Dynamic population variation genetic programming with Kalman operator for power system load modeling

Authors:
Yanyun Tao;Minglu Li;Jian Cao
Affiliations:
School of Electronic Information and Electrical Engineering, Shanghai JiaoTong University, Shanghai, China;School of Electronic Information and Electrical Engineering, Shanghai JiaoTong University, Shanghai, China;School of Electronic Information and Electrical Engineering, Shanghai JiaoTong University, Shanghai, China
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Year:
2010

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Abstract

According to the high accuracy of load model in power system, a novel dynamic population variation genetic programming with Kalman operator for load model in power system is proposed. First, an evolution load model called initial model in power system evolved by dynamic variation population genetic programming is obtained which has higher accuracy than traditional models. Second, parameters in initial model are optimized by Kalman operator for higher accuracy and an optimization model is obtained. Experiments are used to illustrate that evolved model has higher accuracy 4.6∼48% than traditional models and It is also proved the performance of evolved model is prior to RBF network. Furthermore, the optimization model has higher accuracy 7.69∼81.3% than evolved model.