Identification of Transformer Oil incipient Faults Based on the Integration between Different DGA Techniques
Ward, S., Ibrahim, S., EL-Faraskoury, A., A. Mansour, D., Badawi, M. (2023). Identification of Transformer Oil incipient Faults Based on the Integration between Different DGA Techniques. EKB Journal Management System, 6(1), 412-421. doi: 10.21608/dusj.2023.291093
Sayed Abo El-Sood Ward; Shimaa A. Ibrahim; Adel EL-Faraskoury; Diaa-Eldin A. Mansour; Mohamed Badawi. "Identification of Transformer Oil incipient Faults Based on the Integration between Different DGA Techniques". EKB Journal Management System, 6, 1, 2023, 412-421. doi: 10.21608/dusj.2023.291093
Ward, S., Ibrahim, S., EL-Faraskoury, A., A. Mansour, D., Badawi, M. (2023). 'Identification of Transformer Oil incipient Faults Based on the Integration between Different DGA Techniques', EKB Journal Management System, 6(1), pp. 412-421. doi: 10.21608/dusj.2023.291093
Ward, S., Ibrahim, S., EL-Faraskoury, A., A. Mansour, D., Badawi, M. Identification of Transformer Oil incipient Faults Based on the Integration between Different DGA Techniques. EKB Journal Management System, 2023; 6(1): 412-421. doi: 10.21608/dusj.2023.291093

Identification of Transformer Oil incipient Faults Based on the Integration between Different DGA Techniques

Delta University Scientific Journal
Volume 6, Issue 1, April 2023, Page 412-421  XML PDF (507.59 K)
Document Type: Original research papers
DOI: 10.21608/dusj.2023.291093
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Authors
Sayed Abo El-Sood Ward1; Shimaa A. Ibrahim* 2; Adel EL-Faraskoury* 3; Diaa-Eldin A. Mansour* 4; Mohamed Badawi* 2
1Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt Faculty of Engineering, Delta University for Science and Technology, Gamasa, Egypt
2Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt
3Extra High Voltage Research Centre, Egyptian Electricity Holding Company, Egypt
44Electrical Power Engineering Department, School of Electronics, Communications and Computer Engineering, Egypt-Japan University of Science and Technology, Alexandria, Egypt Electrical Power and Machines Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt
Abstract
Premature  diagnosis of transformer oil faults enables the operator for diagnosing the transformer condition and hence operating the transformer continuously without outage. DGA is one of the most popular chemical tests used for fault diagnosis and there are many techniques which have been developed in this regard. This paper presents a proposed DGA technique for transformer oil fault identification based on the results of the recently published techniques. The proposed technique has been constructed based on the integration between the outputs of two recently established DGA techniques; Conditional probability and artificial neural network. A total of 532 datasets, obtained from the Egyptian Electricity Transmission Company (EETC) with known faults, have been used for designing and testing the proposed technique. The proposed fault diagnostic technique's accuracy attained 86.6 %, which is higher than the results of the combined techniques; 81.7% for ANN and 82.8% for conditional probability technique. The proposed developed technique came to the conclusion that integrating several DGA techniques, with higher accuracy, enhances fault detection's overall accuracy.  
Keywords
Power transformers; Condition monitoring; Dissolved gas analysis (DGA); ANN; Conditional probability technique
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