https://doi.org/10.5370/KIEE.2026.75.5.985

최유미(Yoomi Choi) ; 구자열(Jayeol Ku) ; 김해인(Haein Kim)

This study investigates structural heterogeneity in the price responsiveness of residential electricity demand between single-person and multi-person households in Korea. Using micro-data from the Household Income and Expenditure Survey (2019-2024) combined with KEPCO’s progressive tariff schedule, we applied the Two-Stage Least Squares (2SLS) method. This approach effectively controls for the endogeneity inherent in Increasing Block Tariffs (IBT) by incorporating marginal price and difference variables. The empirical results indicate that while residential electricity demand is generally price-inelastic, single-person households exhibit significantly lower elasticity (in absolute terms) than multi-person households. This rigidity is attributed to single-person households being concentrated in the lowest tariff block, which diminishes price sensitivity, and having a high proportion of base load from essential appliances. Consequently, this study suggests that future Demand Side Management (DSM) policies and tariff reforms must adopt a sophisticated approach that accounts for the distinct consumption characteristics and limited price responsiveness of single-person households in the era of demographic changes.

https://doi.org/10.5370/KIEE.2026.75.5.997

김상훈(Sang-Hoon Kim) ; 이혜규(Hye-Gyu Lee) ; 황평익(Pyeong-Ik Hwang)

This paper proposes an optimization-based method to pre-evaluate load transfer feasibility in distribution networks under line outage conditions. Unlike conventional studies that focus on post-fault restoration assuming feasible load transfer, the proposed approach assesses the feasibility itself while quantitatively identifying operational constraint violations. A mixed-integer linear programming model is formulated by incorporating slack variables to represent voltage and line current constraint violations, enabling systematic classification of infeasible scenarios. To improve computational efficiency, nonlinear power flow constraints are approximated using a polyhedral formulation. The proposed method is applied to the IEEE 94-bus distribution test system under all single-line outage scenarios, and the results confirm that the approach effectively distinguishes feasible, constraint-violating, and structurally infeasible load transfer cases, providing useful insights for distribution network planning.

https://doi.org/10.5370/KIEE.2026.75.5.1006

이윤영(Yun-Young Lee) ; 송성윤(Sungyoon Song)

The increasing penetration of renewable energy resources has raised concerns about voltage response characteristics and dynamic stability in weak grid conditions. Conventional grid strength indices, such as SCR, have limitations in capturing these dynamic characteristics. This paper evaluates voltage stability from a grid robustness perspective by combining static robustness indices with time-domain dynamic analysis. SCR, ESCR, WSCR, and SCRIF are used to identify vulnerable buses, and their effectiveness is validated through time-domain dynamic simulations. Results show that low-SCRIF buses exhibit poorly damped voltage responses and higher sensitivity to controller parameters. In addition, the effectiveness of STATCOM-based voltage support and POD control is demonstrated, showing improved voltage response and damping characteristics in weak-grid conditions

https://doi.org/10.5370/KIEE.2026.75.5.1017

이종훈(JongHoon Lee) ; 손다빈(Dabin Son) ; 한상욱(Sangwook Han)

in low-inertia power systems. In the Korean power system, Governor-Free (GF) performance is conventionally assessed using a fixed evaluation framework with a 10 s grace period and a 50 s assessment window after a frequency event. However, this approach does not adequately reflect generator-specific dynamic characteristics and may include the effects of Automatic Generation Control (AGC) in the GF assessment result. This paper proposes a generator-specific GF-only evaluation window based on the dynamic characteristics of the GGOV1 governor model. By considering actuator delay, fuel system dynamics, and turbine response, the effective GF response interval was derived as 2.3?7 s for the target generator. Case studies using actual system event data show that the conventional fixed window can distort the actual GF contribution of individual generators, whereas the proposed method more clearly captures the initial primary frequency response while reducing AGC influence. The proposed approach can improve the fairness and physical consistency of GF performance assessment in low-inertia power systems.

https://doi.org/10.5370/KIEE.2026.75.5.1029

임성수(Seong-Su Lim) ; 김경훈(Gyeong-Hun Kim) ; 이진오(Jin-Oh Lee)

