A new procedure for determination of insulators contamination in electrical distribution networks

Amini, MohamadAli; Sedighi, Ali

doi:10.1016/j.ijepes.2014.03.034

Cited by 20 publications

(4 citation statements)

References 6 publications

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“…Contamination in insulators is a difficult problem to assess in inspections of the electrical system, depending on climatic conditions there may be variation in surface conductivity [25]. The variation in surface conductivity can be caused by a higher relative humidity of the air [26], which makes the identification of a critical condition difficult, since the system may not present faults on days with good climatic conditions [27].…”

Section: Insulator's Datasetmentioning

confidence: 99%

Classification of insulators using neural network based on computer vision

Stefenon

Corso

Nied

et al. 2021

IET Generation Trans & Dist

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Insulators of the electrical power grid are usually installed outdoors, so they suffer from environmental stresses, such as the presence of contamination. Contamination can increase surface conductivity, which can lead to system failures, reducing the reliability of the network. The identification of insulators that have their properties compromised is important so that there are no discharges through its insulating body. To perform the classification of contaminated insulators, this paper presents computer vision techniques for the extraction of contamination characteristics, and a neural network (NN) model for the classification of this condition. Specifically, the Sobel edge detector, Canny edge detection, binarization with threshold, adaptive binarization with threshold, threshold with Otsu and Riddler–Calvard techniques will be evaluated. The results show that it is possible to have an accuracy of up to 97.50% for the classification of contaminated insulators from the extraction of characteristics with computer vision using the NN for the classification. The proposed model is more accurate than well‐established models such as support‐vector machine (SVM), k‐nearest neighbor (k‐NN), and ensemble learning methods. This showed that optimizing the model's parameters can make it superior to solve the problem in question.

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Section: Insulator's Datasetmentioning

confidence: 99%

Classification of insulators using neural network based on computer vision

Stefenon

Corso

Nied

et al. 2021

IET Generation Trans & Dist

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show abstract

“…Under the real situation, the material in the calculation area around the insulator is set to air. From the wind tunnel test results of North China Electrical Power University [30], it can be seen that during the test, ESDD is always maintained at approximately 287 g/cm 2 , which has little effect on the amount of pollution. Therefore, the influence of ESDD is ignored in this study.…”

Section: Setting Of Physical Property Conditionsmentioning

confidence: 99%

“…However, in humid environment, fog, dew and drizzle may condense on the insulator surface in the form of separated water droplets, which will dissolve the pollution particles on the insulator surface and ionize. Then, deterioration of insulation performance and pollution flashover in severe cases will occur, causing huge losses to the power system [2][3][4][5]. When the water droplets condense on the insulator surface, it will not only distort the electrical field distribution, but also change the movement track of pollution particles, and finally affect the contamination characteristics.…”

Section: Introductionmentioning

confidence: 99%

Influence of Electrical Field Distortions Induced by Water Droplets on the Contamination Characteristics of an Insulator

Lv¹,

Wang²,

Wang³

2021

Fluid Dynamics &Amp; Materials Processing

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When separated water droplets condense on the surface of a composite insulator, the electrical field on the insulator surface is distorted. In turn, such distortions change the trajectories of pollution particles. In this study, the COMSOL software is used to simulate such a process for the FXBW4-10/100 composite insulator with or without water droplets condensation under a 10 kV DC voltage. The influence of the wind speed and particles concentration on the contamination characteristics of the considered 110 kV insulator is analyzed. The results show that: 1) in the presence of water droplets on the insulator surface, the ratio of electrical field force and gravity acting on the particles is large; 2) the contamination on the insulator surface increases with the wind speed; 3) when the wind speed is small, the relationship between the contamination amount and the pollution concentration is essentially linear.

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“…Studying the pollution accumulation characteristics of the insulator under natural environment is the foundation of external insulation, and it is important to ensure the safety of power grids [1][2][3][4]. Accurate measurement and prediction of natural contamination on insulator surface is significant for taking the appropriate measures to prevent pollution flashover in power system.…”

Section: Introductionmentioning

confidence: 99%