Hierarchical Proximal Policy Optimization for Wide-Area Damping Control of Inter-Area Oscillations in Power Systems with DFIG Wind Integration

Abstract

This paper presents a hierarchical reinforcement learning framework for wide-area damping control (WADC) of inter-area oscillations in the IEEE 39-bus power system with DFIG wind farm integration driven by real variable wind speed data recorded in the Ottawa region (June 2025). A two-level architecture is developed: a base-level Proximal Policy Optimization (PPO) agent learns damping control policies from a high-fidelity Simulink model, while a meta-level controller adaptively tunes PPO hyperparameters between training batches using three complementary feedback signals (immediate, moving-window, and cumulative-historical deltas). The agent observes six PMU signals and outputs a single continuous DFIG reactive power modulation command. Training spans 35 batches of 20 episodes (700 total, ≈121 million timesteps). Evaluation against a conventional PSS baseline demonstrates 62–88% MSE reduction in generator active power oscillations across all three areas, 49% peak deviation reduction at the DFIG bus, 58.5% voltage sag reduction at the PCC, 46.5% frequency nadir improvement, and 60.8% peak angular separation reduction between areas, confirming that a DFIG wind farm replacing conventional synchronous generation can simultaneously serve as an effective wide-area damping controller.