Electricity Regulatory Authority (Standard of Performance) Rules

IEEE Guide for Electric Power Distribution Reliability Indices

Electricity Regulatory Authority (Standard of Performance) Rules, 2012 Rule 1

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Electricity Regulatory Authority Law ELECTRICITY REGULATORY AUTHORITY (STANDARD OF PERFORMANCE) RULES, (SL 11 of 2012) In exercise of the powers conferred by sections 66 and 89(3) of the Electricity Regulatory Authority Law (2010 Revision), the Authority, after consultation with the Governor and the licensee, makes the following Rules —

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(Regulations 2 and 7(17)) ANSI/IEEE Std 762-1987(R2002) (Revision of ANSI/IEEE Std 762, originally issued for trial use in 1980) IEEE Standard Definitions for Use in Reporting Electric Generating Unit Reliability, Availability, and Productivity

Sponsor Power Systems Engineering Committee of the IEEE Power Engineering Society

Reaffirmed March 20, 2002 IEEE-SA Standards Board

Approved September 19, 1985 IEEE Standards Board

Approved August 1, 2002 American National Standards Institute

IEEE-SA Standards Board Approved September 19, 1985 Recognized as an American National Standard (ANSI)

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reaffirmed, it is reasonable to conclude that its contents, although still of some value, do not wholly reflect the present state of the art. Users are cautioned to check to determine that they have the latest edition of any IEEE Standard.

Comments for revision of IEEE Standards are welcome from any interested party, regardless of membership affiliation with IEEE. Suggestions for changes in documents should be in the form of a proposed change of text, together with appropriate supporting comments.

Interpretations: Occasionally questions may arise regarding the meaning of portions of standards as they relate to specific applications. When the need for interpretations is brought to the attention of IEEE, the Institute will initiate action to prepare appropriate responses. Since IEEE Standards represent a consensus of all concerned interests, it is important to ensure that any interpretation has also received the concurrence of a balance of interests. For this reason IEEE and the members of its technical committees are not able to provide an instant response to interpretation requests except in those cases where the matter has previously received formal consideration. Comments on standards and requests for interpretations should be addressed to:

Secretary, IEEE Standards Board 345 East 47th Street New York, NY 10017 USA

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Foreword

(This Foreword is not a part of ANSI/IEEE Std 762-1987, IEEE Standard Definitions for Use in Reporting Electric Generating Unit Reliability, Availability, and Productivity.)

Measures of generating unit performance have been defined, recorded, and utilized by the electric power industry for over 40 years. Initially, only a few terms, such as forced outage rate and scheduled outage rate, were needed. The increased focus on generating unit performance in recent years has caused regulatory agencies and the industry to place a greater emphasis on performance measures.

These contemporary constraints have amplified the difficulties that evolved from having generating unit statistics compiled by different organizations to meet their own specific needs. In the past these difficulties have included the interpretation of data within a given system by an outside agency and the correlation of data among the various systems.

The current problems have made clear the need for a standard to overcome these difficulties by providing terminology and indexes for use in existing data systems or in future systems. This standard is directed toward allowing for a meaningful exchange of electric generating unit performance data while attempting to retain as much of existing systems as possible.

No attempt is made here to standardize or to recommend methodologies or procedures for the collection of unit performance data. Furthermore, no attempt is made here to address the special requirements of electric generating units limited by fuel supplies, resources such as water (hydro), or environmental restrictions. It is expected that the methods used will continue to vary from system to system according to individual needs. What is attempted is to specify certain common terms and indexes that must be obtainable from each data base to provide for a basis of information exchange.

The task force has attempted to keep the list of terms and indexes as brief as possible. Performance cannot be measured by a single parameter, and several indexes are required to indicate the ability of a generating unit to produce power when called upon. The use of any single index to measure the performance of a unit or a class of units is misleading. This requirement has necessitated the inclusion of all of the terms and indexes as given here.

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Some indexes are based on period hours. By use of such a common base, simple additive relationships between various indexes result, and the use of period hours gives sets of indexes that sum to 100%, as described in Appendix C. Other

indexes are not based on period hours. For example, in the statistic forced outage rate (see 7.16), (service hours forced outage hours) is used as a base because forced outage rate is intended to estimate the probability of forced outage during the times when there is no planned or maintenance outage. For other than base load service, further modifications are needed to estimate this probability correctly. It is the intent of the task force to define sufficient data categories (states, times, capacity levels) so that suitable indexes for all types of units can be calculated.

It should be noted that even the use of all the indexes and terms cannot identify the underlying and sometimes compelling reasons for lost performance.

This standard was prepared by the Power Plant Productivity Definitions Task Force of the Applications of Probability Methods Subcommittee of the Power Systems Engineering Committee, whose members were as follows:

The following persons were on the balloting committee that approved this document for submission to the IEEE Standards Board:

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When the IEEE Standards Board approved this standard on September 19, 1985, it had the following membership:

The task force wishes to dedicate this work to the memory of Veazey M.Cook, a pioneer in the application of generating unit outage data in system planning studies. The format and many of the terms used in this standard can be traced to Veazey Cook's work.

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CLAUSE 1.

2.

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6.3 6.4 6.5

6.6 6.7

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An American National Standard

IEEE Standard Definitions for Use in Reporting Electric Generating Unit Reliability, Availability, and Productivity

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NOTE — A unit generally enters the active state on its service date.

3.1.1 Available The state in which a unit is capable of providing service, whether or not it is actually in service and regardless of the capacity level that can be provided.

3.1.1.1 In Service The state in which a unit is electrically connected to the system.

3.1.1.2 Reserve Shutdown The state in which a unit is available but not in service.

NOTE — This is sometimes referred to as economy shutdown.

3.1.2 Unavailable The state in which a unit is not capable of operation because of operational or equipment failures, external restrictions, testing, work being performed, or some adverse condition. The unavailable state persists until the unit is made available for operation, either by being synchronized to the system (in-service state) or by being placed in the reserve shutdown state.

3.1.2.1 Planned Outage The state in which a unit is unavailable due to inspection, testing, nuclear refueling, or overhaul. A planned outage is scheduled well in advance.

3.1.2.1.1 Basic Planned Outage The planned outage state that is originally scheduled and of a predetermined duration.

3.1.2.1.2 Extended Planned Outage The planned outage state that is the extension of the basic planned outage beyond its predetermined duration.

NOTE — Extended planned outage applies only when planned work exceeds predetermined duration. The extension, due to a condition discovered during the planned outage that has forced the extension of the planned outage, is to be classified as Class 1 unplanned outage (see 3.1.2.2.2).

Startup failure would result in Class 0 unplanned outage (see 3.1.2.2.1).

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Figure 1—Relation Between Unit States

3.1.2.2 Unplanned Outage The state in which a unit is unavailable but is not in the planned outage state.

NOTES:

1 — When an unplanned outage is initiated, the outage is to be classified according to one of five classes, as defined in 3.1.2.2.1 through 3.1.2.2.5. Unplanned outage Class 0 applies to a start-up failure and Class 1 applies to a condition requiring immediate outage. Also, unplanned outage starts when planned outage ends but is extended due to unplanned work. Classes 2, 3, and 4 apply to outages where some delay is possible in time of removal of the unit from service. The class (2, 3, or 4) of outage is to be determined by the amount of delay that can be exercised in the time of removal of the unit. The class of outageis not made more urgent if the time of removal is advanced due to favorable conditions of system reserves or availability of replacement capacity for the predicted duration of the outage. However, outage starts when the unit is removed from service or is declared unavailable when it is not in service.

2 — During the time the unit is in the unplanned outage state, the outage class is determined by the outage class that initiates the state.

3 — In some cases, the opportunity exists during unplanned outages to perform some of the repairs or maintenance that would have been performed during the next planned outage. If the additional work extends the outage beyond that required for the unplanned outage, the remaining outage should be reported as a planned outage.

4 — Unlike planned outages, unplanned outages do not have a fixed duration that can be estimated each year.

3.1.2.2.1 Class 0 Unplanned Outage (Starting Failure) An outage that results from the unsuccessful attempt to place the unit in service

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(see 3.1.3.1).

3.1.2.2.2 Class 1 Unplanned Outage (Immediate) An outage that requires immediate removal from the existing state.

NOTE — A Class 1 unplanned outage can be initiated from either the in-service or reserve shutdown states. A Class 1 unplanned outage can also be initiated from the planned outage state.

See Note in 3.1.2.1.2.

3.1.2.2.3 Class 2 Unplanned Outage (Delayed) An outage that does not require immediate removal from the in-service state but requires removal within 6 h.

