Papers by Shipon Chandra Barman
Eman Research Publishing LLC, USA, 2025
Photovoltaic (PV) technologies continue to expand as the global demand for sustainable energy acc... more Photovoltaic (PV) technologies continue to expand as the global demand for sustainable energy accelerates. Yet, while advances in solar cell materials have improved conversion efficiency, the reliability and performance of photovoltaic modules still depend heavily on manufacturing precision. Among the most critical stages is the lamination process, where temperature, pressure, and curing time collectively determine structural bonding, electrical stability, and defect formation. This study explored how controlled adjustments in these parameters influence module performance during manufacturing. A full factorial Design of Experiments (DOE) framework was applied to examine lamination temperature (140-160 °C), pressure (0.70-0.90 MPa), and curing duration (12-18 min). Sixty experimental runs were conducted using monocrystalline silicon photovoltaic cells, while performance metrics-including power output, defect rate, adhesion strength, thermal stability, and electrical deviation-were systematically measured. Statistical Process Control (SPC) tools and process capability indices (Cp and Cpk) were used to evaluate production stability and reproducibility. Results suggest that incremental increases in lamination temperature, pressure, and curing duration generally improved module performance. Power output increased from 282.94 W to 287.62 W, while defect rates declined from 5.36% to 3.56%. Mechanical adhesion strength improved from 2.53 N/mm² to 2.95 N/mm², accompanied by enhanced thermal stability. Among the experimental runs, the optimal configuration-155 °C lamination temperature, 0.85 MPa pressure, and 18 min curing-produced 286.74 W output with a defect rate of only 2.83% and the highest observed adhesion strength of 3.01 N/mm². Overall, the findings highlight how carefully optimized lamination parameters can significantly enhance electrical efficiency, structural durability, and process reliability. These insights may contribute to more stable photovoltaic manufacturing systems and, perhaps more importantly, support the broader advancement of scalable and dependable solar energy technologies.
IEEE, 2025
Recent advancements in smart technologies tender the use of artificial technologies in the energy... more Recent advancements in smart technologies tender the use of artificial technologies in the energy systems more practical. The focus of this paper is on designing an innovative smart energy system with the aid of cloud computing. It aims to address certain primary issues in energy demand forecasting, load balancing, and automation in decentralized control management systems. A proposed system which utilizes hybrid architecture based on integrated predictive analytics and control distributed automation and decision systems, across multiple diverse energy resources, demonstrates transformative control automation and predictive control capabilities, using edge artificial intelligence, such energy clouds, and federated control. The artificial intelligence system provides edge control within federated control architecture on the energy cloud, the system analytics predictive control and control functions which offer automated decision-making. The decisions made in the automation of several energy assets boost the system predictive control capabilities. The enhanced predictive control capabilities offer reduction in operational energy systems, operational cost and carbon, and overall system carbon footprint the operational energy system efficiency. The cloud artificial intelligence systems bring transformative changes to the operational energy system efficiency, operational cost, and overall system carbon footprint. It paves the way toward a new, self-governing paradigm for smart and autonomous ecosystems sustainable energy systems.
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Papers by Shipon Chandra Barman