Remaining Useful Lifetime Prediction of Distribution Transformer Using Dynamic Multi-Scale Attention-based CNN-LSTM

Abstract

Oil-immersed transformers are critical assets in the energy industry linking most power utilities to end-users. Their failure results in prolong outages, leading to huge revenue loss incurred during downtimes and replacement cost. In extreme cases, transformers in an unhealthy state poses a significant threat to the safety of grid operators. Interestingly, traditional reactive and preventive methods have been inefficient in determining when legitimate maintenance actions are due, often leading to either early over-maintenance of healthy transformers or late under-maintenance of serviceable and unhealthy transformers. Predictive maintenance based on determining the remaining useful lifetime (RUL) acts as an actionable step that resolves these challenges by delivering exactly the most appropriate time to undertake maintenance whiles ensuring optimal utilization of resources which saves maintenance cost, reduces downtimes and ensures operator safety and grid reliability. This work proposed an advanced Dynamic Multi-Scale Attention (DMSA) model and leverages on multi-modal data fusion from electrical, mechanical, thermal, and environmental sources to provide an improved data-driven solution for accurate prediction of the RUL of distribution transformers. This technique addressed the drawbacks of employing single modality approaches in capturing complex operational interactions. In this work, dynamic scaling model is incorporated to adaptively adjust the attention weights based on the importance of the input features. For short term predictions, the proposed model experimentally achieved an enhanced performance of 0.2300 mean absolute error and 0.9872 coefficient of determination value. Additionally, the DMSA CNN-LSTM model demonstrated accurate prediction, evidenced by a concordance correlation coefficient value of 0.9936. These statistical gains were achieved in a computational time of 587.3387s, demonstrating superior scalability in the event of real time deployment. Furthermore, the long-term prediction was performed using Prophet to fit the data which predicted a RUL of 25 years at 95% confidence interval which corresponded with the reference standard in IEEE STD C57.91.