Inverter-based resources are rapidly increasing across distribution networks, so steady-state analysis and operational optimization must faithfully capture their grid-support behavior, especially Volt-Var droop with a dead-band. However, the deadband creates a non-differentiable point at the boundary, which can delay or derail Newton-Raphson power flow (PF) convergence. In optimal power flow (OPF), the droop relation follows different rules across regions, and it cannot be posed directly with conditional statement inside an optimization model. To address these issues, the PF model smooths the dead-band boundary with a circular and cubic spline, and uses an analytic Jacobian to achieve reliable convergence. The OPF model expresses the droop relation through linear equality and inequality constraints, selecting the active region with binary variables so the formulation remains a mixed-integer linear programming without conditional statement. This paper validates the proposed models through case studies with multiple inverters, confirming stable PF convergence, plausible reactive-power sharing, and economically coherent dispatch, while quantifying how the dead-band width and droop slope influence voltage profiles and operating cost.

https://doi.org/10.5370/KIEE.2026.75.5.1039

김시준(Sijun Kim) ; 정진형(Jinhyung Jeung) ; 위영민(Young-Min Wi)

This paper proposes a day-ahead load forecasting method that improves interpretability through multi-seasonal decomposition and component-wise prediction. System load exhibits superimposed trend, seasonalities, and irregular fluctuations, which limits direct forecasting with a single model. MSTL (Multi-Seasonal Trend decomposition using Loess) decomposes the load into trend, weekly, daily, and residual components, and VMD (Variational Mode Decomposition) further separates the residual into multiple frequency modes. The trend, seasonal, and VMD-based residual components are predicted using LSTM (Long Short-Term Memory) models. Case studies on the hourly nationwide gross system load during normal days in 2024 show that the proposed method achieves a MAPE of 1.89% and an RMSE of 1572.07 MW, outperforming a single LSTM model with a MAPE of 2.44% and an RMSE of 2268.86 MW. In addition, the proposed framework enables identification of dominant error sources, demonstrating improved accuracy and interpretability.

https://doi.org/10.5370/KIEE.2026.75.5.1048

송천호(Cheon-Ho Song) ; 김성현(Seong-Hyeon Kim) ; 강영재(Young-Jae Kang) ; 임명섭(Myung-Seop Lim)

To address potential inverter failures and ensure high reliability, dual-inverter and dual-stator motor configurations are increasingly adopted in aerospace drive systems, such as Urban Air Mobility, which require robust fault-tolerant operation. This study focuses on a dual-stator vernier motor as a promising candidate for safety-critical applications due to its high power density and redundancy. However, the dual-stator topology introduces a significantly larger number of design variables compared to single-stator designs, leading to an increased computational burden during the initial design and analysis phases. To overcome these challenges, this paper proposes a novel computation-time reduction methodology based on an analytical framework using conformal mapping. Unlike traditional methods such as magnetic equivalent circuits or subdomain methods, the proposed approach eliminates the need for complex network configurations and the re-derivation of boundary conditions for various geometries. By employing the Schwarz-Christoffel (S-C) transformation, complex slotted air-gap regions are mapped into a normalized rectangular domain, enabling efficient and intuitive field analysis. The validity of the proposed method was verified through comparison with Finite Element Analysis (FEA) under load conditions. The results demonstrate that the proposed technique achieves high accuracy in predicting air-gap flux density and torque characteristics. Notably, the proposed method achieved a 92.3% reduction in computation time compared to FEA, proving its superior efficiency for the rapid design of motors with complex structures. Finally, this paper discusses a hybrid modeling approach to incorporate magnetic nonlinearity in future research to further enhance analytical precision.

https://doi.org/10.5370/KIEE.2026.75.5.1053

강현준(Hyeon Jun Kang) ; 한석민(Seok Min Han) ; 이원빈(Won Bin Lee) ; 이진환(Jin Hwan Lee)

This paper presents the optimal design of an outer­rotor surface­mounted permanent magnet synchronous motor for washing machine applications using explorative Particle Swarm Optimization(ePSO)­Mesh Adaptive Direct Search(MADS). In the ePSO­MADS algorithm, ePSO is used for global search, while MADS is employed for local search. Cost and torque ripple are selected as the optimization objectives. The electromagnetic characteristics of the base and optimal models are evaluated using finite element analysis. In addition, the trends in the cross­sectional areas of core, coil, and magnets across optimization iterations are examined to elucidate the mechanism underlying material cost reduction.

