《電子技術(shù)應(yīng)用》
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客户侧窃电态势感知及智能预警关键技术的研究
2021年电子技术应用第12期
陈文瑛1，龙 跃1，傅 宏2，杨芾藜2，周 川2
1.国网重庆市电力公司，重庆400010；2.国网重庆市电力公司营销服务中心，重庆400010
摘要： 客户侧窃电行为不仅造成电能资源大量流失，同时造成线路负荷过载引发火灾等重大安全事故。针对当前客户侧窃电行为的多样性与隐蔽性特征，以约束客户侧窃电行为为目的，设计了客户侧窃电态势感知及智能预警关键技术。考虑客户侧窃电行为的多样性与隐蔽性特性，选取额定电压偏离度、电压不平衡率与电流不平衡率等6个客户侧窃电态势感知指标，利用RBF神经网络构建客户侧窃电态势感知模型，将所选取的6个指标与相关数据作为模型输入，通过动态K均值聚类算法优化模型，模型输出结果即为客户侧窃电态势感知结果。基于感知结果，通过声光报警装置与智能设备实现智能预警，实验结果显示，该技术能够有效抑制客户侧窃电行为。
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
Chen Wenying1，Long Yue1，Fu Hong2，Yang Fuli2，Zhou Chuan2
1.State Grid Chongqing Electric Power Company，Chongqing 400010，China； 2.State Grid Chongqing Electric Power Company Marketing Service Center，Chongqing 400010，China
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

0 引言

    作為一種重要的能源，電能既普遍應(yīng)用于人們?nèi)粘Ｉ钆c工作中，又對(duì)社會(huì)經(jīng)濟(jì)發(fā)展與國(guó)防安全產(chǎn)生直接影響[1]。在科技飛速發(fā)展與能源格局改變的大環(huán)境下，提升能源利用率與電能傳輸?shù)陌踩?、可靠性是?dāng)前電力行業(yè)關(guān)注的重點(diǎn)目標(biāo)[2]。電能的損失不僅是由于電網(wǎng)線路內(nèi)的電阻與設(shè)備轉(zhuǎn)換造成的，客戶側(cè)竊電同樣是電能損失的主要途徑[3]?，F(xiàn)實(shí)生活中，客戶側(cè)端用電設(shè)備的顯著提升令電能的消耗也顯著提升，部分客戶為“節(jié)約成本”紛紛利用不同方式實(shí)施竊電行為，造成電能資源大量流失，嚴(yán)重制約了我國(guó)電力產(chǎn)業(yè)發(fā)展的穩(wěn)定性[4]。同時(shí)，客戶側(cè)為實(shí)施竊電行為，私自改造電路，令電網(wǎng)內(nèi)產(chǎn)生嚴(yán)重線路負(fù)荷過載的問題，這些問題極易導(dǎo)致火災(zāi)等重大安全事故[5]。針對(duì)當(dāng)前具有多樣性與隱蔽性特性的竊電方法[6]，研究一種有效的客戶側(cè)竊電態(tài)勢(shì)感知及智能預(yù)警關(guān)鍵技術(shù)具有重要意義。




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作者信息：

陳文瑛1，龍  躍1，傅  宏2，楊芾藜2，周  川2

(1.國(guó)網(wǎng)重慶市電力公司，重慶400010；2.國(guó)網(wǎng)重慶市電力公司營(yíng)銷服務(wù)中心，重慶400010)




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