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Islam, Md Shoriful
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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A Comprehensive Review of AI-Driven DC Arc Fault Detection in Photovoltaic Systems Islam, Md Shoriful
Control Systems and Optimization Letters Vol 3, No 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i2.208

Abstract

Photovoltaic (PV) systems now account for over 1.3% of global electricity generation and are expanding rapidly. DC arc faults pose severe safety risks among PV system faults, including fire hazards, equipment damage, and system failures. Traditional protection methods such as overcurrent devices and threshold-based detection often fail to distinguish arc faults from normal system noise reliably due to series arcs' intermittent and low-current nature. To address these problems, intelligent methods, including machine learning (ML), deep learning (DL), and hybrid approaches, have emerged as promising solutions offering superior accuracy, adaptability, and real-time performance. This paper presents a state-of-the-art intelligent approach for DC arc fault detection in PV systems. We explicitly compare ML and DL algorithms, highlighting trade-offs in computational efficiency, data requirements, and hardware constraints. Key implementation challenges, limited real-world datasets, and high computational costs for edge deployment are analyzed. Future directions focus on bridging gaps such as edge computing for real-time detection, synthetic data generation, and interpretable AI models. The findings aim to advance PV safety standards and enable scalable renewable energy integration.
Computation and Analysis of Highly Stable and Efficient Non-toxic Perovskite CsSnGeI3 Based Solar Cells to Enhance Efficiency Using SCAPS-1D Software Hossain, Md Momin; Khan, Md Yakub Ali; Halim, Md Abdul; Elme, Nafisa Sultana; Islam, Md Shoriful
Signal and Image Processing Letters Vol 5, No 2 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v5i2.66

Abstract

This paper examines the physical, optical, and electrical characteristics of cesium tin-germanium triiodide based single halide Perovskite absorption materials in order to provide the best photovoltaic application. In light of the diversification of the use of natural resources, perovskite solar cells are becoming more and more essential for capturing renewable energy. In this research work, a cesium tin–germanium triiodide (CsSnGeI3) perovskite-based solar cell (PSC) has been reported to achieve a high-power-conversion efficiency (PCE).  CsSnGeI3 perovskite-based solar cell has been proposed for the Pb and toxic-free (Al/FTO/ TiO2/CsSnGeI3/Mo) structure simulated in Solar Cell Capacitance Simulator (SCAPS-1D software. At first aluminum, fluorine-doped tin oxide, Titanium dioxide, cesium tin–germanium triiodide and   Molybdenum have been inserted into SCAPS and simulated using specific temperature. In this simulation, the electron transport layer (ETL) FTO, the buffer layer  TiO2, and the absorber layer  CsSnGeI3 were all used. Utilizing variations in thickness including absorber and buffer, defect density, operating temperature, back contact work function, series and shunt resistances, acceptor density, and donor density, the performance of the proposed photovoltaic devices was quantitatively assessed. Throughout the simulation, the absorber layer thickness was held constant at 1.6 ?m, the buffer layer at 0.05 ?m, and the electron transport layer at 0.5 ?m. A solar cell efficiency of 24.75%, an open-circuit voltage of 0.95 volts, a short-circuit current density of 30.61 mA/cm2, and a fill factor (FF) of 85.42% have all been recorded for the  CsSnGeI3 absorber layer. Our ground-breaking findings unequivocally show that CsSnGeI3-based PSC is a strong contender to quickly overtake other single-junction solar cell technologies as the most effective.
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.
A Comprehensive Review of AI-Driven Forecasting and Energy Management for DC Microgrids with High Renewable Integration 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.v3i3.245

Abstract

The global transition toward decarbonization has led to a greater integration of renewable energy sources (RES) into power systems, facilitating the widespread adoption of direct current (DC) microgrids. DC microgrids are particularly compatible with modern power systems because they support solar photovoltaic systems, batteries, and electronic loads. Despite these advantages, high levels of intermittent RES introduce challenges related to power balance, voltage stability, and reliable operation. Artificial Intelligence (AI) has emerged as a critical tool, enabling advanced forecasting and intelligent energy management systems (EMS) to address these issues. This comprehensive review examines state-of-the-art AI-based methods for DC microgrids, analyzing a wide range of studies from simulation-based models to real-world experimental pilots. It starts with an overview of the system architecture and operational challenges, followed by a novel taxonomy of AI approaches. The review critically compares machine learning for forecasting and reinforcement learning for real-time control, highlighting their respective performance in handling uncertainty. AI-driven EMS strategies, especially reinforcement learning for optimal scheduling, are detailed. The symbiotic relationship between accurate forecasting and robust EMS is explored, along with challenges such as data dependency and model explain ability, for which emerging solutions, such as federated learning and explainable AI (XAI), are discussed. The paper concludes by outlining future research directions, such as federated learning and standardized benchmarks. It underscores this review's key contribution by providing an integrated framework that bridges the gap between AI-driven forecasting and control for resilient and efficient DC microgrid operation.
Co-Authors Elme, Nafisa Sultana Halim, Md Abdul Hossain, Md Momin Khan, Md Yakub Ali