标题： 基于深度强化学习的非线性电力系统无模型负荷频率控制 显示英文标题

标题： Model-Free Load Frequency Control of Nonlinear Power Systems Based on Deep Reinforcement Learning

摘要： 负荷频率控制（LFC）在电力系统中被广泛用于稳定频率波动并保证电能质量。 然而，大多数现有的LFC方法依赖于精确的电力系统建模，并通常忽略系统的非线性特性，从而限制了控制器的性能。 为了解决这些问题，本文提出了一种基于深度确定性策略梯度（DDPG）框架的无模型LFC方法，用于非线性电力系统。 所提出的方法建立了一个模拟器网络来模拟电力系统动态。 在定义动作-价值函数后，模拟器网络被用于控制动作评估，而不是批评者网络。 然后，通过基于零阶优化（ZOO）和反向传播算法估计策略梯度，对演员网络控制器进行了有效优化。 仿真结果及相应的比较表明，设计的控制器能够生成适当的控制动作，并对非线性电力系统具有很强的适应性。 显示英文摘要

摘要： 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.