As the demand for digital systems that require dc power increases rapidly, the importance of stable dc power supply methods increases. Various elements, such as renewable energy and load, are configured in the power grid that supplies dc, and they are connected in various forms and power patterns. These elements have different starting and operating characteristics, which can be disadvantageous to the design of controllers for power converters to maintain a stable bus voltage in the dc power grid. Therefore, in a dc power system, an energy storage system with active bidirectional power control technology that can minimize transient conditions, such as overshoot, which can occur when a load is connected or power accelerates rapidly, is significantly important. This article proposes a novel power control method capable of compensating for the instability of the dc bus voltage owing to the transient state in the dc power supply system. This method minimizes overshoot and ensures fast responsiveness and system stability by calculating the flux linkage of an inductor in a bidirectional dc–dc converter and applying an excitation voltage without pulsewidth modulation switching. The performance and validity of the proposed transient high-speed controller are verified through simulations and experiments.

Multistack and multimotor powertrain systems have significant potential for improving the efficiency and performance of fuel cell electric vehicles (FCEVs) compared to conventional powertrain systems. To achieve a superior powertrain system, the major components such as the stack, motor, and transmission of the multistack and multimotor systems should be optimized. To analyze the energy efficiency and dynamic performance of the FCEV, an FCEV analysis model was developed. This model included a two‐stack and two‐motor powertrain system (2S2M) employing a stack power and motor torque distribution strategy. An optimization problem was formulated with stack transition power, motor torque distribution, and transmission gear ratios as the optimization variables and hydrogen consumption and acceleration time as the objectives for efficiency and performance measures, respectively. An artificial neural network (ANN) model‐based optimization method was used to address the computational burden of multiobjective optimization. The optimization results highlighted the Pareto front for the FCEV employing 2S2M, showing a trade‐off relationship between the efficiency and performance of the FCEV. Compared to the conventional powertrain system, the 2S2M can reduce hydrogen consumption and acceleration time by up to 7.9% and 6.2%, respectively. An analysis of the distribution of optimal solutions and a comparison of the Pareto fronts for each optimization variable highlighted the necessity for the proposed system optimization method. Furthermore, a comparison between the FCEV and ANN models in terms of computational time for the optimization demonstrated the effectiveness of the ANN model‐based multiobjective optimization.

Stratospheric drones operating in extreme environments are very important for predicting reliability and are high-efficiency, high-performance, and lightweight power units. Multilevel inverters are suitable for application as power conversion units for stratospheric drones. A guideline is needed to evaluate whether it is suitable for practical application from a reliability perspective among various multilevel topologies. Existing reliability prediction models cannot reflect the operating characteristics of multilevel inverters. In this paper, we analyze the driving characteristics of each topology from the perspective of half-bride, which is the basic configuration of multilevel inverters, and we propose a fault tree analysis (FTA) design with three operating modes. The proposed method has the advantage of being able to easily analyze the failure rate by expanding to single-phase and three-phase and to analyze the failure rate according to changes in modulation index (MI) and power factor (PF). The failure rates of the proposed method and the part count method are analyzed using MIL-HDBK-217F. We also analyze the impact of different various operating characteristics on the failure rate. From a reliability perspective, we provide a variety of guidelines for selecting a multilevel topology that fits the operation conditions.

This study proposes an artificial neural network-based diagnostic technique for diagnosing the aging status of the Insulated Gate Bipolar Transistor (IGBT), a core component of a three-phase inverter system installed in a stratospheric solar drone, and predicting their remaining useful life (RUL). Due to environmental constraints that make it difficult to collect actual operating data, 15,625 training data sets were constructed through MATLAB/Simulink-based inverter system simulations that reflect various IGBT aging conditions. The learning data was processed to use the change rate of the inverter three-phase voltage and voltage components (positive sequence, negative sequence, zero sequence) according to the symmetric coordinate method, based on the change in the on-state resistance (R_on) value of the IGBT as input feature data. Based on this, we implemented and trained a classification model to predict the Risk Level and a regression model to predict the Combined Aging Index. The classification model achieved 94.49% overall accuracy and 97.61% accuracy for severe aging prediction, while the regression model attained a coefficient of determination (R²) of 0.8718, RMSE of 2.56, and MAE of 1.84, demonstrating strong predictive performance. Feature contribution analysis further identified the reverse-phase voltage change rate as the most critical indicator, accounting for 34.2% of the predictive power in inverter aging diagnosis. This study verified the robustness of the model through simulations that reflected a noisy operating environment. Future validation using operational data and the inclusion of various failure modes can enhance the generalization performance of the predictive model. The proposed technique can be used as a diagnostic technology for predictive maintenance and assurance of reliability in aircraft inverter systems, which require high reliability.

This study proposes a novel current controller to enhance the transient response characteristics and control responsiveness of energy storage systems (ESSs) in DC power systems. To achieve this, an Enhanced Transient High-Speed Controller (ETHSC) is introduced, which improves upon the conventional Transient High-Speed Controller (THSC) by incorporating real-time error compensation for excitation voltage application time. The ETHSC calculates the inductor’s flux linkage $\lambda$ in real-time and corrects errors caused by the nonlinear characteristics of inductance, ensuring more stable control performance. This study further analyzes the limitations of the conventional THSC and mathematically formulates the ETHSC architecture, introducing a real-time inductance correction algorithm to enhance transient response and stability. The proposed controller was implemented in a boost converter topology and compared with the conventional THSC. Through PSIM simulations and a 500 W prototype experiment, the ETHSC demonstrated its ability to maintain fast transient response while significantly reducing overshoot caused by inductor nonlinearity. These results confirm that the ETHSC provides a more stable and efficient control strategy compared to conventional transient controllers, making it a promising solution for high-performance DC power systems.

The global population is rapidly increasing, and the urban population is on an even faster trend; therefore, the population density is expected to rise. As the number of people in cities grows, the demand for high-rise buildings is anticipated to increase to address the problem of limited land resources. Therefore, efficient energy management using distributed resources has become increasingly important. Elevators are a vital vertical means of transportation in high-rise buildings, and reducing the weight of their components can lead to favorable conditions for energy utilization and increased speed. Therefore, this study presents an elevator system that supplies power inside an elevator car by eliminating the traveling cable and applying a small-capacity energy storage system (ESS). Additionally, we propose a charging algorithm suitable for the proposed system. Generally, batteries have sensitive electrical properties among the distributed energy resources (DERs). Therefore, controlling the stable maintenance of the transient state of the charging current—even when the DC power is unstable or the load changes rapidly in a system requiring fast charging—is crucial. Owing to the nature of the elevator system to be applied, discontinuous charging is frequent, and the active and efficient management of the battery state of charge (SOC) may be challenging. In addition, since it is necessary to be able to charge as much as possible during a short discontinuous charging time, a current control algorithm with a stable and high-speed response is required. The proposed transient high-speed tracking controller (THSTC) is a method for tracking the time of applying an inductor’s excitation voltage without pulse–width modulation (PWM) switching, which is less sensitive to the controller gain values and has fast responsiveness as well as stable transient response characteristics. The proposed method has good dynamic characteristics with a simple control structure without a complex design, which is useful for systems with repeated discontinuous charging. We validate the performance and effectiveness of the proposed controller through simulations and experiments.