3.1.2.2.4 Class 3 Unplanned Outage (Postponed) An outage that can be postponed beyond 6 h but requires that a unit be removed from the in-service state before the end of the next weekend.

NOTE — Classes 2 and 3 can only be initiated from the inservice state.

3.1.2.2.5 Class 4 Unplanned Outage (Deferred) An outage that will allow a unit outage to be deferred beyond the end of the next weekend but requires that a unit be removed from the available state before the next planned outage.

3.1.2.3 Repair Urgency When a planned or unplanned outage is initiated, the urgency with which repair activities are carried out is classified according to one of three classes as defined in 3.1.2.3.1 through 3.1.2.3.3.

3.1.2.3.1 Maximum Effort Repairs were accomplished in the shortest possible time.

3.1.2.3.2 Normal Effort Repairs were carried out with normal repair crews working normal shifts.

3.1.2.3.3 Low-Priority Effort Repairs were carried out with less than a normal effort.

3.1.3 Starting Attempt The action to bring a unit from shutdown to the in-service state. Repeated

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initiations of the starting sequence without accomplishing corrective repairs are counted as a single attempt.

3.1.3.1 Starting Failure The inability to bring a unit from some unavailable state or reserve shutdown state to the in-service state within a specified period. The specified period may be different for individual units. Repeated failures within the specified starting period are to be counted as a single starting failure.

3.1.3.2 Starting Success The occurrence of bringing a unit from some unavailable state or the reserve shutdown state to the in-service state within a specified period. The specified period may be different for individual units.

3.2 Deactivated Shutdown The state in which a unit is unavailable for service for an extended period of time because of its removal for economy or reasons not related to the equipment.

Under this condition, a unit generally requires weeks of preparation to make it available.

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Figure 2—Unit Capacity Levels

4.1 Maximum Capacity (MC) The maximum capacity that a unit can sustain over a specified period of time.

The maximum capacity can be expressed as gross maximum capacity (GMC) or net maximum capacity (NMC). To establish this capacity, formal demonstration is required. The test should be repeated periodically. This demonstrated capacity level shall be corrected to generating conditions for which there should be minimum ambient restriction. When a demonstration test has not been conducted, the estimated maximum capacity of the unit shall be used.

4.2 Dependable Capacity The maximum capacity, modified for ambient limitations for a specified period of time, such as a month or a season.

4.3 Available Capacity The dependable capacity, modified for equipment limitation at any time.

4.4 Seasonal Derating The difference between maximum capacity and dependable capacity.

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4.5 Unit Derating The difference between dependable capacity and available capacity.

4.6 Planned Derating That portion of the unit derating that is scheduled well in advance.

4.6.1 Basic Planned Derating The planned derating that is originally scheduled and of predetermined duration.

4.6.2 Extended Planned Derating The planned derating that is the extension of the basic planned derating beyond its predetermined duration.

4.7 Unplanned Derating That portion of the unit derating that is not a planned derating. Unplanned derating events are classified according to the urgency with which the derating needs to be initiated, as defined in 4.7.1 through 4.7.4.

4.7.1 Unplanned Derating, Class 1 (Immediate) A derating that requires an immediate action for the reduction of capacity.

4.7.2 Unplanned Derating, Class 2 (Delayed) A derating that does not require an immediate reduction of capacity, but requires a reduction of capacity within 6 h.

4.7.3 Unplanned Derating, Class 3 (Postponed) A derating that can be postponed beyond 6 h, but requires a reduction of capacity before the end of the next weekend.

4.7.4 Unplanned Derating, Class 4 (Deferred) A derating that can be deferred beyond the end of the next weekend, but requires a reduction of capacity before the next planned outage.

4.8 Installed Nameplate Capacity The full-load continuous gross capacity of a unit under specified conditions, as calculated from the electric generator nameplate based on the rated power factor.

NOTE — The nameplate rating of the electric generator may not be indicative of the unit maximum or dependable capacity, since some other item or equipment (such as the turbine) may limit unit output.

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5.7 Forced Outage Hours (FOH) The number of hours a unit was in a Class 0, 1, 2, or 3 unplanned outage state.

5.8 Maintenance Outage Hours (MOH) The number of hours a unit was in a Class 4 unplanned outage state.

5.9 Deactivated Shutdown Hours (DSH) The number of hours a unit was in the deactivated shutdown state.

5.10 Period Hours (PH) The number of hours a unit was in the active state.

5.11 Unit Derated Hours (UNDH) The available hours during which a unit derating was in effect.

5.11.1 In-Service Unit Derated Hours (IUNDH) The in-service hours during which a unit derating was in effect.

5.11.2 Reserve Shutdown Unit Derated Hours (RSUNDH) The reserve shutdown hours during which a unit derating was in effect.

5.12 Planned Derated Hours (PDH) The available hours during which a basic or extended planned derating was in effect.

5.12.1 In Service PlAnned Derated Hours (IPDH) The in-service hours during which a basic or extended planned derating was in effect.

5.12.2 Reserve Shutdown Planned Derated Hours (RSPDH) The reserve shutdown hours during which a basic or extended planned derating was in effect.

5.13 Unplanned Derated Hours (UDH) The available hours during which an unplanned derating was in effect.

5.13.1 In-Service Unplanned Derated Hours (IUDH) The in-service hours during which an unplanned derating was in effect.

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5.13.2 Reserve Shutdown Unplanned Derated Hours (RSUDH) The reserve shutdown hours during which an unplanned derating was in effect.

5.14 Forced Derated Hours (FDH) The available hours during which a Class 1, 2, or 3 unplanned derating was in effect.

5.14.1 In-Service Forced Derated Hours (IFDH) The in-service hours during which a Class 1, 2, or 3 unplanned derating was in effect.

5.14.2 Reserve Shutdown Forced Derated Hours (RSFDH) The reserve shutdown hours during which a Class 1, 2, or 3 unplanned derating was in effect.

5.15 Maintenance Derated Hours (MDH) The available hours during which a Class 4 unplanned derating was in effect.

5.15.1 In-Service Maintenance Derated Hours (IMDH) The in-service hours during which a Class 4 unplanned derating was in effect.

5.15.2 Reserve Shutdown Maintenance Derated Hours (RSMDH) The reserve shutdown hours during which a Class 4 unplanned derating was in effect.

5.16 Seasonal Derated Hours (SDH) The available hours during which a seasonal derating was in effect.

5.17 Equivalent Hours (E) The number of hours a unit was in a time category involving unit derating, expressed as equivalent hours of full outage at maximum capacity. Both unit derating and maximum capacity shall be expressed on a consistent basis, gross or net. Equivalent hours can be calculated for each of the time categories in 5.11through 5.16. The symbol designation for the equivalent hours is formed by adding an E in front of the symbol for the corresponding time designation (for example, equivalent unit derated hours is designated EUNDH). Equivalent hours can be calculated from the following equation:

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where

E( ) = equivalent hours in the time category represented by parentheses, which can be any one of the time categories in 5.11 through 5.16 D( )i = the derating for the time category shown in parentheses, after the ith change in either available capacity (unit deratings) or dependable capacity (seasonal deratings)

NOTE — In order to apportion equivalent hours among the various time categories, appropriate ground rules shall be established in the reporting system so that after each change in either available capacity or dependable capacity, the sum of all subcategories of unit derating is equal to the unit derating.

Ti = the number of hours accumulated in the time category of interest between the ith and the (i + 1)th change in either available capacity (unit deratings) or dependable capacity (seasonal deratings)

MC = maximum capacity

5.18 Deactivation Date The date a unit was placed into the deactivated shutdown state.

5.19 Reactivation Date The date a unit was returned to the active state from the deactivated shutdown state.

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MG = period hours · maximum capacity = PH · MC GMG = PH · GMC NMG = PH · NMC

6.3 Available Generation (AG) The energy that could have been generated by a unit in a given period if operated continuously at its available capacity.

6.4 Unavailable Generation (UG) The difference between the energy that would have been generated if operating continuously at dependable capacity and the energy that would have been generated if operating continuously at available capacity. This is the energy that could not be generated by a unit due to planned and unplanned outages and unit deratings.

UG = (planned outage hours + unplanned outage hours + equivalent unit derated hours) · maximum capacity = (POH + UOH + EUNDH) · MC

6.5 Seasonal Unavailable Generation (SUG) The difference between the energy that would have been generated if operating continuously at maximum capacity and the energy that would have been generated if operating continuously at dependable capacity, calculated only during the time the unit was in the available state.

