A Hybrid CNN-LSTM Approach for Transformer Remaining Useful Life Prediction Using Dissolved Gas Analysis; [하이브리드 접근법을 이용한 유중가스 기반 변압기 잔여수명 예측]

초록

Power transformers are critical assets in electrical grids, and accurately predicting their remaining useful life (RUL) is essential for predictive maintenance strategies. This paper presents a novel hybrid CNN-LSTM approach for Dissolved Gas Analysis (DGA) data that prioritizes computational efficiency while maintaining high prediction accuracy. The approach combines CNN for extracting spatial patterns from multi-dimensional gas concentrations and LSTM for modeling temporal dependencies, integrated with Cox proportional hazards regression for probabilistic survival predictions. Using 30 years of DGA data, the proposed model achieved a C-index of 0.822, comparable to LSTM-only model while reducing training time by 89.3%. This 9.3× faster training speed makes the model highly suitable for industrial deployment where rapid model updates are essential. Optimization techniques including CBAM and mixed-precision training further enhanced efficiency. Copyright © The Korean Institute of Electrical Engineers This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

키워드