| 基于ELM-SVM模型与电能计量大数据的窃电识别技术研究 |
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| 引用本文： | 县国成,王永攀,高俊,浮海,杨斌,武旭.基于ELM-SVM模型与电能计量大数据的窃电识别技术研究[J].陕西电力,2022,0(9):82-89. |
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| 作者姓名： | 县国成 王永攀 高俊 浮海 杨斌 武旭 |
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| 作者单位： | （1.国网甘肃省电力公司天水供电公司，甘肃天水 741000；2.兰州交通大学自动化与电气工程学院，甘肃兰州 730070） |
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| 摘 要： | 窃电现象破坏社会供用电秩序，严重时更会阻碍新型配电系统建设的发展。为了更精确地识别窃电行为，提出了一种基于极限学习机（ELM）与支持向量机（SVM）相结合的窃电智能识别模型。利用电能计量大数据，分析窃电用户数据状态指标，构建窃电指标评价体系；利用指标评价体系训练窃电智能识别模型，进而以ELM-SVM预测模型来识别窃电用户。该方法有效集合了ELM算法与SVM算法的优点，算例表明，识别模型的识别准确率可达97.8%，说明ELM-SVM结合方法是可行的，实现了对用户窃电行为的高精度、高效性预测识别。
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| 关 键 词： | 电能计量大数据 窃电智能识别 极限学习机 支持向量机 评价体系 |
Power Theft Identification Technology Based on ELM-SVM Model and Big Data of Electric Energy Measurement |
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| XIAN Guocheng,WANG Yongpan,GAO Jun,FU Hai,YANG Bin,WU Xu.Power Theft Identification Technology Based on ELM-SVM Model and Big Data of Electric Energy Measurement[J].Shanxi Electric Power,2022,0(9):82-89. |
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| Authors: | XIAN Guocheng WANG Yongpan GAO Jun FU Hai YANG Bin WU Xu |
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| Affiliation: | （1. State Grid Gansu Tianshui Power Supply Company，Tianshui 741000，China； 2. School of Automation and Electrical Engineering， Lanzhou Jiaotong University， Lanzhou 730070，China） |
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| Abstract: | The phenomenon of stealing electricity destroys the order of social power supply and consumption， and even hinders the development of new distribution system construction when it is serious. In order to identify the behavior of stealing electricity more accurately， an intelligent recognition model of stealing electricity based on the combination of extreme learning machine （ELM） and support vector machine （SVM） is proposed. By using the big data of electric energy measurement，the data status indicators of power theft users are analyzed， and the evaluation system of power theft indicators is built. The index evaluation system is used to train the intelligent identification model of stealing electricity，and the ELM-SVM prediction model is used to identify the users of stealing electricity. This method effectively integrates the advantages of ELM algorithm and SVM algorithm. The example shows that the recognition accuracy of the recognition model can reach 97.8%， which shows the feasibility of ELM-SVM combined method. The high-precision， high-efficient prediction and recognition of users’ stealing behavior are realized. |
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| Keywords: | big data of electric energy measurement intelligent identification of stealing electricity extreme learning machine support vector machine evaluation system |
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