A combined unsupervised learning approach for electricity theft detection and loss estimation

IF 1.6 Q4 ENERGY & FUELS IET Energy Systems Integration Pub Date : 2023-02-22 DOI:10.1049/esi2.12094
Liangcai Xu, Zhenguo Shao, Feixiong Chen
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引用次数: 1

Abstract

Electricity theft is a great trouble for power companies. As the means of tampering with smart meters continue to increase, the electricity theft behaviours become more diversified and covert, which are difficult to be identified using the existing electricity theft detection method. In addition, the existing methods usually cannot estimate the economic losses caused by electricity theft. To address these issues, a combined unsupervised learning approach for electricity theft detection and loss estimation is proposed in this study. First, three anomaly measurement indexes including the mean index, fluctuation index, and trend index are proposed to capture different anomalies respectively. Then, based on historical electricity consumption data, we develop two unsupervised learning techniques including the sample-to-subsamples decomposition algorithm and clustering algorithm to obtain the typical ranges of index values, and the load samples whose index values are not in the typical ranges will be considered fraudulent. Furthermore, three anomaly measurement indexes are combined to judge whether the load sample is fraudulent, and the user whose most load samples are judged fraudulent will be considered as an electricity thief. Finally, an economic loss estimation method is proposed, which quantifies the losses of electricity theft. Numerical experiments are carried out based on the Irish smart meter dataset, and the results demonstrate the effectiveness and the superior performance of the proposed method compared with a series of electricity theft detection methods.

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一种结合无监督学习的电力盗窃检测和损失估计方法
窃电是电力公司的一大麻烦。随着智能电表篡改手段的不断增加,窃电行为变得更加多样化和隐蔽,用现有的窃电检测方法难以识别。此外,现有的方法通常无法估计偷电造成的经济损失。为了解决这些问题,本研究提出了一种用于电力盗窃检测和损失估计的组合无监督学习方法。首先,提出了均值指数、波动指数和趋势指数三种异常度量指标,分别捕捉不同的异常;然后,基于历史用电量数据,我们开发了样本到子样本分解算法和聚类算法两种无监督学习技术来获得指标值的典型范围,指标值不在典型范围内的负荷样本将被视为欺诈。结合三个异常测量指标判断负载样本是否欺诈,被判断为欺诈负载样本最多的用户将被视为偷电者。最后,提出了一种量化窃电损失的经济损失估计方法。基于爱尔兰智能电表数据集进行了数值实验,与一系列电盗窃检测方法相比,结果证明了该方法的有效性和优越性能。
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来源期刊
IET Energy Systems Integration
IET Energy Systems Integration Engineering-Engineering (miscellaneous)
CiteScore
5.90
自引率
8.30%
发文量
29
审稿时长
11 weeks
期刊最新文献
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