A 10kV Distribution Network Line Loss Prediction Method Based on Grey Correlation Analysis and Improved Artificial Neural Network

被引：0
|
作者：
Zhang Y. [1 ]
Wang Z. [1 ]
Liu L. [2 ]
Deng C. [2 ]
Sun Y. [2 ]
Wang X. [2 ]
Han X. [2 ]
机构：
[1] School of Electrical and Electronic Engineering, North China Electric Power University, Changping District, Beijing
[2] China Electric Power Research Institute, Haidian District, Beijing
来源：
Dianwang Jishu/Power System Technology | 2019年 / 43卷 / 04期
关键词：
10kV distribution network line loss; Adaptive genetic algorithm; Artificial neural network; Grey correlation analysis;
D O I：
10.13335/j.1000-3673.pst.2018.1193
中图分类号：
学科分类号：
摘要：
To estimate the level of 10kV distribution network line loss more integrally and accurately, a 10kV distribution network line loss prediction method based on grey correlation analysis and improved artificial neural network is proposed. Relevancies between 15 electrical indexes and 10kV distribution network line loss are analyzed with grey correlation, and the best electrical characteristic indexes are selected after checked with practical data of 10kV distribution network. To overcome difficulty in determining the number of nodes in hidden layer, predictive performancesof the line loss prediction model under different neural network structures are analyzed with cross validation and test-and-error methods. Considering slow convergence and existence of local minimum in conventional BP neural network (BPNN), adaptive genetic algorithm (AGA) is adopted to improve BP neural network (AGA-BPNN), and comparedwith RBF neural network (RBFNN) and conventional BP neural network. After calculating actual data of 10kV lines, minimum prediction error for 3 methods above are 6.71%, 12.95% and 17.05% respectively, proving better convergence and accuracy of AGA-BPNN. © 2019, Power System Technology Press. All right reserved.
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页码：1404 / 1410
页数：6
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