https://doi.org/10.5370/KIEE.2026.75.5.1060

신민혁(Min-Hyeok Shin) ; 정문석(Mun-Seok Jung) ; 김성민(Sung-Min Kim) ; 엄재부(Jae-Boo Eom) ; 정태욱(Tae-Uk Jung)

In this paper, we suggested an eddy current type electromagnetic (EM) damper to reduce vibration generated during the operation of a drum washing machine. The proposed damper consists of a copper cylinder and permanent magnets, and generates damping force through eddy current while maintaining a simple mechanical structure. To enhance the magnetic flux linking the copper cylinder, an iron spacer was inserted between the permanent magnets, resulting in increased current density and damping force, as verified through finite element analysis (FEA). In addition, a back-yoke was installed inside the copper cylinder to establish an effective magnetic path, suppress flux leakage, and further increase the magnetic flux density within the conductor, leading to additional improvement in damping performance. Furthermore, the effects of copper and yoke thickness on damping force were investigated, and optimal design parameters were obtained using the response surface method (RSM). The optimal model with a back-yoke exhibited significantly higher damping force compared to the model without a yoke. Frequency-dependent analysis confirmed that the damping force is proportional to velocity, demonstrating viscous damping characteristics and effective vibration suppression under varying dynamic conditions. The results demonstrate that the proposed structure effectively improves magnetic flux utilization.

https://doi.org/10.5370/KIEE.2026.75.5.1068

안종찬(Jong-Chan An) ; 송민우(Min-Woo Song) ; M.J.M.A Rasul(M.J.M.A Rasul) ; 김종훈(Jong-Hoon Kim)

Lithium iron phosphate (LFP) batteries are widely used in electric vehicles and energy storage systems because of their thermal safety and low cost. However, their intrinsic voltage hysteresis complicates accurate state-of-charge (SOC) estimation by creating different voltage responses during charge and discharge. This study proposes an SOC estimation method that combines a discrete Preisach model (DPM) with an equivalent-circuit-model-based extended Kalman filter(EKF). The proposed method identifies DPM parameters using weighted least squares and ridge regularization with a bias term, improving numerical stability and reducing hysteresis-induced uncertainty under dynamic operating conditions. To evaluate practical applicability, dynamic stress test (DST) and federal urban driving schedule (FUDS) profiles were applied at 25 °C, 45 °C, and 0 °C. Experimental results showed that the proposed EKF+DPM(WLS+Ridge+bias) achieved the lowest mean absolute error (MAE) at all temperatures, confirming improved SOC estimation accuracy in terms of average error. In particular, at 0 °C, the MAE decreased from 3.03% to 1.45% under DST and from 3.61% to 0.58% under FUDS compared with the conventional EKF. By contrast, the conventional EKF+DPM approach increased the maximum error and MAE under some conditions, especially at low temperature, indicating that stable DPM parameter estimation is critical to performance. These results demonstrate that the proposed method can stably suppress SOC estimation errors despite temperature variation and dynamic load changes, providing improved reliability and robustness for real-time battery management system applications.

https://doi.org/10.5370/KIEE.2026.75.5.1077

하태빈(Tae-bin Ha) ; 이상력(Sang-ryuk Lee) ; 김태윤(Tae-yoon Kim) ; 송민우(Min-woo Song) ; 김종훈(Jing-hoon Kim)

Lithium-ion batteries exhibit different degradation pathways under diverse operating environments, and in real-world applications, reliable life prediction is often required using only early-stage operating data before sufficient long-term history is accumulated; considering the time and cost of data collection, transfer learning becomes essential. However, when operating conditions change, data distributions readily shift, and prediction performance can deteriorate if a model trained under a single condition is directly applied or if the transfer intensity is determined solely based on time-series linearity or distribution-level similarity. To mitigate both domain discrepancy and limited early-data constraints, this study proposes an ICA-guided transfer learning strategy that determines the transfer intensity using only the first 200 cycles. First, eight aging-profile datasets were obtained using INR21700-33J cells, and a Seq2Seq?LSTM pre-trained model was developed using capacity- and voltage-based health indicators augmented with rate-of-change and exponential moving average features. During transfer, initial similarity was assessed via Pearson correlation of the 200-cycle capacity time series; however, we found that time-series similarity alone can lead to performance degradation under certain conditions. To address this limitation, we additionally incorporated similarities of Peak 1 and Peak 2 extracted from incremental capacity (IC) curves. Specifically, when IC-peak correlation was high, conservative transfer focusing on the output layer was applied, whereas low IC-peak correlation triggered stronger transfer by unfreezing the decoder along with the output layer. The results demonstrate that the proposed ICA-peak-based decision rule consistently secures high explanatory power across all target sets with stable prediction performance.

