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Improved Genetic Algorithms And Their Applications In Power Systems

Posted on:2005-03-05Degree:MasterType:Thesis
Country:ChinaCandidate:L D QinFull Text:PDF
GTID:2132360152468875Subject:Power system and its automation
Abstract/Summary:PDF Full Text Request
This paper aims at providing us with improved genetic algorithms and their applications in power systems, especially on the aspects of economic dispatch and reactive power optimization. Firstly, this paper surveys applications of genetic algorithms to reactive power optimization and economic dispatch of power system, secondly, it proposes a queen-bee evolution based genetic algorithm for solving the economic dispatch problem of power system. This queen-bee evolution is similar to nature in that the queen-bee plays a major role in reproduction process and this proposed algorithm can enhance the optimization capability of genetic algorithms. Numerical results on two actual systems of 6 generators and 13 generators respectively show that the proposed algorithm is faster and more robust than the conventional genetic algorithm, thirdly, it proposes a hybrid multi-objective genetic algorithm for optimizing the multi-objective problems in the economic dispatch of power system. This proposed algorithm differs from other multi-objective genetic algorithms in its selection procedure, crossover procedure and mutation procedure. The selection procedure selects individuals for a crossover operation based on a weighted sum of multiple objective functions. The characteristic feature of the selection procedure is that the weights attached to the multiple functions are not constant but randomly specified for each selection. Furthermore, the crossover procedure and mutation procedure adaptively adjust the crossover probability, crossover position and mutation probability based on fuzzy logic technology. Numerical results on an actual system of 13 generators show that this proposed algorithm is effective, finally, it presents an improved genetic algorithm to reactive power optimization. This approach adaptively adjusts the crossover probability,crossover position and mutation probability based on fuzzy logic technology. The proposed method has been applied to the IEEE30 buses power systems. The computation results show that this approach can find optimal solution more efficiently.
Keywords/Search Tags:Improved Genetic Algorithm, Power System, Economic Dispatch, Reactive Power Optimization, Queen-bee Evolution, Hybrid Multi-objective Genetic Algorithm, Fuzzy Adaptive Genetic Algorithm
PDF Full Text Request
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