Detection and Mitigation of Data Manipulation Attacks in AC Microgrids

Abstract

This paper presents a resilient control framework for distributed frequency and voltage control of AC microgrids under data manipulation attacks. In order for each distributed energy resource (DER) to detect any misbehavior on its neighboring DERs, an attack detection mechanism is first presented using a Kullback-Liebler (KL) divergence-based criterion. An attack mitigation technique is then proposed that utilizes the calculated KL divergence factors to determine trust values indicating the trustworthiness of the received information. Moreover, DERs continuously generate a self-belief factor and communicate it with their neighbors to inform them of the validity level of their own outgoing information. DERs incorporate their neighbors' self-belief and their own trust values in their control protocols to slow down and mitigate attacks. It is shown that the proposed cyber-secure control effectively distinguishes data manipulation attacks from legitimate events. The performance of proposed secure frequency and voltage control techniques is verified through the simulation of microgrid tests system implemented on IEEE 34-bus test feeder with six DERs.