https://doi.org/10.5370/KIEE.2025.74.6.1003

김민철(Minchol Kim) ; 조형준(Hyeong-Jun Jo) ; 정태영(Tae-Young Jyung) ; 정기석(Ki-Seok Jeong) ; 김수배(Soobae Kim)

This paper explores the dynamic modeling and simulation of a Fuel Cell and Electrolyzer Cell system within the PSCAD/EMTDC environment, emphasizing its potential for frequency stabilization in modern power grids. This study integrates the Fuel Cell and Electrolyzer Cell system with DC/DC converters, and inverters to assess its performance in mitigating frequency deviations during rapid load variation events. For this assessment, a PSCAD/EMTDC-based model is applied to the WECC 9-bus system, considering two distinct characteristics: (1) Time Delay, where the activation of the Fuel Cell and Electrolyzer Cell is delayed following a load change, and (2) Ramp Rate, where the rate of power output adjustment is controlled during load fluctuations. Simulation results demonstrate that the Fuel Cell and Electrolyzer Cell system, modeled in PSCAD/EMTDC, can effectively stabilize grid frequency, providing a faster and more reliable response compared to traditional synchronous generator-based governor systems.

https://doi.org/10.5370/KIEE.2025.74.6.1012

김동현(Dong-Hyun Tony Kim) ; 김진호(Jin-Ho Kim) ; 노재형(Jae Hyung Roh) ; 박종배(Jong-Bae Park)

This study investigates the impact of Reserve Cascading on electricity market efficiency, with a particular focus on energy prices, reserve prices, and total generation costs across scenarios defined by different reserve requirements. Reserve Cascading allows surplus higher-tier reserves to be reallocated to meet lower-tier demands, optimizing generator dispatch, reducing reliance on additional resources, and enhancing overall market stability. The analysis reveals that energy and reserve prices exhibit notable fluctuations depending on the reserve requirements of each scenario. Despite these price variations, Cascading consistently demonstrates its ability to lower total generation costs across all scenarios by minimizing generator dispatch needs and improving operational efficiency. The study highlights the value of integrating Cascading into electricity market design as a strategic tool to enhance economic efficiency and market reliability.

https://doi.org/10.5370/KIEE.2025.74.6.1022

윤민호(Min-Ho Yoon) ; 최승수(Seung-Su Choi) ; 임성훈(Sung-Hun Lim)

This paper focuses on symmetrical component analysis applied to fault detection in power systems with a three-phase reactor-type superconducting fault current limiter (SFCL). Symmetrical component analysis is a fundamental method for analyzing asymmetric faults in power systems, especially in three-phase systems. The study analyzes the behavior of SFCL in limiting fault currents and its effect on the symmetrical components of voltage, current, and impedance during faults. Although SFCL is effective in limiting fault currents, it interferes with the operation of conventional overcurrent relays in fault detection due to the reduced fault current levels. This paper discusses these challenges and highlights the need for improving existing symmetrical component analysis methods to better accommodate the unique characteristics of superconducting devices. In the context of future power system protection, the study suggests that more accurate fault analysis and protection methods, which consider the behavior of superconducting devices, are essential for enhancing system stability and reliability. Furthermore, this study conducts symmetrical component analysis of impedance and superconducting devices, contributing to a deeper understanding of fault characteristics and improving the accuracy of fault detection and system protection.ㅍ

https://doi.org/10.5370/KIEE.2025.74.6.1030

왕천(Chen Wang) ; 장호천(Haotian Zhang) ; 이민주(Minju Lee) ; 이명훈(Myoung Hoon Lee) ; 문준(Jun Moon)

In recent years, the integration of renewable energy sources into power systems has increased their complexity, making automated control and management more challenging. To address this issue, we propose OC-LSTM, a deep reinforcement learning (DRL) algorithm which integrates option-critic DRL with the long short-term memory (LSTM) neural network to efficiently manage power systems. The OC-LSTM algorithm extracts temporal features from the power system using the LSTM network and leverages the option-critic (OC) framework in DRL to learn policies for adjusting the system's topology, ensuring secure and efficient power transmission. Experimental results demonstrate that the OC-LSTM algorithm outperforms standard DRL algorithms during training, and ablation studies further confirm the effectiveness of LSTM in extracting power system features. Additionally, the OC-LSTM algorithm allows stable operation of the IEEE 5-Bus, IEEE 14-Bus and L2RPN WCCI 2020 power systems for 60 consecutive hours without the need for human intervention.

