A Hybrid Approach for Maximum Power Point Tracker on PV Systems.

EasyChair Preprint 13970

Abstract

As the usage of photovoltaic is emerging to palliate the degradation of the atmosphere and the natural environment. Photovoltaic (PV) systems appear to be the key for green energy production for households and industrial sectors. However, due to the dynamic change of load for a PV system or the variations of weather conditions, most PV systems are equipped with a maximum power point tracker (MPPT) to operate at their optimum capabilities. However, the efficiency of the MPPT controller varies from the techniques and algorithms used thus affecting the operations of the PV system. This paper proposes a hybrid technique based on Artificial neural network (ANN) combines with perturbation and observation (P&O) or incremental conductance (IC) algorithms using MATLAB Simulink for tracking the ideal maximum power point under uniform and sudden change of weather conditions. A comparison is depicted with conventional techniques, the findings suggest that the hybrid ANN-IC technique has an improved accuracy of 98% and response time 0,154 seconds to MPP under uniform weather. The model presents a fast-tracking response under rapidly changing conditions for a convergence time of 1,013 us.

Keyphrases: Artificial Neural Network (ANN), Boost converter, Incremental Conductance (IC), Maximum power point (MPP) tracker, Perturbation and Observation (P&O), Photovoltaic systems

@booklet{EasyChair:13970,
  author    = {Ely Ondo Ekogha and Pius Adewale Owolawi},
  title     = {A Hybrid Approach for Maximum Power Point Tracker on PV Systems.},
  howpublished = {EasyChair Preprint 13970},
  year      = {EasyChair, 2024}}