Artificial Intelligence Techniques for Predicting the Flashover Voltage on Polluted Cup-Pin Insulators

Salem, Ali Ahmed; Rahman, Rahisham Abd; Al-Gailani, Samir A.; Kamarudin, M. S.; Othman, N. A.; Jamail, Nor Akmal Mohd

doi:10.1007/978-3-030-33582-3_35

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…It is observed that the flashover voltage has a greater effect on composite insulators than on porcelain insulators under uniform contamination. Non-uniformity of contamination has been investigated on the bottom and top along with the insulator leakage distance [8]. As suggested by [8], a significant influence in the flashover voltage (FOV) value caused by the uneven pollution degree (bottom/top), appeared to be about 28%-30% which is greater than FOV with the uniform contamination type.…”

Section: Introductionmentioning

confidence: 97%

“…Non-uniformity of contamination has been investigated on the bottom and top along with the insulator leakage distance [8]. As suggested by [8], a significant influence in the flashover voltage (FOV) value caused by the uneven pollution degree (bottom/top), appeared to be about 28%-30% which is greater than FOV with the uniform contamination type. According to [9], the flashover voltage stress on the porcelain insulator strings is reduced by raising the non-uniformity level of the fan-shaped non-uniform pollution.…”

Section: Introductionmentioning

confidence: 97%

“…So, it is still important to have more research work concerning insulator pollution. In recent years, several types of researches on the deposition of pollution have been executed [7][8][9][10][11][12][13][14][15]. In [7] reports, the impact of the distribution of pollutants on flashover voltage of various types of insulators was analyzed in a uniform method.…”

Section: Introductionmentioning

confidence: 99%

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Prediction Flashover Voltage on Polluted Porcelain Insulator Using ANN

Salem¹,

Rahman²,

Ghanem³

et al. 2021

Computers, Materials &Amp; Continua

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This paper aims to assess the effect of dry band location of contaminated porcelain insulators under various flashover voltages due to humidity. Four locations of dry bands are proposed to be tested under different severity of contamination artificially produce using salt deposit density (SDD) sprayed on an insulator. Laboratory tests of polluted insulators under proposed scenarios have been conducted. The flashover voltage of clean insulators has been identified as a reference value to analyze the effect of contamination distribution and its severity. The dry band dimension has been taken into consideration in experimental tests. The flashover voltage has been predicted using an artificial neural network (ANN) technique based on the laboratory test data. The ANN approach is constructed with five input data (geometry the insulator and parameters of contamination) and flashover voltage as the output of the model. Results indicated that the pollution distribution based on the proposed scenario has a significant influence on the flashover voltage performances. Validation of the ANN model reveals that the relative error values between the experimental results and the prediction appeared to be within 5%. This indicates the significant efficiency of the ANN technique in predicting the flashover voltage insulator under test.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction Flashover Voltage on Polluted Porcelain Insulator Using ANN

Salem¹,

Rahman²,

Ghanem³

et al. 2021

Computers, Materials &Amp; Continua

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“…Salem et al [22] combined Adaptive Neuro Fuzzy Inference System (ANFIS) with ANN and used insulator height, diameter, form factor, creepage distance along with Equivalent Salt Deposit Density (ESDD) as input parameters to train the model. In Reference [23], the authors applied dimensional analysis to the proposed ANFIS-based ANN network by establishing a relationship between critical flashover voltage and leakage current. The arc constant of the mathematical model for obtaining the test data was optimized using a Genetic Algorithm (GA) for improved results.…”

Section: Introductionmentioning

confidence: 99%

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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