Comparison of artificial intelligence techniques to failure prediction in contaminated insulators based on leakage current

Medeiros, Alessandro; Sartori, Andreza; Stefenon, Stéfano Frizzo; Meyer, Luiz Henrique; Nied, Ademir

doi:10.3233/jifs-211126

Cited by 41 publications

(25 citation statements)

References 49 publications

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“…There are several faults that can occur in the electrical power system because it is mostly installed outdoors [19]. One of the causes of failures in the electrical power system is the presence of contamination on the insulating components resulting in greater surface conductivity [20].…”

Section: Related Work and Considered 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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Section: Related Work and Considered 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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“…The development of the fault can be linked to the increase in a measure of the insulator's condition. The leakage current is a measure that can be used to evaluate the time series measured according to the increase of contamination, thus being possible to forecast the development of a failure by the variation of this measure [2]. Regarding the time series forecasting, several approaches are being studied to obtain a better performance with lower computational cost, such as the ensemble learning models which will be presented in the next section.…”

Section: Outdoor Polymeric Insulatorsmentioning

confidence: 99%

“…Like the compared models, NAR is applied for multi-step prediction of a sequence of values in a time series 2 . In this approach, when external feedback is missing, the closed-loop can continue to predict using internal feedback.…”

Section: B Nonlinear Autoregressivementioning

confidence: 99%

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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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“…However, in the presence of moisture a conductive path can be generated which decreases the insulation between the high-voltage phases and ground [40]. When cracks and fissures occur in the insulator, the rate of contamination deposition may accelerate and consequently increases its leakage current, making it more susceptible to partial discharge events and flashovers [41].…”

Section: Insulators Contaminationmentioning

confidence: 99%

Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models

Neto¹,

Stefenon²,

Meyer³

et al. 2022

Preprint

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To improve the monitoring of the electrical power grid it is necessary to evaluate the influence of contamination in relation to leakage current and its progression to a disruptive discharge. In this paper, insulators were tested in a saline chamber to simulate the increase of salt contamination on their surface, and to evaluate the supportability of these components. From the time series forecasting of the leakage current, it is possible to evaluate the development of the fault before a flashover occurs. Choosing which method to use is always a difficult task since some models may have higher computational effort. In this paper, for a complete evaluation, the long short-term memory (LSTM), group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), bootstrap aggregation (bagging), sequential learning (boosting), random subspace, and stacked generalization (stacking) ensemble learning models are analyzed. A review and comparison of these well-established methods for time series forecasting is performed. From the results of the best structure of the model, the hyperparameters are evaluated and the Wavelet transform is used to obtain an enhanced model.

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Comparison of artificial intelligence techniques to failure prediction in contaminated insulators based on leakage current

Cited by 41 publications

References 49 publications

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

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

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

Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models

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