Online condition monitoring of surge arresters based on third-harmonic analysis of leakage current

2017 ◽  
Vol 24 (4) ◽  
pp. 2274-2281 ◽  
Author(s):  
Ibrahim A. Metwally ◽  
Mohamed Eladawy ◽  
E. A. Feilat
Keyword(s):  
Harmonic Analysis ◽  
Leakage Current ◽  
Condition Monitoring ◽  
Third Harmonic ◽  
Surge Arresters

Thermal Image and Leakage Current Diagnostic as a Tool for Testing and Condition Monitoring of ZnO Surge Arrester

2013 ◽  
Vol 64 (4) ◽  
Author(s):  
Novizon N. ◽  
Zulkurnain Abdul Malek ◽  
Nouruddeen Bashir ◽  
N. Asilah
Keyword(s):  
Leakage Current ◽  
Condition Monitoring ◽  
Thermal Imaging ◽  
Harmonic Component ◽  
Strong Relationship ◽  
Thermal Image ◽  
Aging Condition ◽  
Third Harmonic ◽  
Surge Arresters ◽  
Thermal Data

This paper presents a study proposing a method to assess the condition of metal-oxide surge arresters. Thermal data using thermal imaging as well as the leakage current third harmonic component were used as tools to investigate the surge arrester aging condition.  Artificial Neural Network was employed to classify surge arrester condition with the temperature profile, ambient temperature and humidity as inputs and third harmonic leakage current as target. The results indicated a strong relationship between the thermal profile and leakage current of the surge arrester. This finding suggests the viability of this method in condition monitoring of surge arrester.

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

2017 ◽  
Vol 7 (3) ◽  
pp. 593
Author(s):  
Novizon Novizon ◽  
Zulkurnain Abdul-Malek ◽  
Aulia Aulia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Leakage Current ◽  
Thermal Image ◽  
Histogram Method ◽  
Good Tool ◽  
Third Harmonic ◽  
Surge Arresters ◽  
Long Time ◽  
Artificial Neural

<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>

Surge Arrester Leakage Current Analysis by Using Particle Swarm Optimization Technique

2014 ◽  
Vol 554 ◽  
pp. 608-612
Author(s):  
Vahabi Mashak Saeed ◽  
Zulkurnain Abdul-Malek ◽  
Nabipour Afrouzi Hadi ◽  
Chin Leong Wooi ◽  
Amir Hesam Khavari
Keyword(s):  
Leakage Current ◽  
Condition Monitoring ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Measurement Techniques ◽  
Particle Swarm ◽  
Operating Voltage ◽  
Surge Arresters ◽  
Particle Swarm Optimization Technique ◽  
Total Leakage

Surge arresters, as protective equipment, are used to limit any overvoltage in a power system resulting from various sources. The surge arrester affects from degradation due to the continuous operating voltage system as well as due to repeated lightning current discharge. Therefore, condition monitoring and health diagnostics of surge arresters are a necessary issue. Hence, as a feasible solution, a condition monitoring based on leakage current measurement techniques was selected to tackle the problem of age diagnostics of surge arresters. The Particle Swarm Search Algorithm was introduced as a method for extracting the third harmonic resistive component, Ir3rd, from the total leakage current. To employ this method for extracting Ir3rd, codes were developed in Matlab. The setting of Particle Swarm Search Algorithm was configured for extracting Ir3rd and accuracy of 94% was obtained.

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

2014 ◽  
Vol 554 ◽  
pp. 598-602 ◽  
Author(s):  
Yusuf Novizon ◽  
Abdul Malek Zulkurnain ◽  
Abdul Malek Zulkurnain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Leakage Current ◽  
Thermal Image ◽  
Critical Parameters ◽  
Thermal Images ◽  
Third Harmonic ◽  
Surge Arresters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

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.

scholarly journals Extracting the Resistive Current Component from a Surge Arrester’s Leakage Current without Voltage Reference

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1257
Author(s):  
Vid Vončina ◽  
Jože Pihler ◽  
Miro Milanovič
Keyword(s):  
Leakage Current ◽  
Data Transfer ◽  
Electronic Device ◽  
Theoretical Background ◽  
Voltage Reference ◽  
Current Component ◽  
Surge Arresters ◽  
Transfer Unit ◽  
Control Centre ◽  
Resistive Component

This article presents the development of the theoretical background and the design of an electronic device for monitoring the condition of a gapless Metal Oxide Surge Arrester (MOSA). The device is intended to be used online. Because of the inaccessibility and possible remote location of most surge arresters, it is equipped with a communication system, allowing for the device to convey the measurement of the surge arrester characteristics under any conditions. It is possible to determine the condition of the MOSA by gathering measurements of the surge arrester’s resistive component of leakage current. The leakage current information is sent via data transfer unit to a server and, after interpretation, will be forwarded to the authorised personnel through the surge arrester control centre.

scholarly journals Harmonic Analysis of the Output Voltage of a Third-Harmonic-Injected Inverter for LSM Drives

2005 ◽  
Vol 125 (2) ◽  
pp. 133-139 ◽  
Author(s):  
Hidenori Shigeeda ◽  
Akinobu Okui ◽  
Hirofumi Akagi
Keyword(s):  
Harmonic Analysis ◽  
Output Voltage ◽  
Third Harmonic
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