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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.Energies 2018, 11, 3516 2 of 26 and in the center of a big city [6], because sunlight is available almost everywhere. Photovoltaic electricity can be produced as close as possible to its place of consumption in a decentralized way [7], directly to the user, which makes it accessible to a large part of the world's population.Much research and development are conducted about the critical elements of photovoltaic energy; starting with energy generation [6], conversion, then injection into the network [8], as well as energy management [9]. The key problem with photovoltaic energy generation is the difficulty of achieving the highest energy yield for PV panels. The voltage of a PV panel strongly depends on the connected load due to the non-linear behavior of the PV cell [10]. Therefore, various Maximum Power Point Tracking (MPPT) algorithms have been established to allow panels operate in optimal conditions, and thus, to track the maximum power point [6,11]. Among these algorithms are "Perturbation & Observation (P&O)" [12] and "incremental conductance (InCon)" [13] which are the most used due to the simplicity of their implementation. However, the abovementioned methods are constrained by the amplitude of the injected perturbations, which determines the importance of the oscillations around the Maximum power point (MPP) as well as the convergence time. To overcome this problem, several algorithms have been developed based on techniques derived from artificial intelligence such as Fuzzy Logic [14], Neural Network [15], Neuro-Fuz...

Summary In this study, a nonlinear control of a 5‐level T‐type converter topology‐based multiterminal voltage source converter high‐voltage direct current system is proposed. The idea of the proposed control is to combine the backstepping control and direct power control with virtual flux concept into one controller able to improve the performance of the transmission system. In the other side, the use of a 5‐level space vector modulation with a balancing strategy based on effective use of the redundant switching states of the converters voltage vectors guarantees the objective of maintaining balanced voltages in DC‐capacitors. Finally, simulations of the 5‐level T‐type multiterminal voltage source converter high‐voltage direct current system validate the effectiveness of the proposed control law. The obtained results are compared with those performed by a conventional Proportional Integrator (PI) controller. These outcomes allow to exhibit excellent transient response during a range of operating conditions.

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This paper proposes a Virtual Flux Predictive Direct Power Control (PDPC) for a five-level T-type multi-terminal Voltage Source Converter High Voltage Direct Current (VSC-HVDC) transmission system. The proposed PDPC scheme is based on the computation of the average voltage vector using a virtual flux predictive control algorithm, which allows the cancellation of active and reactive power tracking errors at each sampling period. The active and reactive power can be estimated based on the virtual flux vector that makes AC line voltage sensors not necessary. A constant converter switching frequency is achieved by employing a multilevel space vector modulation, which ensures the balance of the DC capacitor voltages of the five-level t-type converters as well. Simulation results validate the efficiency of the proposed control law, and they are compared with those given by a traditional direct power control. These results exhibit excellent transient responses during range of operating conditions.