This letter proposes a data-driven hybrid deep learning method for dynamic total transfer capability (TTC) control. It leverages deep learning (DL) to achieve fast prediction of TTC and reduce the problem complexity, while the deep reinforcement learning (DRL) method, e.g., proximal policy optimization (PPO), is enhanced by competitive learning (CL) to obtain a better generalization of the DRL agents. This also allows us to deal with system stochasticity. Comparison results with other model-based alternatives on the IEEE 39-bus system highlight the advantages of the proposed method for variable unseen and insecure scenarios.

中文翻译：

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混合深度学习用于动态总传输能力控制

这封信提出了一种用于动态总传输能力（TTC）控制的数据驱动的混合深度学习方法。它利用深度学习（DL）来实现TTC的快速预测并降低问题的复杂性，而深度强化学习（DRL）方法（例如近端策略优化（PPO））则通过竞争性学习（CL）进行了增强以获得更好的DRL代理的一般化。这也使我们能够处理系统随机性。与其他基于模型的替代品在IEEE 39总线系统上的比较结果突显了所提出的方法在各种看不见的和不安全的情况下的优势。