2012
DOI: 10.1002/widm.1043
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Data mining for oil‐insulated power transformers: an advanced literature survey

Abstract: Knowledge discovery in database and data mining (DM) have emerged as high profile, rapidly evolving, urgently needed, and highly practical approaches to use dissolved gas analysis (DGA) data to monitor conditions and faults in oil‐immersed power transformers. This study reviews different DM approaches to oil‐immersed power transformer maintenance by discussing historical developments and presenting state‐of‐the‐art DM methods. Relevant publications covering a broad range of artificial intelligence methods are … Show more

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Cited by 11 publications
(3 citation statements)
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“…Fault gases can be categorized into three [7,8,[33][34][35][36][37][38] The quantity and ratios of these gases will vary depending on whether a fault is developing in the transformer or not. A critical evaluation of the variation of gases present can precisely determine the underlying condition and the extent to which internal damages have been occasioned [39]. The leading causes of gas formation within an inservice transformer are either of thermal or electrical origins.…”
Section: Analysis Of Dissolved Gasesmentioning
confidence: 99%
“…Fault gases can be categorized into three [7,8,[33][34][35][36][37][38] The quantity and ratios of these gases will vary depending on whether a fault is developing in the transformer or not. A critical evaluation of the variation of gases present can precisely determine the underlying condition and the extent to which internal damages have been occasioned [39]. The leading causes of gas formation within an inservice transformer are either of thermal or electrical origins.…”
Section: Analysis Of Dissolved Gasesmentioning
confidence: 99%
“…In this section some literature on the topic of using data mining (DM) techniques in power transformer data and fault prediction is exposed. Huang, Huang, and Sun (2012) reviewed different DM approaches to oil-immersed power transformer maintenance. It focused more on diagnosis, identifying the cause of failure after the failure has occurred, with brief mentions to prognosis of faults.…”
Section: Related Literaturementioning
confidence: 99%
“…The techniques include graphical analyses like scattered plot and curve fitting, as well as statistical analyses such as hypothesis tests and correlation coefficients [3][4][5][6][7][8]. Apart from that, mathematical techniques such as weightings-based health index formula, fuzzy logic, artificial neural network, principal component analysis, analytic hierarchy process, grey relational analysis, support vector machine and particle swarm optimisation have also been proposed for condition assessment of in-service power transformers [4,[9][10][11][12][13][14][15].…”
Section: Introductionmentioning
confidence: 99%