中圖分類號(hào)： TN06；TM711
文獻(xiàn)標(biāo)識(shí)碼： A
DOI：10.16157/j.issn.0258-7998.211614
中文引用格式： 陳文瑛，龍躍，傅宏，等. 客戶側(cè)竊電態(tài)勢(shì)感知及智能預(yù)警關(guān)鍵技術(shù)的研究[J].電子技術(shù)應(yīng)用，2021，47(12)：69-73.
英文引用格式： Chen Wenying，Long Yue，F(xiàn)u Hong，et al. Research on key technologies of situation awareness and intelligent early warning of electricity theft on customer side[J]. Application of Electronic Technique，2021，47(12)：69-73.

Research on key technologies of situation awareness and intelligent early warning of electricity theft on customer side

Abstract： The customer side electricity stealing behavior not only causes the massive loss of power resources, but also causes the overload of line load, leading to fire and other major safety accidents. Aiming at the diversity and concealment characteristics of the current electricity stealing behavior in the side toilets, the key technologies of situation awareness and intelligent early warning of electricity stealing on the customer side are studied for the purpose of restraining the electricity stealing behavior on the customer side. Considering the diversity and concealment of customer side power stealing behavior, six customer side power stealing situation awareness indicators are selected, including rated voltage deviation, voltage imbalance rate and current imbalance rate，etc. The RBF neural network is used to build the customer side power stealing situation awareness model. The selected six indicators and related data are used as the model inputs, and the dynamic K-means clustering algorithm is used to optimize the model. The output of the model is the customer side power stealing situation awareness result. Based on the sensing results, intelligent early warning is realized by sound light alarm device and intelligent device. The experimental results show that the technology can effectively suppress the customer side electricity stealing behavior.

Key words : customer side；electricity theft；situation awareness；intelligent early warning；perception index