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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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Research Article

Power Transformer Fault Diagnosis using DGA and Artificial Intelligence

Author(s): Seyed Javad Tabatabaei Shahrabad, Vahid Ghods* and Mohammad Tolou Askari

Volume 13, Issue 4, 2020

Page: [579 - 587] Pages: 9

DOI: 10.2174/2213275912666190212124133

Price: $65

Abstract

Background: Power transformers are one of the most applicable electricity network devices which transmit output power of the generator to the network through increasing voltage and decreasing current. Due to high cost of such devices and cost of disconnecting device upon failure, disconnection and failure of the transformer should be avoided as much as possible.

Objective: In addition, in order to increase reliability and reduce maintenance costs, such devices should be monitored constantly. Internal faults ionize and warm up oil and as a result, gases like carbon dioxide, methane, ethane, ethylene and acetylene are produced. Various methods have been proposed for diagnosing fault in power transformers where one of the most well-known methods is dissolved gas analysis (DGA). DGA in oil is one of the effective tools for diagnosing initial faults in transformers.

Method: Common fault detection methods using oil-dissolved gas analysis include Dornemburge, Duval’s triangle, IEC/IEEE standard, key gases and Rogers. In recent years, artificial intelligence like genetic algorithm, fuzzy logic and neural networks have been used to detect faults using DGA. In this paper, support vector machine (SVM) and decision tree are used to detect internal faults in power transformers.

Results: By evaluation of the proposed methods, total accuracies of classifiers using SVM and decision tree were 90% and 97.5%, respectively.

Conclusion: Decision tree shows better performance and it is suggested as a proper method for obtaining promising results.

Keywords: Power transformer, oil immersed transformer, DGA, fault diagnosis, artificial intelligence, support vector machine (SVM)

Graphical Abstract

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