Original Article
Photovoltaic Systems
Qinghai Li; A. Bourhani Said; Yusen Yan; Hui Zhou; Yanguo Zhang
Abstract
This study presents the design and simulation of a concentrating photovoltaic-thermal (CPVT) hybrid system that integrates spectral beam splitting (SBS) technology. The system utilizes conventional heliostat mirrors with dual-axis tracking to concentrate sunlight onto both a monocrystalline photovoltaic ...
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This study presents the design and simulation of a concentrating photovoltaic-thermal (CPVT) hybrid system that integrates spectral beam splitting (SBS) technology. The system utilizes conventional heliostat mirrors with dual-axis tracking to concentrate sunlight onto both a monocrystalline photovoltaic module and a thermal receiver. Nb2O5/SiO2/K9 spectral filter, with high transmittance in the 580–1100 nm range, is used to separate high-energy photons for photovoltaic conversion and low-energy photons for thermal energy production. The optimal installation position and angle of the mirrors and components were determined, and simulations were conducted to assess the system’s performance. The proposed hybrid system achieved a photovoltaic electrical efficiency of 18.00%, a thermal efficiency of 34.29%, and an overall system electrical efficiency of 26.57%. These results highlight the effectiveness of both the spectral filter and the concentration system, positioning this hybrid solution as a promising approach to maximize solar energy utilization.
Original Article
Applications of Machine Learning Algorithms in Renewable Energies
Pavan Gangwar; Aishvarya Narain
Abstract
This paper presents various maximum power point tracking (MPPT) techniques for solar photovoltaic (SPV) systems operating under partial shading conditions (PSCs). Traditional methods such as perturb and observe (P&O) used as a base model which face significant challenges in accurately identifying ...
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This paper presents various maximum power point tracking (MPPT) techniques for solar photovoltaic (SPV) systems operating under partial shading conditions (PSCs). Traditional methods such as perturb and observe (P&O) used as a base model which face significant challenges in accurately identifying the maximum power point (MPP) in the power-voltage curve. To overcome these challenges other optimization techniques like particle swarm optimization (PSO), grey wolf optimization (GWO), and cuckoo search algorithm (CSA) and machine learning (ML) is used. A multilayer perceptron (MLP) based MPPT framework designed to predict duty cycles based on SPV voltage and current inputs. Simulation results shows that the MLP-based approach achieves faster convergence in power output, and improved voltage stability compared to P&O, PSO, GWO, and CSA methods. The result highlights the potential of integrating ML techniques into SPV systems to enhance efficiency in challenging PSC scenarios. This research contributes to the advancement of sustainable solar energy technologies by leveraging adaptive intelligence for optimal energy harvesting.