Reliability assessment of electrical power systems using genetic algorithms

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Title: Reliability assessment of electrical power systems using genetic algorithms
Author: Samaan, Nader Amin Aziz
Abstract: The first part of this dissertation presents an innovative method for the assessment of generation system reliability . In this method , genetic algorithm (GA ) is used as a search tool to truncate the probability state space and to track the most probable failure states . GA stores system states , in which there is generation deficiency to supply system maximum load , in a state array . The given load pattern is then convoluted with the state array to obtain adequacy indices . In the second part of the dissertation , a GA based method for state sampling of composite generation -transmission power systems is introduced . Binary encoded GA is used as a state sampling tool for the composite power system network states . A linearized optimization load flow model is used for evaluation of sampled states . The developed approach has been extended to evaluate adequacy indices of composite power systems while considering chronological load at buses . Hourly load is represented by cluster load vectors using the k -means clustering technique . Two different approaches have been developed which are GA parallel sampling and GA sampling for maximum cluster load vector with series state revaluation . The developed GA based method is used for the assessment of annual frequency and duration indices of composite system . The conditional probability based method is used to calculate the contribution of sampled failure states to system failure frequency using different component transition rates . The developed GA based method is also used for evaluating reliability worth indices of composite power systems . The developed GA approach has been generalized to recognize multi -state components such as generation units with derated states . It also considers common mode failure for transmission lines . Finally , a new method for composite system state evaluation using real numbers encoded GA is developed . The objective of GA is to minimize load curtailment for each sampled state . Minimization is based on the dc load flow model . System constraints are represented by fuzzy membership functions . The GA fitness function is a combination of these membership values . The proposed method has the advantage of allowing sophisticated load curtailment strategies , which lead to more realistic load point indices .
URI: http : / /hdl .handle .net /1969 .1 /1054
Date: 2004-11-15


Reliability assessment of electrical power systems using genetic algorithms. Available electronically from http : / /hdl .handle .net /1969 .1 /1054 .

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