Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation

Abstract

Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.

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

“…Several select the optimal training parameters of the TWSVM classifier, and finally, the actual fault samples and random tests were used to verify the validity of the model. Hazlee Azil Illias and Wee Zhao Liang [23] proposed a transformer fault diagnosis model based on hybrid SVM and improved evolutionary particle swarm optimization (SVM-MEPSO), which used a stepwise regression approach for data reduction and the results show that the hybrid SVM-MEPSO time-varying acceleration coefficient (TVAC) technology can obtain the highest accuracy compared with other PSO algorithms. The optimal hybrid DGA feature subset (OHFS) was selected from three feature sets by using genetic algorithm-support vector machine-feature screen (GA-SVM-FS) model and used as input of the improved social group optimization (ISGO) optimized multi-SVM classifier to develop a transformer fault diagnosis model which achieved the highest fault diagnosis accuracy (92.86%) compared with other diagnostic models [24].…”

“…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.…”

DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

“…Substituting Equations (6) and (7) into Equation (5), the problem of constructing an optimal hyperplane is translated into a dual quadratic programming problem:…”

“…Support vector machine (SVM) is a supervised machine learning method used to solve classification and regression problems, firstly proposed by Vapnik on the basis of statistical learning theory [5]. SVM was applied in many fields, such as economics [6], electrics [7], and medical science [8]. Especially in the field of cancer diagnosis, many studies have already proven the excellent performance of SVM classifier [9][10][11].…”

Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

“…Illias et al [89] proposed a hybrid modified evolutionary PSO-time varying acceleration coefficient-ANN for power transformer fault diagnosis. Meanwhile, Illias and Zhao [253] employed hybrid SVM-modified evolutionary PSO to identify transformer faults based on DGA. Wang et al [254] developed a new hybrid evolutionary algorithm combining PSO and BP algorithm, called HPSO-BP algorithm, to select optimal value of probabilistic neural network parameters for power transformer fault diagnosis.…”