Echo state network applied for classification of medium voltage insulators

Stefenon, Stéfano Frizzo; Seman, Laio Oriel; Neto, Nemesio Fava Sopelsa; Meyer, Luiz Henrique; Nied, Ademir; Yow, Kin Choong

doi:10.1016/j.ijepes.2021.107336

Cited by 64 publications

(36 citation statements)

References 60 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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“…where i is each index of the histogram and N M is the dimensions of the image. From p, considering i ∈ 0, • • • , L − 1, wherein L is the total amount of intensity in the image, other measures are obtained, such as the mean (4), variance (5), asymmetry (6), kurtosis (7), energy (8), and entropy (10).…”

Section: Feature Extractionmentioning

confidence: 99%

“…Researchers have conducted promising studies regarding the evaluation of contamination in insulators installed outdoors using artificial intelligence strategies [6][7][8]. According to Abouzeid et al [9], flashover of the insulator occurs when contaminants cover the surface of the insulator, resulting in a reduction in the surface resistance.…”

Section: Introductionmentioning

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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“…Liu et al (2020) proposed a hybrid time-series prediction approach with the binary grey wolf algorithm and echo state networks (BGWO-ESN). Beyond time series forecasting, the echo state networks have also been applied in other problems, including mainly classification (Stefenon et al, 2022), detection (Steiner et al, 2021), and image segmentation (Abdelkerim et al, 2020). However, the conventional ESN architectures still lack the ability to handle complicated tasks.…”

Section: Introductionmentioning

confidence: 99%

Grey Wolf Optimization–Based Deep Echo State Network for Time Series Prediction

Chen

Zhang

2022

Front. Energy Res.

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The Echo State Network (ESN) is a unique type of recurrent neural network. It is built atop a reservoir, which is a sparse, random, and enormous hidden infrastructure. ESN has been successful in dealing with a variety of non-linear issues, including prediction and classification. ESN is utilized in a variety of architectures, including the recently proposed Multi-Layer (ML) architecture. Furthermore, Deep Echo State Network (DeepESN) models, which are multi-layer ESN models, have recently been proved to be successful at predicting high-dimensional complicated non-linear processes. The proper configuration of DeepESN architectures and training parameters is a time-consuming and difficult undertaking. To achieve the lowest learning error, a variety of parameters (hidden neurons, input scaling, the number of layers, and spectral radius) are carefully adjusted. However, the optimum training results may not be guaranteed by this haphazardly created work. The grey wolf optimization (GWO) algorithm is introduced in this study to address these concerns. The DeepESN based on GWO (GWODESN) is utilized in trials to forecast time series, and therefore the results are compared with the regular ESN, LSTM, and ELM models. The findings indicate that the planned model performs the best in terms of prediction.

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Echo state network applied for classification of medium voltage insulators

Cited by 64 publications

References 60 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

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

Grey Wolf Optimization–Based Deep Echo State Network for Time Series Prediction

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