Power & Electrical Engineering

A Load Transfer Method for Power Distribution Networks Based on Graph Theory and Improved Fuzzy Genetic Algorithm

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  • School of Electric Power,South China University of Technology,Guangzhou 510640,Guangdong,China
武志刚(1975-),男,博士,副教授,主要从事电力系统仿真、复杂网络理论研究

Received date: 2015-03-26

  Revised date: 2015-06-15

  Online published: 2015-11-01

Supported by

Supported by the National High-Tech R&D Program of China (2012AA050209)

Abstract

In view of the fault recovery of complicated power distribution networks,a load transfer method for power distribution networks is proposed based on the graph theory and the improved fuzzy genetic algorithm. First,the to-pological structure and component parameters of feeders are integrated together by means of the JGraphT - based da-ta modeling of power distribution networks,and by taking advantage of the topological structure of power distribution networks explicitly,the tedious node encoding rules and the storage modes in the form of miscellaneous adjacency lists or matrices are avoided. Next,with the help of the graph theory algorithms integrated in JGraphT,different types of fault sections are quickly distinguished and the topological constraint is rapidly determined. Then,a for-ward - backward sweep method based on the recursive graph theory is developed. Finally,an improved fuzzy genet-ic algorithm is proposed according to the characteristics of power distribution networks. In the proposed algorithm,the initial solutions and genetic operators of the genetic algorithm are dynamically adjusted so as to improve the opti-mizing performance of the proposed algorithm,and the fuzzy inputs and fuzzy rules are revised reasonably to im-prove the convergence rate of the genetic algorithm and avoid the premature convergence. Simulation results show that the proposed algorithm is superior to the other methods in terms of power flow calculation and optimization effi-ciency.

Cite this article

Wu Zhi-gang Ma Yi-song . A Load Transfer Method for Power Distribution Networks Based on Graph Theory and Improved Fuzzy Genetic Algorithm[J]. Journal of South China University of Technology(Natural Science), 2015 , 43(12) : 48 -54,70 . DOI: 10.3969/j.issn.1000-565X.2015.12.007

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