Insulation failure detection in transformer winding using cross-correlation technique with ANN and k-NN regression method during impulse test

Rahmatian, M.; Vahidi, B.; Ghanizadeh, Ahmad; Gharehpetian, Gevork B.; Alehosseini, H.A.

doi:10.1016/j.ijepes.2013.04.020

Cited by 24 publications

(9 citation statements)

References 25 publications

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“…As with other strategies, the stacking ensemble performs a combination of weak models to result in a model with greater processing capacity (meta-model). For a classification task, beyond the SVR the weak learners can be, for instance, support vector machine (SVM) [58]- [60], k-nearest neighbors (k-NN) [61]- [63], or decision trees [64]- [66]. The artificial neural network will take as inputs the results of the weak learners and it will return the final predictions based on these [67].…”

Section: Ensemble Learning Modelmentioning

confidence: 99%

Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods

et al. 2022

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Outdoor insulators may experience stress due to severe environmental conditions, such as pollution and contamination. Through the identification of partial discharges by ultrasonic noise, it is possible to assess the possibility of a power grid failure occurring. In this paper, ensemble models are used to analyze an ultrasonic signal from an ultrasonic microphone Pettersson M500. As the insulators are susceptible to developing irreversible failures, it will be evaluated whether the ultrasonic signal will remain over time, so that it is possible to assess whether the discharges being captured can result in a failure in contaminated polymeric insulators, evaluated in a high voltage laboratory under controlled conditions. The ensemble models were used in this paper because they typically require less computational effort than techniques based on deep learning and have acceptable performance for the problem at hand. The bagging, boosting, random subspace, bagging plus random subspace, and stacked generalization ensemble models are evaluated, and the best result of each model is used to compare the differences between the models. The bagging ensemble learning model proved to be faster and have lower error than other ensemble models, long short-term memory (LSTM), and nonlinear autoregressive (NAR).

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Section: Ensemble Learning Modelmentioning

confidence: 99%

Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods

et al. 2022

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“…The k-NN is a memory-based algorithm, so all computation is postponed until the classification phase, since the learning process consists of memorizing objects [60]. One of the advantages of k-NN is that the model is simple; thus, it requires less computational effort than methods based on deep learning because, during training, the algorithm only stores objects.…”

Section: Model Architecturementioning

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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“…The k-NN is a memory-based algorithm, so all computation is postponed until the classification phase, since the learning process consists of memorizing objects [40]. One of the advantages of k-NN is that the model is simple, thus it requires less computational effort than methods based on deep learning because during training the algorithm only stores objects.…”

Section: Model Architecturementioning

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

Insulation failure detection in transformer winding using cross-correlation technique with ANN and k-NN regression method during impulse test

Cited by 24 publications

References 25 publications

Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods

Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods

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

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

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