Transformer winding type recognition based on FRA data and a support vector machine model

Mao, Xiaoan; Wang, Zhongdong; Crossley, P.A.; Jarman, Paul; Fieldsend-Roxborough, Andrew; Wilson, Gordon

doi:10.1049/hve.2019.0294

Cited by 16 publications

(11 citation statements)

References 27 publications

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“…After evaluating the best configuration of the model, a benchmarking with the decision tree [68], ensemble [69][70][71], support vector machine (SVM) [72][73][74], and the multilayer perceptron models are presented. The pictures of the insulators were taken before the measurement of the NSDD, so that there was no influence from the operator on the contamination; if the contamination did not meet the requirement of IEC 60815 (Annex C) [52], the process was repeated from the beginning.…”

Section: Benchmarkingmentioning

confidence: 99%

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

et al. 2021

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Contamination on insulators may increase the surface conductivity of the insulator, and as a consequence, electrical discharges occur more frequently, which can lead to interruptions in a power supply. To maintain reliability in an electrical distribution power system, components that have lost their insulating properties must be replaced. Identifying the components that need maintenance is a difficult task as there are several levels of contamination that are hard to notice during inspections. To improve the quality of inspections, this paper proposes using k-nearest neighbors (k-NN) to classify the levels of insulator contamination based on images of insulators at various levels of contamination simulated in the laboratory. Computer vision features such as mean, variance, asymmetry, kurtosis, energy, and entropy are used for training the k-NN. To assess the robustness of the proposed approach, a statistical analysis and a comparative assessment with well-consolidated algorithms such as decision tree, ensemble subspace, and support vector machine models are presented. The k-NN showed up to 85.17% accuracy using the k-fold cross-validation method, with an average accuracy higher than 82% for the multi-classification of contamination of insulators, being superior to the compared models.

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Section: Benchmarkingmentioning

confidence: 99%

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

et al. 2021

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“…The challenge when utilizing FRA to diagnose transformer active parts is mainly in the correct interpretation of deviations from current and reference measurements. Studies investigating FRA interpretation use numerical indices [10,11], white-box modeling [12,13] and artificial intelligence algorithms [14][15][16][17] to objectively assess frequency response traces obtained from real cases [8,18], laboratory experiments [19] and simulation studies [20][21][22]. Different approaches for the interpretation of FRA measurements are reported in recent literature on the application of intelligent classifiers.…”

Section: Frequency Response Analysismentioning

confidence: 99%

Frequency Response Analysis Interpretation Using Numerical Indices and Machine Learning: A Case Study Based on a Laboratory Model

et al. 2021

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Frequency response analysis is a powerful tool for mechanical fault diagnostics in power transformers. However, interpretation schemes still today depend on expert analyses, mainly because of the complex structure of power transformers. One of the fundamental shortcomings of experimental investigations is that mechanical deformations cannot be managed on real transformers to obtain data for different scenarios because they are too destructive. To address this issue in a systematic way, the current research used a specially designed laboratory transformer model that allows mechanical defects to be introduced so its frequency response can be evaluated under different conditions. The key feature of this model is the non-destructive interchangeability of its winding sections, allowing reproducibility and repeatability of frequency response measurements. Numerical indices were compared over key performance indicators (linearity, sensitivity and monotonicity). The analysis indicated that comparative standard deviation offered promising results for evaluation of mechanical deformations on the laboratory winding model given its monotonic behaviour, sensitivity and linear increase with fault severity. Additionally, support vector machine learning, radial basis function neural network and the statistical k-nearest neighbour method were used for fault classification with different strategies and configurations. While limited data from different transformers are used in the available literature, the approach discussed here considers 371 measurements from the same transformer model. The test results are supportive and demonstrate great accuracy when machine learning is used for winding fault classification.INDEX TERMS Condition monitoring, frequency response analysis interpretation, machine learning, numerical indices, power transformers, radial basis function, support vector machines.

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“…After evaluating the best configuration of the model, a benchmarking with the decision tree [48], ensemble [49,50] and support vector machine (SVM) [51][52][53] models is presented. The pictures of the insulators were taken before the measurement of the NSDD, so that there was no influence from the operator on the contamination, if the contamination did not meet the requirement of IEC 60815 (Annex C) [32] the process would be repeated since the start.…”

Section: Benchmarkingmentioning

confidence: 99%

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

Corso¹,

Perez²,

Stefenon³

et al. 2021

Preprint

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The contamination on the insulators may increase its surface conductivity and, as a consequence, electrical discharges occur more frequently, which can lead to interruptions in the power supply. To maintain reliability in the electrical distribution power system, components that have lost their insulating properties must be replaced. Identifying the components that need maintenance, is a difficult task as there are several levels of contamination that are hardly noticed during inspections. To improve the quality of inspections, this paper proposes to use the k-nearest neighbours (k-NN) to classify the levels of insulator contamination, based on the image of insulators at various levels of contamination simulated in the laboratory. Using computer vision features such as mean, variance, asymmetry, kurtosis, energy, and entropy are used for training the k-NN. To assess the robustness of the proposed approach, statistical analysis and a comparative assessment with well-consolidated algorithms such as decision tree, ensemble subspace, and support vector machine models are presented. The k-NN showed results of up to 85.17 % accuracy using the k-fold cross-validation method, with an average accuracy higher than 82 % for multi-classification of the contamination of the insulators, being superior to the compared models.

show abstract

Transformer winding type recognition based on FRA data and a support vector machine model

Cited by 16 publications

References 27 publications

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

Frequency Response Analysis Interpretation Using Numerical Indices and Machine Learning: A Case Study Based on a Laboratory Model

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

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