Cooperative Fault-Tolerant Control for Source-Network-Load-Storage-Based Active Distribution Network Under Communication and Actuator Faults

Active distribution networks (ADNs) with integrated source-network-load-storage units play a vital role in supporting renewable energy integration and improving system flexibility. However, during long-term operation, inevitable communication faults and actuator faults may severely impair critical control objectives, including frequency regulation, voltage stability, active power sharing, and state-of-charge (SoC) equalization. These faults introduce significant risks to system stability and reliable operation, highlighting the need for effective resilience mechanisms. To address this challenge, this paper develops a fault-tolerant control framework that combines real-time fault estimation with resilient control design. First, a deep neural network (DNN)-based estimator is constructed to identify the real-time effects of communication and actuator faults without relying on detailed physical models. Based on the estimated information, a DNN-assisted fault-tolerant controller is then designed to ensure frequency and voltage recovery, equitable power sharing among distributed generators (DGs), and balanced SoC management of energy storage systems (ESSs), even under concurrent communication and actuator faults. This forms a data-driven compensation mechanism that enables resilient performance without relying on conventional reallocation or redundancy-based strategies. To further demonstrate the effectiveness of the proposed method, quantitative simulation results indicate that, compared with existing approaches, the frequency error is reduced by approximately 97%, the voltage error by 60–74%, and the active and reactive power errors by around 90%. Finally, the MATLAB/Simulink analysis of an ADN testing system verifies that these improvements significantly enhance the robustness and reliability of source-network-load-storage ADNs under both normal and faulty conditions, providing a promising solution for resilient operation in modern power systems.