Using dissolved gas analysis results to detect and isolate the internal faults of power transformers by applying a fuzzy logic method

Noori, Masoud; Effatnejad, Reza; Hajihosseini, Payman

doi:10.1049/iet-gtd.2017.0028

Cited by 43 publications

(30 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Energies 2019, 12, 4170 2 of 18 DGA interpretation methods [1], including key gas method [2,3], IEC three-ratio method [4,5], Duval triangle method [6], Rogers ratio method [7] and Dornenburg ratio method [8], Duval pentagon [9], Mansour pentagon method [10,11], etc., are available to identify the different types of faults occurring in operating transformers. Although the commonly used methods are simple and effective in transformer fault diagnosis, they suffer from defects such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which will affect the reliability of fault analysis [12].With the development of artificial intelligence (AI), machine learning and pattern recognition methods have been widely used in power transformer fault diagnosis, including artificial neural network (ANN) [13][14][15], support vector machine (SVM) [16][17][18][19][20][21][22][23][24], probabilistic neural network [25,26], Bayesian neural network [27], fuzzy logic [28][29][30], deep belief network [31], expert system [32,33], which make up for the shortcomings of the traditional DGA methods, directly or indirectly improve the accuracy of transformer fault diagnosis, and provide a new idea for high-precision transformer fault diagnosis. Although these methods have achieved good results, there are also some shortcomings.…”

mentioning

confidence: 99%

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

et al. 2019

View full text Add to dashboard Cite

Dissolved gas analysis (DGA) is a widely used method for transformer internal fault diagnosis. However, the traditional DGA technology, including Key Gas method, Dornenburg ratio method, Rogers ratio method, International Electrotechnical Commission (IEC) three-ratio method, and Duval triangle method, etc., suffers from shortcomings such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which affect the reliability of fault analysis. Grey wolf optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 and it is easy for the original GWO to fall into the local optimum. This paper presents a new meta-heuristic method by hybridizing GWO with differential evolution (DE) to avoid the local optimum, improve the diversity of the population and meanwhile make an appropriate compromise between exploration and exploitation. A fault diagnosis model of hybrid grey wolf optimized least square support vector machine (HGWO-LSSVM) is proposed and applied to transformer fault diagnosis with the optimal hybrid DGA feature set selected as the input of the model. The kernel principal component analysis (KPCA) is used for feature extraction, which can decrease the training time of the model. The proposed method shows high accuracy of fault diagnosis by comparing with traditional DGA methods, least square support vector machine (LSSVM), GWO-LSSVM, particle swarm optimization (PSO)-LSSVM and genetic algorithm (GA)-LSSVM. It also shows good fitness and fast convergence rate. Accuracies calculated in this paper, however, are significantly affected by the misidentifications of faults that have been made in the DGA data collected from the literature.

show abstract

mentioning

confidence: 99%

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

et al. 2019

View full text Add to dashboard Cite

show abstract

“…RRM can be applied when gas concentration exceeds the specified limit rather than double the specified limit as in DRM [13,19]. Fault identification in this method is based on gas ratios C2H2/C2H4, CH4/H2, C2H4/C2H6 and six types of fault (including normal condition) is identified [11,12].…”

Section: A the Proposed Fuzzy Logic Model For Incipient Fault Diagnomentioning

confidence: 99%

“…In this method percentage concentration of CH4 , C2H2, and C2H4 to total three gases are plotted along the sides of the triangle. Triangle is divided into seven fault regions PD: Partial Discharge, T1: Thermal Fault less than 300 C, T2: Thermal Fault between 300 to 700 C, T3:Thermal Fault more than 700 C, D1: Low Energy Discharge (Sparking), D2: High Energy Discharge (Arcing), DT: Mix of Thermal and Electrical Faults [18,19]. Duval triangle method provides more accurate and consistent diagnosis than other ratio methods.…”

Section: A Duval Trianglementioning

confidence: 99%

Fuzzy Logic Expert System for Incipient Fault Diagnosis of Power Transformers

Husain¹

2018

IJEEI

View full text Add to dashboard Cite

Condition monitoring of power transformers improves the security and reliability of an electrical power system. It protects the transformers from failures, and avoids huge revenue loss to utilities and customers. The fault diagnosis of transformers is carried out by concentrations of several dissolved gasses. An accurate fault diagnosis of transformers has been a critical problem for diagnostic experts of transformers. In this article, a novel fuzzy logic model has been proposed to determine the transformer incipient faults. It incorporates the information obtained from dissolved gas analysis test. Further, the proposed model also incorporates conventional fault diagnosis methods viz. Duval Triangle, Doernenburg, Rogers, and IEC ratio code methods. The proposed fuzzy logic models short out the problems occur in the conventional fault diagnosis methods of transformers.

show abstract

“…Hence, appropriate methods and diagnostic procedures must be chosen, in order to allow the detection of defects occurring solely in these parts of the transformer. The best option seems to be the use of methods that are non-invasive, easy to apply, and give an intuitive view about the initial selection of transformers on healthy units and units with suspected or developed defect [5,10,[16][17][18][19]. Among these methods, one of the most important is the analysis of the gases dissolved in oil (DGA), which constitutes a part of the fundamental measurements within the first level of a diagnostic procedure of a transformer in service [9].…”

Section: Introductionmentioning

confidence: 99%

“…With an increase in temperature, hydrocarbons are generated in the following sequence, methane-ethane-ethylene-acetylene, but H2 concentration is low in this case. Thermal decomposition of cellulose is associated with the generation of large amounts of carbon monoxide and lesser amounts of carbon dioxide [4,5,9,13,[16][17][18][19][20]. The conclusions about the transformer's technical condition, based on the results of the chromatographic analysis of the oil sample taken from the transformer, is typically carried out in accordance with the general scheme presented in Figure 1 [20].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of the Results of Diagnostic Measurements with an Internal Inspection of Oil-Filled Power Transformers

Piotrowski

Rózga

Kozak³

2019

Energies

View full text Add to dashboard Cite

This article presents a description of four independent case studies concerning situations when power transformers were directed to internal inspection. This inspection was the result of a specific case of a routine diagnostic procedure that was carried out and, where the transformer was switched off by a Buchholz gas relay. The case studies described were selected such that they represented situations when availability of historical data on the previous measurements was limited and a quick diagnosis had to be made on the basis of the results from the last measurement. In all of the cases presented here, the analysis of the gases dissolved in oil had played an important role in the detection of the defects that turned out to be dangerous for further exploitation of the transformers considered. The first signal about a possible developing defect was elicited solely from the measurements of the oil samples taken from the transformer in service. However, more detailed recognition and initial localization of the defect was possible after additional supplementary measurements (winding resistance, sweep frequency response analysis, etc.), which required the transformer to be switched off. The conducted sequence of actions, based on the developed diagnostic procedure, indicated the possibility of effective and early withdrawal of the transformer from operation, before it underwent a serious failure.

show abstract

Using dissolved gas analysis results to detect and isolate the internal faults of power transformers by applying a fuzzy logic method

Cited by 43 publications

References 17 publications

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

Fuzzy Logic Expert System for Incipient Fault Diagnosis of Power Transformers

Comparative Analysis of the Results of Diagnostic Measurements with an Internal Inspection of Oil-Filled Power Transformers

Contact Info

Product

Resources

About