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Dianli Zidonghua Shebei / Electric Power Automation Equipment
Volumn 26, Issue 5, 2006, Pages 23-27
GA parameter setting and its application in load modeling
Author keywords

Control parameter; Electric power system; Genetic algorithm; Load modeling; Parameter range
Indexed keywords

ELECTRIC LOADS; GENETIC ALGORITHMS; MATHEMATICAL MODELS; PROBABILITY;
EID: 33749031258     PISSN: 10066047     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (18)
References (11)
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  • 3
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    • Diversity enhancing genetic algorithm for voltage and reactive power optimization
    • ZHANG Yong-jun, REN Zhen, TANG Zhuo-yao, et al. Diversity enhancing genetic algorithm for voltage and reactive power optimization[J]. Electric Power Automation Equipment, 2003, 23(1): 18-24.
    • (2003) Electric Power Automation Equipment , vol.23 , Issue.1 , pp. 18-24
    • Zhang, Y.-J.1    Ren, Z.2    Tang, Z.-Y.3
  • 4
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    • The generalized induction motor model and its description ability to synthetic loads of electric power system
    • LI Xin-ran, HE Ren-mu, ZHOU Wen, et al. The generalized induction motor model and its description ability to synthetic loads of electric power system[J]. Automation of Electric Power Systems, 1999, 23(9): 23-27.
    • (1999) Automation of Electric Power Systems , vol.23 , Issue.9 , pp. 23-27
    • Li, X.-R.1    He, R.-M.2    Zhou, W.3
  • 5
    • 19044373356 scopus 로고    scopus 로고
    • Comparative study of genetic algorithm and traditional optimization in power load modeling
    • LI Xin-ran, LIU Yan-yang, CHEN Hui-hua, et al. Comparative study of genetic algorithm and traditional optimization in power load modeling[J]. Journal of Hunan University, 2005, 32(2): 29-32.
    • (2005) Journal of Hunan University , vol.32 , Issue.2 , pp. 29-32
    • Li, X.-R.1    Liu, Y.-Y.2    Chen, H.-H.3
  • 6
    • 0033341899 scopus 로고    scopus 로고
    • Approaches to identifiability analysis of electric load models
    • JU Ping, ZHAO Xia-yang, LI Dong-hui. Approaches to identifiability analysis of electric load models[J]. Automation of Electric Power Systems, 1999, 23(19): 29-33.
    • (1999) Automation of Electric Power Systems , vol.23 , Issue.19 , pp. 29-33
    • Ju, P.1    Zhao, X.-Y.2    Li, D.-H.3
  • 7
    • 33749031233 scopus 로고    scopus 로고
    • Chinese source
  • 8
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    • The power system load model structure and the parameter recognizes
    • Baoding: North China Electric Power University
    • ZHANG Hong-bin. The power system load model structure and the parameter recognizes[D]. Baoding: North China Electric Power University, 2003.
    • (2003)
    • Zhang, H.-B.1
  • 9
    • 4444364597 scopus 로고    scopus 로고
    • Adaptive genetic algorithm to improve group premature convergence
    • WU Hao-yang, ZHU Chang-chun, CHANG Bing-guo, et al. Adaptive genetic algorithm to improve group premature convergence[J]. Journal of Xi'an Jiaotong University, 1999, 33(11): 27-30.
    • (1999) Journal of Xi'an Jiaotong University , vol.33 , Issue.11 , pp. 27-30
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    • Reactive power optimization of integrative power system based on improved genetic algorithm
    • SHENG Zhao-jun, LIU Han. Reactive power optimization of integrative power system based on improved genetic algorithm[J]. Electric Power Automation Equipment, 2004, 24(4): 27-29.
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    • de JONG K A. An analysis of the behavior of a class of genetic adaptive systems[D]. East Lansing, USA: University of Michigan, 1975.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.