Chung-Yuen Won
성균관대학교 공과대학 기술경영학과 · 공학
Chung-Yuen Won 교수의 연구실은 에너지 전환과 지속 가능한 에너지 시스템 구현을 위한 핵심 기술인 태양광 발전, 에너지 저장 시스템(ESS), 그리고 스마트 에너지 관리 기술에 중점을 두고 있습니다. 특히 최적 출력 추적(MPPT), 배터리 상태 제어, 인공신경망 기반 에너지 관리 시스템, 고효율 전력변환 회로 설계 등 전력전자 및 제어 이론을 융합한 응용 연구를 진행하고 있습니다. 연구는 실증적 실험과 시뮬레이션을 기반으로 하여 실생활 적용 가능성을 확보하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Studies on photovoltaic systems are increasing because of a large, secure, essentially exhaustible and broadly available resource as a future energy supply. However, the output power induced in the photovoltaic modules is influenced by an intensity of solar cell radiation, temperature of the solar cells and so on. Therefore, to maximize the efficiency of the renewable energy system, it is necessary to track the maximum power point of the input source. In this paper, a new maximum power point tra
Aging increases the internal resistance of a battery and reduces its capacity; therefore, energy storage systems (ESSs) require a battery management system (BMS) algorithm that can manage the state of the battery. This paper proposes a battery efficiency calculation formula to manage the battery state. The proposed battery efficiency calculation formula uses the charging time, charging current, and battery capacity. An algorithm that can accurately determine the battery state is proposed by appl
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 tr
In this paper, an interleaved soft switching boost converter for a Photovoltaic Power Conditioning System (PV-PCS) with high efficiency is proposed. In order to raise the efficiency of the proposed converter, a 2-phase interleaved boost converter integrated with soft switching cells is used. All of the switching devices in the proposed converter achieve zero current switching (ZCS) or zero voltage switching (ZVS). Thus, the proposed circuit has a high efficiency characteristic due to low switchi
A chattering alleviation control algorithm was applied to a vector-controlled induction motor servo system to practically eliminate the chattering problem. The strategy consists of hybridizing the conventional sliding mode control with linear state feedback. The servo system with the proposed control strategy was analyzed. studied by simulation, and verified experimentally in the laboratory. The performance of the drive is shown to be practically free from chattering problems. The system simulat
This paper presents a stator winding temperature detection method for permanent magnet synchronous motors (PMSMs) using a motor parameter estimation method. PMSM performance is highly dependent on the motor parameters. However, the motor parameters vary with temperature. It is difficult to measure motor parameters using a voltage equation without additional sensors. Herein, a stator winding temperature estimation method based on a d-axis current injection method is proposed. The proposed estimat
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