Privacy-Preserving Energy Theft Detection in Microgrids: A State Estimation Approach
Privacy-Preserving Energy Theft Detection in Microgrids: A State Estimation Approach
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DOI：
10.1109/tpwrs.2015.2406311
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发表时间：
2016-03
影响因子：
6.6
通讯作者：
Sergio Salinas;P. Li
中科院分区：
文献类型：
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作者：
Sergio Salinas;P. Li
电能盗窃是电力系统中的一个突出问题，它造成了巨大的经济损失，威胁着电网的可靠性。最近，智能电网已被提出作为下一代电力系统，以现代化现有电网并提高其效率，可持续性和安全性。智能电网的关键技术包括智能电表，使系统运营商能够从用户那里收集实时电力消耗数据，以及微电网，使用户能够拥有和控制可再生资源。然而，智能电网容易受到网络攻击，从而使窃取能源变得更加容易。大多数现有的能源盗窃检测方案需要收集来自用户的实时功耗数据，即，用户的加载配置文件，这侵犯了他们的隐私。在本文中，我们首先提出了一个集中的能源盗窃检测算法，利用卡尔曼滤波器，称为SEK。它可以有效地识别能源小偷，但不能保护用户的隐私。然后，基于SEK，我们开发了一个隐私保护的能量盗窃检测算法称为PPBE，通过分解卡尔曼滤波器到两个并行和松散耦合的过滤器，私下发现的能量小偷。最后，我们进行彻底的隐私分析和广泛的模拟来验证我们提出的算法。
Energy theft is a notorious problem in electric power systems, which causes great economic losses and threatens the reliability of the power grid. Recently, the Smart Grid has been proposed as the next-generation power system to modernize the current grid and improve its efficiency, sustainability, and security. Key technologies of the Smart Grid include smart meters, which allow system operators to collect real-time power consumption data from users, and microgrids, which allow users to own and control renewable resources. However, the Smart Grid is vulnerable to cyber attacks, thus making stealing energy much easier in it. Most existing energy theft detection schemes require the collection of real-time power consumption data from users, i.e., users' load profiles, which violates their privacy. In this paper, we first propose a centralized energy theft detection algorithm utilizing the Kalman filter, called SEK. It can efficiently identify the energy thieves but cannot protect users' privacy. Then, based on SEK, we develop a privacy-preserving energy theft detection algorithm called PPBE, which privately finds the energy thieves by decomposing the Kalman filter into two parallel and loosely coupled filters. Finally, we conduct thorough privacy analysis and extensive simulations to validate our proposed algorithms.