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Contact Name
Alfian Ma'arif
Contact Email
alfian_maarif@ieee.org
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alfian_maarif@ieee.org
Editorial Address
Jl. Empu Sedah No. 12, Pringwulung, Condongcatur, Kec. Depok, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia
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INDONESIA
Control Systems and Optimization Letters
ISSN : -     EISSN : 29856116     DOI : 10.59247/csol
Control Systems and Optimization Letters is an open-access journal offering authors the opportunity to publish in all fundamental and interdisciplinary areas of control and optimization, rapidly enabling a safe and sustainable interconnected human society. Control Systems and Optimization Letters accept scientifically sound and technically correct papers and provide valuable new knowledge to the mathematics and engineering communities. Theoretical work, experimental work, or case studies are all welcome. The journal also publishes survey papers. However, survey papers will be considered only with prior approval from the editor-in-chief and should provide additional insights into the topic surveyed rather than a mere compilation of known results. Topics on well-studied modern control and optimization methods, such as linear quadratic regulators, are within the scope of the journal. The Control Systems and Optimization Letters focus on control system development and solving problems using optimization algorithms to reach 17 Sustainable Development Goals (SDGs). The scope is linear control, nonlinear control, optimal control, adaptive control, robust control, geometry control, and intelligent control.
Articles 11 Documents
Search results for , issue "Vol 3, No 3 (2025)" : 11 Documents clear
DC Arc Fault Detection in Microgrids: A Comprehensive Review of Challenges, Advances, and Future Directions Islam, Md Shoriful
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.244

Abstract

DC arc faults in residential, commercial, and industrial DC microgrids pose significant safety and reliability challenges, including potential fire hazards, equipment damage, and system downtime. Despite advancements in detection technologies, accurately detecting and mitigating DC arc faults remains difficult due to the dynamic nature of microgrids, fluctuating load conditions, and the absence of zero-crossing points in DC systems. This review provides a thorough analysis of existing DC arc-fault detection methods, including time-domain, frequency-domain, time-frequency analysis, and machine learning techniques, and compares their performance in terms of accuracy, robustness, and real-time applicability. The review highlights the principles, advantages, and limitations of each approach, addressing key challenges such as noise interference, low-current arc detection, and the need for real-time processing. Furthermore, it discusses recent developments in hybrid detection systems, high-frequency signal processing, and deep learning models as promising solutions to enhance detection accuracy and system reliability, while also addressing practical implementation challenges. Finally, the review outlines future research directions, emphasizing the importance of adaptive algorithms, standardized testing protocols, and integration with emerging grid technologies. This review distinguishes itself by providing a systematic comparison of detection paradigms and a synthesized roadmap for future research, bridging the gap between theoretical advances and practical implementation in diverse microgrid environments.

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