https://doi.org/10.5370/KIEE.2025.74.6.1041

장호천(Haotian Zhang) ; 왕천(Chen Wang) ; 이민주(Minju Lee) ; 이명훈(Myoung Hoon Lee) ; 문준(Jun Moon)

With the increasing complexity of modern power systems due to the access of large-scale renewable energy sources, minimizing operational costs while achieving stable grid operation has become a core challenge in power scheduling and optimization. Energy dispatch, topology control and emergency load shedding are key measures to improve power system stability and flexibility. However, the outputs of their traditional control policies rely on predefined rules or mathematical optimization models, which are prone to computational bottlenecks and response lags in high-dimensional dynamic environments, making it difficult to meet the demands of smart grids. In recent years, deep reinforcement learning (DRL) has gradually become a cutting-edge technology for power system scheduling and control by virtue of its powerful adaptive learning and decision optimization capabilities. According to the existing research, DRL can improve the flexibility and anti-interference ability of the power grid by learning the optimal policies through autonomous interaction, surpassing the real-time decision-making ability of traditional optimization methods in high-dimensional state space. In this paper, we systematically review the applications of DRL in energy dispatch, topology control and emergency load shedding, focus on its optimization policies, technological breakthroughs and applicability, and analyze the current challenges and future research directions.

https://doi.org/10.5370/KIEE.2025.74.6.1058

이지민(Jimin Lee) ; 이규섭(Gyu-Sub Lee)

Large-scale offshore wind power systems are regarded as a key solution for achieving carbon neutrality due to their advantages in public acceptance and efficiency. While wind power technology continues to advance rapidly, there remains a lack of research focused on analyzing grid codes such as voltage ride through, and reactive power capability and power system planning to meet those standards in terms of offshore wind farm integration. This paper proposes an optimization-based method for determining the optimal capacity of reactive power compensation equipment to satisfy the reactive power capability requirements specified in grid codes for offshore wind power integration systems. A test system representing an offshore wind farm is implemented to validate the proposed approach and derive the optimal capacities of the compensation equipment. ㅍ

https://doi.org/10.5370/KIEE.2025.74.6.1065

김예중(Yejung Kim) ; 김병민(Byeongmin Min) ; 김명진(Myungchin Kim) ; 곽주식(Joosik Kwak)

Droop control is a representative control approach that enables decentralized autonomous power sharing for IBR driven power systems. Despite its advantages that contribute to increased reliability and modulairty, however, this approach often results in unavoidable frequency and voltage deviations. In addition, the power sharing performance could be affected by line impedance. To address these challenges, this study considerers application of a nonlinear droop control method aimed at reducing frequency and voltage deviations and showing satisfactory power sharing performance. Verification results demonstrate that the considered droop control approach realizes limited deviation of voltage and frequency compared to the conventional droop control. With the considered nonlinear droop approach, autonomous operation of IBR based power networks with improved power quality and satisfactory power sharing could be achieved.

https://doi.org/10.5370/KIEE.2025.74.6.1074

보우믹 비두트(Biddut Bhowmik) ; 곽주식(Joosik Kwak) ; 김성열(Sung-Yul Kim)

This paper explores integrating grid-forming inverters (GFMs) with a solar photovoltaic (PV) and Proton Exchange Membrane (PEM) electrolyzer system to assess the impact on hydrogen production and system efficiency. The solar PV system, modeled in PSCAD, generated a power profile used as input for a PEM electrolyzer model in Simulink. Two scenarios were analyzed: without and with grid-forming control. Results show that grid-forming control enhances power delivery stability, leading to higher hydrogen production and improved efficiency. This study contributes to Power-to-Gas (P2G) efforts, where green hydrogen is produced from renewable energy. Without GFM, the PEM electrolyzer experienced lower hydrogen output and efficiency losses due to power fluctuations, while the GFM scenario yielded a more consistent power input, optimizing hydrogen yield and system efficiency. The findings underscore the role of GFMs in enhancing the performance of renewable energy-driven electrolyzers.

