Optimal control of novel fuel cell-based DVR using ANFISC-MOSSA to increase FRT capability of DFIG-wind turbine

Abstract

Although Doubly fed induction generator (DFIG)-based wind turbine has several significant advantages, any voltage fluctuation can severely disturb its normal and safe operation, i.e., it is characteristically very sensitive. Hence, fault ride-through is determined as a prominent benchmark for DFIG which must be enhanced by a voltage compensator. Dynamic voltage restorer (DVR), a voltage fluctuation mitigator, is connected in-series with distribution and sub-transmission lines so that DFIG can appropriately operate during any type of disturbance. In this paper, a new two switches boost converter (TSBC) coupled with quinary multilevel inverter (QMLI) is proposed which is fed by fuel cell to produce staircase sinusoidal voltage for DVR. The proposed step-up DC/DC presents two important advantages: low current ripple and low switch stress, while QMLI can create high-step staircase sinusoidal voltage without considering a number of switches. TSBC-QMLI-based DVR has been controlled by neuro fuzzy inference system controller (ANFISC). As the control problem is multi-objective and complex, Multi-Objective Salp Swarm Algorithm (MOSSA) has been applied to optimally tune the ANFISC’s parameters, and then compared with Non-dominated Sorting Genetic Algorithm II (NSGA-II). To verify and validate the compensation capability of proposed DVR, it has been thoroughly evaluated under different voltage disturbances. Eventually, the simulation results have validated the compensation capability of the suggested TSBC-QMLI-based DVR which is accurately controlled by MOSSA-based ANFISC to mitigate the balanced and unbalanced voltage sag and swell along with the flicker and harmonic voltage.