Adaptive Backstepping Controller Design Based MPPT of the Single-Phase GridConnected PV System

Abstract

The environmental condition changes lead to obvious fluctuation in photovoltaic panels’ output power. Therefore, to make efficient use of photovoltaic (PV) systems the maximum power point tracking (MPPT) controllers are required. Many classical methods are proposed to track the MPP, but they will lead to a high power drop when rapid changes in the atmospheric conditions occur, which necessities a robust controller with high performance. In such a manner, the proposed controller is designed for this purpose. There are two stages of the proposed controller: The artificial neural network (ANN) based the first stage that generates the PV panel optimal voltage and the second phase consists of a non-linear adaptive backstepping control, which is able to follow this optimum voltage by acting on the DC/DC boost converter’s duty cycle. The input-output linearization technique is based the suggested controller. The last is robust and safe from the parameters fluctuation, load variation, and the atmospheric condition changes. In the proposed control design, unknown and estimated converter parameters are assumed, especially the inductor and input capacitor, considering an adequate Lyapunov function. A single-phase inverter connects the boost converter to the grid. However, for this connection to be made the power factor should be unified and the inverter current should be synchronized with the grid voltage. The sliding mode controller (SMC) is designed to operate on the inverter duty cycle for the resolution of these tasks. The controller is used to enhance the robustness and the system's rapidity. Besides, the DC bus is regulated using the proportional and integral (PI) controller. Matlab/Simulink software is used to simulate the overall system. Furthermore, for accurate results, the proposed controller is compared with perturb & observe (P&O), ANN-backstepping sliding mode (ANN-BSMC), and ANN-backstepping integral sliding mode (ANN-BISMC) techniques. The findings show that the proposed method outweighs other methods in terms of tracking rapidity, steady-state error and the fluctuations around the MPP under severe assumptions.