An Intelligent Parameter Identification Method of DFIG Systems Using Hybrid Particle Swarm Optimization and Reinforcement Learning

Abstract

Precise modeling of power systems is vital to ensure stability, reliability, and secure operations. In power industrial settings, model parameters can become skewed over time due to prolonged device usage or modifications made to the control systems. Doubly-Fed Induction Generator (DFIG), one of the most prevalent generators in wind farms, is sensitive to transient occurrences. Consequently, parameter calibration of DFIG becomes a crucial focal point in power system planning and operational studies. In this paper, two baseline approaches are first developed to identify the potentially harmful parameters of the DFIG system, including the Particle Swarm Optimization (PSO) method and the state-of-the-art off-policy Reinforcement Learning (RL) method, Soft Actor-Critic (SAC). The outcomes demonstrated that the SAC method outperformed PSO, resulting in an impressive reduction of 74.67% Mean Squared Error (MSE) and a more efficient testing period. In further exploration, a novel hybrid approach called SAC-PSO is developed, with SAC being the teacher of PSO to tackle scenarios with multiple potential solutions. The results exhibited an even greater enhancement over using SAC alone, leading to a remarkable reduction of 87.84% MSE during the testing phase. The proposed method can also effectively apply to a power plant incorporating multiple wind generators.

Keywords

• Deep reinforcement learning (DRL)
• doubly-fed induction generator (DFIG)
• hybrid SAC-PSO
• parameter calibration
• parameter identification
• particle swarm optimization (PSO)