Browsing by Author "Shiva Kumar, B.S."

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An adaptive modeling for bifacial solar module levelized cost and performance analysis for mining application
(John Wiley and Sons Ltd, 2024) Shiva Kumar, B.S.; Kunar, B.M.; Murthy, C.S.N.
Power density and efficiency typically dominate design approaches for power electronics. However, cost optimality is in no way guaranteed by these strategies. A design framework that minimizes the (i) levelized cost of electricity (LCOE), (ii) collection of light, and (iii) irradiance of the generation system is proposed as a solution to this flaw. From an improvement of the swarm behavior optimization model to get a minimum LCOE of solar panel, we design to optimize height, tilt angle, azimuth angle, and some parameters to solve the objective function and LCOE improvement problem to obtain the optimal design problem. In adaptive salp swarm optimization (ASSO), this change's proposed model producer swarm behavior is regarded as an adaptive process that keeps the algorithm from prematurely converging during exploration. The proposed algorithm's performance was confirmed using benchmark test functions, and the results were compared with those of the salp swarm optimization (SSO) and other efficient optimization algorithms. LCOE condition as far as “land-related cost” and “module-related cost” demonstrates that the optimal design of bifacial farms is determined by the interaction of these parameters. This proposed model can be used to evaluate visibility on building surfaces that are suitable for mining applications like crushing. Experimentation results show Minimum LCOE AS 0.05 (€/Kw)minimum irradiance and collection light as 336.23(w/m2) and 83.02%n proposed framework model. The swarm optimization method is contrasted with the optimal parameters derived from a conventional solver. © 2023 John Wiley & Sons Ltd.
Performance analysis of a 50 MW grid-connected solar PV system for sustainable mining operations
(World Researchers Associations, 2025) Shiva Kumar, B.S.; Kunar, B.M.; Murthy, C.S.N.
The strategic use of renewable resources has become essential for guaranteeing energy security in response to rising energy demands. Mining operations require creative solutions to provide steady energy delivery because of their high energy needs and dependence on continuous power. A technically sound and financially advantageous option for extensive energy integration in these industries is grid-connected photovoltaic (PV) system. The performance of 50 MW grid-connected solar PV power plant in Peddapalli, a major mining hub with ideal solar conditions, is assessed in this study. With an average yearly temperature of 27.3°C and a mean solar insolation of 4.97 kWh/m2, the plant uses a seasonal tilt approach to maximize solar energy capture. A quarterly energy yield of 15,798.192 MWh, a capacity utilization factor (CUF) of 17.68% and a performance ratio (PR) of 86.12% are examples of key performance measures. By successfully integrating solar PV technology into mining operations, operational expenses and carbon emissions are decreased while issues with energy stability are resolved. This study provides helpful insights for utilizing renewable energy in energy-intensive businesses by highlighting the necessity of robust architecture, stable buildings and efficient energy management to maintain a consistent power supply. © 2025, World Researchers Associations. All rights reserved.
Performance Evaluation and Machine Learning Analysis of 3 kW Grid-Connected Bifacial Solar Photovoltaic Systems
(Springer, 2025) Shiva Kumar, B.S.; Kunar, B.M.; Murthy, C.S.N.
Rooftop solar panels with dual functions are a promising sustainable energy source given the world’s rapid urbanization, particularly in densely populated nations like India. Numerous studies have shown how well the System Advisory Model (SAM) performs when estimating the energy yield of bifacial solar photovoltaic (BSPV) systems. This study uses a popular photovoltaic design software to compare the output of a 3 kW grid-connected BSPV system. The SAM simulation forecasts a production of 4864.5 kWh, whereas the empirical data shows that the system’s yearly energy output is 4321.7 kWh. The operational plant’s performance ratio is about 81.6%, which closely agrees with the 81.2% that SAM projected. Further, the relationship between various input parameters and power output of a BSPV generated from SAM simulation results was investigated using machine learning (ML) models. Among the ML models tested, the linear regression (LR) model delivered the best performance for a 3 kW BSPV system in a specific location. © The Institution of Engineers (India) 2025.