Intelligent anomaly identification in cyber-physical inverter-based systems

•Cyber-physical anomaly identification in microgrid is presented.•Anomalies due to cyber-attacks and faults are considered.•Multi class support vector machine-based anomaly identification technique is given.•Multi class support vector machine (MSVM) detected 80% of the anomalies.•MSVM provided higher efficiency than artificial neural networks and Naive Bayes. Modern cyber-physical systems have become more autonomous and distributed with the inclusion of advanced control architectures and communication networks. Power electronics-based inverters that employ extensive communication structures are integral part of such systems. The controllers for inverter-based systems rely on communication networks that make them vulnerable to cyber-physical anomalies. The cyber anomalies occur due to malicious attacks targeting the communication layer and physical anomalies are caused by power system faults in the physical layer of the microgrid. In this work, an intelligent anomaly identification (IAI) technique for such systems is presented utilizing data driven artificial intelligence tools that employ multi class support vector machines (MSVM) for anomaly classification and localization. The effects of cyber anomalies such as false data injection and denial of service attacks that target the communication network are considered in this work. In addition, the physical anomalies due to power system faults are also considered. The proposed technique utilizes statistical features extracted from measurements for optimal learning of a dual of MSVM classifiers. The mean absolute percentage error is used as a performance metric and the results are validated by comparing to artificial neural network, Naive Bayes classification and using real time simulations in OPAL-RT.