https://doi.org/10.5370/KIEE.2026.75.5.1086

박준희(Jun-Hui Park) ; 성가연(Ga-Yeon Seong) ; 이민규(Min-Gyu Lee) ; 이상윤(Sangyoon Lee)

This study proposes an optimal power operation scheduling strategy for battery swapping charging station (BCSS) integrated with a photovoltaic (PV) system, explicitly considering nodal carbon intensity and the uncertainties of PV generation. The primary objective is to minimize the total operation cost, which comprises i) power purchasing cost for battery charging, ii) the revenue from power selling to the grid, and iii) the carbon cost associated with power injection, while satisfying electric vehicle battery swapping requirements. To address the stochastic uncertainty of PV generation, we formulate the scheduling problem using a Wasserstein metric-based distributionally robust optimization approach. Finally, simulation results demonstrate the comparative performance of the proposed method under varying cost coefficients and the distributional ambiguity parameters, such as the sample size and confidence level.

https://doi.org/10.5370/KIEE.2026.75.5.1096

손성재(Seong-Jae Son) ; 이승엽(SeungYeop Lee) ; 정길성(Gil-Sung Jung) ; 김찬호(Chan-Ho Kim)

In this study, a co-simulation environment capable of simultaneously analyzing the powertrain and thermal management systems was developed to ensure efficient thermal management performance of a 55-kW class agricultural electric tractor. The powertrain analysis model calculates the energy consumption according to vehicle power demand while deriving loss characteristics, which are provided as inputs to the thermal management system model. The thermal management system model performs thermal analysis by incorporating the loss characteristics of the powertrain. For model validation, load and temperature data were obtained during plowing operations using the 55-kW electric tractor, and a minimum prediction accuracy of 90.8% was achieved through comparison with the simulation results. The developed analysis model enables preliminary verification of system design for improving the performance and energy efficiency of the electric tractor at the simulation level.

https://doi.org/10.5370/KIEE.2026.75.5.1103

김형준(Hyeongjun Kim) ; 이세희(Se-Hee Lee)

High Energy Arcing Fault (HEAF) is a critical accident type in electrical equipment. Unlike ordinary fires, HEAF can release large of energy in a very short time, producing extremely high temperatures, rapid pressure rises, and strong mechanical shocks. Such phenomena pose severe risks of equipment damage and secondary hazards, and thus cannot be regarded simply as fire events. Instead, they require dedicated engineering approaches and quantitative assessment methods. Accurate understanding of HEAF mechanisms and numerical analysis of the released energy, including its thermal and fluid dynamic characteristics, are essential for accident prevention and for establishing design criteria. Fire Dynamics Simulation (FDS) provides a useful framework for this purpose, as it enables scenario-based modeling that incorporates arc initiation location, released energy, heat release rate, and pressure effects. Through such simulations, it becomes possible to predict fire development patterns and to estimate the Zone of Influence (ZOI) with greater precision. In this study, a numerical HEAF model for medium voltage switchgear was developed, focusing on a generator-fed fault scenario. The impact of HEAF was evaluated by comparing cases with and without the application of the radiant fraction according to arc power. The analysis results indicate that, while slight variations appear depending on the radiant fraction, enclosure damage and ZOI remain largely unchanged. This finding supports the use of maximum arc power with radiant fraction as an efficient analysis method. Overall, the FDS-based HEAF modeling approach provides a useful tool for assessing HEAF risks, supporting equipment design, improving protective measures, and contributing to regulatory development.

https://doi.org/10.5370/KIEE.2026.75.5.1108

임종필(Jong-Pil Lim) ; 박흥석(Hung-Sok Park) ; 나진용(Jin-Yong Na) ; 박민솔(Min-Sol Park) ; 김동규(Dong-Kyu Kim) ; 고현상(Hyeon-Sang Ko) ; 정채균(Chae-Kyun Jung) ; 최장영(Jang-Young Choi)

