Transformer Mechanical Integrity Evaluation Via Unsupervised Neural Network (UNN) In Smart Grid Network

Zul Hasrizal, Bohari and Mohd Hafiz, Jali and Mohamad Faizal, Baharom and Mohamad Na'im, Mohd Nasir and Nik Mohd Fariz, Mohd Nawi and Yasmin Hanum, Md Thayoob (2016) Transformer Mechanical Integrity Evaluation Via Unsupervised Neural Network (UNN) In Smart Grid Network. 2015 IEEE International Conference On Control System, Computing And Engineering (ICCSCE). pp. 167-171. ISSN 978-147998252-3

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Abstract

This paper describes the classification of mechanical integrity of transformers using unsupervised neural networks (UNN). Transformers are the integral part of electrical system or smart grid networks since the last century. Self-Organizing Maps (SOM) is one type of UNN the widely used to do assessment on any system such as biomedical engineering, load contingency analysis and etc. The application of CIGRE standard and SOM in the research are enhancing the ability to do mechanical integrity assessment on the transformers for condition monitoring. Motivation for this research is to fill in the gap of excess FRA raw data for better assessment. This research proved that the new proposed method using SOM integrated with CIGRE standard able to do mechanical examination especially on core, winding and magnetic part of the transformer compared to current OMICRON SFRAnalyzer tool that employed Chinese Standard.

Item Type: Article
Subjects: T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Electrical Engineering
Depositing User: Mohd Hannif Jamaludin
Date Deposited: 29 Nov 2016 06:29
Last Modified: 29 Nov 2016 06:29
URI: http://eprints.utem.edu.my/id/eprint/17703
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