This paper presents a Matlab/Simulink model of grid connected photovoltaic (PV) system, including a PV array, a maximum power point tracking boost converter and a grid interactive, a control system. A space vector pulse width modulation (SVPWM) has been widely applied in the current control of three-phase voltage source inverters (VSI). The proposed system consists of two main controllers: a boost converter and a grid interactive voltage source inverter. The boost converter is used to regulate the PV voltage and track the MPP of PV array. A perturbation and observation (P&O)is used as MPPT method and it determines the system operation point according of rapidly changing atmospheric conditions. A cascaded control structure with an outer dc link voltage control loop and an inner current control loop is used. The currents are controlled in a synchronous orthogonal d, q frame using a decoupled feedback control. The reference current of proportional-integral (PI) d-axis controller is extracted from the dc-side voltage regulator by applying the energy balancing control. Furthermore, in order to achieve a unity power factor, the q-axis reference is set to zero. The SMC proposed in this paper, to control of current uses the integral sliding mode. Furthermore, classical controls PI compared with the SMC. The results show that the SMC technique is more suitable than that classical control (PI).

This study presents a design and an implementation of a robust Maximum Power Point Tracking (MPPT) for a stand-alone photovoltaic (PV) system with battery storage. A new control scheme is applied for the boost converter based on the combination of the adaptive perturb and observe fuzzy logic controller (P&O-FLC) MPPT technique and the backstepping sliding mode control (BS-SMC) approach. The MPPT controller design was used to accurately track the PV operating point to its maximum power point (MPP) under changing climatic conditions. The presented MPPT based on the P&O-FLC technique generates the reference PV voltage and then a cascade control loop type, based on the BS-SMC approach is used. The aims of this approach are applied to regulate the inductor current and then the PV voltage to its reference values. In order to reduce system costs and complexity, a high gain observer (HGO) was designed, based on the model of the PV system, to estimate online the real value of the boost converter’s inductor current. The performance and the robustness of the BS-SMC approach are evaluated using a comparative simulation with a conventional proportional integral (PI) controller implemented in the MATLAB/Simulink environment. The obtained results demonstrate that the proposed approach not only provides a near-perfect tracking performance (dynamic response, overshoot, steady-state error), but also offers greater robustness and stability than the conventional PI controller. Experimental results fitted with dSPACE software reveal that the PV module could reach the MPP and achieve the performance and robustness of the designed BS-SMC MPPT controller.

This paper studies innovative application of sliding mode control (SMC) for a Hybrid Renewable Energy System (HRES) in grid-connected and autonomous modes of operation. The considered HRES includes a photovoltaic (PV), wind turbine (WT) based on a Permanent Magnet Synchronous Generator (PMSG). The PV generator is coupled to the common DC bus via a DC/DC converter. The latter is controlled by an MPPT algorithm based on the Adaptive Perturbation and Observation Algorithm Method (APOAM) to search the optimum working of this source. A SMC is utilized to manage the PV voltage to achieve the Maximum Power Point (MPP) by altering the obligation duty cycle. The battery interfaced by a bidirectional buck-boost DC/DC converter can be charged or discharged depending on the production situation. On the one hand, the wind turbine conversion chain is equipped with a PMSG and a rectifier controlled to regulate the operating point of the wind turbine to its optimum value. During a Stand-Alone Mode (SAM) operation, the Voltage Source Converter (VSC) was used for controlling the output voltage in terms of amplitude and frequency delivered to the AC load. However, in Grid-Connected Mode (GCM) operation, the VSC was adapted to control the electrical parameters of the grid. To better appreciate the advantages of the proposed SMC approach, we have proposed a series of comparative tests with the conventional PI control in the operating modes GC and SA and under different scenarios. The proposed control strategy has undeniable advantages in terms of control performance and very low total harmonic distortion THD value compared with the conventional PI control. Finally, It is concluded that the proposed approach improves the quality and provides a stable operation of the HRES.

In this paper, we introduce a novel direct maximum power point tracking (MPPT) approach that combines the backstepping controller (BC) and the super-twisting algorithm (STA). The direct backstepping super-twisting algorithm control (BSSTAC) MPPT was developed to extract the maximum power point (MPP) produced by a photovoltaic (PV) generator connected to the battery through a boost dc-dc converter. To reduce the number of sensors required for the BSSTAC implementation, a high gain observer (HGO) was proposed to estimate the value of the state of the PV storage system from measurements of the PV generator voltage and current. The suggested technique is based on the quadratic Lyapunov function and does not employ a standard MPPT algorithm. Results show that the suggested control scheme has good tracking performance with reduced overshoot, chattering, and settling time as compared to the prevalent MPPT tracking algorithms such as perturb and observe (P&O), conventional sliding mode control (CSMC), backstepping controller (BSC), and integral backstepping controller (IBSC). Finally, real-time findings using the dSPACE DS 1104 software indicate that the generator PV can accurately forecast the MPP, as well as the efficacy of the suggested MPPT technique. The provided approach's effectiveness has been validated by a comprehensive comparison with different methods, resulting in the greatest efficiency of 99.88% for BSSTAC.