This paper presents an analysis of the allowable current of submarine power cables, considering the specific characteristics of their actual installation environments. As the expansion of offshore wind farms and submarine transmission networks accelerate globally, the demand for reliable submarine cables has surged. However, due to the absence of design standards for submarine cables, their ampacity is calculated by applying standard for underground cables. These traditional methods fail to reflect unique seabed conditions, such as soil thermal resistivity and ambient temperature. In this study, finite element method (FEM) analysis was performed on an AC 154kV XLPE 3-core 500mm² submarine cable to evaluate how these environmental parameters influence the cable's thermal behavior and ampacity. The reliability of the numerical model was validated by comparing the results with analytical calculations based on the international standard, IEC 60287. The results reveal that environmental factors significantly affect ampacity estimation, with soil thermal resistivity showing a more pronounced influence than ambient temperature. Specifically, under buried installation conditions, it was confirmed that soil thermal resistivity acts as the more dominant governing factor in determining submarine cable ampacity. These results suggest that incorporating site-specific environmental data is essential for optimizing submarine cable design and ensuring operational reliability.

https://doi.org/10.5370/KIEE.2026.75.5.1115

홍건호(Geonho Hong) ; 서현빈(Hyeonbin Seo) ; 오준화(Junhwa Oh) ; 조에디(Eddie Cho) ; 김태은(Taeeun Natalie Kim) ; 황지창(Jichang Hwang) ; 문기량(Giryang Moon) ; 오세용(Seyong Oh)

This paper examines a trigger-assisted vision module that complements a club-mounted inertial measurement unit (IMU) for golf swing measurement and impact-centered sensing, with a focus on club-face angle estimation. IMU sensors provide kHz-rate motion trajectories, but drift accumulation and impact-induced disturbances can degrade absolute orientation measurements. We conduct a feasibility study with an experimental setup that captures high-speed global-shutter stereo images under external illumination. To obtain impact-centered frames without continuous storage, the system writes frames to a ring buffer and retains only a short window of frames when a frame-level ball-presence event occurs. The system detects three markers on a club-head fixture and reconstructs their 3D positions using stereo geometry and OpenCV-based geometric estimation. The club-face angle is computed as a relative rotation with respect to a base reference frame captured in a nominal square configuration. We evaluate the end-to-end acquisition and face-angle estimation pipeline using a fixture-based angle test with ground-truth angle settings, and we quantify errors by comparison with an independent top-view measurement. The results indicate that the proposed frame-limited, impact-centered vision measurements can provide repeatable angle references and can support future integration with IMU measurements through precise time alignment and fusion.

https://doi.org/10.5370/KIEE.2026.75.5.1124

정가은(Ga-Eun Jung) ; 이준엽(Jun-Yeop Lee) ; 이석주(Seok-Ju Lee)

This study proposes the design of a physics-informed artificial intelligence (AI) reduced-order thermal model for real-time core temperature prediction in motor dynamometer systems. The stator and rotor core temperatures of electric motors are difficult to measure directly and have traditionally been estimated using high-fidelity finite element method (FEM) simulations. However, the substantial computational burden of FEM-based approaches limits their applicability in real-time monitoring and control environments. To overcome this limitation, a lumped parameter thermal model that captures inter component heat transfer, ambient temperature effects, and current dependent loss mechanisms is integrated with a physics-informed neural network (PINN) framework. The proposed method embeds the governing thermal dynamics into the learning process, enabling the simultaneous identification of neural network weights and physically interpretable thermal parameters through a composite loss function that minimizes both temperature data mismatch and physics residuals. Furthermore, the model is structured in an ordinary differential equation (ODE) form to ensure numerical efficiency and stability under varying operating conditions. Extensive validation under multiple load profiles demonstrates that the proposed approach preserves FEM level prediction accuracy while achieving about five orders of magnitude reduction in computation time. Consequently, the developed model provides a practical foundation for real-time thermal monitoring, digital twin implementation, and advanced performance evaluation of motor dynamometer systems in industrial applications.

https://doi.org/10.5370/KIEE.2026.75.5.1137

최훈(Hun Choi)