SUG = equivalent seasonal derated hours · maximum capacity = ESDH · MC

6.6 Reserve Generation (RG) The energy that a unit could have produced in a given period but did not, because it was not required by the system. This is the difference between available generation and actual generation.

6.7 Derated Generation (DG) The generation that was not available due to unit deratings.

DG = equivalent unit derated hours · maximum capacity = EUNDH · MC

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7.1 Planned Outage Factor (POF)

7.2 Unplanned Outage Factor (UOF)

7.3 Forced Outage Factor (FOF)

7.4 Maintenance Outage Factor (MOF)

7.5 Unavailability Factor (UF)

7.6 Availability Factor (AF)

7.7 Service Factor (SF)

7.8 Seasonal Derating Factor (SDF) The fraction of maximum generation that could not be produced due to seasonal deratings:

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7.9 Unit Derating Factor (UDF) The fraction of maximum generation that could not be produced due to unit deratings:

7.10 Equivalent Unavailability Factor (EUF) The fraction of maximum generation that could not be produced due to unit deratings and planned and unplanned outages:

7.11 Equivalent Availability Factor (EAF) The fraction of maximum generation that could be provided if limited only by outages and deratings:

7.12 Gross Capacity Factor (GCF)

7.13 Net Capacity Factor (NCF)

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NOTE — Net capacity factor calculated using this equation can be negative during a period when the unit is shutdown. For meaningful pooling of data on several units, net capacity factor can be defined to be zero when the unit is shutdown.

7.14 Gross Output Factor (GOF)

7.15 Net Output Factor (NOF)

7.16 Forced Outage Rate (FOR)

7.17 Equivalent Forced Outage Rate (EFOR)

7.18 Mean Service Time to Outage

7.18.1 Mean Service Time to Forced Outage (MSTFO)

7.18.2 Mean Service Time to Maintenance Outage (MSTMO)

7.18.3 Mean Service Time to Planned Outage (MSTPO)

NOTE — In 7.18.1, only forced outages occurring from in-service state are considered. The name for the index could be ―mean service time to in-service forced outage.‖ However, for simplicity in-service is not included in the name. This note is also applicable to 7.18.2 and 7.18.3.

Indexes similar to 7.18.1, 7.18.2, and 7.18.3 can also be calculated for outages that occur during reserve shutdown state.

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7.19 Mean Outage Duration

7.19.1 Mean Forced Outage Duration (MFOD)

7.19.2 Mean Maintenance Outage Duration (MMOD)

7.19.3 Mean Planned Outage Duration (MPOD)

NOTE — Similar to 7.18, outage hours and number of outages in 7.19 include outages that occur from in-service state only.

7.20 Starting Reliability (SR)

7.21 Cycling Rate (CR)

Annex A

Correlation Between Unit State and Capacity Derating Definitions in This Standard and Those Formerly Used by the Industry

(Informative)

(These Appendixes are not a part of ANSI/IEEE Std 762-1987, EEE Standard Definitions for Use in Reporting Electric Generating Unit Reliability, Availability, and Productivity.)

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Annex B

Transitions Between States (Informative) Section 3 defines three primary unit states:

1) Available 2) Unavailable 3) Deactivated shutdown

These three states are mutually exclusive and exhaustive. A unit will be in exactly one of these states at all times. Thus, these states divide calendar time into nonoverlapping segments. The available and unavailable states are each divided into additional, mutually exclusive states. The available state is divided

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into in- service and reserve shutdown states, and the unavailable state is divided into planned and unplanned outage states. These four secondary states, together with the deactivated shutdown state, also form a mutually exclusive and exhaustive set. Finally, the planned outage state is divided into basic and extended planned outage states. Also, the unplanned outage state is divided into five outage classes, according to the urgency with which the outage is initiated.

Like the other states, the unplanned outage classes are defined to be mutually exclusive. The unit state structure can also be described by starting with the lowest level states. Thus, there are ten basic states:

1) In service 2) Reserve shutdown 3) Basic planned outage 4) Extended planned outage 5) Class 0 unplanned outage 6) Class 1 unplanned outage 7) Class 2 unplanned outage 8) Class 3 unplanned outage 9) Class 4 unplanned outage 10) Deactivated shutdown

These basic states are defined to be mutually exclusive and exhaustive. By grouping various subsets of the basic states together, each of the secondary and primary states can be formed.

Once a unit is in a state, it remains in that state until a transition event occurs that causes the unit to move to another state. The possible transition events can be shown by use of a state transition matrix. Figure B.1 shows a state transition matrix for the ten basic states. The left side of the matrix shows the possible unit states before a transition event. The top row of the matrix shows the (same) possible unit states after a transition event. Thus, each (nondiagonal) element of the matrix can be used to describe a transition event from the state on the left to the top state. Figure B.1 shows the transition events that are possible according to the definitions in Section 3. The elements denoted by "x" are not possible.

By looking on a particular row of Fig B.1, the possible transition events that can terminate a state can be seen. By looking at a particular column of Fig the possible transition events that can initiate a state can be seen.

Detailed definitions for the transition events in Fig have not been included in this

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standard. However, in actual reporting generating unit performance, it is the transition event occurrence times that are in fact reported, from which the state duration times are then calculated. Therefore, the reporting instructions that implement the collection of unit performance data should give careful

consideration to defining precisely and clearly the exact point in time at which the various transitions take place.

Figure B.1—State Transition Matrix

Annex C

Relationships Between Period-Hour-Based Performance Indexes (Informative) For purposes of measuring and improving the performance of individual generating units, it is common to emphasize measures that are based on period hours. The performance indexes in Section 7 provide a unified set of period- hourbased indexes (called factors), as follows:

AF = availability factor UF = unavailability factor EAF = equivalent availability factor EUF = equivalent unavailability factor FOF = forced outage factor MOF = maintenance outage factor UOF = unplanned outage factor = FOF + MOF

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POF = planned outage factor SDF = seasonal derating factor UDF = unit derating factor

These indexes are unified in the sense that they are related in the following ways:

Equation C1 shows that equivalent availability can be obtained by subtracting the unit derating factor and the seasonal derating factor from the availability factor.

Equation C2 shows that equivalent unavailability can be obtained by adding the unit derating factor, but not the seasonal derating factor, to the unavailability factor.

Equation C3 shows that the availability and unavailability factors add to 100%.

Equation C4 shows that the equivalent availability, equivalent unavailability, and seasonal derating factor also add to 100%. However, equivalent availability and equivalent unavailability alone do not, in general, add to 100%, because this sum does not include the effect of seasonal deratings.

Equation C5 shows that the unavailability factor is the sum of the planned and unplanned outage factors (unplanned outage factor is the sum of maintenance outage factor and forced outage factor).

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Substituting Eq C5 into Eq C2 produces Eq C6, which shows that equivalent unavailability is the sum of the planned and unplanned outage factors and the unit derating factor.

Substituting Eq C6 into Eq C4 produces Eq C7. This last equation shows that there are four recognized sources of energy loss: planned outages (full), unplanned outages (full), unit deratings, and seasonal deratings. Each energy loss is represented by a separate index: POF, UOF, UDF, and SDF, respectively.

These indexes are defined in such a way as to be additive. Therefore, the total per unit energy loss is the sum of the four indexes, and the remaining per unit energy not lost is called equivalent availability factor (EAF).

In order for the four energy loss indexes to be additive, as in Eq C7, it is necessary that the capacity loss due to each source be separated. This means, for example, that a unit cannot simultaneously be subject to full outage and unit derating.

Similarly, a unit cannot simultaneously be subject to both seasonal derating and full outage. In order to achieve nonoverlapping energy definitions, the task force agreed to assign full (maximum) unit capacity to the full outage state. This means that both unit deratings and seasonal deratings are considered to end when a full outage starts, as far as the calculation of the unit derating factor (UDF) and the seasonal derating factor (SDF) are concerned.

In order to further illustrate the relationship between the period-hour-based performance indexes, Fig C1 shows capacity versus time diagram (all capacity values must be either gross or net). The total height of the diagram is maximum capacity (MC), and the total width of the diagram is period hours (PH). Thus, the total area Y of the diagram is

Y = MC · PH

This is the total megawatthour (MWh) of energy that could have been generated during the period if operating continuously at MC.

The area Y is divided into several vertical segments by the various time

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designations in Section 5. The vertical segments involving available hours are further divided into sections to show the energy associated with seasonal derating, unit derating, discretionary reduction, and actual generation. All of the performance factors in Section 7 that are based on period hours can be expressed as simple ratios of the areas in Fig C.1 as follows:

Figure C.1—Relation Between Time and Energy Terms

Time Indexes

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Energy Indexes

NOTE — Capacity factor is GCF or NCF depending on gross or net basis used for capacity.

