In this paper, a high-efficiency and low-cost active cell-to-cell balancing circuit for the reuse of electric vehicle (EV) batteries is proposed. In the proposed method, a battery string is divided into two legs to transfer the charge from each cell in one leg to that in the other and a bidirectional CLLC resonant converter is used to transfer energy between the selected cells. Thanks to the proposed structure, the number of bidirectional switches and gate drivers can be reduced by half compared to the conventional direct cell-to-cell topologies, thereby achieving lower cost for the system. The CLLC converter is used to transfer the charge, and it is designed to work at resonant frequencies to achieve zero-voltage zero-current switching (ZVZCS) for all the switches and diodes. Consequently, the system’s efficiency can be enhanced, and hence, the fuel economy of the system can also be improved significantly. To verify the performance of the proposed active cell-balancing system, a prototype is implemented for balancing the three EV battery modules that contain twelve lithium-ion batteries from xEV. The maximum efficiency achieved for the charge transfer is 89.4%, and the balancing efficiency is 96.3%.

As the demand for eco-friendly energy increases, hydrogen energy and liquid hydrogen storage technologies are being developed as an alternative. Hydrogen has a lower liquefaction point and higher thermal conductivity than nitrogen or neon used in general cryogenic systems. Therefore, the application of hydrogen to cryogenic systems can increase efficiency and stability. This paper describes the design and analysis of a cryogenic cooling system for an electric propulsion system using liquid hydrogen as a refrigerant and energy source. The proposed aviation propulsion system (APS) consists of a hydrogen fuel cell, a battery, a power distribution system, and a motor. For a lab-scale 5 kW superconducting motor using a 2G high-temperature superconducting (HTS) wire, the HTS motor and cooling system were analyzed for electromagnetic and thermal characteristics using a finite element method-based analysis program. The liquid hydrogen-based cooling system consists of a pre-cooling system, a hydrogen liquefaction system, and an HTS coil cooling system. Based on the thermal load analysis results of the HTS coil, the target temperature for hydrogen gas pre-cooling, the number of buffer layers, and the cryo-cooler capacity were selected to minimize the thermal load of the hydrogen liquefaction system. As a result, the hydrogen was stably liquefied, and the temperature of the HTS coil corresponding to the thermal load of the designed lab-scale HTS motor was maintained at 30 K.

Predicting the output power of wind generators is essential to improve grid flexibility, which is vulnerable to power supply variability and uncertainty. Digital twins can help predict the output of a wind turbine using a variety of environmental data generated by real-world systems. This paper dealt with the development of a physics-based output prediction model (P-bOPM) for a 10 MW floating offshore wind turbine (FOWT) for a digital twin. The wind power generator dealt with in this paper was modeled considering the NREL 5 MW standard wind turbine with a semi-submersible structure. A P-bOPM of a 10 MW FOWT for a digital twin was designed and simulated using ANSYS Twin Builder. By connecting the P-bOPM developed for the digital twin implementation with an external sensor through TCP/IP communication, it was possible to calculate the output of the wind turbine using real-time field data. As a result of evaluating the P-bOPM for various marine environments, it showed good accuracy. The digital twin equipped with the P-bOPM, which accurately reflects the variability of the offshore wind farm and can predict the output in real time, will be a great help in improving the flexibility of the power system in the future.

The application of cross-linked polyethylene (XLPE) cables to voltage sourced converter (VSC)-based high voltage direct current (HVDC) systems has already been technically verified and has become common, and thermoplastic (TP) is attracting attention as an insulation material for next-generation cables due to the recent development of material-related technologies. However, studies related to TP cables are mainly focused on improving material properties, and studies related to cable systems are insufficient. In this paper, XLPE and TP cables were designed for application to VSC-based HVDC systems, and major characteristics such as electric field distribution and thermal stability were compared and analyzed through overvoltage simulation. The insulation design method of HVDC cable was presented, and the design was performed using XLPE and TP insulation materials. The temperature and electric field profiles of the cables were also analyzed through a finite element method simulation. To analyze the performance of the designed cable, it was simulated with the PSCAD/EMTDC program. Based on the simulation results, the major characteristics of XLPE and TP cables were compared and analyzed. Results showed that in the case of TP cables, insulation properties were excellent, but thermal conductivity was relatively low; therefore, countermeasures are needed.

