ABSTRACT
Solar photovoltaic (PV) panels and solar thermal collectors are two major technologies used to extract electrical and thermal energy, respectively, from solar irradiation. PV panels convert only 15–20% of incident solar radiation into electricity. The remaining radiation elevates the panel’s surface temperature, which badly affects the conversion efficiency and reduces the overall lifespan of the panel. Hence, in this paper, we investigate three cooling techniques developed in-house to maintain the PV panel’s surface temperature: passive cooling, active cooling, and combined cooling. A novel PV/T-PCM system was designed and fabricated to conduct a detailed experimental analysis of each of the cooling methods used on the performance of PV panel in the form of temperature and electrical efficiency of the panel. The temperature of the panel surface got reduced by a maximum of 36% at 1,050 W/m2, and as an effect of that, the electrical efficiency was also improved by around 42% in the case of a combined cooling technique. Finally, empirical correlations were also developed to predict the electrical efficiency and PV panel surface temperature using the experimental data of present investigation considering investigated system and operating parameters. Based on the experimental results, the coefficient of determination (R2), for the correlation of electrical efficiency, was found to be 0.90 and 0.9188 for surface temperature that indicate good confidence level for both the correlations. As a result, the correlations and results are more important from an application standpoint for the design and development of the PV/T system.
Nomenclature
η | = | Electrical efficiency of PV panel |
P | = | Power produced by the PV panel |
V | = | Voltage |
I | = | Current |
Is | = | Value of solar irradiation |
Apanel | = | PV panel surface area |
T | = | PV panel surface temperature (°C) |
t | = | Time in seconds |
wt% | = | Weight percent |
Abbreviations
PCM | = | Phase Change Material |
PV/T | = | Photovoltaic/Thermal |
AMF | = | Aluminum Metal Foam |
SWCNT | = | Single-Walled Carbon Nano Tube |
NOCT | = | Nominal Operating Cell Temperature |
ANFIS | = | Adaptive Neuro-Fuzzy Inference Systems |
AARD | = | Absolute Average Relative Deviation |
MSE | = | Mean Squared Error |
LPM | = | Liter Per Minute |
Acknowledgements
The authors are very grateful to Department of Hydro and Renewable Energy of Indian Institute of Technology (IIT) Roorkee and Department of Mechanical & Industrial Engineering, IIT Roorkee for providing the essential support for the experimental work.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Funding
Notes on contributors
Ankit Dev
Ankit Dev is working as an assistant professor in the department of mechanical engineering, SR University, Warangal, India and has extensive working experience in the field of solar pv thermal energy systems.
Ravi Kumar
Ravi Kumar is currently working as a professor in the mechanical & industrial engineering department, IIT Roorkee.
Aditya Kumar
Aditya Kumar is working as an assistant professor in the department of energy & environment, NIT Tiruchirappalli, India and has extensive working experience in the field of nanofluids and solar pv thermal energy systems.