Deep-Learning-Directed Preventive Dynamic Security Control via Coordinated Demand Response
This addresses dynamic security challenges in power systems, but it is incremental as it applies a known deep learning method to a specific fault scenario.
The paper tackled the problem of predicting out-of-step conditions from three-phase short-circuit faults in power systems to enhance dynamic security, achieving effective early prediction and robustness across various operating conditions.
Unlike common faults, three-phase short-circuit faults in power systems pose significant challenges. These faults can lead to out-of-step (OOS) conditions and jeopardize the system's dynamic security. The rapid dynamics of these faults often exceed the time of protection actions, thus limiting the effectiveness of corrective schemes. This paper proposes an end-to-end deep-learning-based mechanism, namely, a convolutional neural network with an attention mechanism, to predict OOS conditions early and enhance the system's fault resilience. The results of the study demonstrate the effectiveness of the proposed algorithm in terms of early prediction and robustness against such faults in various operating conditions.