Aggregate static load modeling in power grid with environmental characteristics

Author

Ao Pei ; Mu Long-hua

Author_Institution

Sch. of Electron. & Inf. Eng., Tongji Univ., Shanghai, China

Volume

3

fYear

2011

fDate

8-9 Sept. 2011

Firstpage

217

Lastpage

220

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 modeling. 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

coal; environmental factors; mining; particle swarm optimisation; pattern classification; pattern clustering; power grids; radial basis function networks; RBF neural network; aggregate static load modeling; clustering method; coal mine; environmental characteristics; improved PSO algorithm; load data classification; power grid; underground load model; Aggregates; Algorithm design and analysis; Approximation algorithms; Clustering algorithms; Clustering methods; Data models; Load modeling; K-means clustering algorithm; aggregate static load model; particle swarm clustering algorithm; radial basis neural network; subtractive clustering algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Engineering and Automation Conference (PEAM), 2011 IEEE

Conference_Location

Wuhan

Print_ISBN

978-1-4244-9691-4

Type

conf

DOI

10.1109/PEAM.2011.6135049

Filename

6135049