Authors: Dr. RajaGopal Kayapati
Abstract: Electrical power distribution systems are critical infrastructures that require robust fault detection and repair mechanisms to ensure uninterrupted service. Traditional fault detection systems often struggle with accuracy and real-time adaptability. This paper proposes a hybrid machine learning (ML) framework that integrates ensemble learning and deep learning models to predict faults and recommend repair actions in power distribution systems. The proposed system combines the strengths of decision trees, random forests, and long short-term memory (LSTM) networks to improve accuracy, precision, and response time. Experimental results on benchmark electrical datasets demonstrate a significant performance improvement over conventional models. This hybrid approach provides utility companies with a scalable, intelligent fault management solution, thereby reducing downtime and maintenance costs.
DOI: http://doi.org/10.61137/ijsret.vol.11.issue3.135
