Study on Insulator Flashover Voltage Gradient Correction Considering Soluble Pollution Constituents

Zhang, Dongdong; Zhang, Zhijing; Jiang, Xingliang; Yang, Zhongyi; Jing, Zhao; Li, Yongfu

doi:10.20944/preprints201609.0042.v1

2016

DOI: 10.20944/preprints201609.0042.v1

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Study on Insulator Flashover Voltage Gradient Correction Considering Soluble Pollution Constituents

Dongdong Zhang¹,

Zhijing Zhang²,

Xingliang Jiang³

et al.

Abstract: Natural polluted insulator surfaces are always coated with various kinds of soluble constituents, and those constituents affect flashover performance differentially. Currently this fact is not considered either in laboratory experiments or field pollution degree measurements, causing the existing insulation selection method to be deficient. In this paper a systematical research on insulator flashover voltage gradient correction involving types of soluble pollution constituents was presented. Taking typical typ… Show more

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Cited by 3 publications

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

et al. 2020

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Natural Contamination and Surface Flashover on Silicone Rubber Surface under Haze–Fog Environment

et al. 2017

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Anti-pollution flashover of insulator is important for power systems. In recent years, haze-fog weather occurs frequently, which makes discharge occurs easily on the insulator surface and accelerates insulation aging of insulator. In order to study the influence of haze-fog on the surface discharge of room temperature vulcanized silicone rubber, an artificial haze-fog lab was established. Based on four consecutive years of insulator contamination accumulation and atmospheric sampling in haze-fog environment, the contamination configuration appropriate for RTV-coated surface discharge test under simulation environment of haze-fog was put forward. ANSYS Maxwell was used to analyze the influence of room temperature vulcanized silicone rubber surface attachments on electric field distribution. The changes of droplet on the polluted room temperature vulcanized silicone rubber surface and the corresponding surface flashover voltage under alternating current (AC), direct current (DC) positive polar (+), and DC negative polar (−) power source were recorded by a high speed camera. The results are as follows: The main ion components from haze-fog atmospheric particles are NO 3 − , SO 4 2− , NH 4 + , and Ca 2+ . In haze-fog environment, both the equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) of insulators are higher than that under general environment. The amount of large particles on the AC transmission line is greater than that of the DC transmission line. The influence of DC polarity power source on the distribution of contamination particle size is not significant. After the deposition of haze-fog, the local conductivity of the room temperature vulcanized silicone rubber surface increased, which caused the flashover voltage reduce. Discharge is liable to occur at the triple junction point of droplet, air, and room temperature vulcanized silicone rubber surface. After the deformation or movement of droplets, a new triple junction point would be formed, which would seriously reduce the dielectric strength of room temperature vulcanized silicone rubber.

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Experimental Study and Modeling of the Effect of ESDD/NSDD on AC Flashover of SiR Outdoor Insulators

et al. 2022

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Pollution flashover occurs when soluble and nonsoluble materials cover the surface of an insulator, and this may ultimately cause a reduction in its performance. In this paper, the common type of sodium chloride (NaCl) was used as a soluble pollutant (ESDD) and kaolin as a nonsoluble pollutant (NSDD). Samples of silicone rubber (SiR) insulators were selected for this study and fabricated at the Advanced High Voltage Engineering Research Centre (AHIVEC) at Cardiff University. The samples were preconditioned and polluted according to standard specifications. Additionally, the AC voltage ramp technique was used to achieve flashover (FOV) voltage with different pollution levels. The aim of this work was to investigate the effect of nonsoluble materials on flashover characteristics to understand their interaction with dry-band arcs by using FOV electrical equations and experimental data. The test results show that the FOV voltage of the silicone rubber insulator substantially decreased with the increase in both ESDD and NSDD values. It was also identified from these results that the dry-band arcs were considerably influenced by both ESDD and NSDD levels. This impact can be quantified by determining the variation of discharge parameters (N, n). Based on the FOV equations and experimental data, a mathematical model was suggested, taking into account the effect of both ESDD and NSDD.

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Study on Insulator Flashover Voltage Gradient Correction Considering Soluble Pollution Constituents

Cited by 3 publications

References 9 publications

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

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

Natural Contamination and Surface Flashover on Silicone Rubber Surface under Haze–Fog Environment

Experimental Study and Modeling of the Effect of ESDD/NSDD on AC Flashover of SiR Outdoor Insulators

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