An Investigation of Intelligent and Conventional Maximum Power Point Tracking Techniques for Uniform Atmospheric Conditions

Abstract

In recent years, power generation from photovoltaic (PV) system has received great attention compared to other renewable sources. Due to nonlinear characteristics of PV cells, the maximum allowable power level from PV panel changes with atmospheric parameters which are solar irradiance and temperature. In this context, maximum power point tracking (MPPT) algorithms are essential to maximize the output power of PV panel for any solar irradiance and temperature values. In the literature, various MPPT techniques have been studied to deliver maximum power from PV systems. Hence, this study discusses intelligent control techniques, which are called fuzzy logic controller (FLC) and neural network controller (NNC), and compares efficiency performance and convergence speed to conventional perturb & observe (P&O) and incremental conductance (Inc. Cond.) tracking techniques for MPPT of PV system.

In this paper, 150W PV panel model is investigated for different atmospheric conditions in MATLAB. Results of simulation show that NNC based and FLC based MPPTs have 4.66% better tracking accuracy than conventional P&O and Inc. Cond. under standard test condition (STC). NNC based MPPT has best iteration response rate among the other MPPTs under uniform atmospheric conditions. Therefore, the NNC based MPPT presents best superior quality in terms of efficiency and convergence speed for PV systems among the other MPPTs.        

Keywords

• PV Model,Maximum Power Point Tracking,Perturb & Observe,Incremental Conductance,Fuzzy Logic Control,Neural Network Control

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