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Annex D

Glossary of Terms and Abbreviations (Informative)

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(Regulation 5(2)) 1366TM IEEE Guide for Electric Power Distribution Reliability Indices

IEEE Power Engineering Society

Sponsored by the Transmission and Distribution Committee

IEEE Guide for Electric Power Distribution Reliability Indices

Sponsor Transmission and Distribution Committee of the IEEE Power Engineering Society

Approved 26 April 2004 American National Standards Institute

Approved 10 December 2003 IEEE-SA Standards Board

Grateful acknowledgment is made to the Edison Electric Institute for the permission to use the following source material:

Pages 28–30 of the June 2001, Edison Electric Institute 2000 Reliability Report.

Abstract: Distribution reliability indices and factors that affect their calculations are defined in this guide. The indices are intended to apply to distribution systems, substations, circuits, and defined regions.

Keywords: circuits, distribution reliability indices, distribution systems, electric power, reliability indices

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Introduction

(This introduction is not part of IEEE Std 1366-2003, IEEE Guide for Electric Power Distribution Reliability Indices.)

This Guide has been updated to clarify existing definitions and to introduce a statistically based definition for classification of major event days. The working group created a methodology, 2.5 Beta Method, for determination of major event days. Once days are classified as normal or major event days, appropriate analysis and reporting can be conducted. After this document is balloted, the working group will continue to investigate the major event definition by reviewing catastrophic events and days with zero events to determine if enhancements are warranted.

Patents Attention is called to the possibility that implementation of this standard may require use of subject matter covered by patent rights. By publication of this standard, no position is taken with respect to the existence or validity of any patent rights in connection therewith. The IEEE shall not be responsible for identifying patents for which license may be required by an IEEE standard or for conducting inquiries into the legal validity or scope of those patents that are brought to its attention.

Notice to users Errata Errata, if any, for this and all other standards can be accessed at the following URL: http://standards.ieee.org/reading/ieee/updates/errata/index.html. Users are encouraged to check this URL for errata periodically.

Interpretations

Current interpretations can be accessed at the following URL:

http://standards.ieee.org/reading/ieee/interp/index.html.

Participants At the time this standard was completed, the Working Group on System Design had the following membership:

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The following members of the balloting committee voted on this standard.

Balloters may have voted for approval, disapproval, or abstention.

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When the IEEE-SA Standards Board approved this standard on 10 December 2003, it had the following membership:

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CONTENTS 1.1 Scope.

1.2 Purpose. 1

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3.8 interruption duration: The time period from the initiation of an interruption to a customer until service has been restored to that customer. The process of restoration may require restoring service to small sections of the system (see 5.3.2) until service has been restored to all customers. Each of these individual steps should be tracked collecting the start time, end time and number of customers interrupted for each step.

3.9 interruptions caused by events outside of the distribution system: Outages that occur on generation, transmission, substations, or customer facilities that result in the interruption of service to one or more customers. While generally a small portion of the number of interruption events, these interruptions can affect a large number of customers and last for an exceedingly long duration.

3.10 lockout: Refers to the final operation of a recloser or circuit breaker in an attempt to isolate a persistent fault, or to the state where all automatic reclosing has stopped. The current-carrying contacts of the overcurrent protecting device are locked open under these conditions.

3.11 loss of service: A complete loss of voltage on at least one normally energized conductor to one or more customers. This does not include any of the power quality issues such as: sags, swells, impulses, or harmonics.

3.12 major event: Designates an event that exceeds reasonable design and or operational limits of the electric power system. A Major Event includes at least one Major Event Day (MED).

3.13 major event day: A day in which the daily system SAIDI exceeds a threshold value, TMED. For the purposes of calculating daily system SAIDI, any interruption that spans multiple calendar days is accrued to the day on which the interruption began. Statistically, days having a daily system SAIDI greater than TMED are days on which the energy delivery system experienced stresses beyond that normally expected (such as severe weather). Activities that occur on major event days should be separately analyzed and reported. (See 4.5.)

3.14 momentary interruption: A single operation of an interrupting device that results in a voltage zero. For example, two circuit breaker or recloser operations (each operation being an open followed by a close) that momentarily interrupts service to one or more customers is defined as two momentary interruptions.

3.15 momentary interruption event: An interruption of duration limited to the period required to restore service by an interrupting device.

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NOTE—Such switching operations must be completed within a specified time of 5 min or less.

This definition includes all reclosing operations that occur within five minutes of the first interruption. For example, if a recloser or circuit breaker operates two, three, or four times and then holds (within 5 min of the first operation), those momentary interruptions shall be considered one momentary interruption event.

3.16 outage (electric power systems): The state of a component when it is not available to perform its intended function due to some event directly associated with that component.

NOTES:

(1) An outage may or may not cause an interruption of service to customers, depending on system configuration.

(2) This definition derives from transmission and distribution applications and does not apply to generation outages.

3.17 planned interruption: A loss of electric power that results when a component is deliberately taken out of service at a selected time, usually for the purposes of construction, preventative maintenance, or repair.

NOTES:

(1) This derives from transmission and distribution applications and does not apply to generation interruptions.

(2) The key test to determine if an interruption should be classified as a planned or unplanned interruption is as follows: if it is possible to defer the interruption, the interruption is a planned interruption; otherwise, the interruption is an unplanned interruption.

3.18 planned outage: The state of a component when it is not available to perform its intended function due to a planned event directly associated with that component.

3.19 reporting period: The time period from which interruption data is to be included in reliability index calculations. The beginning and end dates and times should be clearly indicated. All events that begin within the indicated time period should be included. A consistent reporting period should be used when comparing the performance of different distribution systems (typically one calendar year) or when comparing the performance of a single distribution system over an extended period of time. The reporting period is assumed to be one year unless otherwise stated.

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3.20 step restoration: A process of restoring interrupted customers downstream from the interrupting device/component in stages over time.

3.21 sustained interruption: Any interruption not classified as a part of a momentary event. That is, any interruption that lasts more than 5 minutes.

3.22 total number of customers served: The average number of customers served during the reporting period. If a different customer total is used, it must be clearly defined within the report.

3.23 unplanned interruption: An interruption caused by an unplanned outage.

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LT = Total connected kVA Load Served

CN = Total Number of Customers who have Experienced a Sustained Interruption during the Reporting Period

CNT(k>n) = Total Number of Customers who have Experienced more than n Sustained Interruptions and Momentary Interruption Events during the Reporting Period.

k = Number of Interruptions Experienced by an Individual Customer in the Reporting Period

TMED = Major event day identification threshold value.

4.2 Sustained interruption indices

4.2.1 System average interruption frequency index (SAIFI) The system average interruption frequency index indicates how often the average customer experiences a sustained interruption over a predefined period of time.

Mathematically, this is given in Equation (1).

(1)

To calculate the index, use Equation (2) below.

(2)

4.2.2 System average interruption duration index (SAIDI) This index indicates the total duration of interruption for the average customer during a predefined period of time. It is commonly measured in customer minutes or customer hours of interruption. Mathematically, this is given in Equation (3).

(3)

To calculate the index, use Equation (4).

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(4)

4.2.3 Customer average interruption duration index (CAIDI) CAIDI represents the average time required to restore service. Mathematically, this is given in Equation (5).

(5)

To calculate the index, use Equation 6.

(6)

4.2.4 Customer total average interruption duration index (CTAIDI) This index represents the total average time in the reporting period that customers who actually experienced an interruption were without power. This index is a hybrid of CAIDI and is similarly calculated except that those customers with multiple interruptions are counted only once. Mathematically, this is given in Equation (7).

(7)

To calculate the index, use Equation (8).

(8)

NOTE— In tallying Total Number of Customers Interrupted, each individual customer should only be counted once regardless of number of times interrupted during the reporting period. This applies to 4.2.4 and 4.2.5.

4.2.5 Customer average interruption frequency index (CAIFI) This index gives the average frequency of sustained interruptions for those customers experiencing sustained interruptions. The customer is counted once regardless of the number of times interrupted for this calculation.

Mathematically, this is given in Equation (9).

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(9)

To calculate the index, use Equation (10)

(10) 4.2.6 Average service availability index (ASAI) The average service availability index represents the fraction of time (often in percentage) that a customer has received power during the defined reporting period. Mathematically, this is given in Equation (11).

(11)

To calculate the index, use Equation (12).

