Hoang-Long Dang

The mitigation of output DC ripple in three-phase current source rectifiers represents a significant design challenge, directly impacting the quality of the delivered power. To address this issue, this paper introduces a novel control framework based on the concept of Virtual Space Vector (VSV) modulation. The fundamental operating principle of the proposed strategy is to systematically reduce the dwell time of the active vector with the largest magnitude within each control sector. This principle is realized through a two-fold process. First, a set of virtual vectors is constructed, with each being a linear combination of two adjacent active switching vectors. Second, within every switching period, the desired current reference vector is synthesized not by the original active vectors, but by a combination of two neighboring virtual vectors and a zero vector. Comprehensive simulation results corroborate the effectiveness of this approach in attenuating DC-link current ripple.

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.

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.

The intricate spectrum of arc faults elicited by diverse load types introduces a complex and formidable challenge in residential series arc fault detection. Series DC arc faults pose a significant concern as they can potentially instigate fire incidents and exert adverse ramifications on power systems if left undetected. Nonetheless, their detection within practical power systems remains challenging, predominantly attributed to the meager arc current magnitude, the absence of a discernible zero-crossing interval, and the manifestation of multifarious aberrant behaviors contingent upon the diverse array of power loads and controllers. Importantly, the conventional safeguards, notably encompassing protection fuses, may exhibit inefficacy in promptly activating during the occurrence of series DC arc faults. The ramifications of undiscerned arc faults are profound, with the potential for engendering erroneous operational modes within power systems, thereby amplifying the risk of material and human casualties. In light of these exigencies, the development of an efficacious detection mechanism targeting series arc faults within DC systems becomes a paramount imperative. This research proposed a preprocessing signal to eliminate the switching noises, which could degrade the performance of artificial machine learning algorithms. The diagnosis results valid the effectiveness of the proposed diagnosis scheme for all ranges of switching frequencies.

Direct current (DC) networks play a pivotal role in the growing integration of renewable energy sources. However, the occurrence of DC arc faults can introduce disruptions and pose fire hazards within these networks. In order to ensure both safety and optimal functionality, it becomes imperative to comprehend the characteristics of DC arc faults and implement a dependable detection system. This paper introduces an innovative arc fault detection algorithm that leverages current filtering based on the empirical rule in conjunction with intelligent machine learning techniques. The core of this approach involves the sampling and subsequent filtration of current using the empirical rule. This filtering process effectively amplifies the distinctions between normal and arcing states, thereby enhancing the overall performance of the intelligent learning techniques integrated into the system. Furthermore, this proposed diagnosis scheme requires only the signal from the current sensor, which reduces the complexity of the diagnosis scheme. The results obtained from the detection process serve to affirm the effectiveness and reliability of the proposed DC arc fault diagnosis scheme.

The reliability of the electronic converter is a vital concern in an industrialized area. Capacitors are critical in electronic converters and are more likely to fail than other electronic gears. Due to aging, the capacitor progressively loses its original quality and capacitance, and the equivalent series resistance escalates. Hence, condition monitoring is a fundamental procedure for evaluating capacitor health that affords prognostic repairs to guarantee stability in power networks. The ESR and capacitance of the capacitor are commonly employed to estimate the condition grade. This study proposes an estimation scheme that utilizes the source current to assess the health condition of an aluminum capacitor. Several advanced intelligence techniques are adopted to estimate the parameters of an AEC in a three-phase inverter system. First, different signals used as inputs, such as input power, capacitor current, voltage, and power, output current, voltage, and power, are analyzed using fast Fourier transform and discrete wavelet transform analysis. Then, various indexes of the analyzed signals, such as RMS, average, median, and variance, are used as the inputs in learning models to monitor the AEC’s parameters. In addition, various input signals are combined to obtain the best combinations for capacitor monitoring. The estimated results prove that utilizing the source current combined with selected indexes improves the monitoring accuracy of the AEC’s health status.

Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical system. DC arc faults are often sorted into two types: series and parallel arcs. A series arc may be the outcome of discharging links in electrical wiring. By contrast, the parallel arc occurs between two electric wires, or between a link and a ground, owing to contamination or poor isolation. The currents in a system with an arc fault are considerably greater when the arc parallel in nature than when the arc is series in nature. In this paper, the electric activities of a network are investigated for the duration of series and parallel arc failures in both the time and frequency domains. The arcing behavior investigated is selected to allow for the identification of series and parallel arcs. The sorting of electrical arcs in an accurate and reliable manner is useful for electrical protection schemes. The identification process used here is based on data related to different domains, such as load current and voltage. In this study, eight learning techniques are investigated with the aim of detecting series and parallel arc faults. The arc behaviors were studied in the various domains. We used the load current and voltage characteristics as an statistic for categorizing a given arc failure. This study could be beneficial to enhance the stability and reliability of arc-fault detectors.