Wind farm parameter optimization identification method based on multi-agent SAC
Year: 2025
Type: article
Abstract: As more wind farms are integrated into power grid, the safe and stable operation of power system becomes increasingly challenged, making accurate wind farm modeling particularly important. Based on multi-agent soft actor critic (SAC) deep reinforcement learning (DRL), this method identifies wind farm parameters under multiple fault conditions. The method compares reactive power output curves between the detailed model and multi-agent SAC identified model, ultimately obtaining high-accuracy parameters. Finally, the effectiveness and superiority of the proposed method are verified by comparing with the identification results of Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO).
Source: Energy Reports
Authors: Qiu Quan Deng, Cui Yun Luo, Yin Wu, Guang Ming Li, Xie Jin Ling, Zhen Cheng Liang
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,040
OpenAlex ID: W4411260882