(12) NOTE—There are 8760 hours in a non-leap year, 8784 hours in a leap year.

4.2.7 Customers experiencing multiple interruptions (CEMIn) This index indicates the ratio of individual customers experiencing more than n sustained interruptions to the total number of customers served. Mathematically, this is given in Equation (13).

(13)

To calculate the index, use Equation (14).

(14)

NOTE—This index is often used in a series of calculations with n incremented from a value of one to the highest value of interest.

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4.3 Load based indices

4.3.1 Average system interruption frequency index (ASIFI) The calculation of this index is based on load rather than customers affected.

ASIFI is sometimes used to measure distribution performance in areas that serve relatively few customers having relatively large concentrations of load,

predominantly industrial/commercial customers. Theoretically, in a system with homogeneous load distribution, ASIFI would be the same as SAIFI.

Mathematically, this is given in Equation (15).

(15)

To calculate the index, use Equation (16).

(16)

4.3.2 Average system interruption duration index (ASIDI) The calculation of this index is based on load rather than customers affected. Its use, limitations, and philosophy are stated in the ASIFI definition in 4.3.1.

Mathematically, this is given in Equation (17).

(17)

To calculate the index, use Equation (18).

(18)

4.4 Other indices (momentary)

4.4.1 Momentary average interruption frequency index (MAIFI) This index indicates the average frequency of momentary interruptions.

Mathematically, this is given in Equation (19).

(19)

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To calculate the index, use Equation (20).

(20)

4.4.2 Momentary average interruption event frequency index (MAIFIE) This index indicates the average frequency of momentary interruption events.

This index does not include the events immediately preceding a lockout.

Mathematically, this is given in Equation (21).

(21)

To calculate the index, use Equation (22).

(22)

4.4.3 Customers experiencing multiple sustained interruption and momentary interruption events (CEMSMIn) This index is the ratio of individual customers experiencing more than n of both sustained interruptions and momentary interruption events to the total customers served. Its purpose is to help identify customer issues that cannot be observed by using averages. Mathematically, this is given in Equation (23).

(23)

To calculate the index, use Equation (24).

(24)

4.5 Major event day classification The following process (―Beta Method‖) is used to identify MEDs. Its purpose is to allow major events to be studied separately from daily operation, and in the process, to better reveal trends in daily operation that would be hidden by the

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large statistical effect of major events. This approach supersedes previous major event definitions (see Annex A for sample definitions). For more technical detail on derivation of the methodology refer to Annex B.

A major event day is a day in which the daily system SAIDI exceeds a threshold value, TMED. The SAIDI index is used as the basis of this definition since it leads to consistent results regardless of utility size and because SAIDI is a good indicator of operational and design stress. Even though SAIDI is used to determine the major event days, all indices should be calculated based on removal of the identified days.

In calculating daily system SAIDI, any interruption that spans multiple days is accrued to the day on which the interruption begins.

The major event day identification threshold value, TMED, is calculated at the end of each reporting period (typically one year) for use during the next reporting period as follows:

a) Collect values of daily SAIDI for five sequential years ending on the last day of the last complete reporting period. If fewer than five years of historical data are available, use all available historical data until five years of historical data are available.

b) Only those days that have a SAIDI/Day value will be used to calculate the TMED (do not include days that did not have any interruptions).

c) Take the natural logarithm (ln) of each daily SAIDI value in the data set.

d) Find  (Alpha), the average of the logarithms (also known as the log-average) of the data set.

e) Find  (Beta), the standard deviation of the logarithms (also known as the log- standard deviation) of the data set.

f) Compute the major event day threshold, TMED, using equation (25).

(25)

g) Any day with daily SAIDI greater than the threshold value TMED that occurs during the subsequent reporting period is classified as a major event day.

Activities that occur on days classified as major event days should be separately analyzed and reported.

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4.5.1 An example of using the major event day definition An example of using the major event day definition to identify major events and subsequently calculate adjusted indices that reflect normal operating performance is shown in this subclause. This subclause illustrates the calculation of the daily SAIDI, calculation of the major event day threshold TMED, identification of major event days, and calculation of adjusted indices. Table 1 gives selected data for all outages occurring on a certain day for a utility that serves 2,000 customers.

(26)

One month of historical daily SAIDI data is used in the following example to calculate the Major Event Day threshold TMED. Five years of historical data is preferable for this method, but printing that many values in this standard is impractical, so only one month is used to illustrate the concept. The example data is shown in Table 2.

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The value of , the log -average, is the average of the natural logs, and equals – 0.555 in this case.

The value of , the log -standard deviation, is the standard deviation of the natural logs, and equals 1.90 in this example.

The value of  + 2.5 is 4.20.

The threshold value TMED is calculated by e(4.20) and equals 66.69 SAIDI per day. This value is used to evaluate the future time period (e.g., the next year).

Table 3 shows example SAIDI/day values for the first month of 1994.

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The SAIDI/day on 1/28/94 (237.49) exceeds the example threshold value (TMED = 66.69), indicating that the distribution system experienced stresses beyond that normally expected on that day. Therefore, 1/28/94 is classified as a major event day. The SAIDI/day for all other days was less than TMED, indicating that normal stresses were experienced on those days.

To complete the example, indices should be calculated for the following two conditions:

a) all events included

b) major event days removed. In most cases, utilities will calculate all of the indices they normally use (e.g., SAIFI, SAIDI and/or CAIDI). For this example, only SAIDI will be shown. 1994 SAIDI for condition one, all events included, is given in Equation (27) below.

(27)

1994 SAIDI for condition two, major event days removed for separate reporting and analysis, is given in equation 28 below.

(28)

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From Table 6, it can be seen that there were eight circuit breaker operations that affected 2000 customers. Each of them experienced 8 momentary interruptions.

There were twelve recloser operations that caused 750 customers to experience 12 momentary interruptions. Some of the operations occurred during one reclosing sequence. To calculate the number of momentary interruption events, only count the total number of reclosing sequences. In this case there were five circuit breaker events (records 1, 3, 4, 7, and 9) that affected 2000 customers.

Each of them experienced 5 momentary interruption events. There were six recloser events (records 2, 5, 6, 8, 10 and 11) that affected 750 customers each of them experienced 6 momentary interruption events.

5.2 Calculation of indices for a system with no major event days

The equations in Clause 4.5 and definitions in Clause 3 should be used to calculate the annual indices (see Equations (29) – (40)). In the example below,

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the indices are calculated by using the equations in 4.2 and 4.4 using the data in Table 4 and Table 5, assuming there were no major event days in this data set.

To calculate CTAIDI and CAIFI, the number of customers experiencing a sustained interruption is required. The total number of customers affected (CN) for this example can be no more than 2000. Since only a small portion of the customer information table is shown it is impossible to know CN; however, it is likely that not all of the 2000 customers on this feeder experienced an interruption during the year. 1800 will be arbitrarily assumed for CN (for your calculations actual information should be used) since the interruption on 9/3 shows that at least 1500 customers have been interrupted during the year.

CTAIDI, CAIFI, CEMIn, and CEMSMIn require detailed interruption information for each customer. The database should be searched for all customers who have experienced more than n interruptions that last longer than five minutes. Assume n is chosen to be 5. In Table 5, customer Willis, J. experienced seven interruptions in one year and it is plausible that other customers also experienced more than five interruptions, both momentary and sustained.

For this example, assume arbitrary values of 350 for CN(k > n), and 750 for CNT(k > n). The number of interrupting device operations is given in Table 6 and is used to calculate MAIFI and MAIFIE. Assume the number of customers downstream of the recloser equals 750. These numbers would be known in a real

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system.

Using the above sample system should help define the methodology and approach to obtaining data from the information systems and using it to calculate the indices.

5.3 Examples The following subclause illustrates two concepts: momentary interruptions and step restoration through the use of examples.

5.3.1 Momentary interruption example To better illustrate the concepts of momentary interruptions and sustained interruptions and the associated indices, consider Figure 1 and Equation 41, Equation 42, and Equation 43. Figure 1 illustrates a circuit composed of a circuit breaker (B), a recloser (R), and a sectionalizer (S).

For this scenario, 750 customers would experience a momentary interruption and 250 customers would experience a sustained interruption. Calculations for SAIFI, MAIFI, and MAIFIE on a feeder basis are shown in Equations 41–43 below.

Notice that the numerator of MAIFI is multiplied by 2 because the recloser took two shots, however, MAIFIE is multiplied by 1 because it only counts the fact that a series of momentary events occurred.

