A New Cloud-Based IoT Solution for Soiling Ratio Measurement of PV Systems Using Artificial Neural Network

Mussawir Ul Mehmood, Abasin Ulasyar (), Waleed Ali, Kamran Zeb, Haris Sheh Zad, Waqar Uddin and Hee-Je Kim ()
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Mussawir Ul Mehmood: Department of Electrical Power Engineering, USPCAS-E, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
Abasin Ulasyar: Department of Electrical Power Engineering, USPCAS-E, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
Waleed Ali: Department of Electrical Power Engineering, USPCAS-E, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
Kamran Zeb: School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
Haris Sheh Zad: Department of Mechanical and Manufacturing Engineering, Pak-Austria Fachhochschule, Institute of Applied Sciences and Technology, Haripur 22620, Pakistan
Waqar Uddin: Department of Electrical Engineering, National University of Technology, NUTECH, Islamabad 44000, Pakistan
Hee-Je Kim: School of Electrical Engineering, Pusan National University, Pusandaehak-ro 63 beon-gil 2, Geumjeong-gu, Busan 46241, Republic of Korea

Energies, 2023, vol. 16, issue 2, 1-14

Abstract: Solar energy is considered the most abundant form of energy available on earth. However, the efficiency of photovoltaic (PV) panels is greatly reduced due to the accumulation of dust particles on the surface of PV panels. The optimization of the cleaning cycles of a PV power plant through condition monitoring of PV panels is crucial for its optimal performance. Specialized equipment and weather stations are deployed for large-scale PV plants to monitor the amount of soil accumulated on panel surface. However, not much focus is given to small- and medium-scale PV plants, where the costs associated with specialized weather stations cannot be justified. To overcome this hurdle, a cost-effective and scalable solution is required. Therefore, a new centralized cloud-based solar conversion recovery system (SCRS) is proposed in this research work. The proposed system utilizes the Internet of Things (IoT) and cloud-based centralized architecture, which allows users to remotely monitor the amount of soiling on PV panels, regardless of the scale. To improve scalability and cost-effectiveness, the proposed system uses low-cost sensors and an artificial neural network (ANN) to reduce the amount of hardware required for a soiling station. Multiple ANN models with different numbers of neurons in hidden layers were tested and compared to determine the most suitable model. The selected ANN model was trained using the data collected from an experimental setup. After training the ANN model, the mean squared error (MSE) value of 0.0117 was achieved. Additionally, the adjusted R-squared ( R 2 ) value of 0.905 was attained on the test data. Furthermore, data is transmitted from soiling station to the cloud server wirelessly using a message queuing telemetry transport (MQTT) lightweight communication protocol over Wi-Fi network. Therefore, SCRS depicts a complete wireless sensor network eliminating the need for extra wiring. The average percentage error in the soiling ratio estimation was found to be 4.33%.

Keywords: internet of things; solar energy; solar efficiency; cloud; edge device; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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