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Reconfiguration Of Distribution Networks Based On Immune Genetic Algorithm

Posted on:2010-09-01Degree:MasterType:Thesis
Country:ChinaCandidate:L J XuFull Text:PDF
GTID:2192360278969376Subject:Power system and its automation
Abstract/Summary:PDF Full Text Request
Network reconfiguration is not only an important method in circulate and control of distribution system, but also an important part of distribution management system. When power is flowing through the net work, every component in distribution network is sure to consume some energy, Then great attention should be paid to the optimal operation of the distribution network., Distribution network reconfiguration is a useful way to the loss reduction.After multifactorial analysis of many distribution network power flow algorithms, computing power loss using hybrid power flow calculation method is put forward. After analyzing the principle and performance of immune algorithm, we analysised the Affinity, the mutation rules and the rules of antibody inhibition, developed a distribution network minimum loss reconfiguration of solution and simulation of the system; Searching speed of this algorithm and the influence to it when changing some parameters are discussed, Then comparing the results of these problems by the improved variable scale immune algorithm to those by other algorithms, the efficiency of the algorithm is validated once more.Based on the proposed mathematical model and algorithm practical program is made by Matlab language. The proposed algorithm in this paper is applied to the IEEE 30-bus system and compares the results of optimization with the advanced genetic algorithm of other paper. The favourable effect can be obtained by using GA in reactive optimization that is in node voltage control., power loss reduction and improve reliability. All of the results show that the proposed algorithm in this paper has better ability of overall searching optimal solution and higher precision.
Keywords/Search Tags:Genetic algorithm, Distribution network reliability, feeder losses, Immune Genetic Algorithm
PDF Full Text Request
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