Improving Frequency Regulation in Power Systems via Noisynet Deep Reinforcement Learning Approach
2023 33rd Australasian Universities Power Engineering Conference (AUPEC)(2023)
摘要
The growing utilization of renewable energy resources in contemporary power systems has presented challenges for conventional model-based load frequency control (LFC) methods. These challenges include the escalation of computational burdens and diminished control performance. In order to address the stochastic disturbances, a novel approach called the Noisynet Deep Deterministic Policy Gradient (DDPG) method is proposed to adjust the agent’s parameters. Comparative analysis between the proposed method, the conventional DDPG method, and a finely-tuned PID method demonstrates that the proposed method yields a superior control policy. Consequently, the Noisynet DDPG method shows significant promise as a means to enhance LFC performance.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要