论文标题
基于杂种AI的异常检测模型，使用相分子测量单元数据
Hybrid AI-based Anomaly Detection Model using Phasor Measurement Unit Data
论文作者
论文摘要
在过去的几十年中，广泛使用信息和通信技术一直是电力系统数字化的主要驱动力。对关键电网基础设施的正确安全监视成为现代电力系统不可或缺的一部分。使用相量测量单元（PMU）来监视功率系统是具有前途未来的技术之一。测量频率增加和数据处理的智能方法可以提高可靠操作电网的能力。增加的网络物理互动既提供了好处和缺点，其中一个缺点以测量数据中的异常形式出现。异常可能是由于电网上的物理故障以及网络层中的干扰，错误和网络攻击引起的。本文旨在开发一种基于混合AI的模型，该模型基于各种方法，例如长期记忆（LSTM），卷积神经网络（CNN）和其他相关的混合算法，用于相sor测量单元数据中的异常检测。这项研究中使用的数据集是由德克萨斯大学获得的，该数据包括来自网格测量结果的真实数据。除了实际数据外，已经分析了已注入异常的错误数据。讨论了预防这种异常的影响和缓解方法。
Over the last few decades, extensive use of information and communication technologies has been the main driver of the digitalization of power systems. Proper and secure monitoring of the critical grid infrastructure became an integral part of the modern power system. Using phasor measurement units (PMUs) to surveil the power system is one of the technologies that have a promising future. Increased frequency of measurements and smarter methods for data handling can improve the ability to reliably operate power grids. The increased cyber-physical interaction offers both benefits and drawbacks, where one of the drawbacks comes in the form of anomalies in the measurement data. The anomalies can be caused by both physical faults on the power grid, as well as disturbances, errors, and cyber attacks in the cyber layer. This paper aims to develop a hybrid AI-based model that is based on various methods such as Long Short Term Memory (LSTM), Convolutional Neural Network (CNN) and other relevant hybrid algorithms for anomaly detection in phasor measurement unit data. The dataset used within this research was acquired by the University of Texas, which consists of real data from grid measurements. In addition to the real data, false data that has been injected to produce anomalies has been analyzed. The impacts and mitigating methods to prevent such kind of anomalies are discussed.
