Power system state estimation is being faced with different types of anomalies. These might include bad data caused by gross measurement errors or communication system failures. Sudden changes in load or generation can be considered as anomaly depending on the implemented state estimation method. Additionally, considering power grid as a cyber physical system, state estimation becomes vulnerable to false data injection attacks. The existing methods for anomaly classification cannot accurately classify (discriminate between) the above-mentioned three types of anomalies, especially when it comes to discrimination between sudden load changes and false data injection attacks. This paper presents a new algorithm for detecting anomaly presence, classifying the anomaly type and identifying the origin of the anomaly, i.e., measurements that contain gross errors in case of bad data, or bus(es) associated with load(s) experiencing a sudden change, or state variables targeted by false data injection attack. The algorithm combines analytical and machine learning (ML) approaches. The first stage exploits an analytical approach to detect anomaly presence by combining $\chi^2$-test and anomaly detection index. The second stage utilizes ML for the classification of anomaly type and identification of its origin, with particular reference to discrimination between sudden load changes and false data injection attacks. The proposed ML based method is trained to be independent of the network configuration which eliminates retraining of the algorithm after network topology changes. The results obtained by implementing the proposed algorithm on IEEE 14 bus test system demonstrate the accuracy and effectiveness of the proposed algorithm.
翻译:电源系统状态估计正面临不同类型的异常现象,其中可能包括严重测量错误或通信系统故障造成的不良数据。根据实施的国家估算方法,负载或发电的突然变化可视之为异常现象。此外,将电网视为网络物理系统,国家估算容易受到虚假数据注入攻击。异常分类的现有方法无法准确分类上述三种异常类型(区分),特别是当它涉及突然负载变化和错误数据注入攻击之间的差别时。本文件介绍了一种新的算法,用于发现异常现象的存在,对异常类型进行分类,并查明异常现象的起源,即如果数据坏,则含有严重错误的测量数据或与经历突然变化的负载相关的总线,或以虚假数据注入攻击为目标的变量。现行算法将分析和机器学习(ML)方法结合起来。第一阶段利用一种分析方法来检测异常现象的存在,将美元=2美元检验和异常现象检测指数结合起来。第二阶段利用ML对异常类型进行分类,并查明异常现象的起源,其中特别提及发生严重数据错误的数据、或与发生突变的负载和虚假再演算结果后的拟议网络测试结果。拟议采用磁测算系统。