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Study On Genetic Simulated Annealing Algorithm Based Energy-saving Reconstruction Of 10 KV Distribution Lines

Posted on:2012-02-11Degree:MasterType:Thesis
Country:ChinaCandidate:L J YangFull Text:PDF
GTID:2132330335453927Subject:Power system and its automation
Abstract/Summary:PDF Full Text Request
Aiming at the problems of the 10 kV medium voltage distribution lines at present, i.e. too large line loss and low power supply reliability, simple and accurate criteria are formulated according to the actual situation to evaluate indexes such as line types, load rate and operation time. The lines selected are judged by implementing risk. Lines without implementing risk will be reconstructed, with measures including line replacement, line switch, double power supply, distribution room switch, etc. How to determine the rehabilitation programs, which can not only reduce line loss but also adapt to urban development and the needs of gradual growth of electricity load and meet the requirements of power quality and reliability, is key issue in reconstruction. A rehabilitation program is some combination of all types of reconstruction projects which are composed of a number of reconstruction contents which are used to reach a common goal. Actually, optimization of reconstruction programs is the combination of optimization of all reconstruction projects. It is a multi-objective uncertain nonlinear mixed integer programming problem. The physical model and mathematical model of distribution lines energy-saving reconstruction are established. Genetic algorithm and simulated annealing algorithm are combined to solve the problem. Based on kinds of operation constraints, optimal scheme of lines is formulated under the objective of minimum ratio of investment to annual loss-reducing benefit, i.e. stable investment recovery period. Calculation results show that the method can effectively reduce the line loss.
Keywords/Search Tags:Distribution line, Reconstruction, Energy-saving, Connection Genetic simulated annealing algorithm, implementing risk
PDF Full Text Request
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