A New Method for Photovoltaic Parameters Extraction Under Variable Weather Conditions Aissa Hali and Yamina Khlifi Abstract This work suggests a new method for extracting parameters from the current-voltage characteristics of photovoltaic (PV) panel to establish translational relations that allows to predict these PV parameters in other conditions of tempera- ture and irradiation levels. This method is based on the knowledge of few selected points which are the short-circuit point, the maximum power point, and the open circuit point. These points are extracted from the current-voltage characteristics pro- vided by the manufacturer’s datasheet. Single diode model is chosen to represent the PV panel which is known as five parameters model. It includes two parasitic resis- tances, diode saturation current, diode ideality factor, and photo-current. The main results of simulations in Matlab environment show that the five PV parameters varies as function of irradiance and temperature. The proposed technique is compared to other published methods applied on Kyocera KC175GHT-2 PV panel. The obtained results show that the new suggested method of predicting PV parameters presents a lower statistical error whatever the weather conditions. Therefore, the new method is validated and more precise than extraction methods reported in the literature. Keywords Matlab · Panel · Photovoltaics parameters · Statistical errors · Trust Region Dogleg algorithm · Translational relations 1 Introduction The overall performance of a photovoltaic (PV) panel is willing to degrade over time, due to exposure weather conditions and aging. Consequently, predicting these degradations is crucial to avoid their destructive influences on photovoltaic energy production. In this framework, the evaluation of photovoltaic panel behavior in differ- A. Hali (B ) · Y. Khlifi Laboratory of Renewable Energy, Embedded Systems and Data Processing, National School of Applied Sciences Oujda, Oujda, Morocco e-mail: haliaissa11@gmail.com Y. Khlifi e-mail: khlifi_yamina@yahoo.fr © Springer Nature Singapore Pte Ltd. 2022 S. Bennani et al. (eds.), WITS 2020, Lecture Notes in Electrical Engineering 745, https://doi.org/10.1007/978-981-33-6893-4_52 565