《電子技術(shù)應(yīng)用》
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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
2021年電子技術(shù)應(yīng)用第12期
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
摘要: 客戶側(cè)竊電行為不僅造成電能資源大量流失,,同時(shí)造成線路負(fù)荷過(guò)載引發(fā)火災(zāi)等重大安全事故。針對(duì)當(dāng)前客戶側(cè)竊電行為的多樣性與隱蔽性特征,,以約束客戶側(cè)竊電行為為目的,,設(shè)計(jì)了客戶側(cè)竊電態(tài)勢(shì)感知及智能預(yù)警關(guān)鍵技術(shù)??紤]客戶側(cè)竊電行為的多樣性與隱蔽性特性,,選取額定電壓偏離度、電壓不平衡率與電流不平衡率等6個(gè)客戶側(cè)竊電態(tài)勢(shì)感知指標(biāo),,利用RBF神經(jīng)網(wǎng)絡(luò)構(gòu)建客戶側(cè)竊電態(tài)勢(shì)感知模型,,將所選取的6個(gè)指標(biāo)與相關(guān)數(shù)據(jù)作為模型輸入,通過(guò)動(dòng)態(tài)K均值聚類算法優(yōu)化模型,,模型輸出結(jié)果即為客戶側(cè)竊電態(tài)勢(shì)感知結(jié)果,。基于感知結(jié)果,通過(guò)聲光報(bào)警裝置與智能設(shè)備實(shí)現(xiàn)智能預(yù)警,,實(shí)驗(yàn)結(jié)果顯示,,該技術(shù)能夠有效抑制客戶側(cè)竊電行為。
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,,F(xiàn)u 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(guò)載的問(wèn)題,這些問(wèn)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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