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5.3.2 Step restoration examples The following case illustrates the step restoration process. A feeder serving 1000 customers experiences a sustained interruption. Multiple restoration steps are required to restore service to all customers. Table 7 shows the times of each step, a description and associated customers interruptions and minutes they were affected in a time line format.

Figure 2 illustrates the example described in Table 7. In this example, all of the customers supplied by the circuit were interrupted at the beginning of step 1.

Service was restored to a portion of those customers at the end of step 1. Service was restored to another portion of those customers at the end of step 2.

Additional customers were interrupted during step 3 (new step 1). Service was restored to additional customers at the end of step 3.

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Table 8 shows the information in a format that explains each step and allows the reader to see the calculation steps.

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utilities also calculate indices on a feeder basis to provide more detailed information for decision making. Averages give general performance trends for the utility; however, using averages will lead to loss of detail that could be critical to decision making. For example, using system averages alone will not provide information about the interruption duration experienced by any specific customer. At the time of this writing, it is difficult for most utilities to provide information on a customer basis. This group envisions that the tracking of specific details surrounding specific interruptions rather than averages will, in the future, be accomplished by improving tracking capabilities. To this end, the working group has included not only the most commonly used indices, but also indices that examine performance at the customer level (e.g., CEMIn).

6.2 Factors that cause variation in reported indices Many factors can cause variation in the indices reported by different utilities.

Some examples of differences in the following:

— level of automated data collection — geography — system design — data classification (e.g., are major events in the data set?, planned interruptions?)

To ensure accurate and equitable assessment and comparison of absolute performance and performance trends over time, it is important to classify performance for each day in the data set to be analyzed as either day-to-day or major event day. Not performing this critical step can lead to false decision making because major event day performance often overshadows and disguises daily performance. Interruptions that occur as a result of outages on customer owned facilities or loss of supply from another utility should not be included in the index calculation.

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Annex A (informative)

Survey of reliability index usage The Working Group on System Design conducted three surveys on distribution reliability index usage. The first one was completed in 1990 and the second was completed in 1995 and the third one was completed in 1997. The purpose of the surveys was to determine index usage and relative index values. In 1990, 100 United States utilities were surveyed, 49 of which responded. In 1995, 209 utilities were surveyed, 64 of which responded. In 1997, 159 utilities were surveyed and 61 responded. Responding utility locations are shown by state in Figure A.1.

Newer surveys are being performed by Edison Electric Institute (EEI). The data provided is not comparable because utilities provided whatever information was easily obtainable. All surveys showed that the most commonly used indices are SAIFI, SAIDI, CAIDI, and ASAI. Figure A.2 shows the percentage of companies using specific indices in 1990. Figure A.3 shows the same information for 1995 and 1997. Figures A.4–A.8 show data on the most commonly used indices given by quartiles where Q1 is the top quartile. The data shown in the Q1 column means that 25% of utilities have an index less than the value shown. For further clarification:

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Q1: 25% of utilities have an index less than the value shown Q2: 50% of utilities have an index less than the value shown (the median value) Q3: 75% of utilities have an index less the value shown Q4:100% of utilities have an index less the value shown

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A.1 Cause codes

In the 1997 survey, cause codes were surveyed. The results are shown below in Figure A.9.

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A.2 Results of question # 7 of the 1999 EEI reliability survey

The following information was provided by the Edison Electric Institute (EEI) based on a survey they performed in 1999. The text is shown exactly as the survey respondents provided the information to EEI.

What definition do you use for major events?

1) Major storm defined as 10% or more of the customer base interrupted in an operating region (based on 8 operating regions) or customers interrupted for 24 hours.

2) Interruptions that result from a catastrophic event that exceeds the design limits of the electric power system, such as an earthquake, tornado, or an extreme storm.

3) A major storm is an event that affects 10% or more of the connected customers with 1% not restored within 24 hours.

4) Ten percent or more of our customers are without power and have been without power for more than 24 hours.

5) The major storm exclusion a criterion is based on a statistical analysis of the last four-year history of reliability data. A cumulative frequency distribution of

the number of locations requiring service restoration work per day is calculated

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for the four-year period. When the frequency of the restoration work exceeds the 98.5 percentile, by company or region the major storm criterion work be met for the all interruptions for that day.

6) Ten percent of customers in a given region affected by an event plus the last customer out greater than 24 hours. All three of the following must be true:

—widespread damage —10 000 or 10% of customers served in area affected —National Weather Service declares severe weather watch or warning for the area

7) Ten percent customer base and 1 customer for 24 hours.

8) More than 15 000 customers out (out of a total customer base of 450 000).

9) As defined by our PUC as named storms, tornados, ice storms, etc.

10) Events where 10% of your customers (meters) have experienced an interruption due to the event.

11) IEEE Std 1366™-1998; Definition 3.12 major event. Company 1 defined as, 10% of the customers within a region without electricity and not restored within a 24 hour period.

12) Ten percent of the entire electric system’s customers must experience an interruption in service and one percent of the entire electric system’s customers must experience an interruption in service for more than 24 hours.

13) Ten percent of customers out of service and restoration time exceeding 24 hours.

14) Named storms, i.e. hurricane, tropical storms, or tornadoes verified by the National Weather Service. Major forest fires are also included. In addition, Company 2 reporting definition does not include planned interruptions. MAIFI is reported as momentary events.

15) (1) Winds in excess of 90 mph OR (2) 1/2 inch of ice and winds in excess of 40 mph.

NOTE— The major storm outage minutes in 1999 were minimal for Company 3 and did not impact the reliability measures.

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16) 0.8 hours x customers served for a month, if the customer hours lost for any one day in that month exceed this value it can be removed from our year-end calculations. Interruptions that result from a catastrophic event that exceeds the design limits of the electric power system, such as an earthquake or an extreme storm. These events shall include situations where there is a loss of power to 10% or more of the customers over a 24-hour period and with all customers not restored within 24 hours.

17) State of Connecticut Department of Public Utility Control – Major Storm Exclusion Definition for 1999 – Any day or 24-hour period, where 31 restoration steps or greater were experienced. For 2000, the UI storm exclusion is based on 35 restoration steps or greater. The change in storm exclusion restoration step threshold, is based on the previous four-year outage history.

18) A period of adverse weather which interrupts 10% or more of the customers served in an operating area, or results in customers being without power for 24 hours or longer.

19) Weather events that cause more than 100 000 customers to be interrupted, with restoration taking at least 24 hours.

20) (1) A Watch or Warning has been issued by the National Weather Service, (2) Extensive mechanical damage has been experienced and (3) More than 6% of the customers served in a region have been affected by outages during a 12-hour period.

21) A major storm is defined as the interruption to 110 000 customers or more which is about 5 percent of our total customers. The 110 000 was arrived at by going out six standard deviations from the mean of all daily cases of trouble.

22) Any outage lasting longer than 48 hours is capped at 48 hours.

23) Any event outage over 10% of the customers in a region AND requiring over 24 hours to restore service to all customers. (PUC definition) Outages occurring during qualifying major storms are not entered into our system, therefore we can only report on 8B, 11B, and 13B below.

24) Determination is subjective, not strictly defined at this time.

25) Tropical storms, hurricanes, tornados, and ice storms.

26) Interruptions that result from a catastrophic event that exceeds the design limits of the electric power system, such as an earthquake or an extreme storm.

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These events shall include situations where there is a loss of power to 10% or more customers in a region over a 24-hour period and with all customers not restored within 24 hours.

27) >10% of customers out of service for >24 hours.

28) 15 000 or more customers out of service.

29) Ten percent of customers in an area (region) interrupted.

30) (1) 10% or more of customers interrupted in a operating area.

And (2) A storm or other large occurrence where customers experience an interruption for 24 or more hours in an operating area.

31) A storm is determined at regional level when in any consecutive 24 hours the cumulative outages reach 15 AND cumulative customer interruption minutes reach 200 000

32) A major storm is defined as an interruption of electric service resulting from conditions beyond the company’s control, which affects at least 10% of the customers in an operating area during the course of an event.

33) Level 3 or above out of 5 according to our emergency plan.

About 5 storms per year excluded.

34) Any day during which the number of interruptions are greater than 3 standard deviations above average.

35) CAIDI for the storm period must be 2.5 times normal. Outside crews required to restore damage. Restoration of damage must require 24 hours or more.

36) Named Storms (i.e. hurricane).

37) Extension mechanical damage to the electric system. Outages involving more than 10% of the customers served by district. More than 1% of the customers serviced have not been restored within 24 hours.

38) 15 000 or more customers outages.

