Power Transformer Fault Diagnosis Using Support Vector Machine and Particle Swarm Optimization

Mo, Wenxiong; Kari, Tusongjiang; Wang, Hongbing; Luan, Lan; Gao, Wensheng

doi:10.1109/iscid.2017.165

Cited by 19 publications

(16 citation statements)

References 11 publications

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“…In order to overcome the difficulties posed by traditional methods in interpreting test results, a major effort has been made to develop intelligent diagnosis in this area. For this purpose, several methods have used artificial intelligence (AI) including expert system (EPS) [10], artificial neural network (ANN) [11], fuzzy logic theory [12], rough sets theory (RST) [13], grey system theory (GST) [14], swarm intelligence (SI) algorithms [15], data mining technology [16], Selforganizing map (SOM) [17], machine learning (ML) [18] and optimized machine learning (OML) [19], to the diagnosis of transformer faults based on DGA data.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Method for Power Transformers Faults Diagnosis Based on Ensemble Bagged Tree Classification and Training Subsets Using Rogers and Gouda Ratios

2022

IJIES

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As a major piece of equipment in the electrical power transmission and distribution, the rapid and accurate assessment of emerging or existing internal faults in power transformers is a key factor in the safe and stable operation of the power grid. This paper proposes a hybrid approach to fault diagnosis for these transformers. This approach is based on ensemble bagged tree classification and training subsets obtain by a conventional pre-processing method. Two pre-processing approaches are performed, the first based on the maximum concentrations of the dissolved gas samples and the second based on the minimum concentrations of the dissolved gas samples. For each training subset, an ensemble classifier is constructed with as inputs the Rogers ratios, Gouda ratios, dissolved gas concentrations and their associations. The proposed hybrid methods are established with 475 samples of training dataset, tested on 117 samples dissolved gas analysis (DGA) data and validated on International Electrotechnical Commission (IEC) TC10 database. The performances of the proposed diagnostic models are evaluated and a comparison is done compared with the following diagnostic methods: IEC ratios method (IRM), Rogers ratios method (RRM), three ratios technique (TRT), Gouda's triangle (GT), and self-organizing map (SOM) clusters. The results found by computer simulations carried out by the matrice laboratory (MATLAB) software show that, of the two pre-processing approaches used, the one based on the minimum sample concentration gives better results than the one based on the maximum concentration. In terms of fault type, the best diagnostic model using the minimum concentration-based pre-processing approach has a diagnostic accuracy of 94.02%, compared to 92.31% for the best diagnostic model using the maximum concentrationbased pre-processing approach. This is lower than the 97.25% for SOM clusters and 96.58% for GT but higher than the 59.83% for RRM, 81.19% for IRM and 93.16% for TRT. In terms of fault severity, the best diagnostic model using the minimum concentration-based pre-processing approach has a diagnostic accuracy of 81.20%, compared to 74.36% for the best diagnostic model using the minimum concentration-based pre-processing approach. This result is lower than the 83.76% for TRT and 89.74% for GT but higher than the 49.57% for RRM, 66.67% for IRM and 78.90% for SOM clusters.

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Section: Introductionmentioning

confidence: 99%

Hybrid Method for Power Transformers Faults Diagnosis Based on Ensemble Bagged Tree Classification and Training Subsets Using Rogers and Gouda Ratios

2022

IJIES

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“…In order to overcome the difficulties posed by traditional methods in interpreting test results, a major effort has been made to develop intelligent diagnosis in this area. For this purpose, several methods have used artificial intelligence (AI) including expert system (EPS) [20], artificial neural network (ANN) [21][22][23], genetic algorithm (GA) [24], fuzzy logic theory [25][26][27], rough sets theory (RST) [28], Grey system theory (GST) [29], swarm intelligence (SI) algorithms [30,31], data mining technology [32], self-organizing map (SOM) [33] and machine learning (ML) [34][35][36] for the diagnosis of transformer faults based on DGA data. The current existing conventional and intelligent methods are carried out by means of a sample dataset with the corresponding labelled faults.…”

