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Transactions: WSEAS TRANSACTIONS ON POWER SYSTEMS
Transactions ID Number: 52-590
Full Name: Pei AO
Position: Ph.D. Candidate
Age: ON
Sex: Female
Address: 390 Room, Building of Electronics and Information Engineering, No.4800, Caoan Road, Jiading District, Shanghai
Country: CHINA
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E-mail address: aopei16.student@sina.com
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Title of the Paper: Aggregate Static Load Modelling in Power Grid with Environmental Characteristics
Authors as they appear in the Paper: Pei AO , MU Long-hua
Email addresses of all the authors: aopei16.student@sina.com ;lhmu@vip.163.com
Number of paper pages: 10
Abstract: In practice, there are many defects that underground load model is built by using the traditional static load model in coal mine. To enhance the accuracy of load model, a new clustering method based on improved PSO algorithm is presented in this paper. This new clustering method is used to classify the load data in order to reduce the number of load model before modelling. Then, RBF neural network based on improved PSO algorithm is proposed to establish aggregate load model. Verified by an example, compared with traditional static load model, the method in this paper can greatly improve the accuracy of the model.
Keywords: aggregate static load model; particle swarm clustering algorithm; subtractive clustering algorithm; K-means clustering algorithm; radial basis neural network; power grid with environmental Characteristics
EXTENSION of the file: .doc
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