Deep machine learning with grey wolf algorithm and central deference Kalman filter based broken rotor bars detection in induction motor

Autor: Ali Amiri Zaniani, Mehdi Nafar, Mohsen Simab
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: IET Renewable Power Generation, Vol 16, Iss 16, Pp 3519-3530 (2022)
Druh dokumentu: article
ISSN: 1752-1424
1752-1416
DOI: 10.1049/rpg2.12337
Popis: Abstract The present study tries to propose a new method which is using Central deference Kalman Filter (CDKF) as input index of deep machine learning (DML), for simulating state estimation and broken rotor bars (BRBs) diagnosis in induction motors (IMs). In addition, an advanced selective ensemble (DML) method applying the grey wolf optimization (GWO) algorithm to diagnose the BRBs in IMs is proposed in the following study. Here, in the initial step, in order to take sufficient datum within the usual efficiency, it is needed to train the DML network. The outcomes indicate that the offered scheme can more accurately and powerfully detect diverse forms of BRBs with an accuracy of more than 98%. And also, Filter precision is enhanced via changing the sigma points of the filter, however, the stability of the filter enhances more because of its utilization, and the CDKF is further stable and precise in comparison to the Unscented Kalman filter (UKF). The CDKF performance is assessed to estimate the speed and is used as input index of DML to diagnose the broken rotor bar in IMs. The obtained outcomes prove the performance of this combined scheme.
Databáze: Directory of Open Access Journals
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