Renewable energy is being adopted worldwide, and the proportion of offshore wind turbines is increasing. Offshore wind turbines operate in harsh weather conditions, resulting in various failures and high maintenance costs. In this paper, a condition diagnosis model for condition monitoring of an offshore wind turbine has been developed. The generator, main bearing, pitch system, and yaw system were selected as components subject to the condition monitoring by considering the failure rate and downtime of the wind turbine. The condition diagnosis model works by comparing real-time and predictive operating data of the wind turbine, and about four years of Supervisory Control and Data Acquisition (SCADA) data from a 2 MW wind turbine was used to develop the model. A deep neural network and an artificial neural network were used as machine learning to predict the operational data in the condition diagnosis model, and a confusion matrix was used to measure the accuracy of the failure determination. As a result of the condition monitoring derived by inputting SCADA data to the designed system, it was possible to maintain the failure determination accuracy of more than 90%. The proposed condition monitoring system will be effectively utilized for the maintenance of wind turbines.

공장의 에너지 효율을 높이는 방안 중 공정 스케줄링은 제조 공정에서 자원을 최적으로 할당하여 제품의 생산 계획을 수립하는 활동이다. 그러나 야간 근로가 불가피한 경우에는 이러한 전략이 효과적으로 적용되지 않을 수 있다. 또한, 생산 요구량의 지속적인 변화로 인해 실제 공장에서의 적용에 어려움이 있다. 최근에는 전기자동차의 보급이 급증함에 따라 전기자동차 배터리를 에너지 저장 시스템으로 활용하는 기술이 주목을 받고 있다. 이러한 배터리를 활용한 기술은 공장 에너지 관리를 위한 대안이 될 수 있다. 본 논문에서는 전기자동차 배터리를 활용한 공장 에너지 관리 방안을 제안한다. 제안된 방안은 전기자동차 배터리 충전 상태 및 TOU(Time-of-use)를 고려하여 PSCAD/EMTDC 소프트웨어에서 분석된다. 제안된 방안은 예측된 전력 사용량과 TOU를 고려하여 수립된 공정 스케줄링과 비교 분석된다. 결과적으로 공정 스케줄링은 하루에 4,152원, 제안된 방안은 7,286원의 전기 요금을 절감하였다. 본 논문을 통해 공장 에너지 관리를 위해 전기자동차 배터리 활용 가능성을 확인할 수 있었다.

In seaports, the automatic Grab-Type Ship Unloader (GTSU) stands out for its ability to automatically load and unload materials, offering the potential for substantial productivity improvement and cost reduction. Developing a fully automatic GTSU, however, presents a unique challenge: the system must autonomously determine the position of the cargo hold and the coordinates of the working point and identify potential hazards during material loading and unloading. This paper proposes AI models designed to detect cargo holds, extract working points, and support collision risk warnings, utilizing both the LiDAR sensor and the camera in the GTSU system. The model for cargo hold detection and collision warning was developed using image data of the cargo hold and grab, employing the You Only Look Once model. Concurrently, the model responsible for extracting the coordinates of working points for the GTSU system was designed by integrating the cargo hold detection and point cloud processing models. After testing the AI models for the lab-scale GTSU, the results show that the cargo hold detection and collision warning models achieve an accuracy of approximately 96% and 90%, respectively. Additionally, the working point coordinates extracted from the sensor system show a deviation of 5–10% compared to traditional measurements.

Wind turbines playing a critical role in renewable energy generation, accurately assessing their operational status is crucial for maximizing energy production and minimizing downtime. This study conducts a comparative analysis of different neural network models for wind turbine condition diagnosis, evaluating their effectiveness using a dataset containing sensor measurements and historical turbine data. The study utilized supervisory control and data acquisition data, collected from 2 MW doubly-fed induction generator-based wind turbine system (Model HQ2000), for the analysis. Various neural network models such as artificial neural network, long short-term memory, and recurrent neural network were built, considering factors like activation function and hidden layers. Symmetric mean absolute percentage error were used to evaluate the performance of the models. Based on the evaluation, conclusions were drawn regarding the relative effectiveness of the neural network models for wind turbine condition diagnosis. The research results guide model selection for wind turbine condition diagnosis, contributing to improved reliability and efficiency through advanced neural network-based techniques and identifying future research directions for further advancements.

A saturated iron-core type superconducting fault current limiter (SI-SFCL) can effectively restrict the magnitude of the fault current and alleviate the strain on circuit breakers in DC power systems. Design of a superconducting coil (SC), which is one of the key tasks in the SI-SFCL design, requires guaranteeing a sufficient magnetic field, ensuring optimization of the shape and size, minimizing the wire cost, and satisfying the safety and stability of operation. Generally, finite element method (FEM) is used to calculate and evaluate the operating characteristics of SCs, from which it is possible to determine their optimal design parameters. When the coil is complex and large, the simulation time may range from hours to days, and if input parameters change even slightly, the simulations have to be redone from scratch. Recent advances in deep learning represent the ability to be effective for modeling and optimizing complex problems from training data or in real-time. In this paper, we presented a combination of the FEM simulation and deep Q-network (DQN) algorithm to optimize the SC design of a lab-scale SI-SFCL for a DC power system. The detailed design process and options for the SC of SI-SFCL were proposed. In order to analyze the characteristics related to the electromagnetic properties and operational features of the SC, a 3D FEM model was developed. Then, a DQN model was constructed and integrated with the FEM simulation for training and optimizing the design parameters of the SC in real-time. The obtained results of this study have the potential to effectively optimize the design parameters of large-scale SI-SFCL development for high-voltage DC power systems.