Section: Introductionmentioning

confidence: 99%

Interpreting dissolved gases in transformer oil: A new method based on the analysis of labelled fault data

Nanfak

Eke

Kom

et al. 2021

IET Generation Trans & Dist

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In this contribution, a new dissolved gas analysis (DGA) method combining key gases and ratio approaches for power transformer fault diagnostic is presented. It is based on studying subsets and uses the five main hydrocarbon gases including hydrogen (H 2 ), methane (CH 4 ), ethane (C 2 H 6 ), ethylene (C 2 H 4 ), and acetylene (C 2 H 2 ). The proposed method uses 475 samples from the dataset divided into subsets formed from the maximum and minimum(s) concentrations of the whole dataset. It has been tested on 117 DGA sample data and validated on the International Electrotechnical Commission (IEC) TC10 database. The performance of the proposed diagnostic method was evaluated and compared with the following diagnostic methods: IEC ratios method, Duval's triangle (DT), three ratios technique (TRT), Gouda's triangle (GT), and self-organizing map (SOM) clusters. The results found were analysed by computer simulations using MATLAB software. The proposed method has a diagnosis accuracy of 97.42% for fault types, as compared to 93.16% of TRT, 96.58% of GT method, 97.25% of SOM clusters method and 98.29% of DT method. However, in terms of fault severity, the proposed method has a diagnostic accuracy of 90.59% as compared to 78.90% of SOM clusters method, 83.76% of TRT, 88.03% of DT method, and 89.74% of GT method.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…9,10 The quantitative method can be roughly divided into statistical analysis-based and nonstatistical analysis-based methods. 11 The former is mainly composed of principal component analysis (PCA), 12,13 partial least squares (PLS), 14,15 independent component analysis (ICA), 16,17 analytic hierarchy process, statistical pattern classifiers, 18 reliability analysis, 19 and the most recently developed support vector machine (SVM), [20][21][22] whereas the latter includes complex network, [23][24][25][26] fuzzy logic, [27][28][29][30][31][32][33][34][35][36][37] information fusion, 38,39 evidential reasoning, [40][41][42][43][44] and multiple decision making. [45][46][47][48][49] The notion of intuitionistic fuzzy set (IFS) can be viewed as a natural generalization of usual fuzzy set.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis based on TOPSIS method with Manhattan distance

Jiang

Wang

Deng

et al. 2019

Advances in Mechanical Engineering

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Fault diagnosis is important for the maintenance of machinery equipment. Due to the randomness and fuzziness of fault, the relationship between fault types and their characteristics are complicated. Therefore, the determination of fault type is a challenging part of machinery fault diagnosis with the traditional method. To tackle this problem, a fault diagnosis approach based on the technique for order performance by similarity to ideal solution with Manhattan distance is presented in this article. First, the similarity measure between the fault model and the detection sample is constructed based on the Manhattan distance. Then, the similarity is transformed into intuitionistic fuzzy set and the generated intuitionistic fuzzy set is fused by the intuitionistic fuzzy weighted averaging operator. On this basis, the technique for order performance by similarity to the ideal solution approach is utilized to obtain the final rank to ascertain the fault type. The proposed method can handle an intricate relationship between multiple fault types and their various fault characteristics and better express uncertain information. Finally, a fault diagnosis example of the machine rotor and comparative study are conducted to illustrate the application and the effectiveness of the proposed method.

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Power Transformer Fault Diagnosis Using Support Vector Machine and Particle Swarm Optimization

Cited by 19 publications

References 11 publications

Hybrid Method for Power Transformers Faults Diagnosis Based on Ensemble Bagged Tree Classification and Training Subsets Using Rogers and Gouda Ratios

Hybrid Method for Power Transformers Faults Diagnosis Based on Ensemble Bagged Tree Classification and Training Subsets Using Rogers and Gouda Ratios

Interpreting dissolved gases in transformer oil: A new method based on the analysis of labelled fault data

Fault diagnosis based on TOPSIS method with Manhattan distance

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