Enhancing HVDC transmission line fault detection using disjoint bagging and bayesian optimization with artificial neural networks and scientometric insights.

Autor: Yousaf MZ; Center for Renewable Energy and Microgrids, Huanjiang Laboratory, Zhejiang University, Zhuji, Zhejiang, 311816, China.; School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China.; School of Electrical and Information Engineering, Hubei University of Automotive Technology, Shiyan, 442002, China., Singh AR; Department of Electrical Engineering, School of Physics and Electronic Engineering, Hanjiang Normal University, Shiyan, 442000, Hubei, People's Republic of China. arvindsinghwce@gmail.com., Khalid S; Center for Renewable Energy and Microgrids, Huanjiang Laboratory, Zhejiang University, Zhuji, Zhejiang, 311816, China., Bajaj M; Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun, 248002, India. thebestbajaj@gmail.com.; Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan. thebestbajaj@gmail.com.; College of Engineering, University of Business and Technology, Jeddah, 21448, Saudi Arabia. thebestbajaj@gmail.com., Kumar BH; Department of Electrical and Electronics Engineering, Mohan Babu University, Tirupati, India., Zaitsev I; Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Beresteyskiy, 56, Kyiv-57, 03680, Ukraine. zaitsev@i.ua.; Center for Information-Analytical and Technical Support of Nuclear Power Facilities Monitoring, National Academy of Sciences of Ukraine, Akademika Palladina Avenue, 34-A, Kyiv, Ukraine. zaitsev@i.ua.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Oct 09; Vol. 14 (1), pp. 23610. Date of Electronic Publication: 2024 Oct 09.
DOI: 10.1038/s41598-024-74300-z
Abstrakt: DC grid fault protection techniques have previously faced challenges such as fixed thresholds, insensitivity to high-resistance faults, and dependency on specific threshold settings. These limitations can lead to elevated fault currents in the grid, particularly affecting multi-modular converters (MMCs) vulnerability to large fault current transients. This paper proposes a novel approach that combines the disjoint-based Bootstrap Aggregating (Bagging) technique and Bayesian optimization (BO) for fault detection in DC grids. Disjoint partitions reduce variance and enhance Ensemble Artificial Neural Network (EANN) performance, while BO optimizes EANN architecture. The proposed approach uses multiple transient periods instead of a fixed time to train the model. Transient periods are segmented into multiple 1 ms intervals, and each interval trains a separate neural network. In this way, a robust local relay is created that does not require high-speed communication systems. Additionally, a discrete wavelet transform (DWT) is applied to select detailed coefficients of the transient fault current, measured at the DC line's sending terminal for fault protection. EANN is trained in comprehensive offline data that considers noise impact. Simulation results demonstrate the scheme's ability to detect faults as high as 400 Ω accurately. This makes it a robust, reliable, and effective solution for fault detection on high-voltage direct current (HVDC) transmission lines. Lastly, this research provides the first-ever scientometric analysis of HVDC transmission line fault protection using neural network algorithms, highlighting major research themes and trends. The scientometric analysis was based on a dataset of 136 available research articles from the Scopus database from the last ten years. Therefore, this research provides valuable insights into the use of ANN for HVDC transmission line fault protection.
(© 2024. The Author(s).)
Databáze: MEDLINE
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