Leakage current waveform matching method based on combination of multi-machine learning and weighted voting mechanism

Through multi-machine learning combined with weighted voting mechanism, the problem of leakage current type identification in the existing technology is solved, and the accurate matching and type identification of leakage current waveforms are achieved, which improves the accuracy and robustness of detection.

CN119691462APending Publication Date: 2025-03-25STATE GRID JIANGXI ELECTRIC POWER CO GANZHOU POWER SUPPLY BRANCH
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Patent Information

Application Number
CN202411737727.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-29
Publication Date
2025-03-25

AI Technical Summary

Technical Problem

The existing leakage current detection methods fail to effectively utilize actual fault leakage or electric shock signals as action criteria, making it difficult to accurately identify the leakage current type.

Method used

The leakage current waveform matching method combined with the weighted voting mechanism is adopted. The current data is preprocessed through wavelet transformation, Z-score outlier processing and min-max normalization processing, and the features of the time domain, frequency domain and time frequency domain are extracted, and the support vector machine, random forest, K nearest neighbor, BP neural network and RBF neural network are trained. The final waveform matching and type identification are achieved in combination with the weighted voting mechanism.

Benefits of technology

It improves the precise matching and classification identification capabilities of leakage current waveforms, improves the classification accuracy and robustness of the model, and reduces the possible deviations and misclassification problems caused by a single model.

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Abstract

The invention, which relates to the technical field of leakage current detection, discloses a leakage current waveform matching method based on combination of multi-machine learning and a weighted voting mechanism, comprising the following steps: S1, preprocessing current data; s2, feature extraction of the current data; s3, establishing and training a multi-machine learning model; and S4, performing leakage current waveform matching of a multi-machine learning model in combination with a weighted voting mechanism. According to the leakage current waveform matching method combining multi-machine learning with the weighted voting mechanism, the classification accuracy and robustness of the model are effectively improved through the multi-model weighted voting mechanism in the accurate matching and classification identification of the leakage current waveform; the method has the specific effects that multiple features are extracted from three dimensions of a time domain, a frequency domain and a time-frequency domain, and the features of the leakage current waveform are comprehensively and deeply described, so that the classification model can more fully identify the features of different types of leakage currents, and the identification accuracy of the model is greatly improved.
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