Voltage/VAR Control in Active Distribution Networks via DRL Under False Data Injection Attacks on Distributed PV Systems

Owing to its capability to handle uncertainties and provide rapid responses, Deep Reinforcement Learning (DRL) has been widely applied to Volt-Var Control (VVC) in Active Distribution Networks (ADNs). However, existing studies still present two main limitations. First, the characteristics of power equipment have not been adequately integrated with the action properties of DRL, which may compromise the control performance. Second, current DRL-based VVC methods for ADNs remain insufficiently resilient to False Data Injection Attacks (FDIAs) targeting Photovoltaic systems (PVs), significantly increasing the risks of voltage instability and operational insecurity in distribution networks. To address these challenges, a novel segmented power-constraint method is proposed to reconcile the mismatch between the control characteristics of traditional PV inverters and the action-generation mechanism of DRL agents. Furthermore, by incorporating a Distribution-Based Correction Observer into the twin delayed deep deterministic policy gradient algorithm, the proposed method enhances the resilience of DRL-based control against corrupted PV measurement data. This enables the agent to maintain reliable decision-making capabilities even when PV data are compromised. Simulation results demonstrate that the proposed method effectively enhances voltage stability, reduces power losses, and maintains robust control performance under false data injection attacks.