Effect of desert environmental conditions on the flashover voltage of insulators

Hamza, Abdel Salam; Abdel-Gawad, Nagat M. K.; Arafa, B. A.

doi:10.1016/s0196-8904(01)00174-1

Cited by 33 publications

(6 citation statements)

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“…Other studies simulated the leakage current while accounting for amount of salt in the contamination layer [6] . Other experimental studies were made on the effect of desert pollution on polymeric insulator [7,8] . In another study, leakage current was estimated using the FEMLAB software with different conductivities of contamination layer [9] .…”

Section: Introductionmentioning

confidence: 99%

Risk assessment of desert pollution on composite high voltage insulators

El-Shahat

Anis

2014

Journal of Advanced Research

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Transmission lines located in the desert are subjected to desert climate, one of whose features is sandstorms. With long accumulation of sand and with the advent of moisture from rain, ambient humidity and dew, a conductive layer forms and the subsequent leakage current may lead to surface discharge, which may shorten the insulator life or lead to flashover thus interrupting the power supply. Strategically erected power lines in the Egyptian Sinai desert are typically subject to such a risk, where sandstorms are known to be common especially in the spring. In view of the very high cost of insulator cleaning operation, composite (silicon rubber) insulators are nominated to replace ceramic insulators on transmission lines in Sinai. This paper examines the flow of leakage current on sand-polluted composite insulators, which in turn enables a risk assessment of insulator failure. The study uses realistic data compiled and reported in an earlier research project about Sinai, which primarily included grain sizes of polluting sand as well as their salinity content. The paper also uses as a case study an ABB-designed composite insulator. A three-dimensional finite element technique is used to simulate the insulator and seek the potential and electric field distribution as well as the resulting leakage current flow on its polluted surface. A novel method is used to derive the probabilistic features of the insulator’s leakage current, which in turn enables a risk assessment of insulator failure. This study is expected to help in critically assessing – and thus justifying – the use of this type of insulators in Sinai and similar critical areas.

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Section: Introductionmentioning

confidence: 99%

Risk assessment of desert pollution on composite high voltage insulators

El-Shahat

Anis

2014

Journal of Advanced Research

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“…These seed parameters are used as the key in random number generation from the TD-ERCS map. Random sequences are denoted by x n and y n in Equations ( 1) and (2). Machine learning algorithms were trained by taking 100 bootstrap samples, and 44 observations were made for each bootstrap sample.…”

Section: Bootstrapping Methodsmentioning

confidence: 99%

“…High-voltage stresses and artificial rain and fog are a few examples. The critical flashover voltage of insulators depends on the insulator design, Electronics 2020, 9, 1620 2 of 21 surface roughness, orientation, rain, humidity, temperature, fogs, Ultraviolet (UV) radiations, wind speed, direction, and distance from the pollution source [1,2]. Although the performance of outdoor insulators is affected by many parameters, pollution deposition on the insulator surface is considered a major factor in the deteriorating performance of insulators.…”

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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“…It has been estimated that 20–50 per cent of unscheduled outages are caused by vegetation in the Northeast US (Simpson and van Bossuyt, 1996). Additionally, the natural desert environment generates wind-borne dust and sand that interfere with insulators, reducing effective distribution and potentially resulting in flashover outages (Hamza et al ., 2002; Sima et al ., 2010). The desert environment effect ranges from dust deposition to the worst case of sand storms, when strong winds lead to sand particle saltation at high speeds occasionally leading to suspension (Hamza et al ., 2002).…”

Section: Methodsmentioning

confidence: 99%

Valuing the Reliability of the Electrical Power Infrastructure: A Two-stage Hedonic Approach

Maliszewski¹,

Larson²,

Perrings³

2012

Urban Studies

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The reliability of electrical power supply is amongst the conditions that inform house purchase decisions in all urban areas. Reliability depends in part on the conditions of the power generation and distribution infrastructures involved, and in part on environmental conditions. Its value to homeowners may be capitalised into the value of the house. In this paper, a hedonic pricing approach is used to estimate the capitalised value of the reliability offered by distribution infrastructures and the environmental conditions with which they interact in Phoenix, Arizona. A first stage estimates the impact of infrastructure and environmental conditions on reliability. In a second stage, the capitalised value of reliability from the marginal willingness to pay for reliability revealed by house purchase decisions is estimated and used to infer the value of both infrastructural characteristics and environmental conditions.

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Effect of desert environmental conditions on the flashover voltage of insulators

Cited by 33 publications

References 1 publication

Risk assessment of desert pollution on composite high voltage insulators

Risk assessment of desert pollution on composite high voltage insulators

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

Valuing the Reliability of the Electrical Power Infrastructure: A Two-stage Hedonic Approach

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