Mutual inductor fault diagnosis method and system based on intelligent algorithm, and storage medium

By combining multimodal data input and multi-task deep learning models, the problems of noise sensitivity and high computational complexity in transformer fault diagnosis are solved, accurate diagnosis and early warning of transformer faults are achieved, and the operation and maintenance management efficiency of the power system is improved.

CN120722265AActive Publication Date: 2025-09-30CETSDEC CO LTD
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Patent Information

Application Number
CN202511165226.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-20
Publication Date
2025-09-30
Estimated Expiration
2045-08-20

AI Technical Summary

Technical Problem

Existing transformer fault diagnosis methods have high computational complexity and strong noise sensitivity in complex environments, resulting in low diagnostic efficiency and insufficient accuracy, making it difficult to achieve accurate fault detection.

Method used

Multimodal data input and multi-task deep learning model are adopted to perform signal denoising through bandpass filter, adaptive filter and wavelet transform. Multimodal input vector is constructed by combining time domain features, zero-sequence features and environmental parameters, and fault diagnosis is performed using multi-task deep learning model.

Benefits of technology

Accurate diagnosis of transformer faults is achieved, the accuracy and reliability of fault detection are improved, misdiagnosis and missed diagnosis are reduced, and the safe and stable operation of the power system is ensured.

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Abstract

The invention relates to the technical field of fault diagnosis, and discloses a mutual inductor fault diagnosis method and system based on an intelligent algorithm, and a storage medium. The method comprises the following steps: acquiring an output signal of a secondary side of a mutual inductor at a preset sampling frequency, performing de-noising processing on the output signal, and converting the output signal into time sequence data; performing time domain feature extraction on the time sequence data to obtain a first data feature and a second data feature of the target physical quantity, extracting a zero-sequence current component and a zero-sequence voltage component from the time sequence data, and calculating a phase difference between the zero-sequence current and the zero-sequence voltage as a zero-sequence feature; whether the transformer is abnormal or not is judged, if yes, a multi-modal input vector is constructed based on the time domain features, the zero sequence features, the environmental parameters, the temperature parameters and historical fault records, the multi-modal input vector is input into a pre-trained multi-task deep learning model, and the deep learning model outputs a fault diagnosis result. According to the invention, the fault diagnosis precision of the mutual inductor is improved.
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