Frequency stability prediction of renewable energy penetrated power systems using CoAtNet and SHAP values

As the complexity of power systems increases, traditional model-driven methods for online frequency stability prediction (FSP) encounter constraints in both accuracy and efficiency. To enhance the accuracy and efficiency of FSP, an data-driven method using CoAtNet and SHAP values is proposed. By leveraging the combination of convolution and attention mechanisms, CoAtNet addresses the limitation of traditional deep learning approaches that may not be able to extract data features comprehensively. Moreover, selecting all features as input into a deep-learning model may cause a substantial computation burden. It is thus impractical for CoAtNet to perform FSP of large-scale power systems. For this problem, this paper develops a SHAP values-based feature selection method to select the effective features as input. This process greatly reduces the numerical complexity, maintaining a high prediction performance. Additionally, the marginally stable situation of the system frequency is ignored by most researchers. A frequency security index to identify marginally stable situations is thus employed to generate the data labels, which are classed as “absolute security”, “relative security”, and “insecurity”. Finally, verified by the comparison simulation, the proposed model outperforms other models with accuracies of 98.80% on the modified IEEE 39-bus system and 99.04% on the modified ACTIVSg500 system.