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

Abstract

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.

System identification of photovoltaic system based on fractional-order model

“…Table 3 displays a comparison of the FOM frequency response with the IOM frequency response. In [21], an IOM for the PV system is built using the system identification approach and shows an accuracy of 93.98%, so the constructed FOM in this paper shows better accuracy.…”

“…This technique used experimental input-output data and a nonlinear Hammerstein-Wiener model to extract the dynamic model of integer order but did not show the transfer function. In [21], a new MPPT is proposed based on system identification. An integer-order model (IOM) is driven, whereas the temperature is the model input and the reference voltage at MPP is the output of the IOM.…”

A new adaptive MPPT technique using an improved INC algorithm supported by fuzzy self-tuning controller for a grid-linked photovoltaic system

“…Due to the nonlinearity of the grid-connected PV systems and the uncertainty of the PV-generated power due to climatic fluctuations, setting the PID controller parameters for MPPT is a challenging issue. This challenge necessitated some adjustments and adaptations to the PID controller-based MPPT approaches [35,36]. Some other modifications were presented to improve the INC technique.…”

Optimization of fractional order PI controller to regulate grid voltage connected photovoltaic system based on slap swarm algorithm

“…To that end, the first two benefits stem from the fact that meta-heuristics (salp optimization technique) examine and solve optimization issues by focusing just on the inputs and outputs [46], [47]. In other words, meta-heuristics see optimization as a black box.…”