Reliable P-wave detection is essential in seismic monitoring and early warning systems, as it determines how quickly and accurately an event is identified. The 3σ-IMAV method, which tracks variations in impact momentum derived from seismic jerk signals, has shown stable performance under noisy conditions with low computational cost. However, the influence of its internal parameters on detection performance has not been fully examined. This study investigates the sensitivity of key parameters in the 3σ-IMAV algorithm using real seismic records from the IRIS global network. The IMAV window length, threshold scaling factor, and minimum exceedance duration are systematically varied, and their effects are evaluated using event-based metrics, including detection success rate, false detection rate, late detection rate, and detection time error. The results quantify performance trade-offs in parameter selection and offer empirical evidence for selecting optimal parameters across diverse IRIS seismic records.

https://doi.org/10.5370/KIEE.2026.75.5.1145

나건우(Kunwoo Na) ; 김영승(Youngseoung Kim) ; 김우용(Wooyong Kim)

Accurate state-of-charge (SOC) estimation for lithium iron phosphate (LFP) batteries is challenging due to their flat open-circuit voltage (OCV) plateaus and significant path-dependent voltage hysteresis. This study investigates the hysteresis characteristics of LFP batteries and proposes a time-efficient modeling method. Conventionally, characterizing hysteresis requires measuring the OCV curve across the full SOC range (0-100%) with fine steps, a process that typically consumes approximately 164.3 hours. To address this inefficiency, we propose a 'Half Range' method that exploits the symmetry of hysteresis voltage around 50% SOC. By characterizing only the 0-50% or 50-100% range, the experimental duration is reduced to approximately 44.8 hours, achieving a time saving of about 73%. Hysteresis parameters were identified using the nonlinear least squares method. Experimental validation demonstrates that the proposed method maintains modeling accuracy comparable to the conventional Full Range method (RMSE: 0.336 mV), with the Upper Half model achieving an RMSE of 0.098 mV. Consequently, the proposed method significantly reduces experimental cost and time while preserving accuracy, providing a practical and efficient tool for LFP battery analysis and SOC estimation.

https://doi.org/10.5370/KIEE.2026.75.5.1152

박동근(Dong-Geun Park) ; 황돈하(Don-Ha Hwang) ; 이호영(Ho-Young Lee)

This study used the FDS(Fire Dynamics Simulator) to assess how grid size affects the prediction of HRR(Heat Release Rate) and computational time in electrical cable tray fires, through mesh sensitivity analysis. Two scenarios with different boundary and ventilation conditions were modelled. In Scenario 1, an open three tray was ignited by a 40 kW propane burner positioned beneath the lowest tier for 10 minutes. Scenario 2 involved five trays in two rooms connected by an open door with forced ventilation. Grid sizes of 0.05 m, 0.1 m and 0.2 m were tested. The predicted HRR was compared with experimental results to compute local and global errors. In Scenario 1, the 0.05 m grid produced the lowest global error of 0.337 %, while the 0.1 m grid demonstrated comparable accuracy, offering greater efficiency, and was therefore selected as the optimal option. In Scenario 2, the 0.05 m grid produced the smallest local and global errors and was selected as optimal.

https://doi.org/10.5370/KIEE.2026.75.5.1162

원승용(Seung Yong Won) ; 강정원(Jeong Won Kang)

Urban railways provide high punctuality and large passenger capacity as a key means of public transportation. However, congestion tends to be concentrated on certain lines and sections, especially during morning and evening rush hour, which poses a safety risk beyond simple inconvenience. So this study introduce to develop a system that estimates train congestion using load and stress data measured by electric trains, and transmits data over commercial wireless and urban rail closed wired networks to display information in real-time on central control centers and station platforms. In addition, this study aims to verify the reliability of displayed congestion information. By implementing a real-time congestion transmission-display system based on load and stress sensors, this study can be applied to the revised railway safety management regulations and provide passengers with a safer and more efficient boarding environment on urban railways.

https://doi.org/10.5370/KIEE.2026.75.5.1170

김홍옥(Hong-Ok Kim) ; 구경완(Kyung-Wan Koo)