In this paper, a high-efficiency and low-cost active cell-to-cell balancing circuit for the reuse of electric vehicle (EV) batteries is proposed. In the proposed method, a battery string is divided into two legs to transfer the charge from each cell in one leg to that in the other and a bidirectional CLLC resonant converter is used to transfer energy between the selected cells. Thanks to the proposed structure, the number of bidirectional switches and gate drivers can be reduced by half compared to the conventional direct cell-to-cell topologies, thereby achieving lower cost for the system. The CLLC converter is used to transfer the charge, and it is designed to work at resonant frequencies to achieve zero-voltage zero-current switching (ZVZCS) for all the switches and diodes. Consequently, the system’s efficiency can be enhanced, and hence, the fuel economy of the system can also be improved significantly. To verify the performance of the proposed active cell-balancing system, a prototype is implemented for balancing the three EV battery modules that contain twelve lithium-ion batteries from xEV. The maximum efficiency achieved for the charge transfer is 89.4%, and the balancing efficiency is 96.3%.

As the demand for eco-friendly energy increases, hydrogen energy and liquid hydrogen storage technologies are being developed as an alternative. Hydrogen has a lower liquefaction point and higher thermal conductivity than nitrogen or neon used in general cryogenic systems. Therefore, the application of hydrogen to cryogenic systems can increase efficiency and stability. This paper describes the design and analysis of a cryogenic cooling system for an electric propulsion system using liquid hydrogen as a refrigerant and energy source. The proposed aviation propulsion system (APS) consists of a hydrogen fuel cell, a battery, a power distribution system, and a motor. For a lab-scale 5 kW superconducting motor using a 2G high-temperature superconducting (HTS) wire, the HTS motor and cooling system were analyzed for electromagnetic and thermal characteristics using a finite element method-based analysis program. The liquid hydrogen-based cooling system consists of a pre-cooling system, a hydrogen liquefaction system, and an HTS coil cooling system. Based on the thermal load analysis results of the HTS coil, the target temperature for hydrogen gas pre-cooling, the number of buffer layers, and the cryo-cooler capacity were selected to minimize the thermal load of the hydrogen liquefaction system. As a result, the hydrogen was stably liquefied, and the temperature of the HTS coil corresponding to the thermal load of the designed lab-scale HTS motor was maintained at 30 K.

Predicting the output power of wind generators is essential to improve grid flexibility, which is vulnerable to power supply variability and uncertainty. Digital twins can help predict the output of a wind turbine using a variety of environmental data generated by real-world systems. This paper dealt with the development of a physics-based output prediction model (P-bOPM) for a 10 MW floating offshore wind turbine (FOWT) for a digital twin. The wind power generator dealt with in this paper was modeled considering the NREL 5 MW standard wind turbine with a semi-submersible structure. A P-bOPM of a 10 MW FOWT for a digital twin was designed and simulated using ANSYS Twin Builder. By connecting the P-bOPM developed for the digital twin implementation with an external sensor through TCP/IP communication, it was possible to calculate the output of the wind turbine using real-time field data. As a result of evaluating the P-bOPM for various marine environments, it showed good accuracy. The digital twin equipped with the P-bOPM, which accurately reflects the variability of the offshore wind farm and can predict the output in real time, will be a great help in improving the flexibility of the power system in the future.

The application of cross-linked polyethylene (XLPE) cables to voltage sourced converter (VSC)-based high voltage direct current (HVDC) systems has already been technically verified and has become common, and thermoplastic (TP) is attracting attention as an insulation material for next-generation cables due to the recent development of material-related technologies. However, studies related to TP cables are mainly focused on improving material properties, and studies related to cable systems are insufficient. In this paper, XLPE and TP cables were designed for application to VSC-based HVDC systems, and major characteristics such as electric field distribution and thermal stability were compared and analyzed through overvoltage simulation. The insulation design method of HVDC cable was presented, and the design was performed using XLPE and TP insulation materials. The temperature and electric field profiles of the cables were also analyzed through a finite element method simulation. To analyze the performance of the designed cable, it was simulated with the PSCAD/EMTDC program. Based on the simulation results, the major characteristics of XLPE and TP cables were compared and analyzed. Results showed that in the case of TP cables, insulation properties were excellent, but thermal conductivity was relatively low; therefore, countermeasures are needed.