Implementation and Evaluation of Maximum Power Point Tracking (MPPT) Based on Adaptive Neuro-Fuzzy Inference System for Photovoltaic PV System
Implementation and Evaluation of Maximum Power Point Tracking (MPPT) Based on Adaptive Neuro-Fuzzy Inference System for Photovoltaic PV System
The conversion of energy of sunlight into electricity is done using photovoltaic (PV) cells. This paper introduces, comparing, analyzing and evaluating the performance of the PV systems which are operating with MPPTs that work by adaptive neuro fuzzy interference system (ANFIS) technique with other MPPT algorithms such as Perturb and Observe (P&O) algorithm, Fuzzy Logic Control (FLC) algorithm and Artificial Neural Network (ANN) algorithm. These algorithms work to control the duty cycle (D) of the pulse signal that goes to the switch of the DC-DC converter for maximizing the power generated by the solar panel. The paper also introduces simulating and modeling a general PV panel with some adjustable parameters for modelling any real PV model using its electrical data sheet. In addition, the work tests the model for the influence of changing in operation solar irradiation and operation temperature on I-V and P-V curves. MPPT algorithms are implemented using boost DC-DC converter with constant resistive load. All systems are analyzed and simulated by using MATLAB-Simulink program. Simulation results show that the ANFIS and ANN based MPPT method gives faster response to archive the MPP and is more efficient than FLC MPPT and the P&O MPPT methods.
Keywords:
Photovoltaic cell, maximum power point tracking, fuzzy logic control artificial neural network, adaptive neuro-fuzzy inference system,
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- ISSN: 2146-0604
- Başlangıç: 2011
- Yayıncı: İstanbul Aydın Üniversitesi