4.6 Article

System Identification Based ARV-MPPT Technique for PV Systems Under Variable Atmospheric Conditions

Journal

IEEE ACCESS
Volume 10, Issue -, Pages 51325-51342

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3174107

Keywords

Integrated circuits; Signal processing algorithms; Particle swarm optimization; Optimization; Classification algorithms; Mathematical models; Atmospheric modeling; Energy conversion; energy management; MPPT algorithms; photovoltaic energy; renewable energy; system identification

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In this study, a new MPPT method called SI-based polynomial ARV was proposed for a PV system under uniform irradiance. By using a polynomial model, this method can effectively obtain the reference voltage and has shown high performance in simulation studies.
Different tracking algorithms have been developed to obtain maximum efficiency from the PV power systems under changing atmospheric conditions. Some of these algorithms are effective under uniform irradiance, while the others are effective under partial shading conditions. In this study, a new MPPT method designed with System Identification-SI and named SI-based polynomial ARV is proposed for a PV system under uniform irradiance. The recommended method is also an adaptive reference voltage-ARV-based method. In this method, the model in which the reference voltage is obtained has a simple polynomial structure. This polynomial model has been obtained by assuming that the PV system is a nonlinear black-box type system. The input-output data, which are required for modeling, were created in MATLAB/Simulink environment. Then, by using these data with SI-Toolbox, the mathematical model of the PV system in polynomial structure giving the input-output relationship was obtained. T temperature information was used as model input, which was the generated reference voltage. Simulation studies were carried out under two different changing atmospheric scenarios; and the performance of the proposed method was analyzed. The obtained results were compared with perturb & observe-PO, incremental conductance-IC, constant voltage reference-CVR, artificial neural network-ANN-based ARV, and SI-based nonlinear ARV methods. All simulation results showed that the recommended method is simple structured, applicable, and has high performance.

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