Deep machine learning with grey wolf algorithm and central deference Kalman filter based broken rotor bars detection in induction motor
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...
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ftdoajarticles:oai:doaj.org/article:bcc133a9b8d44bef8db5be929103c1df 2023-05-15T16:01:22+02:00 Deep machine learning with grey wolf algorithm and central deference Kalman filter based broken rotor bars detection in induction motor Ali Amiri Zaniani Mehdi Nafar Mohsen Simab 2022-12-01T00:00:00Z https://doi.org/10.1049/rpg2.12337 https://doaj.org/article/bcc133a9b8d44bef8db5be929103c1df EN eng Wiley https://doi.org/10.1049/rpg2.12337 https://doaj.org/toc/1752-1416 https://doaj.org/toc/1752-1424 1752-1424 1752-1416 doi:10.1049/rpg2.12337 https://doaj.org/article/bcc133a9b8d44bef8db5be929103c1df IET Renewable Power Generation, Vol 16, Iss 16, Pp 3519-3530 (2022) Renewable energy sources TJ807-830 article 2022 ftdoajarticles https://doi.org/10.1049/rpg2.12337 2022-12-30T20:08:57Z 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. Article in Journal/Newspaper DML Directory of Open Access Journals: DOAJ Articles IET Renewable Power Generation 16 16 3519 3530 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Renewable energy sources TJ807-830 |
spellingShingle |
Renewable energy sources TJ807-830 Ali Amiri Zaniani Mehdi Nafar Mohsen Simab Deep machine learning with grey wolf algorithm and central deference Kalman filter based broken rotor bars detection in induction motor |
topic_facet |
Renewable energy sources TJ807-830 |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Ali Amiri Zaniani Mehdi Nafar Mohsen Simab |
author_facet |
Ali Amiri Zaniani Mehdi Nafar Mohsen Simab |
author_sort |
Ali Amiri Zaniani |
title |
Deep machine learning with grey wolf algorithm and central deference Kalman filter based broken rotor bars detection in induction motor |
title_short |
Deep machine learning with grey wolf algorithm and central deference Kalman filter based broken rotor bars detection in induction motor |
title_full |
Deep machine learning with grey wolf algorithm and central deference Kalman filter based broken rotor bars detection in induction motor |
title_fullStr |
Deep machine learning with grey wolf algorithm and central deference Kalman filter based broken rotor bars detection in induction motor |
title_full_unstemmed |
Deep machine learning with grey wolf algorithm and central deference Kalman filter based broken rotor bars detection in induction motor |
title_sort |
deep machine learning with grey wolf algorithm and central deference kalman filter based broken rotor bars detection in induction motor |
publisher |
Wiley |
publishDate |
2022 |
url |
https://doi.org/10.1049/rpg2.12337 https://doaj.org/article/bcc133a9b8d44bef8db5be929103c1df |
genre |
DML |
genre_facet |
DML |
op_source |
IET Renewable Power Generation, Vol 16, Iss 16, Pp 3519-3530 (2022) |
op_relation |
https://doi.org/10.1049/rpg2.12337 https://doaj.org/toc/1752-1416 https://doaj.org/toc/1752-1424 1752-1424 1752-1416 doi:10.1049/rpg2.12337 https://doaj.org/article/bcc133a9b8d44bef8db5be929103c1df |
op_doi |
https://doi.org/10.1049/rpg2.12337 |
container_title |
IET Renewable Power Generation |
container_volume |
16 |
container_issue |
16 |
container_start_page |
3519 |
op_container_end_page |
3530 |
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1766397262464811008 |