Optimal design of electrical power distribution grid spacers using finite element method

Stefenon, Stéfano Frizzo; Seman, Laio Oriel; Ovejero, Raúl García; Leithardt, Valderi Reis Quietinho

doi:10.1049/gtd2.12425

Cited by 33 publications

(23 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shutdowns in the distribution networks result in a lower level of reliability of the grid, which is a negative result for the electricity utility [22]. Research has been conducted to improve the insulation performance of components in the electrical distribution grid [23]. From the need to have a reliable electrical power system, there is the challenge of au-tomating electrical inspections.…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…High voltage polymeric insulators that are used outdoors are susceptible to tracking and erosion due to contamination, since hidrophobicity eventually is lost [25]. From the beginning of these effects there is a tendency to continue until there is a flashover and the insulator is damaged [26].…”

Section: Outdoor Polymeric Insulatorsmentioning

confidence: 99%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

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).

show abstract

“…In addition to time series prediction, ANN shows promise for pattern classification [25][26][27], control [28,29], and optimization. Optimization gains space in this context because of the need to improve the components necessary to maintain the system's operation [30,31]. Especially considering the expansive growth of communication systems [32,33], internet of things [34][35][36], the need for sustainability [37] and [38], technology development [39], and data privacy [40,41].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Machine Learning for Financial Investment Indication

Oliveira¹,

Dazzi²,

Fernandes³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

To support the decision making process of new investors, this paper aims to implement Machine Learning algorithms to generate investment indications. Three artificial intelligence techniques were implemented, namely: Multilayer Perceptron, Logistic Regression and Decision Tree, which performed the classification of investments. The results of the different algorithms were compared to each other using the metrics: accuracy, precision, recall, and F1-score. The Decision Tree was the algorithm that obtained the best classification metrics and an accuracy of 77%.

show abstract

Optimal design of electrical power distribution grid spacers using finite element method

Cited by 33 publications

References 81 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

Machine Learning for Financial Investment Indication

Contact Info

Product

Resources

About