Transformer mechanical integrity evaluation via unsupervised neural network (UNN) in smart grid network

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Bohari Z.H.
Jali M.H.
Baharom M.F.
Nasir M.N.M.
Fariz N.M.
Thayoob Y.H.M.
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Institute of Electrical and Electronics Engineers Inc.
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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. � 2015 IEEE.
Biomedical engineering; Computer networks; Condition monitoring; Conformal mapping; Control systems; Electric power transmission networks; Self organizing maps; Chinese Standard; Contingency analysis; Electrical systems; Integral part; Mechanical integrity; Smart grid; Smart grid networks; Unsupervised neural networks; Smart power grids