Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting

Stefenon, Stéfano Frizzo; Ribeiro, Matheus Henrique Dal Molin; Nied, Ademir; Mariani, Viviana Cocco; Coelho, Leandro dos Santos; Leithardt, Valderi Reis Quietinho; Silva, Luís Augusto; Seman, Laio Oriel

doi:10.1109/access.2021.3076410

Cited by 68 publications

(32 citation statements)

References 49 publications

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“…Other researchers work with ultrasound equipment that can help to forecast insulator contamination. With the correct measurement and information technology set, it is possible to predict contamination with good accuracy of a signal with many nonlinearities [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Sensing Method Using Multiple Quantities for Diagnostic of Insulators in Different Ambient Conditions

Dolník

Šárpataky

Kolcunová

et al. 2022

Sensors

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Insulators are one of the many components responsible for the reliability of electricity supply as part of transmission and distribution lines. Failure of the insulator can cause considerable economic problems that are much greater than the insulator cost. When the failure occurs on the transmission line, a large area can be without electricity supply or other transmission lines will be overloaded. Because of the consequences of the insulator’s failure, diagnostics of the insulator plays a significant role in the reliability of the power supply. Basic diagnostic methods require experienced personnel, and inspection requires moving in the field. New diagnostic methods require online measurement if it is possible. Diagnostic by measuring the leakage current flowing on the surface of the insulator is well known. However, many other quantities can be used as a good tool for diagnostics of insulators. We present in this article results obtained on the investigated porcelain insulators that are one of the most used insulation materials for housing the insulator’s core. Leakage current, dielectric loss factor, capacity, and electric charge are used as diagnostic quantities to investigate porcelain insulators in different pollution conditions and different ambient relative humidity. Pollution and humidity are the main factors that decrease the insulator´s electric strength and reliability.

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Section: Introductionmentioning

confidence: 99%

Sensing Method Using Multiple Quantities for Diagnostic of Insulators in Different Ambient Conditions

Dolník

Šárpataky

Kolcunová

et al. 2022

Sensors

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show abstract

“…When the insulator has its constructive properties damaged, it is necessary to replace it. To avoid disconnection from the network, this task can be performed by a specialized team with the network energized, having a higher cost when it comes to corrective maintenance [50].…”

Section: Datasetmentioning

confidence: 99%

Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures

Stefenon

Singh

Yow

et al. 2022

Sensors

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Power distribution grids are typically installed outdoors and are exposed to environmental conditions. When contamination accumulates in the structures of the network, there may be shutdowns caused by electrical arcs. To improve the reliability of the network, visual inspections of the electrical power system can be carried out; these inspections can be automated using computer vision techniques based on deep neural networks. Based on this need, this paper proposes the Semi-ProtoPNet deep learning model to classify defective structures in the power distribution networks. The Semi-ProtoPNet deep neural network does not perform convex optimization of its last dense layer to maintain the impact of the negative reasoning process on image classification. The negative reasoning process rejects the incorrect classes of an input image; for this reason, it is possible to carry out an analysis with a low number of images that have different backgrounds, which is one of the challenges of this type of analysis. Semi-ProtoPNet achieves an accuracy of 97.22%, being superior to VGG-13, VGG-16, VGG-19, ResNet-34, ResNet-50, ResNet-152, DenseNet-121, DenseNet-161, DenseNet-201, and also models of the same class such as ProtoPNet, NP-ProtoPNet, Gen-ProtoPNet, and Ps-ProtoPNet.

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“…Another very common problem is saline contamination, which is present in coastal regions. These contaminations must be evaluated and monitored by energy utilities, since the salinity combined with an high relative humidity of the air increases the surface conductivity of the insulators and can cause failures in an electrical power system [42].…”

Section: Insulator Contaminationmentioning

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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Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting

Abstract: This work was supported by Junta De Castilla y León-Consejería De Economía Y Empleo: System for simulation and training in advanced techniques for the occupational risk prevention through the design of hybrid-reality environments with ref J118.

Cited by 68 publications

References 49 publications

Sensing Method Using Multiple Quantities for Diagnostic of Insulators in Different Ambient Conditions

Sensing Method Using Multiple Quantities for Diagnostic of Insulators in Different Ambient Conditions

Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures

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

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