Machine-Learning-Based-Fault-Detection-in-Electrical-Equipment

When I began working on electrical systems, I soon understood the significance of early fault detection in providing safety and stability for the system. Immaterial faults in equipment can have devastating effects such as fires or explosions, threaten-ing both life and property. Early detection, therefore, is not only beneficial—it is a necessity. With the machine learning especially the advanced models like the SVM and KNN we could come up with a scalable method of handling real power systems. The models were highly accurate in predicting and SVM was more than 99 percent accurate. Also confirming to this, we came out with ensemble model by bagging and majority voting which also provided improvement to the detection system. Our method started with both the Particle Swarm Optimization (PSO) and feature selection, thus opening the data with preprocessing followed by tuning of the hyperparameters to advance the performance of the model. The produced system was tested on realistic contemporary datasets and it has uniform reliability under varying contexts. Besides assuring network stability and management of operations for supervisors, customer satisfaction and general optimization of industrial systems—highlighting the vital position of intelligent fault detection in securing and optimizing electrical infrastructures.