scholarly journals AN EXPERIMENTAL EVALUATION OF INTELLIGENT FAULT DETECTION AND CLASSIFICATION FOR INDUCTION MOTORS UTILIZING MACHINE LEARNING APPROACHES

2021 ◽  
Vol 29 (2) ◽  
pp. 126-136
Author(s):  
Mahmut KASAP ◽  
Eyüp ÇİNAR ◽  
Ahmet YAZİCİ ◽  
Kemal ÖZKAN
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Experimental Evaluation ◽  
Induction Motors ◽  
Learning Approaches ◽  
Fault Detection And Classification

Fault Detection and Classification in Power System using Machine Learning

2021 ◽  
Author(s):  
Manojna ◽  
Sridhar H. S ◽  
Nikhil Nikhil ◽  
Anand Kumar ◽  
Pratyay Amrit
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Power System ◽  
Fault Detection And Classification

scholarly journals Fault detection in power grids based on improved supervised machine learning binary classification

2021 ◽  
Vol 72 (5) ◽  
pp. 315-322
Author(s):  
Mohammed Wadi
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Power Systems ◽  
Smart Grids ◽  
Binary Classification ◽  
Power Grids ◽  
Supervised Machine Learning ◽  
Learning Approaches ◽  
Smart Meters ◽  
Data Set

Abstract With the increased complexity of power systems and the high integration of smart meters, advanced sensors, and high-level communication infrastructures within the modern power grids, the collected data becomes enormous and requires fast computation and outstanding analyzing methods under normal conditions. However, under abnormal conditions such as faults, the challenges dramatically increase. Such faults require timely and accurate fault detection, identification, and location approaches for guaranteeing their desired performance. This paper proposes two machine learning approaches based on the binary classification to improve the process of fault detection in smart grids. Besides, it presents four machine learning models trained and tested on real and modern fault detection data set designed by the Technical University of Ostrava. Many evaluation measures are applied to test and compare these approaches and models. Moreover, receiver operating characteristic curves are utilized to prove the applicability and validity of the proposed approaches. Finally, the proposed models are compared to previous studies to confirm their superiority.

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