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 paper proposes a model-free LFC method for nonlinear power systems based on deep deterministic policy gradient (DDPG) 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 (ZOO) 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.

翻译：负荷频率控制（LFC）广泛应用于电力系统中，以稳定频率波动并保障电能质量。然而，现有LFC方法大多依赖于精确的电力系统建模，通常忽略系统的非线性特性，限制了控制器的性能。为解决上述问题，本文提出一种基于深度确定性策略梯度（DDPG）框架的非线性电力系统无模型LFC方法。该方法构建了一个仿真器网络用于模拟电力系统动态。在定义动作-价值函数后，仿真器网络替代评论家网络进行控制动作评估。随后，通过基于零阶优化（ZOO）与反向传播算法的策略梯度估计，对演员网络控制器进行高效优化。仿真结果及对比分析表明，所设计的控制器能够生成合适的控制动作，并对非线性电力系统展现出强适应性。