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Abstract
This paper proposes a high performance control scheme for a double function grid-tied double-stage PV system. It is based on model predictive power control with space vector modulation. This strategy uses a discrete model of the system based on the time domain to generate the average voltage vector at each sampling period, with the aim of canceling the errors between the estimated active and reactive power values and their references. Also, it imposes a sinusoidal waveform of the current at the grid side, which allows active power filtering without a harmonic currents identification phase. The latter attempts to reduce the size and cost of the system as well as providing better performance. In addition, it can be implemented in a low-cost control platform due to its simplicity. A double-stage PV system is selected due to its flexibility in control, unlike single-stage strategies. Sliding mode control-based particle swarm optimization (PSO) is used to track the maximum power of the PV system. It offers high accuracy and good robustness. Concerning DC bus voltage of the inverter, the anti-windup PI controller is tuned offline using the particle swarm optimization algorithm to deliver optimal performance in DC bus voltage regulation. The overall system has been designed and validated in an experimental prototype; the obtained results in different phases demonstrate the higher performance and the better efficiency of the proposed system in terms of power quality enhancement and PV power injection.
Highlights
Nowadays, the use of renewable energies is more than necessary; it is a global strategic issue.It is a major tool against global warming [1]
The main contribution of this paper is to propose a high-performance control scheme for double function grid-tied PV system based on model predictive power control (MPPC) with space vector modulation (SVM)
An MPC with SVM for double function grid-tied PV system has been designed validated in an experimental prototype
Summary
The use of renewable energies is more than necessary; it is a global strategic issue. Indirect control strategies have aroused the attention of many researchers; such strategies include Direct Power Control (DPC), that focuses on a predefined switching table to select the appropriate control vector [24,35,36], a predictive direct power control (P-DPC) based on cost function minimization, and predictive current control, that uses the currents as control variables instead of powers as in P-DPC [29,37] These three strategies require current sensors only at the main source; they do not take into consideration the impact of load or filter current compared to conventional ones.
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