Improvement in Transformer Diagnosis by DGA using Fuzzy Logic

Dhote, Nitin K.; Helonde, J. B.

doi:10.5370/jeet.2014.9.2.615

Cited by 19 publications

(13 citation statements)

References 15 publications

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“…The diagnosis on the insulation system of a transformer can be drawn by mapping the oil characteristics of this transformer to a set of rules. A number of researchers applied fuzzy logic techniques to power transformer fault diagnosis .…”

Section: Literature Review On Pattern Recognition Techniques For Tranmentioning

confidence: 99%

Pattern recognition techniques for power transformer insulation diagnosis-a comparative study part 1: framework, literature, and illustration

Cui

Saha

2014

Int. Trans. Electr. Energ. Syst.

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Summary The condition of the insulation system of a power transformer has a significant impact on its overall reliability and serviceability. Transformer oil tests including breakdown voltage, acidity, dielectric dissipation factor, 2‐furfuraldehyde, water content, and dissolved gases analysis have been commonly performed in utility companies to provide information regarding the conditions of transformer insulation. Over the past two decades, various pattern recognition techniques are proposed to interpret the oil tests results and make diagnosis on transformer insulation. However, there are still considerable challenging issues to be investigated before the pattern recognition technique can become a “ready‐to‐use” tool at utility companies. This paper provides a comparative study of pattern recognition techniques for power transformer insulation diagnosis using oil tests results. A general pattern recognition application framework will be outlined in the paper. And a comprehensive literature review on various pattern recognition techniques for transformer insulation diagnosis will be provided in the paper. The important issues for improving the applicability of pattern recognition techniques for transformer insulation diagnosis will also be discussed. A case study will be presented to demonstrate the procedure of applying pattern recognition techniques to practical transformer insulation diagnosis using oil test results. Copyright © 2014 John Wiley & Sons, Ltd.

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Section: Literature Review On Pattern Recognition Techniques For Tranmentioning

confidence: 99%

Pattern recognition techniques for power transformer insulation diagnosis-a comparative study part 1: framework, literature, and illustration

Cui

Saha

2014

Int. Trans. Electr. Energ. Syst.

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“…Similar to the three-ratio method widely used in the fault diagnosis of transformers [18,19], selecting concentration ratio of SF 6 decomposition products as the characteristic quantity may be a more preferred method.…”

Section: Fault Diagnosis With Chemical Methodsmentioning

confidence: 99%

Application of Joint Electro-Chemical Detection for Gas Insulated Switchgear Fault Diagnosis

Li¹,

Tang²,

Li³

2015

Journal of Electrical Engineering and Technology

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-The integrity of the gas insulated switchgear (GIS) is vital to the safety of an entire power grid. However, there are some limitations on the techniques of detecting and diagnosing partial discharge (PD) induced by insulation defects in GIS. This paper proposes a joint electro-chemical detection method to resolve the problems of incomplete PD data source and also investigates a new unique fault diagnosis method to enhance the reliability of data processing. By employing ultra-high frequency method for online monitoring and the chemical method for detecting SF6 decomposition offline, the acquired data can form a more complete interpretation of PD signals. By utilizing DS evidence theory, the diagnostic results with tests on the four typical defects show the validity of the new fault diagnosis system. With higher accuracy and lower computation cost, the present research provides a promising way to make a more accurate decision in practical application.

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“…Nonetheless, there is no unified standard within fuzzy theory for constructing suitable membership functions [10]. Recently, a trapezoidal membership function is usually employed in the health diagnosis of transformers [35,36], and the trapezoidal model is also in accordance with the health condition of transformers [21]. Hence, the trapezoidal model is adopted to obtain the assessing grades in this paper.…”

Section: Fuzzy Set Theorymentioning

confidence: 99%

An Integrated Decision-Making Model for Transformer Condition Assessment Using Game Theory and Modified Evidence Combination Extended by D Numbers

et al. 2016

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Abstract:The power transformer is one of the most critical and expensive components for the stable operation of the power system. Hence, how to obtain the health condition of transformer is of great importance for power utilities. Multi-attribute decision-making (MADM), due to its ability of solving multi-source information problems, has become a quite effective tool to evaluate the health condition of transformers. Currently, the analytic hierarchy process (AHP) and Dempster-Shafer theory are two popular methods to solve MADM problems; however, these techniques rarely consider one-sidedness of the single weighting method and the exclusiveness hypothesis of the Dempster-Shafer theory. To overcome these limitations, this paper introduces a novel decision-making model, which integrates the merits of fuzzy set theory, game theory and modified evidence combination extended by D numbers, to evaluate the health condition of transformers. A four-level framework, which includes three factors and seventeen sub-factors, is put forward to facilitate the evaluation model. The model points out the following: First, the fuzzy set theory is employed to obtain the original basic probability assignments for all indices. Second, the subjective and objective weights of indices, which are calculated by fuzzy AHP and entropy weight, respectively, are integrated to generate the comprehensive weights based on game theory. Finally, based on the above two steps, the modified evidence combination extended by D numbers, which avoids the limitation of the exclusiveness hypothesis in the application of Dempster-Shafer theory, is proposed to obtain the final assessment results of transformers. Case studies are given to demonstrate the proposed modeling process. The results show the effectiveness and engineering practicability of the model in transformer condition assessment.

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Improvement in Transformer Diagnosis by DGA using Fuzzy Logic

Cited by 19 publications

References 15 publications

Pattern recognition techniques for power transformer insulation diagnosis-a comparative study part 1: framework, literature, and illustration

Pattern recognition techniques for power transformer insulation diagnosis-a comparative study part 1: framework, literature, and illustration

Application of Joint Electro-Chemical Detection for Gas Insulated Switchgear Fault Diagnosis

An Integrated Decision-Making Model for Transformer Condition Assessment Using Game Theory and Modified Evidence Combination Extended by D Numbers

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