Water flow cooling is a cost-effective and environmentally friendly way to enhance the performance of a photovoltaic (PV) system. However, most of the studies have investigated a PV unit with water flow cooling in the steady-state condition. Therefore, as the novelty, this study aims at modeling and investigation of the system performance in the transient condition. For a 50W polycrystalline PV module, the annual experimental data is utilized to find its transient response to water flow cooling, which covers temperature and power improvement (ΔT and ΔP). An artificial neural network (ANN) is developed using the experimental data for prediction of ΔT and ΔP. ANN is validated and then, utilized to provide a comprehensive discussion about the impact of effective parameters on the system response. According to the results, for all the investigated cases, there is a threshold value for water flow rate. For instance, when wind velocity and ambient temperature are 1 ms−1 and 25 °C, the threshold values for the solar radiation values of 800, 1000, and 1200 Wm-2 are 0.009, 0.013, and 0.015 kgs−1, respectively. Moreover, irradiance and ambient temperature have great impacts on dimensionless ΔT and ΔP, while wind velocity is the least effective one.

Thermography and machine learning combination for comprehensive analysis of transient response of a photovoltaic module to water cooling / Sohani, A.; Cornaro, C.; Shahverdian, M. H.; Hoseinzadeh, S.; Moser, D.; Nastasi, B.; Sayyaadi, H.; Astiaso Garcia, D.. - In: RENEWABLE ENERGY. - ISSN 0960-1481. - 210:(2023), pp. 451-461. [10.1016/j.renene.2023.04.073]

Thermography and machine learning combination for comprehensive analysis of transient response of a photovoltaic module to water cooling

Hoseinzadeh S.;Nastasi B.;Astiaso Garcia D.
2023

Abstract

Water flow cooling is a cost-effective and environmentally friendly way to enhance the performance of a photovoltaic (PV) system. However, most of the studies have investigated a PV unit with water flow cooling in the steady-state condition. Therefore, as the novelty, this study aims at modeling and investigation of the system performance in the transient condition. For a 50W polycrystalline PV module, the annual experimental data is utilized to find its transient response to water flow cooling, which covers temperature and power improvement (ΔT and ΔP). An artificial neural network (ANN) is developed using the experimental data for prediction of ΔT and ΔP. ANN is validated and then, utilized to provide a comprehensive discussion about the impact of effective parameters on the system response. According to the results, for all the investigated cases, there is a threshold value for water flow rate. For instance, when wind velocity and ambient temperature are 1 ms−1 and 25 °C, the threshold values for the solar radiation values of 800, 1000, and 1200 Wm-2 are 0.009, 0.013, and 0.015 kgs−1, respectively. Moreover, irradiance and ambient temperature have great impacts on dimensionless ΔT and ΔP, while wind velocity is the least effective one.
2023
photovoltaic (PV) systems; water flow cooling; transient response; thermography; machine learning
01 Pubblicazione su rivista::01a Articolo in rivista
Thermography and machine learning combination for comprehensive analysis of transient response of a photovoltaic module to water cooling / Sohani, A.; Cornaro, C.; Shahverdian, M. H.; Hoseinzadeh, S.; Moser, D.; Nastasi, B.; Sayyaadi, H.; Astiaso Garcia, D.. - In: RENEWABLE ENERGY. - ISSN 0960-1481. - 210:(2023), pp. 451-461. [10.1016/j.renene.2023.04.073]
File allegati a questo prodotto
File Dimensione Formato  
Sohani_Thermography_2023.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 6.7 MB
Formato Adobe PDF
6.7 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1678884
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 3
social impact