Deep Reinforcement Learning-Based Adaptive Transient Voltage Control of Power Systems by Distributed Collaborative Modulation of Voltage-Source Converters with Operational Constraints of Current Saturation

This paper presents a novel deep reinforcement learning (DRL)-based method for the adaptive control of transient voltage in power systems. First, we propose a neural network-based nonlinear controller (TVCON) designed to modulate each voltage-source converter (VSC), such as photovoltaic systems or energy storage systems, that actively contributes to transient voltage control. Subsequently, all distributed TVCONs can collaborate to rapidly restore system voltage during fault transients by centrally optimizing their parameters (weight coefficients). Specifically, the optimization is conducted periodically using incremental DRL to efficiently update the TVCONs’ parameters in accordance with the practical operating conditions of the system and VSCs. Consequently, the provision of transient reactive current by VSCs, which have operational constraints related to current saturation, can be feasibly and adaptively controlled by the TVCONs while considering their steady-state active current outputs. Additionally, the inappropriate sacrifice of VSCs’ active current and the resulting adverse impacts can be effectively mitigated. Finally, simulations conducted on a modified IEEE 14-Bus system validate the proposed method.