Hybrid Wavelet-LSTM-Transformer Model for Fault Forecasting in Power Grids

In power grid management, accurate forecasting of faults is the key to providing uninterrupted electricity supply. Errors can result in significant losses of property as well as severe harm. Better alternatives adhere to the complex power grid data that frequently confuse conventional forecasting techniques and produce less-than-ideal forecasts. This research proposes a novel hybrid Wavelet-LSTM-Transformer model for power grid fault prediction. Long Short-Term Memory (LSTM) network sequential learning capabilities, attention processes of the Transformer model, and time-frequency analysis capabilities of wavelet transform are combined in the suggested model. In order to forecast trends, our approach maintains the notion of long-term memory while capturing short-term variations. This paper demonstrates that the developed model outperforms the most comparable work using numerous experimental trials and comparisons. It offers a workable method to boost the worldwide resilience and dependability of power grid systems. The results stress combining several modeling techniques to tackle difficult forecasting problems in important infrastructure sectors.