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39) (1) > 10% of the customers out of service at any one time, reported on a district basis. and (2) Extraordinary storm event such as a tornado, severe winds, etc.

40) A major storm is one which affects 15 000 of our approximately 120 000 customers AND makes an incremental addition of 10 min to company SAIDI.

41) A storm or equipment failure that would cause widespread serious damage throughout the service area in such proportion that available Company 4 forces would be unable to restore service within 48 hours. We designate this as a Level III event – Company 4 has 3 levels of event classifications There were no Level III events in 1999.

42) The major storm exclusion criterion is based on a statistical analysis of the last four-year history of reliability data. A cumulative frequency distribution of the number of locations requiring service restoration work per day is calculated for the four-year period. When the frequency of the restoration work exceeds the 98.5 percentile, by company or region the major storm criterion work be met for the all interruptions for that day.

43) Named storms, tornadoes, ice, events with >10% of customers out.

44) An interruption of electric service resulting from conditions beyond the control of the electric distribution company which affects at least 10% of the customers in an operating area during the course of event for a duration of 5 min each or greater.

45) An interruption of electric service resulting from conditions beyond the control of the electric distribution company which affects at least 10% of the customers in an operating area.

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Annex B

(informative)

Major events definition development

B.1 Justification and process for development of the 2.5 beta methodology

The statistical approach to identifying major event days was chosen over the previous definitions (as shown in A.2) because of the difficulties experienced in creating a uniform list of types of major events, and because the measure of impact criterion (i.e., percent of customers affected) required when using event types resulted in non-uniform identification. The new methodology should fairly identify major events for all utilities. Some key issues had to be addressed in order to consider this work successful. They were as follows:

— Definition must be understandable and easy to apply.

— Definition must be specific and calculated using the same process for all utilities.

— Must be fair to all utilities regardless of size, geography, or design.

— Entities that adopt the methodology will calculate indices on a normalized basis for trending and reporting. They will further classify the major event days separately and report on those days through a separate process.

Daily SAIDI values are preferred to daily customer minutes interrupted (CMI) values for major event day identification because the former permits comparison and computation among years with different numbers of customers served.

Consider the merger of two utilities with the same reliability and the same number of customers. CMI after the merger would double, with no change in reliability, while SAIDI would stay constant.

Daily SAIDI values are preferred to daily SAIFI values because the former are a better measure of the total cost of reliability events, including utility repair costs and customer losses, than the latter. The total cost of unreliability would be a better measure of the size of a major event, but collection of this data is not practical.

The selected approach for setting the major event day identification threshold, known as the ―Two Point Five Beta‖ method (since it is using the lognormal SAIDI values rather than the raw SAIDI values), is preferred to using fixed multiples of standard deviation (e.g. ―Three Sigma‖) to set the

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identification threshold because the latter results in non-uniform MED identification among utilities with different sizes and average reliabilities. The b multiplier of 2.5 was chosen because, in theory, it would classify 2.3 days per year as major events. If significantly more days than this are identified, they represent events that have occurred outside the random process that is assumed to control distribution system reliability. The process and the multiplier value were evaluated by a number of utilities with different sized systems from different parts of the United States and found to correlate reasonably well to current major event identification results for those utilities. A number of alternative approaches were considered. None was found to be clearly superior to Two Point Five Beta.

When a major event occurs which lasts through midnight (for example, a six hour hurricane which starts at 9:00 PM), the reliability impact of the event may be split between two days, neither of which would exceed the TMED and therefore be classified as a major event day. This is a known inaccuracy in the method that is accepted in exchange for the simplicity and ease of calculation of the method.

The preferred number of years of data (five) used to calculate the major event day identification threshold was set by trading off between the desire to reduce statistical variation in the threshold (for which more data is better) and the desire to see

B.1.1 Remarks

To generate the example data, values of a and b were taken from an actual utility data set, and then daily SAIDI/day values were artificially generated using a log normal distribution wi then adjusted to illustrate all aspects of the calculation, e.g. a day in Table 2 was assigned a SAIDI value of zero, and a day in Table 3 was assigned a SAIDI value higher than the computed threshold.

This annex provides a technical description and analysis of the 2.5 method of identifying MEDs in di

method based on the theory of probability and statistics. Fundamental concepts such as probability distribution and expected value are highlighted in italics when they are first used, and provided with a short definition. An undergraduate probability and statistics textbook can be consulted for more complete definitions.

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B.1.2 Beta () method description

A threshold on daily SAIDI is computed once a year (see 4.5). The short version is as follows:

a) Assemble the five most recent years of historical values of SAIDI/day. If less than five years of data is available, use as much as is available.

b) Discard any day in the data set that has a SAIDI/Day of zero.

c) Find the natural logarithm of each value in the data set.

d) Compute the average (, or Alpha) and standard deviation ( or Beta) of the natural logarithms computed in step 3.

e) Compute the threshold TMED = exp (Alpha + 2.5 * Beta).

f) Any day in the next year with SAIDI > TMED is a major event day.

B.2 Random nature of distribution reliability

The reliability of electric power distribution systems is a random process, that is, a process that produces random values of a specific random variable. A simple example of a random process is rolling a die. The random variable is the value on the top face of the die after a roll, which can have integer values between 1 and 6.

In electric power distribution system reliability, the random variables are the reliability indices defined in the guide. These are evaluated on a daily or yearly basis, and take on values from zero to infinity.

B.3 Choice of SAIDI to identify major event days

Four commonly used reliability indices are:

— System Average Interruption Duration Index (SAIDI) — System Average Interruption Frequency Index (SAIFI) — Customer Average Interruption Duration Index (CAIDI) — Average Service Availability Index (ASAI)

These indices are actually measures of unreliability, as they increase when reliability becomes worse.

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An ideal measure of unreliability would be customer cost of unreliability, the dollar cost of power outages to a utility’s customers. This cost is a combination of the initial cost of an outage and accumulated costs during the outage.

Unfortunately, the customer cost of unreliability has so far proven impossible to estimate accurately. In contrast, the reliability indices above are routinely and accurately computed from historical reliability data. However, the ability of an index to reflect customer cost of unreliability indicates the best one to use for major event day identification.

Duration-related costs of outages are higher than initial costs, especially for major events, which typically have long duration outages. Thus a duration-related index will be a better indicator of total costs than a frequency-related index like SAIFI or MAIFI. Because CAIDI is a value per customer, it does not reflect the size of outage events. Therefore SAIDI best reflects the customer cost of unreliability, and is the index used to identify major event days. SAIDI in minutes/day is the random variable used for major event day identification.

The use of Customer Minutes Interrupted per day was also considered. Like SAIDI, CMI is a good representation of customer cost of unreliability. In fact, SAIDI is just CMI divided by the number of customers in the utility. The number of customers can vary from year to year, especially in the case of mergers, and multiple years of data are used to find major event days. Use of SAIDI accounts for the variation in customer count, while use of CMI does not. Therefore SAIDI is preferred.

B.4 Probability distribution of distribution system reliability

B.4.1 Probability density functions and probability of exceeding a threshold value

MEDs will be days with larger SAIDI values. This suggests the use of a threshold value for daily SAIDI. The threshold value is called TMED. Days with SAIDI greater than TMED are major event days. As the threshold increases, there will be fewer days with SAIDI values above the threshold. The relationship between the threshold and the number of days with SAIDI above the threshold is given by the probability density function of SAIDI/day.

The probability density function gives the probability that a specific value of a random variable will appear. For example, for a six sided die, the probability that

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a one will appear in a given roll is 1/6th, and the value of the probability density function of one is 1/6th for this random process.

The probability that a value greater than one will occur is just the sum of the probability densities for all values greater than one. Since each value has a probability density of 1/6th for the example, this sum is just 5/6ths. As the threshold increases, the probability decreases. For example, for a threshold of 4, there are only two values greater than 4, and the probability of rolling one of them is 2/6ths or 1/3rd.

In the die rolling example, the random variable can only have discrete integer values. SAIDI/day is a continuous variable. In this case, the sum is replaced by an integral. The probability p that any given day will have a SAIDI/day value greater than a threshold value T is the integral of the probability density function from the threshold to infinity as shown below in Equation (B.1).

(B.1)

Graphically, the probability is the area under the probability density function above the threshold, as shown in Figure B.1.

If any given day has a probability p of being a major event day, then the expected value [see Equation (B.2)] of the number of major event days in a year is the probability times the number of days in a year.

(B.2)

For example, if p = 0.1, then the expected number of major event days in a year is 36.5. This does not mean that exactly 36.5 MEDs will occur. The actual number will vary due to the randomness of the process.

