Comparative study of different photovoltaic MPPT techniques under various weather conditions

M.Ing. (Electrical Engineering)Abstract: Photovoltaic (PV) energy has become the most growing renewable energy source that serves as an alternative to fossil energy as it is considered cheap, less polluted, etc. Photovoltaic system works under both uniform irradiance and partial shaded weather conditions and exhibits both local peak power and global peak power during partial shaded conditions. Maximum power point tracking (MPPT) techniques are being used in PV systems to track the local power peak. However, MPPT systems may fail to track the global peak power. Online MPPT techniques such as Perturb&Observe (P&O) and incremental conductance (IC) are considered economical and easy to implement. However, these online methods underachieve due to some flaws such as oscillating power near maximum power point (MPP) and poor response owing to the sudden change in irradiance with P&O and IC. MPPT techniques are algorithms used in photovoltaic (PV) system to extract maximum power from the PV panel. Offline techniques such as the curve fitting polynomial (CFP) technique use prediction and fitting method in order to track the MPP. However, lower order CFP does not extract maximum power from the panel due to inaccurate fitting of the real P-V curve. The use of supervised machine learning techniques which include artificial neural network (ANN) and artificial neuro-fuzzy inference system (ANFIS) have improved the maximum power point tracking (MPPT) process which increased the PV systems efficiency. This first contribution of this dissertation presents a reconfiguration approach that uses series-connected distributive MPPT (DMPPT). DMPPT is used to track the global peak in order to extract maximum power from the PV array system under uniform irradiance and partial shaded weather conditions. The second contribution is the implementation of improved models of online Perturb&Observe and modified incremental conductance MPPT techniques that work well under sudden change in irradiance, temperature, and with minimal oscillating power near MPP. The third contribution is the state of the art of the sixth-order curve fitting polynomial MPPT technique that can extract maximum power from a PV panel under varied weather conditions. The fourth contribution is to introduce the use of SVM classifier for optimization and MPPT purpose in a PV system..