Investigation of a Real-Time Dynamic Model for a PV Cooling System

Ahmad Manasrah,Yousef Jaradat,Mohammad Masoud,Piero Bevilacqua

doi:10.3390/en15051836

Ahmad Manasrah, Yousef Jaradat + Show 2 more

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https://doi.org/10.3390/en15051836

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Abstract

The cooling of PV models is an important process that enhances the generated electricity from these models, especially in hot areas. In this work, a new, active cooling algorithm is proposed based on active fan cooling and an artificial neural network, which is named the artificial dynamic neural network Fan cooling algorithm (DNNFC). The proposed system attaches five fans to the back of a PV model. Subsequently, only two fans work at any given time to circulate the air under the PV model in order to cool it down. Five different patterns of working fans have been experimented with in this work. To select the optimal pattern for any given time, a back propagation neural network model was trained. The algorithm is a dynamic algorithm since it re-trains the model with new recorded surface temperatures over time. In this way, the model automatically adapts to any weather and environmental conditions. The model was trained with an indoor dataset and tested with an outdoor dataset. An accuracy of more than 97% has been recorded, with a mean square error of approximately 0.02.

Highlights

Several studies have explored the capabilities of Artificial Intelligence (AI) in photovoltaic systems and have reported results in power generation, efficiency improvements, and even the stability of such systems
One study in particular showed that the improved design of an Artificial neural networks (ANN) model significantly enhanced the performance of a maximum power point tracking algorithm (MPPT), which increased the efficiency of the PV system [26]
The results showed that the developed Adaptive Neuro-Fuzzy Inference System (ANFIS) was able to successfully predict the electrical efficiency of PV modules for any given dust particle size

Summary

Introduction

The idea of developing a world that does not rely on fossil fuel sources has been a common objective of most industry-leading nations for years [1]. Several studies have explored the capabilities of AI in photovoltaic systems and have reported results in power generation, efficiency improvements, and even the stability of such systems. ANN models were developed to maximize the power output of solar photovoltaic systems, increasing their efficiencies. One study in particular showed that the improved design of an ANN model significantly enhanced the performance of a maximum power point tracking algorithm (MPPT), which increased the efficiency of the PV system [26]. The results showed that the developed Adaptive Neuro-Fuzzy Inference System (ANFIS) was able to successfully predict the electrical efficiency of PV modules for any given dust particle size. None of the previously cited studies incorporated active or passive cooling systems in their developed models.

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