Markov Transition Fields-Based Dual-Modal Fusion Method on Transient Stability Assessment for Power Systems

There is an extremely urgent need to develop a transient stability assessment method for new power systems with greater rapidity and higher accuracy due to the increased complexity and difficulty caused by massive nonlinear power electronics-dominated generation and loads. In recent years, computing power has increased significantly, meaning that artificial intelligence (AI) algorithms have develop rapidly, and large-scale AI models have become available. Among them, deep learning (DL) algorithms have received more attention due to their inherent advantages, on which assessment strategy and methods are based, but these algorithms are still not sufficiently applicable. Therefore, a Markov Transition Field (MTF)-based dual-modal fusion method for transient stability assessment of power systems is proposed in this paper. First, the influence and effect on transient stability assessment by the fusion of both image modality and time series modality are studied. Then, for enhancing key features, the strategy to convert the time series modality into image modality by MTF is established, which allows the features to be described at multiple time scales and the feature correlation between different time points to be strengthened. Thus, features from image modality and time series modality are extracted, respectively, by Convolutional Neural Networks (CNNs), and gated recurrent units are adopted; the extracted features are further fused by a concatenation fusion method. It is demonstrated by the simulation results that the accuracy of the transient stability assessment is improved effectively by the aforementioned fusion method.