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- W2385865091 abstract "The influence of genetic operations and selection of control parameters on the performance of genetic algorithm's application in load modeling is studied.To overcome the shortcomings of basic genetic algorithm in selection and mutation,proportional selection and linear self-adaptive mutation strategies are designed to improve the behavior of the algorithm.The value ranges of parameters are adjusted to reflect reactive compensation.The improved algorithm can balance the complexity and the effectiveness of GA in load model parameters identification.The developed algorithm is used to identify TVA load model based on measurement-based modeling.And practical modeling process shows that the proposed genetic algorithm is excellent in accelerating convergence,shortening identification time,overcoming the dispersivity of model parameters and improving the identification precision." @default.
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- W2385865091 date "2011-01-01" @default.
- W2385865091 modified "2023-09-24" @default.
- W2385865091 title "Improved Genetic Algorithm and Its Application to Composite Load Model" @default.
- W2385865091 hasPublicationYear "2011" @default.
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