Dynamic model to predict AC critical flashover voltage of nonuniformly polluted insulators under thermal ionization conditions

Shahabi, Saeed; Gholami, Ahmad

doi:10.3906/elk-1603-279

Cited by 1 publication

(2 citation statements)

References 13 publications

(22 reference statements)

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“…All these algorithms exhibit random behavior. For example, in the case of the SCG algorithm, increasing the number of neurons in the hidden layer from [20,10,5] to [30,20,10] reduces the RMSE from 1.22 to 0.59, NRMSE from 0.19 to 0.069, and MAPE from 10.93 to 5.09%. Choosing a certain learning rate for a neural network algorithm is also very important for improved performance.…”

Section: Resultsmentioning

confidence: 99%

“…An improved mathematical model has been proposed in Reference [19] to estimate pollution flashover voltage of ceramic insulators based on dimensional analysis of the flashover influencing parameters. Shahabi et al [20] studied the flashover process of outdoor insulators by adding a random value to the discharge length to account for wind speed, direction, and thermal convection on the discharge. Palangar et al [21] proposed an improved dynamic model for predicting the critical flashover parameters of ceramic insulators by incorporating capacitance in the equivalent circuit of the dry band.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Critical Flashover Voltage of High Voltage Insulators Leveraging Bootstrap Neural Network

et al. 2020

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Understanding the flashover performance of the outdoor high voltage insulator has been in the interest of many researchers recently. Various studies have been performed to investigate the critical flashover voltage of outdoor high voltage insulators analytically and in the laboratory. However, laboratory experiments are expensive and time-consuming. On the other hand, mathematical models are based on certain assumptions which compromise on the accuracy of results. This paper presents an intelligent system based on Artificial Neural Networks (ANN) to predict the critical flashover voltage of High-Temperature Vulcanized (HTV) silicone rubber in polluted and humid conditions. Various types of learning algorithms are used, such as Gradient Descent (GD), Levenberg-Marquardt (LM), Conjugate Gradient (CG), Quasi-Newton (QN), Resilient Backpropagation (RBP), and Bayesian Regularization Backpropagation (BRBP) to train the ANN. The number of neurons in the hidden layers along with the learning rate was varied to understand the effect of these parameters on the performance of ANN. The proposed ANN was trained using experimental data obtained from extensive experimentation in the laboratory under controlled environmental conditions. The proposed model demonstrates promising results and can be used to monitor outdoor high voltage insulators. It was observed from obtained results that changing of the number of neurons, learning rates, and learning algorithms of ANN significantly change the performance of the proposed algorithm.

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