Power system‐oriented extreme weather scenario identification: Hybrid classical‐quantum learning

Abstract Weather‐dependent renewable energy makes modern power systems more sensitive to extreme weather. Therefore, reliable identification of power system‐oriented extreme weather scenarios is important for reliability assessment, resilience analysis, and risk‐oriented operation. However, these scenarios are heterogeneous and suffer from severe class imbalance, which makes reproducible labelling and robust model training difficult. This paper proposes a hybrid classical‐quantum learning framework for power system‐oriented extreme weather scenario identification. First, a structured definition and labelling framework is developed to extract extreme weather scenarios from large‐scale weather‐power datasets by jointly considering meteorological abnormality, power system response, and external event evidence. Second, a quantum generative module is introduced as a training‐only augmentation tool to synthesize minority‐class samples, while validation and testing remain strictly based on real observations. Third, a hybrid classical‐quantum classifier is constructed by embedding variational quantum circuits as trainable feature‐mapping modules within a classical learning pipeline, aiming to examine their feasibility under small‐sample and imbalanced‐data conditions. In addition to conventional generation and classification metrics, risk‐oriented indicators are used to evaluate whether the identified scenarios are meaningful for power system operation. The proposed framework is validated using real‐world weather‐power data from China. The results show that the quantum generative adversarial network preserves minority‐class risk characteristics, while convolutional neural network‐quantum recurrent neural network (CNN‐QRNN) attains the numerically highest minority macro‐ F 1 of 0.2998 among the compared models, with a macro‐ F 1 of 0.3972.