One Diode PV Modeling Under Varying Irradiance

Internet of Things (IoT) is a massive network of connected devices that enables data sharing and analysis for extracting valuable information. Many industries have started to integrate IoT into their devices to increase their businesses' competitiveness. IoT devices which consume less power, ca...

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Main Authors: Qian, C.T.J., Drieberg, M., Soeung, S.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29200 /
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124147942&doi=10.1109%2fICIAS49414.2021.9642628&partnerID=40&md5=35f7bb4763ed9252c3abacb601bac35c
http://eprints.utp.edu.my/29200/
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Summary: Internet of Things (IoT) is a massive network of connected devices that enables data sharing and analysis for extracting valuable information. Many industries have started to integrate IoT into their devices to increase their businesses' competitiveness. IoT devices which consume less power, can be potentially powered up using an energy harvesting system instead of batteries. A photovoltaic (PV) panel converts light energy into electrical energy is used to harvest the power. To predict the behaviour of PV panel, an accurate model is required. Most of the manufacturers provide values of three characteristic points (open circuit point, short circuit point, and maximum power point) at standard test conditions (STC) condition. However, STC condition is not always achieved in reality. Therefore, this paper presents the methodology for modeling an accurate one diode model with two resistors under different irradiance with the help of characteristic points translation technique. The proposed model is applied on a commercial PV panel. Three characteristic points of the model are obtained and validate with the datasheet values. The results achieve a good agreement with a difference below than 5 . The proposed model shows an accuracy improvement when compared to the existing models. © 2021 IEEE.