Integrated ANN-based proactive fault diagnostic scheme for power transformers using dissolved gas analysis

Ghoneim, Sherif S. M.; Taha, Ibrahim B. M.; Elkalashy, Nagy I.

doi:10.1109/tdei.2016.005301

Cited by 148 publications

(89 citation statements)

References 14 publications

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“…while t ≤ Max Iter do for each Wolf i ∈ pack do Update current wolf's position according to Equation (15). end -Update a, A, and C as in Equations (16), (11) and (12).…”

Section: Algorithm 1 Gwo Pseudo-codementioning

confidence: 99%

“…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.…”

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confidence: 99%

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

et al. 2019

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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.

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“…while t ≤ Max Iter do for each Wolf i ∈ pack do Update current wolf's position according to Equation (15). end -Update a, A, and C as in Equations (16), (11) and (12).…”

Section: Algorithm 1 Gwo Pseudo-codementioning

confidence: 99%

mentioning

confidence: 99%

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

et al. 2019

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“…However, these traditional methods may have problems such as missing coding and too absolute coding boundaries. With the development of artificial intelligence technology, artificial neural network (ANN) [7], Bayes network [8], support vector machine (SVM) [9], and other intelligent methods have emerged, which improve the diagnostic accuracy of transformer fault. SVM is a new machine learning method based on the development of statistical learning theory.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of transformer based on modified grey wolf optimization algorithm and support vector machine

Huang

Wang

et al. 2019

IEEJ Transactions Elec Engng

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Power transformers are important pieces of equipment for the operation of power systems. Accurate diagnosis of their fault is closely related to the stable operation of the entire power grid. In order to improve the diagnostic accuracy of transformer fault, the grey wolf optimization (GWO) algorithm is introduced, and the differential evolution mechanism is integrated into the algorithm. Therefore, this paper proposes a transformer fault diagnosis method based on the modified grey wolf optimization algorithm (MGWO) and support vector machine (SVM), so that the application method realizes optimization of the penalty factor and the kernel parameter in SVM. Through the analysis of existing data examples, the SVM model optimized by the MGWO algorithm has the advantages of good generalization and strong predictive ability, and its fault diagnostic accuracy is higher than those of the genetic algorithm, particle swarm optimization algorithm, and GWO algorithm. This method has practical application significance.

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“…However, due to the complexity of the working environment and the process structure of the transformers, these methods are not enough to make a right judgement and cannot judge fault fuzzy boundary. According to [6], their accuracy rates are about in 60%, which means the ratio methods cannot account for the diagnostic criteria completely [7]. In addition, the concentrations of cellulose chemical markers in oil, such as methanol, ethanol and 2-furfural, are used as a determination mark for diagnosing transformer insulation failure, which still present many challenges for an accurate interpretation in real transformers [8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Integrated Method to Diagnose Faults in Power Transformers

Sun

et al. 2018

Energies

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In a smart grid, many transformers are equipped for both power transmission and conversion. Because a stable operation of transformers is essential to maintain grid security, studying the fault diagnosis method of transformers can improve both fault detection and fault prevention. In this paper, a data-driven method, which uses a combination of Principal Component Analysis (PCA), Particle Swarm Optimization (PSO), and Support Vector Machines (SVM) to enable a better fault diagnosis of transformers, is proposed and investigated. PCA is used to reduce the dimension of transformer fault state data, and an improved PSO algorithm is used to obtain the optimal parameters for the SVM model. SVM, which is optimized using PSO, is used for the transformer-fault diagnosis. The diagnostic-results of the actual transformers confirm that the new method is effective. We also verified the importance of data richness with respect to the accuracy of the transformer-fault diagnosis.

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Integrated ANN-based proactive fault diagnostic scheme for power transformers using dissolved gas analysis

Cited by 148 publications

References 14 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

Fault diagnosis of transformer based on modified grey wolf optimization algorithm and support vector machine

A Novel Integrated Method to Diagnose Faults in Power Transformers

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