Monitoring and Controlling Solar Photovoltaic (PV) Performance with Active Cooling System using IoT

Razak, Mohd Faizal Abd; Hasan, Moh.; Jobran, Junaidah Ali Mohd; Jamalli, S A S

doi:10.1088/1742-6596/2107/1/012001

Cited by 2 publications

(1 citation statement)

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“…Four data processing modules were compared and concluded that even though Arduino is cheaper and widelyavailable, it owns limited in-built programming library [29]; PLCs need experts to work with their applications, Raspberry Pi is limited due to Linux operating system, but they are effective and important part of our technologies. A novel I-V curve tracer was presented based on IoT using a Raspberry Pi as the hardware level and mariaDB, Python, and Grafana as software [61]. The development of the Monitoring and Controlling system of Solar Photovoltaic (PV) Performance with an Active Cooling System using IoT is expounded, and I-V curve tracing of a monocrystalline PV generator under real operating conditions is successfully conducted.…”

Section: Introductionmentioning

confidence: 99%

Neural Network based Active Cooling System with IoT Monitoring and Control for LCPV Silicon Solar Cells

et al. 2023

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One of the methods of increasing output power of solar cells is increasing the concentration ratio using lenses and mirrors. Due to the temperature increase of silicon solar cells with concentrating lenses the system must be provided with an active cooling system. PV systems cooled with active or passive cooling methods using air, water or other substances are not taking into account the relationship between the power of solar radiation and operating power of the pump used in the cooling system, which is sufficiently complicated and little studied. This paper proposes a new active energy-efficient cooling system for silicon LCPV (Low Concentrating Photovoltaic). The idea of the proposed cooling system is to make the pump operate as efficiently as possible relative to the power of solar radiation and initial temperature of the heated solar cell. Performed experiments show that at fixed temperature and fixed solar radiation there is the most optimal pump operating power. Neural network is used to find this optimal pump power. Using neural networks requires large computing resources and development software which is not often available on local control computing devices nearby solar power plants. IoT (Internet of Things) technologies allow not only remote monitoring and control, but also forecasting the consumption of the cooling system depending on the current temperature and solar radiation power. Thus, energy efficiency of the cooling system is improved using neural networks and IoT technologies. The simulation of the cooling system operation was performed using various algorithms of the cooling system operation. The proposed active cooling system for silicon LCPV using IoT technologies allows the power consumption to be reduced by 60% compared to algorithms based on the threshold value of temperature.INDEX TERMS IoT monitoring and control, neural networks, Bi-LSTM, XGBoost, LCPV silicon solar cells, active cooling system.BATYRBEK ZHOLAMANOV was born in Aral, Kyzylorda, Kazakhstan, in 1999. He received the B.S. and M.S. degrees in radio engineering, electronics, and telecommunications from Al-Farabi Kazakh National University, in 2020 and 2022, respectively, where he is currently pursuing the Ph.D. degree in radio engineering, electronics, and telecommunications. He was a Research Assistant with the

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Section: Introductionmentioning

confidence: 99%