Sangshin Kwak

In recent years, the pulse-width-modulation (PWM) converter has been found to have extensive applications in renewable energy, industrial fields, and others. The high efficiency requirement is crucial to operating a PWM rectifier in various applications, in addition to the fundamental control objectives of sinusoidal grid currents and the correct DC bus voltage. Additionally, in practical application, another issue arises when the grid voltage frequently experiences distortion, leading to a distorted grid current and a significant rise in total harmonic distortion (THD). To resolve these problems, a model predictive virtual flux-based direct power control (MPVFDPC) with improved power loss performance is proposed based on an integrated switching state predetermination strategy. The proposed MPVFDPC for PWM rectifier inherits the merits of both virtual flux control and direct power control, which have fast dynamic performance and the grid current THD is considerably decreased under distorted grid voltage states. The proposed technique aims to minimize switching loss under ideal and distorted grid voltage states by exploiting the discontinuous modulation concept by using a switching state predetermination strategy. The MPVFDPC with switching state predetermination strategy is proven by employing it in experiments as well as simulations in comparison with previous models: predictive direct power control (Conv. MPDPC) and conventional MPVFDPC (Conv. MPVFDPC). The acquired waveforms and quantitative data are employed to prove the effectiveness of the developed algorithm.

DC microgrids are increasingly becoming the backbone of renewable energy integration. Their ability to efficiently manage intermittent sources like solar and wind power is transforming the energy landscape. However, a critical challenge remains in the form of DC arc faults, which can significantly compromise the reliability and safety of these systems. Parallel arc faults represent a particularly challenging scenario due to their unique electrical behavior. Unlike series arc faults, which cause a decrease in system current, parallel arcs can lead to a significant increase in current due to the low resistance path they create. This research delves into the electrical behavior of DC systems during parallel arc faults. By analyzing the source current signals in different domains, the authors aim to identify specific characteristic features of the source current that can serve as reliable indicators combined with artificial learning models for arc fault diagnosis. The findings of this research can have significant implications for the improvement of advanced arc failure recognition systems. This research represents a valuable step towards safe and reliable DC systems by addressing the challenge of parallel arc fault detection.

The expansion of DC electrical distribution systems necessitates advancements in detecting and mitigating DC arc events, a significant contributor to fire accidents in low-voltage DC distribution systems. Detecting DC arc faults poses considerable challenges, making them a major safety concern in DC power lines. Conventional approaches mainly rely on arc current, which can vary during normal operation, potentially leading to false alarms. Moreover, these methods often require manual adjustment of detection thresholds for different systems, introducing the risk of malfunction. This study proposes an advanced arc fault recognition procedure that extracts and utilizes various key features for the DC arc detection. This work investigated new various features, which are the square average, the average, the median, the rms, the peak-to-peak, and the variance values, to find out which one can be the most effective features to detect the DC arc failure. The results of this detection process show good evidence for the effectiveness and reliability of the proposed malfunction detecting plan.

Active front-end (AFE) rectifiers are a well-known solution for industrial applications because of several advantages such as bidirectional power flow, low harmonic distortion of the line currents, and the possibility of the dc-link voltage adjustment. Typically, in three-phase AFE rectifiers, the power is distributed evenly among the phase legs, aiming for a natural balancing of aging over time. However, uneven thermal stress caused by different switching frequencies or design of cooling system and prior replacement of failure power switches might create an unavoidable aging mismatch between phase legs of AFE rectifier. In this article, a per-phase model predictive direct power control with offset voltage injection is proposed. The proposed method notably reduces power loss of the leg having highest aging state to improve the lifespan of rectifier and reduce the maintenance costs. The active power and reactive power of the AFE rectifier are regulated using predicted rectifier voltages, which are modified by injecting proper offset voltage to decrease switching loss in the leg having highest aging state. The minimum power loss in the leg having highest aging state involves avoiding changing the corresponding switching state for two-thirds of the fundamental period. The simulation and experimental results are presented in comparison to conventional model predictive direct power control to confirm the effectiveness of the proposed technique.

DC microgrids are vital for integrating renewable energy sources into the grid, but they face the threat of DC arc faults, which can lead to malfunctions and fire hazards. Therefore, ensuring the secure and efficient operation of DC systems necessitates a comprehensive understanding of the characteristics of DC arc faults and the implementation of a reliable arc fault detection technique. Existing arc-fault detection methods often rely on time–frequency domain features and machine learning algorithms. In this study, we propose an advanced detection technique that utilizes a novel approach based on feature differences between moving intervals and advanced learning techniques (ALTs). The proposed method employs a unique approach by utilizing a time signal derived from power supply-side signals as a reference input. To operationalize the proposed method, a meticulous feature extraction process is employed on each dataset. Notably, the difference between features within distinct moving intervals is calculated, forming a set of differentials that encapsulate critical information about the evolving arc-fault conditions. These differentials are then channeled as inputs for advanced learning techniques, enhancing the model’s ability to discern intricate patterns indicative of DC arc faults. The results demonstrate the effectiveness and consistency of our approach across various scenarios, validating its potential to improve fault detection in DC systems.

DC series arc faults pose a significant threat to the reliability of DC systems, particularly in DC generation units where aging components and high voltage levels contribute to their occurrence. Recognizing the severity of this issue, this study aimed to enhance DC arc fault detection by proposing an advanced recognition procedure. The methodology involves a sophisticated combination of current filtering using the Three-Sigma Rule in the time domain and the removal of switching noise in the frequency domain. To further enhance the diagnostic capabilities, the proposed method utilizes time and frequency signals generated from power supply-side signals as a reference input. The time–frequency features extracted from the filtered signals are then combined with artificial learning models. This fusion of advanced signal processing and machine learning techniques aims to capitalize on the strengths of both domains, providing a more comprehensive and effective means of detecting arc faults. The results of this detection process validate the effectiveness and consistency of the proposed DC arc failure identification schematic. This research contributes to the advancement of fault detection methodologies in DC systems, particularly by addressing the challenges associated with distinguishing arc-related distortions, ultimately enhancing the safety and dependability of DC electrical systems.

Three-phase AC/DC pulse-width-modulated (PWM) converters have been widely employed in various renewable energy systems and industrial applications, which require a high-efficiency power converter operation. This article proposes a technique to reduce switching loss in AC/DC converters by integrating a voltage vector preselection strategy to model predictive virtual flux control. The voltage vector preselection strategy preselects available voltage vectors corresponding to switching states that lead to minimum switching loss in the phase leg, which conducts the highest current. By using preselected voltage vectors, clamping intervals are generated at every fundamental period to maintain the present switching states of the power switches, resulting in the reduction in switching loss. Additionally, by using virtual flux control, the proposed approach can effectively be used under both ideal and distorted source voltage conditions. The proposed method is compared with the conventional model predictive current control and the conventional model predictive virtual flux control. Both a simulation and experiment are performed to validate the correctness and effectiveness of the proposed method, which has been found to decrease the switching loss of an AC/DC converter by up to 15% compared to conventional control schemes at a negligible increase in the input current total harmonic distortion and DC output voltage ripple.