A fault prediction method for oil-immersed transformer and related device

By combining multiple data features and sample equalization processing with an improved DCCNN model, the accuracy and adaptability issues of fault prediction for oil-immersed transformers are solved, achieving more efficient fault trend capture and prediction.

CN121786708BActive Publication Date: 2026-05-12DATANG HYDROPOWER SCI & TECH RES INST CO LTD
2 Cites 0 Cited by

Patent Information

Application Number
CN202610260148.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-03-04
Publication Date
2026-05-12
Estimated Expiration
2046-03-04

AI Technical Summary

Technical Problem

Existing methods for predicting faults in oil-immersed transformers are inadequate in terms of accuracy, adaptability, feature extraction, and output information, and cannot meet the real-time and stability requirements of power systems.

Method used

An improved DCCNN model is adopted, which combines dissolved gas analysis data, fault sensitivity ratio feature data and physical feature data of oil-immersed transformers. The receptive field is expanded by a dilated convolution module, and sample equalization and feature vector fusion are performed to improve the accuracy and adaptability of fault prediction.

Benefits of technology

It improves the accuracy and adaptability of fault prediction for oil-immersed transformers, reduces model bias and false negative rate, and meets the real-time and stability requirements of power systems.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The application provides a fault prediction method and related device of an oil-immersed transformer, and belongs to the technical field of transformer fault prediction. According to the fault stage division result of the oil-immersed transformer, the fault stage in which the current oil-immersed transformer is located is determined; the first data is retained to evolve the law, the second data is extracted to reflect the sensitivity feature of the oil-immersed transformer fault, the third data is extracted to reflect the physical essence feature of the oil-immersed transformer fault, and the first feature vector, the second feature vector and the third feature vector are obtained correspondingly; according to the fault stage in which the current oil-immersed transformer is located, the first feature vector, the second feature vector and the third feature vector are fused to obtain a fused channel feature vector; and the fused channel feature vector is input into an improved DCCNN model with determined parameters to obtain a fault prediction result of the oil-immersed transformer. The application solves the problem that the accuracy of the fault prediction of the oil-immersed transformer is not high.
Need to check novelty before this filing date? Find Prior Art