Applications of tunable-Q factor wavelet transform and AdaBoost classier for identification of high impedance faults: Towards the reliability of electrical distribution systems-Reference-Cited by-同舟云学术

Applications of tunable-Q factor wavelet transform and AdaBoost classier for identification of high impedance faults: Towards the reliability of electrical distribution systems

Published:2024-06-11 Issue: Volume: Page:
ISSN:0144-5987
Container-title:Energy Exploration & Exploitation
language:en
Short-container-title:Energy Exploration & Exploitation

Author:

Joga S. Ramana Kumar¹,Sinha Pampa²,Manoj Vasupalli³,Sura Srinivasa Rao⁴,Pudi Vasudeva Naidu⁵,Ibrahim Nagwa F.⁶,Alkuhayli Abdulaziz⁷,Hussein Mahmoud M.⁸⁹,Khaled Usama⁸,Mbadjoun Wapet Daniel Eutyche¹⁰^ORCID,Beroual Abderrahmane¹¹,Mahmoud Mohamed Metwally⁸^ORCID

Affiliation:

1. EEE Department, Dadi Institute of Engineering and Technology, Anakapalle, India

2. School of Electrical Engineering, KIIT Deemed to be University, Bhubaneswar, India

3. EEE Department, GMR Institute of Technology, Rajam, India

4. GITAM School of Technology, GITAM Deemed To Be University, Visakhapatnam, India

5. EEE Department, Matrusri Engineering College, Hyderabad, India

6. Electrical Department, Faculty of Technology and Education, Suez University, Suez, Egypt

7. Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh, Saudi Arabia

8. Electrical Engineering Department, Faculty of Energy Engineering, Aswan University, Aswan, Egypt

9. Department of Communications Technology Engineering, Technical College, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq

10. National Advanced School of Engineering, Universit´e de Yaound´e I, Yaound´e, Cameroon

11. AMPERE Lab UMR CNRS 5005, Ecole Centrale de Lyon, University of Lyon, Ecully, France

Abstract

This study presents a novel approach that employs a mixture of the tunable-Q wavelet transform (TQWT) and enhanced AdaBoost to address the issue of high impedance fault (HIF) recognition in power distribution networks. Traditional overcurrent protection relays frequently have lower fault current levels than normal current, making it exceedingly difficult to detect this HIF problem with the necessity to use a quick and effective approach to find HIF problems. Since the TQWT performs better with signals that exhibit oscillatory behavior, it has been utilized to extract special features for the training of the improved AdaBoost model. The procedure is accelerated by calculating the Kourtosis (K) value for each level and selecting the ideal level of decomposition to minimize computing work. Faulted zones are categorized using an enhanced AdaBoost approach. Under normal, noisy, and unbalanced conditions, the recommended approach is applied to an imbalanced 123-bus test system and an IEEE 33-bus test system. The efficiency of the recommended method is also being assessed for imbalanced distribution networks incorporating dispersed generation into real-time platforms. This procedure is quick compared to previous methods since it uses an upgraded AdaBoost classifier and optimal decomposition level.

Publisher

SAGE Publications

Link

https://journals.sagepub.com/doi/pdf/10.1177/01445987241260949

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