基于社群特征的配電網(wǎng)異常用電行為分析
董津辰，雷景生
（上海電力學院 計算機科學與技術學院，上海 200090）
摘 要：針對目前配電網(wǎng)異常用電行為精度欠佳、效率低下、人力資源耗費量大等問題，在海量用電數(shù)據(jù)中利用數(shù)據(jù)挖掘技術實現(xiàn)異常用電數(shù)據(jù)的精確查找與定位。通過引入社群習慣的行業(yè)季節(jié)用電水平等異常分類指標，對可能存在非技術性損耗（NTL）的配網(wǎng)用戶進行分析和檢測，利用改進粒子群LM 神經(jīng)網(wǎng)絡算法建立了有效的異常用電行為的自動識別模型。實驗結果表明：該模型能夠有效地提取用電特征，實現(xiàn)對異常用戶的檢測，具有較強的識別能力和較高的實用性。
關鍵詞：異常用電；非技術性損耗；社群特征；改進粒子群算法
中圖分類號：TM744 文獻標識碼：A 文章編號：1007-3175(2019)01-0014-06
Abnormal Power Consumption Behavioural Analysis of Power Distribution Network Based on Association Characteristic
DONG Jin-chen, LEI Jing-sheng
（College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 200090, China）
Abstract: In order to solve the problem of poor accuracy, low efficiency, and high consumption of human resources in abnormal power consumption of power distribution network, this paper used data mining technology to accurately locate abnormal power consumption data in magnanimity power utilization data. The network users who might have non-technical loss (NTL) were analyzed and detected by using the industry's seasonal power consumption level of the community's habits and other abnormal classification indicators. The improved particle swarm LM neural network optimization algorithm was utilized to establishe an effective automatic recognition model for abnormal power consumption. The experimental results show that this model can effectively extract the electricity characteristics and realize the detection of abnormal users with strong recognition ability and high practicability.
Key words: abnormal power consumption; non-technical loss; community feature; improved particle swarm optimization
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