Abstract
Classic and intelligent techniques aim to locate and track the maximum power point of photovoltaic (PV) systems, such as perturb and observe (P&O), fuzzy logic (FL), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFISs). This paper proposes and compares three intelligent algorithms for maximum power point tracking (MPPT) control, specifically fuzzy, ANN, and ANFIS. The modeling of a single-diode equivalent circuit-based 3 kWp PV plant was developed and validated to achieve this purpose. Then, the MPPT techniques were designed and applied to control the buck–boost converter’s switching device of the PV plant. All three methods use the ambient conditions as input variables: solar irradiance and ambient temperature. The proposed methodology comprises the study of the dynamic response for tracking the maximum power point and the power generated of the PV systems, and it was compared to the classic P&O technique under varying ambient conditions. We observed that the intelligent techniques outperformed the classic P&O method in tracking speed, tracking accuracy, and reducing oscillation around the maximum power point (MPP). The ANN technique was the better control algorithm in energy gain, managing to recover up to 9.9% power.
Highlights
maximum power point tracking (MPPT) algorithms based on the artificial neural networks (ANNs), fuzzy logic (FL), and adaptive neuro-fuzzy inference systems (ANFISs) intelligent techniques to track the the brief discussion above,varying the present research proposesand developing
The results show that the performance of the MPPT algorithm depends more on the quality than the quantity of the training data [41]
Using buck–boost converters as an MPPT component of PV systems is uncommon, but they are important for regions with a dry and sunny tropical climate
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Ahmed and Salam [23] developed and analyzed another MPPT algorithm of a PV system with a buck–boost converter This time, they created a modified P&O algorithm, and Riaz et al [24] affirmed it to be an effective solution for detecting uniform and partial shading conditions. The researchers compared fuzzy and P&O as MPPT control techniques They concluded that the buck–boost converter offered a better performance. Authors analyzed the resulting qualitatively of error (with five for each) were the input variables, the efficiency duty cycleand switching and concluded thatMFs the buck–boost converter proved to haveand better a good signal was the output variable. MPPT algorithms based on the ANN, FL, and ANFIS intelligent techniques to track the the brief discussion above,varying the present research proposesand developing.
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