https://doi.org/10.5370/KIEE.2025.74.6.1081

김용헌(Yong-Heon Kim) ; 최지우(Ji-Woo Choi) ; 석민수(Min-Su Seok) ; 곽상신(Sang-Shin Kwak)

This paper proposes a real-time ac arc detection method that utilizes frequency-domain feature analysis and Random Forest algorithms to extract key frequency components sensitive to arc faults. These selected components are then used as inputs to a lightweight deep learning model. The proposed approach significantly reduces input dimensionality and computational complexity, while achieving approximately a 50% reduction in detection time without compromising detection accuracy. The deep learning model is designed based on 1D Convolutional layers, Inverted Residual (IR) Blocks, and Squeeze-and-Excitation (SE) structures, and employs non-linear activation functions such as LeakyReLU and h-swish to enhance representation capability. Experimental validation was conducted under various load conditions and circuit configurations in compliance with IEC 62606 standards, and the proposed model maintained high detection accuracy even in complex electrical environments. This study demonstrates the feasibility of implementing a real-time arc detection system on an embedded platform using Raspberry Pi 5.

https://doi.org/10.5370/KIEE.2025.74.6.1087

배진용(Jin-Yong Bae)

This study proposes a 3-phase 4-wire zigzag filter that is applicable to a 100 kW electric vehicle (EV) fast charger. The 30 kW class four power converter linked in parallel results in a current imbalance at the input terminal, leading to a sharp, unbalanced current at the neutral point of a 100 kW high-capacity rapid charging apparatus. Moreover, owing to increased harmonic noise, decreased power factor, increased reactive power, decreased active power, decreased efficiency, and electromagnetic wave generation, the unbalanced current at the neutral point is a primary cause of EV charger failures. This study proposes an ideal 3-phase 4-wire zigzag filter for a 100 kW EV fast charger and experimentally verifies that it lowers the neutral point unbalanced current, eliminating harmonics and reaching a peak efficiency of 95.632%.

https://doi.org/10.5370/KIEE.2025.74.6.1097

차명준(Myeong-Jun Cha) ; 박해찬(Hae-Chan Park) ; 장진수(Jin-Su Jang) ; 김래영(Rae-Young Kim)

This paper explores the advancement of electric vehicle (EV) technology as a solution to global warming, with a specific focus on the role of Vehicle-to-Load (V2L) technology. V2L allows EV batteries to supply energy to homes, buildings, or power grids. The implementation of this system relies on efficient DC-DC converters and DC-AC inverters. This study presents an improved predictive model for the magnetizing inductance of high-frequency transformers, a critical component in LLC resonant converters widely used in V2L applications. The proposed design method leverages a Multi-objective Optimization Algorithm (MOA) to improve prediction accuracy and support high-density, high-efficiency designs through detailed magnetic flux path analysis and optimization techniques. The validity of the proposed method was confirmed through experiments and finite element analysis on a 500W high-frequency transformer for an LLC resonant converter.

https://doi.org/10.5370/KIEE.2025.74.6.1106

강수권(Su-Gwon Kang) ; 박수성(Su-Seong Park) ; 박해찬(Hae-Chan Park) ; 김래영(Rae-Young Kim)

An LLC resonant converter must be designed for high efficiency and high power density, with a focus on reducing the volume and loss of the transformer, which plays a crucial role in insulation and voltage conversion between input and output. When using a planar core to achieve high power density, the size of parasitic components and transformer losses vary significantly depending on the winding configuration within the limited window area, necessitating careful consideration of various factors. This paper proposes an interleaved serpentine winding method that maximizes the use of the planar core’s window area, minimizes winding losses under high current input, and offers the advantages of low parasitic capacitance and improved assembly. This winding method utilizes litz wire to reduce DC losses and does not require additional space for winding placement compared to the conventional U-type winding method. To verify the effectiveness of the proposed winding method in reducing parasitic capacitance, we compare the capacitive energy distribution with the U-type winding method and derive formulas to calculate the effective capacitance of each winding configuration. Finally, we validate the proposed transformer’s effectiveness through Finite Element Analysis (FEA) simulations and a 7.5kW LLC resonant converter experiment.

