Power transformer fault diagnosis considering data imbalance and data set fusion

Zhang, Yang; Chen, Hongcai; Du, Yaping; Chen, Min; Liang, Jie; Li, Jianhong; Fan, Xiqing; Yao, Xin

doi:10.1049/hve2.12059

Cited by 30 publications

(12 citation statements)

References 50 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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“…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.

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“…Zhang et al. [26] designed an improved under‐sampling algorithm known as self‐paced ensemble (SPE) to mitigate the majority samples participating in training intelligent classifiers. This method is verified to maintain a relatively high diagnosis performance for transformer internal faults.…”

Section: Introductionmentioning

confidence: 99%

“…Once the minority samples or majority samples are updated, the adaptive weights should be recalculated which in turn increases the computational complexity. Zhang et al [26] designed an improved under-sampling algorithm known as self-paced ensemble (SPE) to mitigate the majority samples participating in training intelligent classifiers. This method is verified to maintain a relatively high diagnosis performance for transformer internal faults.…”

Section: Introductionmentioning

confidence: 99%

Addressing imbalance of sample datasets in dissolved gas analysis by data augmentation: Generative adversarial networks

et al. 2022

IET Generation Trans & Dist

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Dissolved gas analysis (DGA) is an efficient technique to diagnose transformer faults; however, the imbalance of the sample datasets is frequently observed in DGA, which has become one of the main challenges for accurate fault diagnosis. A generative adversarial network (GAN) model is proposed to realize the data augmentation for addressing these problems. The GAN model is nested by two backpropagation neural networks (BPNN) containing a generator and a discriminator. The generator produces real-like data and continuously shapes the real-like data approximate to real data. The discriminator works to distinguish between the real-like data and the real data until the real-like data cannot be discriminated. Under this adversarial learning, the generator output obtains the real-like DGA samples to supplement the minority samples. To verify the diagnosis accuracy after the DGA data augmentation, three typically intelligent models, that is, BPNN, support vector machine (SVM) and deep belief network (DBN) are introduced to establish the diagnostic models. The results show that the recognition accuracy of the minority samples acquires average 30%, 25% and 50% increases for the three models respectively. It is shown that the GAN model well balances the sample datasets and strengthens the performance of the fault diagnostic models.

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Power transformer fault diagnosis considering data imbalance and data set fusion

Cited by 30 publications

References 50 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

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

Addressing imbalance of sample datasets in dissolved gas analysis by data augmentation: Generative adversarial networks

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