This study develops an integrated energy-agriculture modeling framework for Agro-Photovoltaic (Agro-PV) systems to quantitatively evaluate the trade-off between electricity generation and crop productivity under shared land-use conditions. Ten years (2013-2022) of climate data from western Chungcheongnam-do, Korea, were analyzed using irradiance-based PV simulations and photosynthetically active radiation (PAR)-driven crop growth modeling. The results indicate that Agro-PV systems produced 60-70% of the electricity yield of standalone photovoltaic installations while maintaining 50-85% of conventional agricultural productivity, depending on transmittance conditions. Under the optimal configuration (transmittance ratio of 40%), the land equivalent ratio (LER) ranged from 1.21 to 1.36, corresponding to an average combined productivity index (CPI) of approximately 0.64. This represents an estimated 28.5% increase in overall land productivity compared with single-use systems. These findings demonstrate that Agro-PV systems can enhance land-use efficiency while simultaneously contributing to carbon neutrality goals and regional food security through sustainable dual-resource utilization.

https://doi.org/10.5370/KIEE.2026.75.5.1179

김영승(Youngseoung Kim) ; 나건우(Kunwoo Na) ; 정융(Yoong Chung) ; 김우용(Wooyong Kim)

Precise thermal management of pouch-type lithium-ion batteries is critical to ensuring operational safety in electric vehicles (EVs). Conventional studies, which typically rely on lumped parameter models, are limited in predicting localized cell temperatures because they ignore surface temperature gradients caused by internal non-uniformities. This study proposes a real-time temperature distribution prediction method using a single reference sensor and a spatial thermal characteristic map optimized for on-board Battery Management Systems (BMS). Using Inverse Heat Transfer Analysis (IHTA), localized entropic coefficients were extracted from six points, reducing experimental time by 95% compared to conventional potentiometric methods. To ensure computational efficiency for real-time applications, these parameters were integrated into a spatial map via a first-order linear model, enabling continuous State of Charge (SOC)-based estimation. Validation on high-capacity pouch cells confirmed high accuracy, with a Root Mean Square Error (RMSE) below 0.7°C and a maximum absolute error under 3.5°C at unmeasured locations. This approach minimizes sensor requirements and complexity, providing a scalable foundation for future expansion into module and pack-level thermal monitoring.

https://doi.org/10.5370/KIEE.2026.75.5.1188

유도현(Dohyeon Yu) ; 장규정(Gyujeong Jang) ; 노상원(Sangwon Noh) ; 김석환(Seokhwan Kim) ; 조희진(Huijin Cho) ; 박서연(Seoyeon Park) ; 최원석(Wonseok Choi and Inseok Seo) ; 서인석()

In this study, boron-doped hard carbon was synthesized and utilized as an anode material to improve the performance of sodium-ion batteries. The synthesized boron-doped hard carbon showed an optimized structure for sodium ion storage because boron atoms were successfully substituted into the carbon lattice. Specifically, the interlayer spacing was expanded to 0.40 nm, which facilitated the movement of sodium ions. Additionally, structural defects formed during the synthesis provided more active sites, making it suitable for high-capacity sodium storage. Through half-cell tests, the boron-doped hard carbon achieved a high discharge capacity of 364.80 mAh/g (an 11 % increase compared to pristine hard carbon) and maintained stable cycling performance over 100 cycles at 0.1 C. Additionally, rate capability tests (0.1 C to 5.0 C) revealed that the boron-doped hard carbon exhibited higher discharge capacities than the pristine hard carbon across all tested C-rates. These results confirm that the boron-doped hard carbon was successfully synthesized and is expected to contribute to enhancing the performance of sodium-ion batteries.

https://doi.org/10.5370/KIEE.2026.75.5.1196

이재건(Jae-gun Lee) ; 심성락(Sung-rak Sim) ; 손진근(Jin-geun Shon)

This paper proposes a robust reactive power control method for Static Var Compensator (SVC) systems under input phase variation and load unbalance. Conventional SVCs regulate total reactive power and thus fail to compensate phase-by-phase reactive power when unbalanced conditions occur. To address this limitation, the proposed method employs a Dual Second-Order Generalized Integrator (DSOGI) to extract in-phase and quadrature components of three-phase voltages and currents, enabling accurate phase tracking and per-phase reactive power estimation. The extracted quantities are used to generate independent firing-angle commands for each TCR branch. PSIM simulations and DSP-based experiments verify that the proposed method achieves unity displacement power factor in all phases, outperforming existing control approaches.

https://doi.org/10.5370/KIEE.2026.75.5.1203

박재현(Jae-Hyeon Park) ; 김태완(Tae-Wan Kim)

