A photovoltaic fault diagnosis method and system based on multi-modal adaptive weighting

By employing a multimodal adaptive weighted photovoltaic fault diagnosis method, which utilizes unsupervised clustering and a dynamic weight predictor, the problem of misjudgment in photovoltaic fault diagnosis under complex environments is solved, and high-precision fault diagnosis is achieved.

CN122173886APending Publication Date: 2026-06-09QINGYUAN ELECTRICITY DESIGN CO LTD
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Applications(China)
Current Assignee / Owner
QINGYUAN ELECTRICITY DESIGN CO LTD
Filing Date
2026-02-03
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing photovoltaic fault diagnosis methods are difficult to adapt to the complex and ever-changing photovoltaic operating environment and are prone to misjudgment.

Method used

A multimodal adaptive weighted photovoltaic fault diagnosis method is adopted. By acquiring historical operating data of photovoltaic equipment, unsupervised clustering and feature extraction are performed to train a dynamic weight predictor. The feature weights are dynamically adjusted to adapt to different operating conditions, and the fault prediction model is combined for diagnosis.

Benefits of technology

It improves the accuracy of photovoltaic fault diagnosis and the generalization ability of the model, and can maintain high diagnostic accuracy under different operating conditions.

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Abstract

The application provides a photovoltaic fault diagnosis method and system based on multi-modal adaptive weighting, relating to the technical field of fault diagnosis, comprising: obtaining historical operation data of photovoltaic equipment for splicing to obtain first features, clustering the first features to obtain multiple working condition clusters and corresponding order degrees; calling different feature extraction networks according to the order degrees to obtain second features; taking the Shapley values of the fault labels in the clusters to the second features as training targets to train a dynamic weight predictor; obtaining data to be analyzed, extracting first features from the data to be analyzed and finding corresponding working condition clusters, and outputting second feature weights using the corresponding dynamic weight predictor; inputting the second features of the data to be analyzed and the corresponding weights into a pre-constructed fault prediction model to obtain a fault diagnosis result. The application significantly improves the accuracy, efficiency and robustness of fault diagnosis by fusing multi-modal data, adaptive feature extraction and dynamic weight adjustment.
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