Affiliation:
1. School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
2. Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
Abstract
DC microgrids are vital for integrating renewable energy sources into the grid, but they face the threat of DC arc faults, which can lead to malfunctions and fire hazards. Therefore, ensuring the secure and efficient operation of DC systems necessitates a comprehensive understanding of the characteristics of DC arc faults and the implementation of a reliable arc fault detection technique. Existing arc-fault detection methods often rely on time–frequency domain features and machine learning algorithms. In this study, we propose an advanced detection technique that utilizes a novel approach based on feature differences between moving intervals and advanced learning techniques (ALTs). The proposed method employs a unique approach by utilizing a time signal derived from power supply-side signals as a reference input. To operationalize the proposed method, a meticulous feature extraction process is employed on each dataset. Notably, the difference between features within distinct moving intervals is calculated, forming a set of differentials that encapsulate critical information about the evolving arc-fault conditions. These differentials are then channeled as inputs for advanced learning techniques, enhancing the model’s ability to discern intricate patterns indicative of DC arc faults. The results demonstrate the effectiveness and consistency of our approach across various scenarios, validating its potential to improve fault detection in DC systems.
Reference38 articles.
1. Operation and Control of Multiterminal HVDC Transmission for Offshore Wind Farms;Liang;IEEE Trans. Power Deliv.,2011
2. A Novel Multiterminal VSC-HVdc Transmission Topology for Offshore Wind Farms;Raza;IEEE Trans. Ind. Appl.,2016
3. Investigation on Arc Behavior During Arc Motion in Air DC Circuit Breaker;Ma;IEEE Trans. Plasma Sci.,2013
4. Sawa, K., Tsuruoka, M., and Yamashita, S. (2014, January 22–26). Fundamental Arc Characteristics at DC Current Interruption of Low Volt-age (<500V), ICEC 2014. Proceedings of the 27th International Conference on Electrical Contacts, Dresden, Germany.
5. Modeling for Series Arc of DC Circuit Breaker;Kim;IEEE Trans. Ind. Appl.,2019
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