An Adaptive Control Algorithm for Maximum Power Point Tracking for Photovoltaic Energy Conversion Systems - A Comparative Study

Ayman Blorfan (1) , Guy Sturtzer (1) , Damien Flieller (1) , Patrice Wira (2) , Jean Mercklé (2)

1 GREEN - Groupe de Recherche en Electrotechnique et Electronique de Nancy
2 MIPS - Modélisation, Intelligence, Processus et Système

This paper presents intelligent methods for the purpose of Maximum Power Point Tracking (MPPT). One is based on a Fuzzy Logic Controller (FLC) and the second is based on an Artificial Neural Network (ANN); they are applied to a converter circuit. The fuzziness determines the size of the perturbed voltage when there are rapid changes of the solar irradiation. A control scheme is presented which allows better control of the converter current reference using voltage and current from the PV system as inputs to the MPPT perturb and observes method. A new approach is also proposed to carry out the model of the photovoltaic array and to predict the maximum power point with an artificial neural network.

Mots clés: Photovoltaic; MPPT; perturb & observe; fuzzy control; neural control; power optimization; active power filter

Domaines: Traitement du signal et de l'image [eess.SP]

Ayman Blorfan, Guy Sturtzer, Damien Flieller, Patrice Wira, Jean Mercklé. An Adaptive Control Algorithm for Maximum Power Point Tracking for Photovoltaic Energy Conversion Systems - A Comparative Study. International Review of Electrical Engineering,IREE, 2014, 9 (3), pp.559-565. ⟨hal-01171183⟩