Abstract
The formulation of a maximum power point tracking (MPPT) control strategy plays a vital role in enhancing the inherent low conversion efficiency of a photovoltaic (PV) module. Keeping in view the nonlinear electrical characteristics of the PV module as well as the power electronic interface, in this paper, a hybrid nonlinear sensorless observer based robust backstepping super-twisting sliding mode control (BSTSMC) MPPT strategy is formulated to optimize the electric power extraction from a standalone PV array, connected to a resistive load through a non-inverting DC–DC buck-boost power converter. The reference peak power voltage is generated via the Gaussian process regression (GPR) based probabilistic machine learning approach that is adequately tracked by the proposed MPPT scheme. A generalized super-twisting algorithm (GSTA) based differential flatness approach (DFA) is used to retrieve all the missing system states. The Lyapunov stability theory is used for guaranteeing the stability of the proposed closed-loop MPPT technique. The Matlab/Simulink platform is used for simulation, testing and performance validation of the proposed MPPT strategy under different weather conditions. Its MPPT performance is further compared with the recently proposed benchmark backstepping based MPPT control strategy and the conventional MPPT strategies, namely, sliding mode control (SMC), proportional integral derivative (PID) control and the perturb-and-observe (P&O) algorithm. The proposed technique is found to have a superior tracking performance in terms of offering a fast dynamic response, finite-time convergence, minute chattering, higher tracking accuracy and having more robustness against plant parametric uncertainties, load disturbances and certain time-varying sinusoidal faults occurring in the system.
Highlights
Electrical energy production has been a challenging task throughout history
The reference peak power voltage is generated via the Gaussian process regression (GPR) based probabilistic machine learning approach that is adequately tracked by the proposed maximum power point tracking (MPPT) scheme
The reference peak power voltage is generated via the GPR based probabilistic machine learning approach that is adequately tracked by the proposed MPPT scheme
Summary
Electrical energy production has been a challenging task throughout history. With the industrialization of countries, the energy demand is growing proportionally. A significant steady-state error was observed in the output during MPP tracking This issue has been addressed in [12] through integral backstepping based nonlinear MPPT algorithm, where the output tracking error was reduced to a minute level due to the integral action. Another nonlinear robust backstepping based MPPT paradigm has been proposed in [14] This stated strategy dealt efficiently with the simultaneous variation of the temperature and irradiance, and it offered significant robustness against time-varying sinusoidal faults and parametric uncertainties occurring in the system. It has the capability of canceling out all the destabilizing effects (i.e., forces or terms) appearing throughout the domain [15,16] Another well-established nonlinear MPPT control strategy is the conventional sliding mode control (CSMC). It artificially increases the plant relative degree and generates a continuous control signal, thereby attenuating chattering
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