Reliable interoperability among heterogeneous power system applications requires validation of both IEC 61968 message structure and CIM payload semantics. This paper proposes a two-layer validation framework for IEC 61968 SOAP messages. In the first layer, XSD-based syntactic validation checks the SOAP message structure, including headers, tags, data types, and namespaces. In the second layer, SHACL-based semantic validation verifies the RDF/XML CIM payload extracted from the SOAP body by checking object relationships and domain constraints. Unlike previous studies that addressed syntactic and semantic validation separately, the proposed framework integrates both in a unified procedure. For evaluation, IEC 61968-compliant test messages with injected syntactic and semantic errors were used. The results show that the framework effectively detects structural and semantic inconsistencies in CIM-compliant message exchanges.

https://doi.org/10.5370/KIEE.2026.75.5.1211

원영제(Young-Je Won) ; 손진근(Jin-Geun Shon) ; 홍성준(Seong-Joon Hong)

This paper proposes a supercapacitor-based compensation scheme to mitigate load-induced voltage drop in grid-forming photovoltaic systems. In standalone PV systems without power compensation, abrupt load changes can cause output voltage drops that degrade system reliability and disrupt connected loads, while battery-based storage is often too slow for sub-second voltage regulation. The proposed approach connects a supercapacitor to the converter DC stage, monitors operating conditions in real time, and injects auxiliary power immediately when a voltage drop is detected. Simulation confirms that the method maintains the inverter output voltage near its reference under rapid load steps and substantially reduces the load-side voltage drop. The proposed scheme can improve power quality in grid-forming applications such as standalone PV systems, microgrids, and electric vehicle charging stations with frequent load variations.

https://doi.org/10.5370/KIEE.2026.75.5.1217

박수진(Su-Jin Park) ; 이현태(Hyun-Tae Lee) ; 최윤진(Yun-Jin Choi) ; 한재영(Jaeyoung Han) ; 박현종(Hyun-Jong Park)

This paper presents a comparative analysis of interior permanent magnet synchronous motors (IPMSM) and surface permanent magnet synchronous motors (SPMSM) with different pole-slot combinations for downsizing electric vehicle (EV) water pump motors. In electric vehicle thermal management systems, water pump motors are required to deliver sufficient flow rate and pressure within limited installation space, necessitating compact and efficient motor designs. Three compact motor models were designed by reducing the stator outer diameter from 70 mm to 58 mm, and the winding turns of each model were adjusted to satisfy identical rated output and permanent magnet usage conditions. The electromagnetic performances of SPMSM and IPMSM models were analyzed under these conditions. Two-dimensional finite element analysis was employed to evaluate torque characteristics, cogging torque, back-electromotive force, and magnetic flux density distributions. The analysis results show that the SPMSM structure exhibits increased cogging torque and torque ripple due to the reduction in pole arc angle to satisfy the identical magnet cross-sectional area condition. The IPMSM with an 8-pole 12-slot configuration provides the lowest back-EMF, ensuring sufficient voltage margin, but exhibits relatively high torque ripple due to the reduced rotor outer diameter. In contrast, the IPMSM with a 4-pole 6-slot configuration exhibits the lowest torque ripple and cogging torque among the compared models, while also showing balanced characteristics in terms of back-EMF and efficiency. The results demonstrate that the IPMSM with a 4-pole 6-slot configuration is the most suitable pole-slot combination for the compact design of EV water pump motors.

https://doi.org/10.5370/KIEE.2026.75.5.1226

김도연(Do Yeon Kim) ; 이도현(Do Hyeon Lee) ; 안동혁(Dong Hyuk Ahn) ; 이병국(Byoung-Kuk Lee)

This paper proposes an optimal driving algorithm for 50 [kW] wireless charging system with multi-coil pad to achieve high efficiency over a wide load range under SAE J2954 misalignment. Two driving strategies are considered, simultaneous driving where all phases are activated and selective driving where only well-coupled phases are activated. The target system employs a three-phase pad composed of DDP coils for phases A and B and a CP coil for phase C, together with a three-phase IPT converter that supports phase selection. Based on loss characteristics under varying load and alignment conditions, the multi-coil driving algorithm is derived to switch between the two strategies and to select the active A/B phase during selective operation. Finally proposed multi-coil driving algorithm is validated the feasibility and efficiency improvement through experiments on a 50 [kW] system.