The Optimal Classification of Partial Discharge Defects within XLPE Cable by Using ANN and Statistical Techniques
Dessouky, S., El Faraskoury, A., El-Mekkawy, S., El Zanaty, W. (2014). The Optimal Classification of Partial Discharge Defects within XLPE Cable by Using ANN and Statistical Techniques. EKB Journal Management System, 18(2), 1-7. doi: 10.21608/pserj.2014.45254
Sobhy S. Dessouky; Adel El Faraskoury; Samer El-Mekkawy; Waleed El Zanaty. "The Optimal Classification of Partial Discharge Defects within XLPE Cable by Using ANN and Statistical Techniques". EKB Journal Management System, 18, 2, 2014, 1-7. doi: 10.21608/pserj.2014.45254
Dessouky, S., El Faraskoury, A., El-Mekkawy, S., El Zanaty, W. (2014). 'The Optimal Classification of Partial Discharge Defects within XLPE Cable by Using ANN and Statistical Techniques', EKB Journal Management System, 18(2), pp. 1-7. doi: 10.21608/pserj.2014.45254
Dessouky, S., El Faraskoury, A., El-Mekkawy, S., El Zanaty, W. The Optimal Classification of Partial Discharge Defects within XLPE Cable by Using ANN and Statistical Techniques. EKB Journal Management System, 2014; 18(2): 1-7. doi: 10.21608/pserj.2014.45254

The Optimal Classification of Partial Discharge Defects within XLPE Cable by Using ANN and Statistical Techniques

Port-Said Engineering Research Journal
Article 1, Volume 18, Issue 2, September 2014, Page 1-7  XML PDF (1012.24 K)
Document Type: Original Article
DOI: 10.21608/pserj.2014.45254
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Authors
Sobhy S. Dessouky1; Adel El Faraskoury2; Samer El-Mekkawy3; Waleed El Zanaty4
1Department of Electrical Engineering – Port Said University, Port Said, Egypt
2Adel A. El Faraskoury is with Egyptian Electricity Holding Company, Extra High Voltage Research Centre, Cairo, Egypt
3S. M. El-Mekkawy is with Department of Electrical Engineering - Port Said University, Port Said, Egypt
4Waleed M. Elzanati is with Department of Electrical Engineering - Port Said University, Port Said, Egyp
Abstract
The classification of medium voltage cable defects is the most important tool to avoid the inaccurate Partial Discharge (PD) measurements. This paper presents a proposed methodology based on pattern recognition technique to classify the PD occurred in cables into two distinct groups: internal and external. The Artificial Neural Networks (ANNs) with different input schemes have been built to obtain the optimal classification. Many statistical features, which extracted by different techniques from measurements, have established the input schemes 3D-pattern PD to improve the performance and classification speed of ANN. In order to obtain the effective statistical features, the study and comparison between all ANN has been finished by evaluating the classification through two parameters: the mean square error (MSE) and the accuracy of neural network. As a result, the proposed approach provides high recognition rate of classification between internal and external PD within Cross-Linked Polyethylene (XLPE) insulated medium voltage cable
Keywords
Partial Discharge; XLPE Power Cables; Pattern Classification; Neural network; Statistical features
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