ANFIS-Based MPPT Strategy for Solar Water Pumping System

Islam Hachemi; Kamel Haddouche; Ahmed Bouzidane; Mustapha Belarbi

doi:10.5935/jetia.v12i58.2888

Autores/as

Islam Hachemi Research Laboratory of Industrial Technologies, Faculty of Applied Sciences, University of Tiaret, B. P. 78 - 14000 Tiaret, Algeria. https://orcid.org/0009-0001-5487-0648 (no autenticado)
Kamel Haddouche Research Laboratory of Industrial Technologies, Faculty of Applied Sciences, University of Tiaret, B. P. 78 - 14000 Tiaret, Algeria. https://orcid.org/0000-0002-7084-0246 (no autenticado)
Ahmed Bouzidane Research Laboratory of Industrial Technologies, Faculty of Applied Sciences, University of Tiaret, B. P. 78 - 14000 Tiaret, Algeria. https://orcid.org/0009-0001-9731-1870 (no autenticado)
Mustapha Belarbi Energy Engineering and Computer Engineering Laboratory, Faculty of Applied Sciences, University of Tiaret, B. P. 78 - 14000 Tiaret, Algeria. https://orcid.org/0000-0002-0614-9944 (no autenticado)

DOI:

https://doi.org/10.5935/jetia.v12i58.2888

Resumen

This paper presents the modeling and control of a standalone solar water pumping system, comprising a PV generator, DC/AC inverter, squirrel-cage motor, centrifugal pump, hydraulic circuit, and MPPT controller. Four machine-learning techniques (ANN, ANFIS, SVM, and GPR) are assessed for predicting the MPP voltage to improve system efficiency. Comparative results show that ANFIS gives the highest prediction accuracy (R² = 0.9992 and RMSE = 0.9552), outperforming GPR and ANN, with SVM providing moderate performance during training and testing phases. The performance of predcitive models was evaluated using training and testing data, assessing accuracy in predicting optimal voltage based on irradiance and temperature. Dynamic simulations under step-change irradiance conditions confirmed that the ML-based MPPT controllers significantly outperformed conventional P&O and INC methods, achieving faster time response. Based on this analysis, an ANFIS-based MPPT strategy with a PI controller is used, leveraging its superior accuracy relatively to the other ML-based MPPT controllers and conventional methods (P&O and INC).