基于改进粒子群算法的前馈神经网络识别用户窃电行为

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Abstract In order to reduce the negative impact of electricity theft on power grid operation, improve the ability of electricity theft inspection and the accuracy of identifying electricity theft users, a feedforward neural network model based on improved particle swarm optimization algorithm (BFO-PSO) is proposed. Based on the electricity consumption data of a city in recent four years, through feature extraction, four features that have a greater impact on electricity stealing behavior are obtained as input samples. A feedforward neural network recognition model based on BFO-PSO algorithm is constructed, and the optimal weight value of BP neural network model are calculated by using algorithm BFO-PSO. By comparing the recognition results of BP neural network model, the genetic algorithm based BP neural network model and the BFO-PSO based BP neural network model, it is found that the BP network model based on BFO-PSO can better identify the power stealing users. The recognition accuracy is as high as 94%, and the training speed is increased by 5%. It is expected to be widely used in the power stealing user recognition.

Key words： stealing electricity improved particle swarm optimization feedforward neural network user identification

Received: 04 July 2022

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LI Qiuhong. Feedforward neural network based on improved particle swarm optimization algorithm for identification of user electricity stealing behavior[J]. Electrical Engineering, 2022, 23(11): 44-48.

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http://dqjs.cesmedia.cn/EN/Y2022/V23/I11/44

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