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

Yıl 2019, Cilt: 3 Sayı: 2, 93 - 100, 10.10.2019

Ekrem Kandemır Numan Sabit Cetin Selim Borekci

Öz

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.        

Anahtar Kelimeler

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

Kaynakça

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