https://doi.org/10.5370/KIEE.2025.74.6.1115

이지현(Ji-hyeon Lee) ; 조현종(Hyun-chong Cho)

In semiconductor manufacturing, defects on wafer maps often arise due to process-related issues. Recognizing and analyzing these defect patterns is crucial for the early detection of manufacturing faults, enabling proactive defect prediction and ultimately enhancing semiconductor yield rates. In real-world manufacturing, wafers commonly exhibit not only singular defects but also multiple concurrent defects, adding complexity to the analysis. Therefore, this study proposes a deep learning-based model for wafer map multi-defect pattern classification. We employ the ViT-B model for classification and utilize TrivialAugment to increase the diversity of wafer map images used in training. Additionally, two optimizers were applied to facilitate the smooth convergence of the model's loss function, allowing for a comparative performance evaluation. On the MixedWM38 dataset, which comprises 38 classes, our model enhanced with TrivialAugment and Adagrad achieved F1-scores of 0.984 for single defects, 0.987 for double defects, 0.982 for triple defects, and 0.986 for quadruple defects.

https://doi.org/10.5370/KIEE.2025.74.6.1122

유래현(Rae-Hyun Yu) ; 김경호(Kyung-Ho Kim)

The use of VR devices is gradually increasing due to the emergence of various applications such as therapy, research, and entertainment in virtual environments. However, one of the most common side effects of increasing usage is mental health, such as dizziness due to eye fatigue caused by high immersion. Therefore, this paper analyzes the health status through HRV(Heart Rate Variability) analysis by obtaining bio-signals using a non-contact method before and after using VR(Virtual Reality) devices. To acquire the signals, we used the TS-CAN(Two-Stream Convolutional Attention Network) method, which is a supervised learning among deep learning models, and used the UBFC-rPPG dataset and our own dataset, which is a face image taken from a webcam with a resolution of 640x480 at 30 fps. The rPPG (Remote Photoplethysmography) data obtained before and after using the VR (Virtual Reality) device were analyzed using HRV (Heart Rate Variability) analysis. The results showed that VR device usage led to a significant increase in BPM (Heart Rate) and LF Power, as well as a decrease in RMSSD, NN50, and SDNN, indicating a shift in autonomic nervous system balance towards sympathetic dominance, consistent with stress response. These findings suggest that VR usage has a notable impact on physiological stress markers, with implications for health monitoring in VR environments. This study emphasizes the utility of rPPG in assessing physiological responses to stress induced by VR usage. The key findings demonstrate that HRV metrics, such as BPM and LF Power, significantly increased, while RMSSD and SDNN decreased, providing objective insights into the stress effects of VR environments. These results underline the potential of non-contact biosignal analysis for health monitoring in Virtual Reality applications.

https://doi.org/10.5370/KIEE.2025.74.6.1130

정성인(Sung-In Jeong)

In order to use electric energy economically and safely, technologies are being developed to identify the power usage condition in terms of electric motor and electric power application facilities. On that basis various technologies that enable energy saving and safe operation are being developed. This study was accomplished to select a double air-gap permanent magnet synchronous motor for energy-saving and for safe operation in electric motor facilities. The torque performance of the motor was analyzed from the economical perspective of the energy efficiency of the motor by the rotation speed and the safety aspect by the geometric shape of the permanent magnet and the pin bolt diameter for safe operation of the rotor composed of the permanent magnet and the iron core. The results of this study provide important information for improving energy saving and safe operation when applying various electric facilities such as elevators, air conditioning, and pumps by selecting a double air-gap motor.

https://doi.org/10.5370/KIEE.2025.74.6.1136

박호근(Hokeun Park) ; 김경화(Kyeong-Hwa Kim)

Steelworks present challenging environments for conventional train control systems due to high temperatures, dust, and limited visibility. This study proposes a GNSS RTK-based train control system that integrates high-precision positioning with wireless communication and evaluates its performance through on-site experiments. The proposed system combines RTK positioning data with wheel sensors and track occupancy information to ensure reliable control at key locations such as stop signals and level crossings. Test results demonstrate that the system effectively follows speed limits and braking curves, even under manual driving conditions, and that the control logic operates reliably in real industrial settings. Additionally, in GNSS-denied areas, the system maintains position continuity through supplementary sensor data. These findings confirm the system's applicability in environments where ground equipment installation is limited.

