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

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@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