Experimental evaluation of dynamic performance of three-phase AC-DC PWM rectifier with PD-type-2 fuzzy neural network controller

Abstract

Diode and thyristor-based rectifier circuits have been widely used in the industry. Due to non-linear structures of these circuits, they draw non-sinusoidal current from AC network as well as cause a low power factor in the AC side. The DC-link voltage of rectifier is affected by the changes in AC network or by the load variations on the DC side. Pulse-width modulated (PWM) rectifiers can eliminate the mentioned power quality problems if they control properly. This study proposes a controller with an adaptive and robust structure based on proportional + derivative type-2 fuzzy neural network (PD-T2FNN) for DC-link voltage control of PWM rectifier. Dynamic performance of PWM rectifier using the proposed controller is evaluated via dSPACE based experimental setup under different operation conditions: Set-point change, step load change in the DC side of the rectifier, set-point change under load and capacitive operation mode. The experimental results are given for traditional PD and proportional + integral and T2FNN controllers to validity performance of the proposed controller. Performances of controllers are evaluated regarding settling time, overshoot, steady-state error and total harmonic distortion. PWM rectifier with PD-T2FNN DClink voltage controller has superior performance for all operating conditions according to performance criteria when compared with other controllers. © The Institution of Engineering and Technology 2019.