https://doi.org/10.5370/KIEE.2025.74.6.1141

원영제(Young-Je Won) ; 손진근(Jin-Geun Shon) ; 김효성(Hyo-Sung Kim)

This paper proposes a hybrid MPPT algorithm, the OCV(Open Circuit Voltage)-Advanced P&O(Perturb and Observe) method, which combines the fast initial search capability of the OCV technique with the precise tracking performance of the Advanced P&O method. This approach reduces the initial MPP search time and minimizes oscillations, improving power generation efficiency in photovoltaic systems. Simulation results using PSIM show that the OCV-Advanced P&O method outperforms conventional P&O and OCV-P&O techniques. Compared to the conventional P&O algorithm, it achieves a shorter initial search time, higher cumulative power output, and reduced oscillations at the MPP. Furthermore, it ensures high stability and efficiency even in dynamic environments with changing irradiance and temperature, making it a reliable solution for enhancing MPPT performance.

https://doi.org/10.5370/KIEE.2025.74.6.1148

박소진(So-Jin Park) ; 조영아(Youngah Cho) ; 최경식(Kyoungshik Choi) ; 김형태(Hyoungtae Kim)

The government launched the “City Gas&Hydrogen Mixing Demonstration Promotion Team” in February 2022 to conduct a legal maintenance and demonstration research related to hydrogen mixing. The team is currently reviewing hydrogen mixing technology utilizing existing natural gas pipelines and safety including hydrogen embrittlement. In line with this government policy, Korea Gas Corporation is conducting various research and development to increase hydrogen utilization and align with the national energy policy direction by mixing hydrogen into natural gas using existing natural gas supply infrastructure and utilizing it in combined power generation facilities. In order to supply mixed gas by injecting hydrogen into natural gas pipelines, study on various mixing technologies such as analysis of hydrogen interchangeability of existing natural gas infrastructure, material balance analysis according to fluid change, and securing gas quality is necessary. In this paper, we introduce the concept of the hydrogen mixing process of the natural gas supply management station and the types of static mixers that can smoothly mix natural gas and hydrogen, evenly distribute the mixed gas concentration in the pipeline, and secure gas quality, and analyze the most suitable mixer through flow analysis.

https://doi.org/10.5370/KIEE.2025.74.6.1153

노수영(Soo-Young Noh) ; 김요한(Yo-Han Kim) ; 김창현(Chang-Hyun Kim)

This paper presents a robust control design for an electromagnetic suspension (EMS) system using a low-cost Hardware-in-the-Loop Simulation (HILS) testbed. The study addresses challenges such as time delay, noise, and disturbances by implementing an optimized DPID control strategy. In the HILS configuration, the controller is executed on a low-cost microprocessor, while the control target is realized either through a PC-based simulation (Case 1) or via an additional microprocessor (Case 2). Serial communication (Case 1) and Inter-Integrated Circuit (I²C) protocol (Case 2) are employed for data transmission between devices to evaluate performance under varying delay conditions. Simulation results show that both configurations achieve stable control within the specified delay limits, with the serial communication setup demonstrating a broader operational range. The proposed low-cost HILS system shows significant potential for both industrial applications and academic research. Future research will focus on evaluating the control system under wireless conditions and improving data communication efficiency.

https://doi.org/10.5370/KIEE.2025.74.6.1160

천효석(Hyo-Seok Cheon) ; 강성호(Seong-Ho Kang) ; 황정원(Jeong-Won Hwang)

In this paper, we propose extending an indoor sensor network for outdoor applications, specifically for efficient patrolling and autonomous drone operations. Reliable Localization is essential for both indoor and outdoor settings. We demonstrate that, in situations allowing group drone operations, outdoor localization can be easily achieved by extending the sensor network outdoors through drone hovering. Simulation and experimental results confirm the performance and potential of outdoor sensor networks for enhancing drone operational efficiency.