Chang-Gyun An

In this paper, we propose a novel switching modulation scheme for three-phase four-leg inverters that enables the separation of the fault points occurring in AC distribution networks. The proposed method is aimed at continuously supplying normal voltage to the remaining healthy phases while applying zero voltage to any faulty phase when a single line-to-ground (SLG) fault occurs in the distribution network. This modulation scheme allows isolation without physically blocking the point of failure. This can prevent unnecessary continuous outages in the AC distribution network. To this end, we propose a novel coordinate transformation method to extract accurate values for the magnitude of unbalanced three-phase voltages. A novel space vector pulse-width modulation (SVPWM) method for switching schemes that are used under fault conditions is also presented. The proposed coordinate transformation considers a plane lying in a three-dimensional (3-D) space that describes the rotation of a three-phase voltage vector. This transformation converts a physical quantity that rotates in 3-D space into a value that moves in two-dimensional (2-D) space. Subsequently, the use of synchronous reference frame (SRF) transformation enables the detection and control of the voltage magnitude in case of an SLG fault. Additionally, switching vectors are selected to apply zero voltage to the faulty phase by analyzing the switching vector diagram of a four-leg inverter. Mathematical analyses conducted for the entire process are detailed herein. Further, the experimental results of four-leg inverters designed for the 10 kW class verify the proposed strategies.

This paper deals with the hybrid fuel cell system connected with energy storage system (ESS). The hybrid-structured two-phase interleaved buck-boost converter is composed of a controller that is robust to input voltage fluctuations. In addition, the output voltage can be stably maintained through high-speed PWM switching control. This paper describes the system configuration, design, operating principle and specifications in detail. The proposed system was verified through the configuration and experimental results of the ethanol power generation fuel cell system.

This paper proposes an energy management system(EMS) based artificial neural network(ANN) for hybrid AC/DC microgrid(MG), which is composed of distributed generation(DG), energy storage system(ESS), variable loads. An ANN is applied to derive the optimal operation mode of the MG and the power reference of the ESS by using the power data of unpredictable DGs and loads. The ANN controller used in EMS was trained by MATLAB deep learning tool box. The effectiveness of the proposed EMS based ANN was verified through the PSIM simulation.

This paper proposes unbalanced load compensation (LUC) method with artificial neural network (ANN) for grid-connected four-leg inverter. The proposed method consists of reference calculation and controllers for compensating the unbalanced current for four-leg inverter based on an ANN system. The ANN based calculation and controllers are trained by MATLAB toolbox using Levenberg-Marquardt. The PSIM simulation is provided to validate the effectiveness of the proposed method.