可再生能源的规模化接入电网具有间歇性和强随机特性，容易引起显著的频率波动。针对多区域互联电网，现有的基于神经网络的多智能体协同算法在面对强随机扰动时，常因梯度爆炸和梯度消失问题导致控制性能并不理想。为此，提出了一种自适应权重残差近端策略优化自动发电控制方法。该方法将智能体策略神经网络的各层之间通过残差连接，并为残差连接添加一个可训练的自适应权重，以取得最优的残差比例，缓解神经网络梯度爆炸和梯度消失问题，从而使得深层网络的训练过程更加稳定和高效，进而更快地获得强随机扰动下的多区域协同最优解，以消除由于强随机扰动所引起的频率波动。通过在IEEE两区域和三区域负荷频率控制系统上的仿真测试验证了所提算法的有效性。且相较于多种强化学习算法，所提算法具有更快的收敛性和更强的稳定性，更高的频率稳定性及控制性能。

The large-scale access of renewable energy to the power grid has intermittent and strong random characteristics, which is easy to cause significant frequency fluctuations. For multi-area power grids, the existing multi-agent cooperative algorithms based on neural networks often have unsatisfactory control performance due to gradient explosion and gradient disappearance in the face of strong random disturbances. To this end, this paper proposes an adaptive weight residual proximal policy optimization method for automatic power generation control. This method connects the layers of the agent policy neural network through residual connections, and adds a trainable adaptive weight to the residual connection to achieve the optimal residual ratio and alleviate the problem of gradient explosion and gradient disappearance of the neural network, so that the training process of the deep network is more stable and efficient, and then the multi-area cooperative optimal solution under strong random disturbance is obtained faster to eliminate the frequency fluctuation caused by strong random disturbance. The effectiveness of the proposed algorithm is verified by simulation tests on IEEE two-area and three-area load frequency control systems. Compared with various reinforcement learning algorithms, the proposed algorithm has faster convergence and stronger stability, higher frequency stability and control performance.