Tae-Gyu Kim

This study proposes an unbalanced current compensation method based on a four-leg inverter using an artificial neural network (ANN) under unbalanced load conditions. Distribution systems exhibit rapid load variations, and conventional filter-based control methods suffer from the drawback of requiring an extended time period to reach a steady state. To address this problem, an ANN is applied to calculate the unbalanced current reference and enhance dynamic performance. Additionally, because of the periodic incorrect output inherent in the ANN, applying it to a proportional–integral controller would result in an error being directly reflected in the current reference. In the aforementioned problem, an ANN is applied to the dq0 coordinate system current controller to compensate for the periodic incorrect output in the current reference calculation. The proposed ANN-based unbalanced current compensation method is validated through PSIM simulations and experiments.

Recently, the establishment of technical standards for grid connection has gained interest in academia and industry. These standards have focused on the reactive power control function of the grid-connected inverter and maintenance of grid operation, and include detailed information about the grid support function. However, remote control communication and control devices for grid support functions, and other distributed sources, such as wind power and energy storage systems, other than inverters have not been addressed. In this paper, the control of the interlinking converter (ILC) in a hybrid microgrid considering low voltage ride-through (LVRT) among grid support functions is investigated. The proposed method consists of an energy management system considering LVRT and a cooperative control scheme. In the energy management system, an algorithm capable of mode selection was constructed by applying the LVRT curve. Then, considering the LVRT situation, the allowable reactive power range of the ILC was mathematically analyzed through the cooperative control of the energy storage device and the ILC. The proposed method enables us to perform active and reactive power control of the ILC in a hybrid distribution network, considering the power factor under various conditions. This functionality, such as supplying reactive power, significantly contributes to the enhanced grid resilience with distributed power sources, including renewable energy. The proposed strategies were verified through experiments after configuring an experimental set of distributed power sources.

In grid-connected operations, a microgrid can solve the problem of surplus power through regeneration; however, in the case of standalone operations, the only method to solve the surplus power problem is charging the energy storage system (ESS). However, because there is a limit to the capacity that can be charged in an ESS, a separate energy management strategy (EMS) is required for stable microgrid operation. This paper proposes an EMS for a hybrid AC/DC microgrid based on an artificial neural network (ANN). The ANN is composed of a two-step process that operates the microgrid by outputting the operation mode and charging and discharging the ESS. The microgrid consists of an interlinking converter to link with the AC distributed system, a photovoltaic converter, a wind turbine converter, and an ESS. The control method of each converter was determined according to the mode selection of the ANN. The proposed ANN-based EMS was verified using a laboratory-scale hybrid AC/DC microgrid. The experimental results reveal that the microgrid operation performed stably through control of individual converters via mode selection and reference to ESS power, which is the result of ANN integration.

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.

This paper proposes an artificial neural network (ANN)-based energy management system (EMS) for controlling power in AC–DC hybrid distribution networks. The proposed ANN-based EMS selects an optimal operating mode by collecting data such as the power provided by distributed generation (DG), the load demand, and state of charge (SOC). For training the ANN, profile data on the charging and discharging amount of ESS for various distribution network power situations were prepared, and the ANN was trained with an error rate within 10%. The proposed EMS controls each power converter in the optimal operation mode through the already trained ANN in the grid-connected mode. For the experimental verification of the proposed EMS, a small-scale hybrid AD/DC microgrid was fabricated, and simulations and experiments were performed for each operation mode.

Joo B, et al. Yonsei Med J. 2021 Nov;62(11):1052-1061. https://doi.org/10.3349/ymj.2021.62.11.1052

Parameter identification of an induction motor has long been studied either for vector control or fault diagnosis. This paper addresses parameter identification of an induction motor under on-load operation. For estimating electrical and mechanical parameters in the motor model from the on-load data, unmeasured initial states and load torque profile have to be also estimated for state evaluation. Since gradient of cost function for the auxiliary variables are hard to be derived, direct optimization methods that rely on computational capability should be employed. In this paper, the univariate dynamic encoding algorithm for searches (uDEAS), recently developed by the authors, is applied to the identification of whole unknown variables with measured voltage, current, and velocity data. Profiles of motor parameters estimated with uDEAS are reasonable, and estimation time is 2 s on average, which is quite fast as compared with other direct optimization methods.