Anomaly Detection Networks and Fuzzy Control Modules for Energy Grid Management with Q-Learning-Based Decision Making

Abstract

Renewable energy generation has attracted the interest of researchers, but it is volatile, and management systems are vulnerable to malicious attacks. Therefore, security issues are of paramount importance for energy management systems. In this paper, we propose a secure Q-learning- based energy network management system (SQEMS), which consists of an anomaly detection module, a fuzzy control module to mitigate attacks, and a decision-making module to manage the energy grid. Experimental results show that the proposed anomaly detection module has excellent performance on malicious suppliers attacks (MS), and the fuzzy control module can further mitigate the negative effects of false predictions. The robustness analysis shows the effectiveness, robustness, and transferability in anomaly detection and energy management.