Predecting power transformer health index and life expectation based on digital twins and multitask LSTM-GRU model

Power transformers play a crucial role in enabling the integration of renewable energy sources and improving the overall efficiency and reliability of smart grid systems. They facilitate the conversion, transmission, and distribution of power from various sources and help to balance the load between different parts of the grid. The Transformer Health Index (THI) is one of the most important indicators of ensuring their reliability and preventing unplanned outages. To this end, this study introduces a proposed new architecture called a Smart Electricity Monitoring System based on Fog Computing and Digital Twins (SEMS-FDT) for monitoring the health performance of transformers by measuring the THI rate in real time. The SEMS-FDT is specifically designed to enable the observation of the transformer's health and performance purposes in real time. The study investigates the role of machine learning (ML) models, including traditional and ensemble methods, in predicting THI and LI (Heat Load Index) by exploring the use of the entire set of features and optimized feature subsets for prediction. To improve the forecasting prediction process and achieve optimal performance a novel multitasks LSTM_GRU model is also proposed. The experimental results demonstrate that there is a promising performance of 2.543, 0.13646, 0.0284, and 0.985 for MSE, MAE, MedAE, and R2 scores respectively. Moreover, the framework is extended by incorporating model explanations, which include global explanations, which provide insights based on the entire dataset, and local explanations, which offer instance-specific explanations. The integration of the proposed model and explainability features provides engineers with comprehensive outcomes regarding the model's result.