Predictive Maintenance of Power Transformers in Distribution Network with Energy Management Using Deep Learning

The reliability and efficiency of the electrical grid network are obtained through proper maintenance of the power transformers. The advanced maintenance of the electrical grid is obtained through modern strategies to enhance the performance of the system. Power transformers are an important part of the electrical distribution systems. Failure of transformers leads to various constraints regarding service interferences with costly outrages. Traditional forms of maintenance rely on manual inspections on fixed timelines which is not able to detect the transformer degradation at early stages. Advanced predictive maintenance with energy management is obtained through deep learning techniques with hybrid optimization techniques. This includes Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). This is done through various sensors and monitoring equipment placed in the distribution network. The extraction of spatial and temporal data from images is obtained through the CNN component. The hidden defects and abnormal functioning of various parts in the transformer are detected through CNN. Oil leakages, overheating and insulation failure are the early signs of transformer failures. The RNN component concentrates on the data generated by sensors and meters in real-time environment. This helps to predict the future demand for power transformers. The sequential patterns are obtained through the LS TM component. They provide accurate details regarding the performance and life of transformers. The integration of energy management with predictive maintenance helps optimise power utility while maintaining the reliable operation of transformers.