Self-organizing maps applied to monitoring and diagnosis of ZnO surge arresters

Lira, George R. S.; Costa, Edson G. da; Almeida, Carlos

doi:10.1109/tdc-la.2010.5762952

Cited by 13 publications

(11 citation statements)

References 2 publications

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“…In this way they have to be ageing resistant while being subjected to constantly applied operating voltage. They must be able to absorb the energy injected during a discharge, and they should subsequently limit the follow current (leakage current) to values small enough for thermally stable operation [6,7]. Fig.…”

Section: Flexible Detection and Metal-oxide Arrestersmentioning

confidence: 99%

Metal-Oxide Arrester Fault Diagnosis based on Flexible Detection System

Yu¹,

Zhu²,

Chen³

et al. 2018

Proceedings of the 2017 3rd International Forum on Energy, Environment Science and Materials (IFEESM 2017)

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Section: Flexible Detection and Metal-oxide Arrestersmentioning

confidence: 99%

Metal-Oxide Arrester Fault Diagnosis based on Flexible Detection System

Yu¹,

Zhu²,

Chen³

et al. 2018

Proceedings of the 2017 3rd International Forum on Energy, Environment Science and Materials (IFEESM 2017)

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“…Calculate the connected components with the seed image Ω0 in the foreground image γ with the experiential limit of neighbor"s difference of gradient value 16, as dictated in (5)…”

Section: Thermography Image Analysismentioning

confidence: 99%

“…Another researcher proposed a monitoring and diagnostic technique using intelligent system. Self-organizing maps which is one kind of artificial neural network used for predictive monitoring ZnO surge arrester activities studied by [5]. The technique based on utilized of thermal profiles data in SOM artificial neural network.…”

Section: Introductionmentioning

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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“…For condition monitoring of surge arresters, many off-line and on-line techniques have been presented in literature such as: power loss method [5], V-I characteristic curve analysis [6], leakage current measurement [7][8][9][10][11], temperature measurement [12][13][14] and electro-magnetic field measurement [15,16].…”

Section: Introductionmentioning

confidence: 99%