Transforming Substation Automation through Deep Learning and Machine Learning

This research explores the transformative impact of DL and ML on substation automation, revealing significant advancements in real-time optimization, fault identification, and predictive maintenance. By employing LSTM-based models, the research enhanced the mean time to failure from 500 to 650 hours, resulting in a 92% accuracy in predictive maintenance. The SVM classifier obtained a defect detection accuracy of 95% in comparison to more conventional approaches. Reinforcement learning was employed to accomplish a 90% increase in operating efficiency and a 30% decrease in interruption. The quantitative results demonstrate that the proposed methods enhance the reliability and efficiency of electrical power distribution networks, thereby enabling more effective adaptive energy management strategies and offering a robust solution to the issues associated with conventional substation automation systems.