DocumentCode :
2091706
Title :
Study on Small Sample Data Parameter Identification for Power System Static Load Model
Author :
Ao Pei ; Mu Long-hua
Author_Institution :
Sch. of Electron. & Inf. Eng., Tongji Univ., Shanghai, China
fYear :
2010
fDate :
28-31 March 2010
Firstpage :
1
Lastpage :
4
Abstract :
Using traditional least squares criterion suitable for big sample data to identify the parameters, the small data quantity, the noise disturbance and the unusual data will bring about some adverse effects. In order to overcome these adverse effects, minimum sum of absolute residual criterion suitable for small sample data is applied to identify model parameters in this article. Genetic algorithm is used to gain the optimal solution of the parameters. Proved by the practical example, higher accuracy and reliability can be obtained by using this method to build model.
Keywords :
genetic algorithms; parameter estimation; power system simulation; absolute residual criterion; genetic algorithm; power system static load model; small sample data parameter identification; Character generation; Data engineering; Encoding; Genetic algorithms; Load modeling; Parameter estimation; Power engineering and energy; Power system analysis computing; Power system modeling; Stochastic processes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Engineering Conference (APPEEC), 2010 Asia-Pacific
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-4812-8
Electronic_ISBN :
978-1-4244-4813-5
Type :
conf
DOI :
10.1109/APPEEC.2010.5448363
Filename :
5448363
Link To Document :
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