Hybrid feature selection–artificial intelligence–gravitational search algorithm technique for automated transformer fault determination based on dissolved gas analysis-Reference-Cited by-同舟云学术

Hybrid feature selection–artificial intelligence–gravitational search algorithm technique for automated transformer fault determination based on dissolved gas analysis

Published:2020-03-05 Issue:8 Volume:14 Page:1575-1582
ISSN:1751-8695
Container-title:IET Generation, Transmission & Distribution
language:en
Short-container-title:IET Generation, Transmission & Distribution

Author:

Illias Hazlee Azil¹,Chan Kai Choon¹,Mokhlis Hazlie¹^ORCID

Affiliation:

1. Department of Electrical Engineering, Faculty of EngineeringUniversity of Malaya50603Kuala LumpurMalaysia

Funder

Ministry of Higher Education, Malaysia

Universiti Malaya

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-gtd.2019.1189

Reference38 articles.

1. Fault diagnosis of power transformer based on support vector machine with genetic algorithm

2. Singh A. Upadhyay G.: ‘Dissolved gas analysis of power transformer using K‐means and support vector machine’.2016 IEEE 1st Int. Conf. on Power Electronics Intelligent Control and Energy Systems (ICPEICES) Delhi India 2016 pp.1–5

3. Intelligent optimization methods for high‐dimensional data classification for support vector machines;Ding S.;Intell. Inf. Manage.,2010

4. Fault diagnosis of power transformers using multi‐class least square support vector machines classifiers with particle swarm optimisation;Zheng H.B.;IET Electr. Power Appl.,2011

5. Transformer incipient fault prediction using combined artificial neural network and various particle swarm optimisation techniques;Illias H.A.;PLoS ONE,2015

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