| Abstrakt: |
In this paper, the controller modelling and design for the Active Switched Capacitor/Switched Inductor Quasi-Z-Source with Multilevel Inverter (ASC/SL-QZSI) related three-phase grid-tied photovoltaic (PV) power system is proposed. The ASC/SL-QZSI control method consists of two phases: these are assessed with the use of the proposed effective controller. The proposed control system is the hybridization of the Student Psychology-Based Optimization (SPBO) and the Radial Basis Function Neural Network (RBFNN), hence it is named SPBO–RBFNN control scheme. The ASC/SL-QZS offers greater boost capability, uses fewer passive components, like inductors and capacitors, and reduces the voltage stress across main inverter switching devices. The expandability of this topology is another advantage. Extra cells can simply be cascaded at the network's impedance if a higher boost rate is required by adding an inductor and three diodes. In the proposed control scheme, SPBO is developed for determining the total PV voltages. The input PV reference voltages and gain parameters of the SPBO are created as output for optimal tuning of the Proportional Integral (PI) controller. RBFNN is trained with offline process and it is used to extract the reference currents of the grid, and the output of RBFNN is provided with SPBO. It delivers the corresponding tuning parameters to accomplish the grid current. With this proper control, the input power is reduced and the current, voltage and frequency conditions of DC-link are regulated. Finally, the performance of the QZS-CMI is executed in MATLAB and the performance is compared with existing methods. [ABSTRACT FROM AUTHOR] |
