Ezhilmathi Nagarathinam¹, Buvana Devaraju², Karthiyayini Jayamoorthy³, Padmavathi Radhakrishnan⁴, Santhana Lakshmi ChandraMohan⁵, Vijayakumar Perumal⁶, Karthikeyan Balakrishnan^7,*

Energy Engineering, Vol.121, No.8, pp. 2129-2142, 2024, DOI:10.32604/ee.2024.052280

Abstract Maximum Power Point Tracking (MPPT) is crucial for maximizing the energy output of photovoltaic (PV) systems by continuously adjusting the operating point of the panels to track the point of maximum power production under changing environmental conditions. This work proposes the design of an MPPT system for solar PV installations using the Differential Grey Wolf Optimizer (DGWO). It dynamically adjusts the parameters of the MPPT controller, specifically the duty cycle of the SEPIC converter, to efficiently track the Maximum Power Point (MPP). The proposed system aims to enhance the energy harvesting capability of solar PV More >

Mame Cheikh Diouf¹, Mactar Faye^1,2,*, Ababacar Thiam^1,2, Alphousseyni Ndiaye^1,2, Vincent Sambou²

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.5, pp. 1275-1284, 2022, DOI:10.32604/fdmp.2022.021972

Abstract The operating temperature is a critical factor affecting the performances of photovoltaic (PV) modules. In this work, relevant models are proposed for the prediction of this operating temperature using data (ambient temperature and solar irradiance) based on real measurements conducted in the tropical region. For each weather condition (categorized according to irradiance and temperature levels), the temperatures of the PV modules obtained using the proposed approach is compared with the corresponding experimentally measured value. The results show that the proposed models have a smaller Root Mean Squared Error than other models developed in the literature More >

Zhongyao Du^1,*, Xiaoying Chen¹, Hao Wang², Xuheng Wang¹, Yu Deng¹, Liying Sun¹

Energy Engineering, Vol.119, No.4, pp. 1419-1438, 2022, DOI:10.32604/ee.2022.020283

Abstract To attain the goal of carbon peaking and carbon neutralization, the inevitable choice is the open sharing of power data and connection to the grid of high-permeability renewable energy. However, this approach is hindered by the lack of training data for predicting new grid-connected PV power stations. To overcome this problem, this work uses open and shared power data as input for a short-term PV-power-prediction model based on feature transfer learning to facilitate the generalization of the PV-power-prediction model to multiple PV-power stations. The proposed model integrates a structure model, heat-dissipation conditions, and the loss… More >

Ramkiran Bhallamudi^1,2, Sudhakar Kumarasamy^3,4,5, Chinnayan Karuppaiyah Sundarabalan^1,*

Energy Engineering, Vol.118, No.6, pp. 1827-1838, 2021, DOI:10.32604/EE.2021.016798

Abstract This study presents an experimental performance of a solar photovoltaic module under clean, dust, and shadow conditions. It is found that there is a significant decrease in electrical power produced (40% in the case of dust panels and 80% in the case of shadow panels) and a decrease in efficiency of around 6% in the case with dust and 9% in the case with the shadow, as compared to the clean panel. From the results, it is clear that there is a substantial effect of a partial shadow than dust on the performance of the More >