A comprehensive comparative study of DGA based transformer fault diagnosis using fuzzy logic and ANFIS models

Khan, Shakeb A.; Equbal, Md. Danish; Islam, Tarikul

doi:10.1109/tdei.2014.004478

Cited by 150 publications

(62 citation statements)

References 14 publications

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“…; 2) carbon oxides -CO and CO 2 ; and (iii) nonfault gases -O 2 and N 2 [4,21]. The formation of these fault gases is a function of temperature and hence the fault type, i.e.…”

Section: Dga and The Concept Of Weighted Dgamentioning

confidence: 99%

“…Furthermore, several soft computing techniques have also been used in various studies to overcome the shortcomings of the different standard methods. Some of the popular soft computing methods that have been adopted to improve the reliability of DGA-based transformer incipient fault identification are fuzzy logic [11][12][13][14], ANNs [15][16][17], wavelet networks [18,19] and the adaptive neuro-fuzzy inference system (ANFIS) [20][21][22][23]. However, these intelligent techniques have their own limitations and hence the degree of the reliability of the method would depend on how these methods are adopted to circumvent their limitations.…”

Section: Introductionmentioning

confidence: 99%

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Transformer incipient fault diagnosis on the basis of energy-weighted DGA using an artificial neural network

Equbal¹,

Khan²,

Islam³

2018

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, a transformer incipient fault diagnosis model has been developed with the help of an artificial neural network (ANN), taking into account the difference in the energy required to produce the different fault gases.The key fault gases are indicative of the fault type prevailing in the transformer. However, in conventional studies, the energy difference in fault gas formation is not considered while adopting the key gas method for fault diagnosis. In this work, a weighting factor has been used to take into account this relative difference in energy requirement for various fault gas formations. The fault gas concentrations have been suitably weighted by their respective weighting factors before being used in the incipient fault diagnosis process. A backpropagation ANN has been appropriately trained using the weighted fault gas concentration for transformer incipient fault identification. The model has been trained to identify fault types as enlisted in the transformer fault-interpreting standard IEC-599. The developed ANN model has been tested for its diagnostic capability using a reported fault database. The comparative diagnosis results presented here show clear improvement in the diagnosis of transformer internal faults using the energy-weighted ANN model over the unweighted ANN model.

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“…; 2) carbon oxides -CO and CO 2 ; and (iii) nonfault gases -O 2 and N 2 [4,21]. The formation of these fault gases is a function of temperature and hence the fault type, i.e.…”

Section: Dga and The Concept Of Weighted Dgamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transformer incipient fault diagnosis on the basis of energy-weighted DGA using an artificial neural network

Equbal¹,

Khan²,

Islam³

2018

Turk J Elec Eng & Comp Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is highly feasible and accurate to predict transformer running states and make future fault classifications based on the trend of each historical gas concentration and the ratio between gas concentrations [2][3][4]. Current methods include oil gas ratio analysis [5][6][7], SVM [8,9] and artificial…”

Section: Introductionmentioning

confidence: 99%

Prediction Method for Power Transformer Running State Based on LSTM_DBN Network

Lin

Yan

et al. 2018

Energies

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It is of great significance to accurately get the running state of power transformers and timely detect the existence of potential transformer faults. This paper presents a prediction method of transformer running state based on LSTM_DBN network. Firstly, based on the trend of gas concentration in transformer oil, a long short-term memory (LSTM) model is established to predict the future characteristic gas concentration. Then, the accuracy and influencing factors of the LSTM model are analyzed with examples. The deep belief network (DBN) model is used to establish the transformer operation using the information in the transformer fault case library. The accuracy of state classification is higher than the support vector machine (SVM) and back-propagation neural network (BPNN). Finally, combined with the actual transformer data collected from the State Grid Corporation of China, the LSTM_DBN model is used to predict the transformer state. The results show that the method has higher prediction accuracy and can analyze potential faults.

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“…In the past few years, some monitoring methods, including windings displacement [2] and hot spot temperature [3], were applied to detect faults of oil-immersed power transformers, however DGA is still a more convenient and effective online monitoring method comparing to the above methods [4]. If an oil-immersed transformer is subjected to electrical or thermal stress, some gases, emitting from its oil-paper insulation system, mainly including H 2 , CH 4 , C 2 H 6 , C 2 H 4 and C 2 H 2 , can be regarded as a symbol of a potential fault [5].…”

Section: Introductionmentioning

confidence: 99%

“…If an oil-immersed transformer is subjected to electrical or thermal stress, some gases, emitting from its oil-paper insulation system, mainly including H 2 , CH 4 , C 2 H 6 , C 2 H 4 and C 2 H 2 , can be regarded as a symbol of a potential fault [5]. Many approaches such as IEEE standard [6], International Electro technical Commission (IEC) Method 60599 [7], and Duval's Triangle [8], have been applied to detect faults of oil-immersed transformers based on the ratios of the gases for many years.…”

Section: Introductionmentioning

confidence: 99%

A New Support Vector Machine Model Based on Improved Imperialist Competitive Algorithm for Fault Diagnosis of Oil-immersed Transformers

Zhang¹,

Wang²,

Liao³

et al. 2017

Journal of Electrical Engineering and Technology

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-Support vector machine (SVM) is introduced as an effective fault diagnosis technique based on dissolved gases analysis (DGA) for oil-immersed transformers with maximum generalization ability; however, the applicability of the SVM is highly affected due to the difficulty of selecting the SVM parameters appropriately. Therefore, a novel approach combing SVM with improved imperialist competitive algorithm (IICA) for fault diagnosis of oil-immersed transformers was proposed in the paper. The improved ICA, which is proved to be an effective optimization approach, is employed to optimize the parameters of SVM. Cross validation and normalizations were applied in the training processes of SVM and the trained SVM model with the optimized parameters was established for fault diagnosis of oil-immersed transformers. Three classification benchmark sets were studied based on particle swarm optimization SVM (PSOSVM) and IICASVM with four multiple classification schemes to select the best scheme for transformer fault diagnosis. The results show that the proposed model can obtain higher diagnosis accuracy than other methods. The comparisons confirm that the proposed model is an effective approach for classification problems.

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A comprehensive comparative study of DGA based transformer fault diagnosis using fuzzy logic and ANFIS models

Cited by 150 publications

References 14 publications

Transformer incipient fault diagnosis on the basis of energy-weighted DGA using an artificial neural network

Transformer incipient fault diagnosis on the basis of energy-weighted DGA using an artificial neural network

Prediction Method for Power Transformer Running State Based on LSTM_DBN Network

A New Support Vector Machine Model Based on Improved Imperialist Competitive Algorithm for Fault Diagnosis of Oil-immersed Transformers

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