الفهرس 
Basic Concepts of Power System Reliability EvaluationOnly 14 pages are availabe for public view
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Abstract
A large percentage of individual customer interruptions originate within the actual
distribution network serving theses customers. It appears obvious; therefore, that this
should be a fruitful area for detailed reliability evaluation and investigation. Protective
equipment is located in a network to protect equipment and to isolate equipment failures
and faults. The type of protective equipment used can have a direct effect on the frequency
and duration of outages experienced by the customer.
The distribution system is an important part of the entire electric system, as it provides
the final link between the bulk power sources and the customer’s facilities. In many cases
these links are radial in nature and therefore susceptible to outage due to a single event. It
has been stated that 90% of all interruptions occurs due to failures in the distribution
system. These outages usually resulted in customer interruptions which are relatively local
in nature and have quite different effects to disturbances in the bulk power network.
The reliability evaluation of a distribution system consists of assessing how adequately
the different parts of the distribution system are able to perform their intended function.
Although many questions are being asked about the quality of service across the world,
presently there are no standardized ways to track reliability between utilities. Some utilities
are using EEI/IEEE approved indices but most utilities have different ways of calculating
the data.
Reliability assessment of a distribution system is usually connected with the system
performance at the customer end, i.e. at the load point. The basic indices normally used to
predict the reliability of a distribution system are: load point failure rate, average outage
duration, and annual unavailability. The basic indices are important from the individual
customer’s point of view but they do not provide an overall appreciation of the system
performance. They are relevant for circuits that are mostly industrial or commercial. An
additional set of indices can be calculated using these three basic indices and the number of
customers/loads connected at each load point in the system. Most of these additional indices
are weighted averages of the basic load point indices. The most common additional or
system indices are: System Average Interruption Frequency Index (SAIFI), System Average
Interruption Duration Index (SAIDI), Customer Average Interruption Duration Index
(CAIDI), Average Service Availability Index (ASAI), Average Service Unavailability Index
(ASUI), Energy Not Supplied (ENS), and Average Energy Not Supplied (AENS). These
system indexes are also calculated by a large number of utilities from system interruption
data and provide valuable indications of historic system performance. It is important to calculate both sets of indices to get a true picture of reliability. The IEEE application of
probability methods subcommittee published a Reliability Test System (RTS) in 1979 and
extended in 1985 and 1989. Also a developed busbar test system defined as the RBTS is given
in 1996. RBTS after its extension to include distribution system contains the main elements
found in practical system.
The purpose of chapter 2 is to perform continuity analysis for a range of alternative
design/operating configurations of the RBTS Bus2.
Both load point reliability indices and system indices have been evaluated. The effect of
distribution facilities and feeder configuration/operation modes on improving load point and
system reliability is assessed. A computer program has been developed which is capable of
assessing reliability of practical distribution systems.
Electrical energy is a vital ingredient in the economic, social, and geographical
development of a region, province, or country. The basic function of an electric power
system is to meet its energy and load demand at the lowest possible cost to its customers
while maintaining acceptable levels of quality and continuity of supply. What constitutes an
“acceptable” level can be examined in terms of cost and worth to the customer of providing
an adequate supply. The economic, social, and political climate in which the electric power
industry now operates has changed drastically in the last few decades. Statistical evaluation
of past system performance and probabilistic evaluation of further performance are of great
value to power system planners and operators. This fact is now widely recognized and
efforts are being made to quantify the worth of electric service reliability. The loss of energy
expectation or Expected Energy Not Supplied (EENS) has been used in conjunction with and
the Interrupted Energy Assessment Rate (IEAR) to estimate future interruption costs
associated with power system deficiencies. The system IEAR is a factor that defines the cost
to a representative customer of each unit of unsupplied energy due to power interruptions
and is a useful index of making decisions related to system reliability.
The basic objective of chapter 3 is to illustrate how to obtain IEAR indices in the area of
distribution systems for each customer load point and for the overall distribution system.
The developed procedure is illustrated by application to the distribution system associated
with the test system designated as the RBTS. The customer IEAR can be utilized in the
conjunction with the system reliability (adequacy) indices in order to perform value-based
reliability analysis and justify new system investment.
A variety of approaches have been used to determine the actual or perceived costs of
customer interruptions. One method which has been used to establish acceptable reliability worth estimate is to survey electrical consumers in order to determine the monetary losses
associated with supply interruptions. The data compiled from these surveys is used to
generate Sector Customer Damage Functions (SCDF). The cost interruption data in $/kW of
peak demand for eight sectors is given in Appendix B. Chapter 3 utilizes the SCDF as the
cost functions in relating the load point reliability indices to worth of service reliability.
Distribution system reliability assessment is a quickly maturing field. It has evolved
from the first EPRI program in 1978, to programs developed and used in-house by utilities,
to commercially available software products. These planning tools are able to predict the
reliability of a distribution system based on system topology and component reliability data.
Unfortunately, these products will never gain widespread use until utilities are confident
that available data is representative of their actual system.
Ideally, a utility will have a large amount of historical data from which it can determine
the reliability of various components such as lines, protection devices, and switches. Most
utilities, however, do not have this information available. Values may be obtained from
published data corresponding to other systems, but this data may not be representative of
the system under consideration. This data discrepancy is most evident when predicted
system reliability indices do not agree with historically computed reliability indices.
Most utilities do not have a substantial amount of historical component reliability data.
Nearly all utilities, however, have historical system reliability data in the form of reliability
indices (e.g., SAIFI, SAIDI, …). When a system is modeled, the reliability indices predicted
by the assessment tool should agree with these historical values. If so, a certain level of
confidence in the model is achieved and more specific reliability results (e.g., the reliability
of a specific load point or the impact of a design change) can be trusted to a higher degree.
When this confidence has been achieved and predicted results match historical results, the
reliability model is said to be validated.
Chapter 4 presents a new method of distribution system reliability model validation. It
first identifies which default component reliability parameters should be modified by
performing a sensitivity analysis on the RBTS test system. It then presents a method of
computing these parameter values so that predicted system index values match historically
computed index values.