FUZZY RELIABILITY ANALYSIS OF DISTRIBUTION SYSTEMS

Abstract

Distribution system Reliability is one of the most important topics in the electric power industry due to its high impact on the cost of electricity and its high correlation with customer satisfaction. Distribution reliability primarily relates to equipment outages and customer interruptions. The distribution system reliability can be evaluated with analytical and simulation methods. The analytical methods for the reliability evaluation are highly developed. These methods use the data such as the failure rate, repair rate switching rate etc which is obtained from the historical data. These are not accurate and contain a lot of uncertainty in them. Fuzzy set theory is an excellent tool for modeling such kind of uncertainty associated with vagueness, imprecision and/ or with lack of information regarding a particular component or system. This thesis describes about the .Markov Modeling and Event Tree Analysis for the Distribution System Reliability Assessment Markov Modeling is a powerful method based on system states and transition between the states. When a fault occurs on the distribution system network the system transitions form the successful state to failure state and once the repair is done it again transitions into success or operating state. The Markov modeling analysis is done by finding the probability of system or component in each state. The data used for the Markov Model in this thesis has been taken from the from the reliability test system proposed by Dr. Roy Billinton. Event tree analysis can be used to recognize the sequential logic of the system. The Event Tree Analysis gives the sequence of operations of the protective devices after the fault has been initiated. This thesis describes the application of fuzzy set theory in the evaluation of reliability of power distribution networks using fuzzy Markov modeling and fuzzy event tree analysis to reduce the uncertainty.