CNN and FiLM-based leakage current type intelligent identification method

By employing a CNN and FiLM-based intelligent leakage current type identification method, a dataset is constructed using multi-channel waveforms and environmental feature parameters. A FiLM conditional modulation module and a multi-task learning framework are introduced to solve the problems of poor anti-interference and low adaptability in existing leakage current detection technologies. This achieves high-precision leakage current type identification and grounding system discrimination, improving the accuracy and robustness of system diagnosis.

CN121765448APending Publication Date: 2026-03-31STATE GRID HENAN ELECTRIC ZHOUKOU POWER SUPPLY
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Patent Information

Application Number
CN202511583598.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-31
Publication Date
2026-03-31

AI Technical Summary

Technical Problem

Existing leakage current detection methods have poor anti-interference capabilities in complex transformer substation environments, high false alarm and false alarm rates, and cannot effectively distinguish between different types of leakage current. They perform poorly, especially when DC components are mixed in and when arc discharge occurs. They also lack adaptability and cannot take advantage of environmental conditions such as humidity, temperature, and grounding resistance.

Method used

A smart leakage current type identification method based on CNN and FiLM is adopted. By collecting multi-channel waveforms and environmental feature parameters, a dataset is constructed. The FiLM conditional modulation module and multi-task learning framework are introduced. The weighted cross-entropy loss function and OneCycleLR dynamic learning rate scheduling strategy are used to achieve high-precision identification of leakage current type and grounding system discrimination, thereby enhancing the discriminativeness of feature representation.

Benefits of technology

It achieves high-precision identification of multiple types of faults such as single-phase grounding and arc-type leakage under complex working conditions. It can adapt to different grounding systems, improve the accuracy and robustness of system diagnosis, reduce the risk of false alarms and missed alarms, and adapt to different environmental changes.

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Abstract

The invention belongs to the technical field of power distribution network leakage current detection, and particularly relates to a CNN and FiLM-based leakage current type intelligent identification method. Comprising the following steps: S1, collecting real-time operation data of a typical power supply area, obtaining leakage current waveforms and related environment characteristic parameters under different working conditions, and constructing an original leakage current sample data set; s2, a CNN-based leakage current classification model is constructed, a FiLM condition modulation module is introduced, a multi-task learning framework is used at the tail of the network, a main task is leakage current type identification, and an auxiliary task is grounding system discrimination; s3, using a weighted cross entropy loss function to alleviate a class sample imbalance problem; an OneCycleLR dynamic learning rate scheduling strategy is introduced; s4, evaluating the performance of the model on the test set, and using the accuracy and the confusion matrix as evaluation indexes; according to the method, high-precision identification of multiple types of faults such as single-phase grounding, arc type electric leakage and direct current system electric leakage is realized, different grounding systems can be adapted, and the accuracy and robustness of system diagnosis are improved.
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