Model-Free Load Frequency Control of Nonlinear Power Systems Based on Deep Reinforcement Learning
Xiaodi Chen, Meng Zhang, Zheng‐Guang Wu, Ligang Wu, Xiaohong Guan
Abstract
Load frequency control (LFC) is widely employed in power systems to stabilize frequency fluctuation and guarantee power quality. However, most existing LFC methods rely on accurate power system modeling and usually ignore the nonlinear characteristics of the system, limiting controllers' performance. To solve these problems, this article proposes a model-free LFC method for nonlinear power systems based on deep deterministic policy gradient framework. The proposed method establishes an emulator network to emulate power system dynamics. After defining the action-value function, the emulator network is applied for control actions evaluation instead of the critic network. Then, the actor network controller is effectively optimized by estimating the policy gradient based on zeroth-order optimization and backpropagation algorithm. Simulation results and corresponding comparisons demonstrate the designed controller can generate appropriate control actions and has strong adaptability for nonlinear power systems.