In this study, an artificial neural-network (ANN)-based space-vector pulse-width modulation (SVPWM) for capacitor voltage balancing of a three-phase three-level neutral-point clamped converter with improved power quality is presented. The neural-network-based controller offers the advantage of very fast implementation of the SVPWM algorithm. This makes it possible to use an application specific integrated circuit chip in place of a digital signal processor. The proposed scheme employs single layer feed-forward neural-networks at different stages along with a control algorithm using modified reference vector for capacitor voltage balancing of an improved power quality three-phase neutral-point clamped converter. In other words, the neural-network receives three-phase voltages and currents as input and generates symmetrical pulse-width modulation waves for three phases of the converter. A simulated digital signal processor (DSP)-based modulator generates the data which are used to train the network by a back-propagation algorithm in the MATLAB Neural Network Toolbox. The simulation of converter with ANN-based space-vector modulator shows excellent performance when compared with that of conventional DSP-based modulator.
In this study, parameter plane synthesis of a three-phase neutral-point clamped bidirectional rectifier has been performed. The converter involves one outer-loop PI voltage controller and two inner-loop proportional-integral (PI) current controllers for closed-loop control. A D-partition technique has been employed for precise design of the current controllers. The performance of the converter has been evaluated using MATLAB/Simulink software. An experimental prototype of the converter has been developed and the experimental investigation of the converter performance in closed loop has been carried out. DSP 'DS1104' of digital signal processing and control engineering (dSPACE) has been used for real-time implementation of the designed controllers. The converter gives a very good performance in steady-state and dynamic state (for rectification as well as inversion modes of operation) using the designed controller parameters.
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