Artificial Neural Network Based Electricity Theft Detection

S. Bakre, A. Shiralkar, S. Shelar, Suchita Ingle
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Abstract

The theft of electricity is a matter of concern for the distribution utility today. The Aggregate Technical and Commercial (AT&C) loss of Maharashtra State Electricity Distribution Company is around 20.72% for the year 2020–21. The main cause of such a higher loss is pilferage or theft of electricity. As per statistics given by various distribution utilities, the theft incidences of three phase HT and LT consumers are under control. However, there is a rising trend in tampering of single phase meters. Various methods of theft detection of single phase meters are in existence, however, tampering of meter by inserting the resistive link in parallel with the meter cannot be detected using these conventional methods. In this paper, a novice technique of tamper detection using Artificial Neural Network is proposed. The proposed method is cost effective and feasible.
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基于人工神经网络的窃电检测
窃电是今天配电公司关心的一个问题。2020-21年,马哈拉施特拉邦配电公司的总技术和商业(AT&C)损失约为20.72%。造成如此高损失的主要原因是盗窃或盗窃电力。根据各配电公司的统计数据,三相HT和LT消费者的盗窃事件得到控制。然而,对单相仪表的篡改呈上升趋势。单相电表的盗窃检测方法多种多样,但传统的检测方法无法检测到并联插入电阻链路对电表的篡改。本文提出了一种基于人工神经网络的篡改检测新技术。该方法具有成本效益和可行性。
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