Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems

An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysi...

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Bibliographic Details
Published in:Energies (Basel) 2017-03, Vol.10 (3), p.394
Main Authors: Hassan, Syed, Li, Hui, Kamal, Tariq, Arifoğlu, Uğur, Mumtaz, Sidra, Khan, Laiq
Format: Article
Language:English
Subjects:
Adaptive algorithms
adaptive control
Controllers
Fuzzy logic
Localization
maximum power point tracking
Neural networks
Photovoltaic cells
photovoltaic systems
Photovoltaics
wavelets
Weather
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Summary:An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the existing literature of Maximum Power Point Tracking (MPPT) techniques, a high performance neuro-fuzzy indirect wavelet-based adaptive MPPT control is developed in this work. The proposed controller combines the reasoning capability of fuzzy logic, the learning capability of neural networks and the localization properties of wavelets. In the proposed system, the Hermite Wavelet-embedded Neural Fuzzy (HWNF)-based gradient estimator is adopted to estimate the gradient term and makes the controller indirect. The performance of the proposed controller is compared with different conventional and intelligent MPPT control techniques. MATLAB results show the superiority over other existing techniques in terms of fast response, power quality and efficiency.
ISSN:1996-1073
1996-1073
DOI:10.3390/en10030394