Description

Early fault detection in power electronic systems (PESs) to maintain reliability is one of the most important issues that has been significantly addressed in recent years. Fault detection in PESs, data mining-based techniques including artificial neural network, machine learning, and deep learning algorithms are introduced. Electrical energy has become an influential factor in the scientific, economic and welfare fields of human daily life. In recent years, the expansion of electrical energy applications and the increase of electrical energy consumers have made distributed generation (DGs) dramatically replace traditional power systems. Then, the fault detection routine in PESs is expressed by introducing signal measurement sensors and how to extract the feature from it. Finally, based on studies, the performance of various data mining methods in detecting PESs faults is evaluated. The results of evaluations show that the deep learning-based techniques given the ability of feature extraction from measured signals are significantly more effective than other methods and as an ideal tool for future applications in power electronics industry are introduced. The system is developed the different classification algorithm such as artificial neural network and random forest for predicting or detecting the fault in power systems effectively.