Artificial Intelligence based Anomaly Detection and Classification for Grid-Interactive Cascaded Multilevel Inverters

The cascaded multi-level inverter (CMI) is becoming increasingly popular for wide range of applications in power electronics dominated grid (PEDG). The increased number of semiconductors devices in these class of power converters leads to an increased need for fault detection, isolation, and self-healing. In addition, the PEDG's cyber and physical layers are exposed to malicious attacks. These malicious actions, if not detected and classified in a timely manner, can cause catastrophic events in power grid. The inverters' internal failures make the anomaly detection and classification in PEDG a challenging task. The main objective of this paper is to address this challenge by implementing a recurrent neural network (RNN), specifically utilizing long short-term memory (LSTM) for detection and classification of internal failures in CMI and distinguish them from malicious activities in PEDG. The proposed anomaly classification framework is a module in the primary control layer of inverters which can provide information for intrusion detection systems in a secondary control layer of PEDG for further analysis.