An Improved Genetic Algorithm and Its Application to Load Model Parameters Identification
The influence of genetic operations and selection of control parameters on the performance of genetic algorithm is studied. To overcome the shortcomings of basic genetic algorithm in selection and mutation, proportional selection and linear selfadaptive mutation strategies are designed so that the selection and mutation probability are dependent on the fitness of load model parameters rather than fixed. The developed algorithm is used to identify TVA load model based on measurementbased modeling. Practical modeling process shows that the proposed genetic algorithm is excellent in accelerating convergence, shortening identification time, overcoming dispersivity of model parameters and improving the identification precision.
Power System Parameter Identification Genetic Algorithm (GA) Proportional Selection Linear Self-adaptive Mutation, TVA Load Modeling
Jie Ma Ming-xiao Han Shu-jun Yan Ming-ming Du
North China Electric Power University, 102206, Beijing, China
国际会议
The International Conference on Electrical Engineering 2009(2009 电机工程国际会议)
沈阳
英文
1-5
2009-07-05（万方平台首次上网日期，不代表论文的发表时间）