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Using the die rolling example, the probability of getting a six in any roll is 1/6th.

Therefore the expected number of sixes in six rolls is 1. However, if the die is rolled six times, there could be six sixes, or zero sixes, or any number in between. As the number of trials goes up, the number of sixes will approach 1/6th of the number of rolls, but for small numbers of rolls there will be some variation from the expected value.

B.4.2 Gaussian, or normal distribution

The expected number of MEDs per year can be computed for any given threshold if the shape of the probability density function is known. The shape of the probability density function is called the probability distribution. Specific types of shapes have specific names. The most well known is the Gaussian distribution, also called the normal distribution or bell curve, shown in Figure B.2.

The Gaussian distribution is completely described by its mean, or average value, (μ or Mu) and its stan alue is at the center of the distribution (at 0 on the x axis in Figure B.2) and the standard deviation is a measure of the spread of the distribution.

An important property of the Gaussian distribution is that the probability of exceeding a given threshold is a function of the number of standard deviations the threshold is from the mean. Equation (B.3) provides mathematical terms.

(B.3)

If the threshold is n standard deviations greater than the mean, and the probability of exceeding the threshold, p(SAIDI > TMED), is a function only of n, and not of

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the mean and standard deviation. Values for this function are found in tables in the backs of probability textbooks and in, for example, standard spreadsheet functions. Table B.1 gives the probability of exceeding the threshold for different number of standard deviations k.

B.4.3 Three sigma

The term ―Three Sigma‖ is often used loosely to designate a rare event. It comes from the Gaussian probability distribution. As Table B.1 shows, the probability of exceeding a threshold that is three standard deviations more than the mean is 0.00135, or one and a half tenths of a percent. If daily SAIDI had a Gaussian probability distribution, it would be relatively easy to agree on a Three Sigma definition for the major event day threshold, TMED. Unfortunately, SAIDI does NOT have a Gaussian distribution. It has a log-normal distribution.

B.5 Log-normal distribution

The random variable in the Gaussian distribution has a range from – to . In real life, many quantities, including distribution reliability, can only be zero or positive. This causes the probability distribution to skew, bunching up near the zero axis and having a long tail to the right. The degree of skewness depends on the ratio of mean to standard deviation. When the standard deviation is small compared to the mean, the log normal distribution looks like the Gaussian distribution, as shown in Figure B.3(b). When it is large compared to the mean, it does not, as shown in Figure B.3(a). Daily reliability data usually has standard deviation values far larger than the mean.

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A consequence of the log-normality of daily reliability data is that the three sigma conditions no longer hold. In particular, the probability of exceeding a given threshold is no longer independent of the values of the average and standard deviation of the distribution. This means that using a method such as Three Sigma

would result in different numbers of MEDs for utilities with different average values of reliability, or with different standard deviation values. This seems inequitable.

Fortunately, the logarithms of log-normal data have a Gaussian distribution. If the average of the logarithms of the data is called , or Alpha, and the standard deviation of the logari mean and standard deviation of a Gaussian distribution and a threshold on the log of the data can be set which is independent of the values of  and ß. Equations (B.4) and (B.5) show these concepts mathematically.

(B.4)

and

(B.5)

The probability of exceeding TMED is a function of k, just as in the Gaussian example. Table B.2 gives these probabilities as well as the expected number of Major Event Days (MEDs) for various values of k.

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B.5.1 Why 2.5?

Given an allowed number of MEDs per year, a value for k is easily computed.

However, there is no analytical method of choosing an allowed number of MEDs/year. The chosen value of k = 2.5 is based on consensus reached among Distribution Design Working Group members on the appropriate number of days that should be classified as Major Event Days. As Table B.2 shows, the expected number of days for k = 2.5 is 2.3 MEDs/year. In practice, the experience of the committee members, representing a wide range of distribution utilities, was that more than 2.3 days were usually classified as MEDs, but that the days that were classified as MEDS were generally those that would have been chosen on qualitative grounds. The performance of different values of k were examined, and consensus was reached on k = 2.5.

B.6 Fairness of the 2.5ß method It is likely that reliability data from different utilities will be compared by utility management, public utilities commissions and other interested parties. A fair MED classification method would classify, on average, the same number of MEDs per year for different utilities.

The two basic ways that utilities can differ in reliability terms are in the mean and standard deviation of their reliability data. Differences in means are attributable to differences in the environment between utilities, and to differences in operating and maintenance practices. Differences in standard deviation are mostly attributable to size. Larger utilities have inherently smaller standard deviations. As discussed above, using the mean and standard deviation of the logs of the data MEDs depend only on the multiplier, and thus should classify the same number of MEDs for large and small utilities, and for utilities with low and high average reliability.

This is not the case for using the mean and standard deviation of the data without taking logarithms first. The expected number of MEDs varies the average and standard deviation. This variation occurs because of the log-normal nature of the reliability probability distribution.

B.7 Five years of data From a statistical point of view, the more data used to calculate a threshold, the

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better. However, the random process producing the data changes over time as the distribution system is expanded and operating procedures are varied. Using too much historical data would suppress the effects of these changes.

The addition of another year of data should have a low probability of changing the MED classification of previous years. A result from order statistics gives the probability that the kth largest value in m samples will be exceeded f times in n future samples [B10]. It is given in Equation (B.5).

(B.5)

For example, if M = 3 years of data then m = 1095 samples. If f = 3 MEDs/year then the largest non-MED is the k = 1095 –9 = 1086th ordered sample. The probability of f = 3 days in the next year of n = 365 samples exceeding the size of the largest non-MED is found from the equation to be 0.194 (19.4%). In Figure B.5 p is plotted against M for several values of f.

The consensus of the Design Working Group members was that 5 years was the appropriate amount of data to collect. They felt that the distribution system would change enough to invalidate any extra accuracy from more than 5 years of data.

Annex C (informative) Internal data subset C.1 Calculation of reliability indices for subsets of data for internal company use

Reliability performance can be assessed for different purposes. It may be advantageous to calculate reliability indices without planned interruptions in order to review performance during unplanned events. In another case, it may be

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advantageous to review only sustained interruptions. Assessment of performance trends and goal setting should be based on normal event days (neglecting the impact of MEDs). Utilities and regulators determine the most appropriate data to use for reliability performance monitoring. When indices are calculated using partial data sets, the basis should be clearly defined for the users of the indices.

At a minimum, reliability indices based on all collected data for a reporting period and analyzed as to normal versus major event day classifications should be provided. Indices based on subsets of collected data may be provided as specific needs dictate.

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Annex D (informative) Bibliography [B1] ―A Nationwide Survey of Distribution Reliability Measurement Practices,‖ IEEE/PES Working Group on System Design, Paper No. 98 WM 218.

[B2] Balijapelli N., Venkata S. S., Christie R. D., ―Predicting Distribution System Performance Against Regulatory Reliability Standards,‖ to appear in IEEE Transactions on Power Delivery.

[B3] Blinton, R. and Allan R. N., ―Reliability Evaluation of Power Systems,‖ Plenum Press, 1984.

[B4] Billinton R., Allan R., Salvaderi L., Applied Reliability Assessment in Electric Power Systems, IEEE Press, New York, 1991.

[B5] Brown R.E., Electric Power Distribution Reliability, Marcel Dekker, New York, 2002.

[B6] Capra, R. A., Gangel, M. W., and Lyon, S.V. ―Underground Distribution System Design for Reliability,‖IEEE Transactions on Power Apparatus and Systems, Vol. PAS-88, No. 6, June 1969, pp. 834-42.

[B7] Christie R.D., ―Statistical Classification of Major Event Days in Distribution System Reliability,‖accepted to IEEE Transactions on Power Delivery.4 [B8] EPRI EL-2018, RP-1356-1, Development of Distribution System Reliability and Risk Analysis Models," Vol. 2, August 1981.

[B9] IEEE 100, The Authoritative Dictionary of IEEE Standard Terms, 7th Edition.5 [B10] Kottegoda N. T., and Rosso R., Statistics, Probability, and Reliability for Civil and Environmental Engineers, McGraw-Hill, New York, 1997.

[B11] Marinello, C. A., ―A Nationwide Survey of Reliability Practices,‖ presented at EEI Transaction and Distribution Committee Meeting, Hershey, PA, October 20, 1993.

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Made by the Authority, after consultation with the Governor and the licensee, the 8th day of March, 2012.

S.B. Cowan Chairman.

D.B.R. Rankine Member.

Mike Herland Member.

D.L. Tibbetts Member.

Sam Young Member.