Diagnostic of silicon carbide surge arresters using leakage current measurements

Kanashiro, Arnaldo Gakiya; Zanotti, Milton; Obase, Paulo Futoshi; Bacega, Wilson Roberto

doi:10.1109/tla.2011.6030987

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…Many researchers conducted research to separate the resistive leakage current from the total leakage current [9], [11][12][13]. The shifted current method claims that it can be separate the resistive leakage current with 95% accuracy [11].…”

Section: Leakage Current Analysismentioning

confidence: 99%

Artificial Neural Network Application for Thermal Image Based Condition Monitoring of Zinc Oxide Surge Arresters

Novizon¹,

Abdul‐Malek²,

Aulia³

2017

IJEECS

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<p>Manual analysis of thermal image for detecting defects and classifying of condition of surge arrester take a long time. Artificial neural network is good tool for predict and classify data. This study applied neural network for classify the degree of degradation of surge arrester. Thermal image as input of neural network was segmented using Otsu’s segmentation and histogram method to get features of thermal image. Leakage current as a target of supervise neural network was extracted and applied Fast Fourier Transform to get third harmonic of resistive leakage current. The classification results meet satisfaction with error about 3%.</p>

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Section: Leakage Current Analysismentioning

confidence: 99%

Artificial Neural Network Application for Thermal Image Based Condition Monitoring of Zinc Oxide Surge Arresters

Novizon¹,

Abdul‐Malek²,

Aulia³

2017

IJEECS

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“…The voltage across C2 is measured by a scope meter (190B Scope Meter Series: Fluke) and connection to a computer where it is recorded and visually displayed with the use of appropriate software. The technical characteristics of the capacitors C1 and C2 were 224pF, 100kV/50Hz and 2μF, 1000V/50Hz, respectively [12]- [16].…”

Section: A Equipment For the Experimentalmentioning

confidence: 99%

Metal Oxide Surge Arresters Modelling in Temporary Overvoltage Conditions

Thipprasert¹,

Chaidee²

2016

IJEEE

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Insulation and protection system as part of the system of high voltage has been improved stability and reliability of distribution system is designed correctly and uses proper equipment. Surge Arrester (SA) is used for protection of lightning and switching surge in distribution system of Provincial Electricity Authority: PEA. This paper to design modelling of Metal Oxide Surge Arresters (MOSA) in 22kV Distribution System by ATP-EMTP program and compares the experimental testing of surge arrester product A. the comparison of test results indicate that leakage currents error less than 4.4 percentage. The proposed model is used for leakage currents of distribution system analysis. Moreover, it will be useful to select or design suitable SAs for using in places with salt conditions, high rainfall, high wind speeds and high humidity.  IndexTerms-surge arrester, predicting lifetime, distribution system

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“…This is due to its good indicator of surge arrester degradation. Some outstanding research [1][2][3][4][5] have successfully employed the leakage current signal for fault detection and diagnosis. However, it is necessary to increase the fault diagnosis capability.…”

Section: Introductionmentioning

confidence: 99%

Correlation between Third Harmonic Leakage Current and Thermography Image of Zinc Oxide Surge Arrester for Fault Monitoring Using Artificial Neural Network

Novizon

Abdul‐Malek²

2014

AMM

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The ageing level of ZnO materials in gapless surge arresters can be determined by using either the traditional leakage current measurements or recently introduced thermal images of the arrester. However, a direct correlation between arrester thermal images and its leakage current (and hence the ageing level) is yet to be established. This paper attempts to find such a correlation using an artificial neural network (ANN). Experimental work was carried out to capture both the thermal images and leakage current of 120kV rated polymeric housed gapless arresters. Critical parameters were then extracted from both the thermal image and the leakage current, before being exported into the artificial neural network tool. Using the leakage current level, the conditions of the arrester are classified as normal, caution, and faulty. The ANN correctly classifies the ageing level using only the thermal image information with an accuracy of 97%, which is highly encouraging.

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Diagnostic of silicon carbide surge arresters using leakage current measurements

Cited by 7 publications

References 6 publications

Artificial Neural Network Application for Thermal Image Based Condition Monitoring of Zinc Oxide Surge Arresters

Artificial Neural Network Application for Thermal Image Based Condition Monitoring of Zinc Oxide Surge Arresters

Metal Oxide Surge Arresters Modelling in Temporary Overvoltage Conditions

Correlation between Third Harmonic Leakage Current and Thermography Image of Zinc Oxide Surge Arrester for Fault Monitoring Using Artificial Neural Network

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