A Review on Diagnosis of Error and Fault Detection By Using Electric Drive and Artifical Intillegence in Smart Machine Learning Techniques of Power System

Authors

  • Piyush Kumar Yadav, Saurabh V Kumar, Dr. Rajnish Bhasker, Anchal Sahu

Abstract

Monitoring an electric vehicle can detect malfunctions in the vehicle's operation capable of causing unforeseen failures and financial losses. This study explores a variety of error detection methods and diagnostic methods for induction motors and presents them. First, an anomaly process or external detection is used to enlarge the file accuracy of detecting broken rotor bars. It is shown how the proposed method can significantly improve network reliability through a single-phase separation process. After that, Miscellaneous findings based on ensemble are used to compare different approaches in combination learning to find broken rotor bars. Finally, a deep neural network is developed to exclude key features that will be used as network input parameters. A deep auto encoder was used to build a high-end model to make predictions of a broken rotor barriers and transmission errors from induction motors with high accuracy.

Published

2020-12-01

Issue

Section

Articles