Attack Detection for Distributed Photovoltaic Generation Systems Leveraging Cyber and Power Side Channel Data

Huaiyu Liu, Yuze Fu, Kaikai Pan, Wenyuan Xu, Chenggang Li, Chen Liu

Abstract: With the growing focus on reducing carbon emissions, there has been a significant increase in the deployment of distributed photovoltaic generation systems. However, their distributed nature makes them vulnerable to adversarial threats. Moreover, these systems can serve as potential entry points or attack surfaces for targeting the power grid. Existing methods are either designed for specific attacks or intrusive to power terminals (e.g., intelligent terminal, communication device). To tackle this issue, this paper introduces a method for detecting and classifying attacks on distributed PV systems leveraging cyber and power side channel data. The proposed method is capable of detecting various attacks rather than specific ones and is non-intrusive for collecting information from power terminals.