A Meta-Strategy Framework With Deep Reinforcement Learning for Damping Interarea Oscillation via High-Voltage DC Transmission

Interarea oscillations pose significant threats to the security and stability of power systems, and these oscillations have to be damped rapidly to prevent potential system failure. Our previous work introduced a deep reinforcement learning (DRL) strategy on top of the power oscillation damping (POD) control architecture using high-voltage direct current (HVdc) transmissions. This strategy effectively generated POD signals based on the wide-area information, thus modulating the transferred power to damp interarea oscillations. However, the performance degrades when there are significant changes in system structure or operating conditions, highlighting a general adaptability issue in DRL-based control schemes. To solve the issue, we propose a novel meta-evolution strategy framework that integrates both meta-strategy optimization (MSO) and evolutionary strategy (ES)-based DRL in this article. The ES algorithm has fewer hyperparameters that are relatively easy to tune than typical DRL algorithms and identifies the optimal policy quickly by exploring the policy parameter space through parallel computations. The MSO introduces a latent variable projecting the power system parameters under different operation conditions to ensure a fast and efficient adaption to unforeseen scenarios. A modified IEEE 39-bus system with HVdc transmission is studied to validate the performance and effectiveness of the proposed meta-strategy in damping interarea oscillations.