Dynamic Population Variation Genetic Programming with Kalman Operator for Power System Load Modeling

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Tao:2010:ICONIP,

• author = "Yanyun Tao and Minglu Li and Jian Cao",
• title = "Dynamic Population Variation Genetic Programming with Kalman Operator for Power System Load Modeling",
• booktitle = "17th International Conference Neural Information Processing (ICONIP 2010) - Theory and Algorithms, Part I",
• year = "2010",
• editor = "Kok Wai Wong and B. Sumudu U. Mendis and Abdesselam Bouzerdoum",
• volume = "6443",
• series = "Lecture Notes in Computer Science",
• pages = "520--531",
• address = "Sydney, Australia",
• month = nov # " 22-25",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1007/978-3-642-17537-4_64",
• bibdate = "2010-11-22",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/iconip/iconip2010-1.html#TaoLC10",
• 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 optimised by Kalman operator for higher accuracy and an optimisation model is obtained. Experiments are used to illustrate that evolved model has higher accuracy 4.6-48percent 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 7.69-81.3percent than evolved model.",
• affiliation = "School of electronic information and electrical engineering, Shanghai JiaoTong University, Dongchuan road 800, 200240 Shanghai, China",
• notes = "10kV and 35kV transformers TanShi substation in China.",

}

Genetic Programming entries for Yanyun Tao Minglu Li Jian Cao

Citations