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This invention relates to a wind power anomaly detection method based on dual-view comparative learning. The method includes the following steps: Step (1): Preprocessing the input data to generate a multi-scale hierarchical sequence; Step (2): Constructing an HPDB module, extracting features in parallel from both views, and fusing them with weights based on the spectrum; Step (3): Adopting a self-supervised learning paradigm, optimizing the network end-to-end by calculating a composite loss function; Step (4): In the testing phase, the model judges anomalies by calculating the weighted sum of the differences between the dual-view representations and the prediction error; Step (5): Using precision, recall, and F1 score to comprehensively evaluate the model's detection capability. This method aims to overcome the challenges faced by existing technologies in processing high-dimensional, non-stationary, and strongly coupled operating data of wind turbine generators, such as insufficient model generalization ability, insensitivity to weak anomalies, and low distinguishability between normal and abnormal patterns.