|1/2010 - 16|
Cluster Classification of Partial Discharges in Oil-impregnated Paper InsulationSURESH, S. D. R. , USA, S.
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PDs, multiple PD sources, oil-impregnated paper insulation, PD classification, cluster analysis
partial(11), discharge(9), insulation(7), power(6), transformer(5), pattern(5), recognition(4), fuzzy(4)
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About this article
Date of Publication: 2010-02-27
Volume 10, Issue 1, Year 2010, On page(s): 90 - 93
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.01016
Web of Science Accession Number: 000275458900016
SCOPUS ID: 77954667129
Recognition of multiple partial discharge (PD) sources in high voltage equipment has been a challenging task until now. The work reported here, aims to recognize multiple PD sources in oil-impregnated paper using Cluster Analysis (CA) and Fuzzy Logic (FL). The typical sources of PD in transformer are identified and the corresponding single source PD defect laboratory models are fabricated. From the measured PD signals, the necessary statistical parameters are extracted by applying CA for classification. A Fuzzy based algorithm has been developed to recognize single source PDs. The developed algorithm has also been applied to recognize multiple PD sources.
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 Influences of Traction Load Shock on Artificial Partial Discharge Faults within Traction Transformer—Experimental Test for Pattern Recognition, Energies, ISSN 1996-1073, Issue 10, Volume 10, 2017.
Digital Object Identifier: 10.3390/en10101556 [CrossRef]
 Comprehensive Evaluation and Application of GIS Insulation Condition Part 1: Selection and Optimization of Insulation Condition Comprehensive Evaluation Index Based on Multi-Source Information Fusion, Tang, Bowen, Tang, Ju, Liu, Yilu, Zeng, Fuping, IEEE Access, ISSN 2169-3536, Issue , 2019.
Digital Object Identifier: 10.1109/ACCESS.2019.2925804 [CrossRef]
 Comprehensive Evaluation and Application of GIS Insulation Condition Part 2: Construction and Application of Comprehensive Evaluation Model Considering Universality and Economic Value, Tang, Bowen, Sun, Yuanzhang, Wu, Siying, Gao, Keli, Yan, Xianglian, Zeng, Fuping, IEEE Access, ISSN 2169-3536, Issue , 2019.
Digital Object Identifier: 10.1109/ACCESS.2019.2925802 [CrossRef]
 Improving pattern recognition accuracy of partial discharges by new data preprocessing methods, Majidi, Mehrdad, Oskuoee, Mohammad, Electric Power Systems Research, ISSN 0378-7796, Issue , 2015.
Digital Object Identifier: 10.1016/j.epsr.2014.09.014 [CrossRef]
 Influences of oil flow speed and temperature on partial discharge properties in transformer oil, Li, Shuaibing, Gao, Bo, Wu, Guangning, 2016 Australasian Universities Power Engineering Conference (AUPEC), ISBN 978-1-5090-1405-7, 2016.
Digital Object Identifier: 10.1109/AUPEC.2016.7749302 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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