Modeling and analysis of distribution reliability indices

• Published in: IEEE transactions on power delivery Vol. 19; no. 4; pp. 1950 - 1955
• Main Authors: Balijepalli, N., Venkata, S.S., Christie, R.D.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.10.2004; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Assessments; Computer simulation; Connection and protection apparatus; Data analysis; Electrical engineering. Electrical power engineering; Electrical power engineering; Exact sciences and technology; Failure analysis; Feeders; Mathematical models; Monte Carlo methods; Operation. Load control. Reliability; Performance analysis; Power electronics, power supplies; Power engineering and energy; Power networks and lines; Power supplies; Power system modeling; Power system planning; Power system reliability; Reliability engineering; Standard deviation; Statistical distributions; Theory. Simulation; Statistical distribution; Power system stability; Power supply; reliability indices; Damage prevention; reliability assessment; Power system simulation; Standard deviation; Mechanical protection; Mathematical model; Distribution network; Monte Carlo method; Data analysis; Outage; Clientele; Protective device; Distribution systems; Power electronics; Computation time; Sensitivity; Electrical network; Numerical simulation; Power system reliability; Planning; Monte Carlo simulation
• ISSN: 0885-8977; 1937-4208
• DOI: 10.1109/TPWRD.2004.829144

Assessment of customer power supply reliability is an important part of distribution system operation and planning. Monte Carlo simulations can be used to find the statistical distribution of the reliability indices, along with their mean and standard deviation. The standard deviation of the reliability indices provides distribution engineers with information on the expected range of the annual values. However, the Monte Carlo simulation usually is a time-consuming computation. In this paper, an efficient Monte Carlo simulation method for distribution system reliability assessment is presented. Analysis of outage data from a practical distribution system is performed to determine the failure and repair models appropriate for use in the Monte Carlo simulation. The sensitivity of the reliability indices to the choice of model is presented. Finally, the impact of protection devices on the statistical distribution of System Average Interruption Frequency Index (SAIFI) for a practical distribution feeder is presented.