Silicon carbide (SiC) MOSFETs, as a member of the emerging technology of wide-bandgap (WBG) semiconductors, are transforming high-power and high-temperature applications due to their superior electrical and thermal properties. Their potential to outperform traditional silicon-based devices, particularly in terms of efficiency and operational stability, has made them a popular choice for power electronics. However, reliability issues about numerous failure types, including gate-oxide degradation, threshold voltage instability, and body diode degeneration, remain serious challenges. This article critically evaluates the key failure mechanisms that affect SiC MOSFET reliability and their impact on device performance. Furthermore, this paper discusses current advances in SiC technology, including both improvements and continued dependability difficulties. Key areas of future study are suggested, with an emphasis on improved material characterization, thermal management, and creative device architecture to improve SiC MOSFET performance and long-term reliability. The insights presented will help to improve the design and testing processes required for SiC MOSFETs’ widespread use in critical high-power applications.

This paper remedies the lack of comparison between studies specifically addressing partial discharge (PD) localization using electrical techniques. It identifies all the elements in need in each technique as well as the equations leading to a precise determination of the discharge site in a cable with a certain length and documents several circuit models set to simulate various types of PD. From the details in this paper, different detection methods can be combined based on the specific requirements of each method for detecting PD. This work thoroughly evaluates several electrical PD detection approaches, including time-based, frequency band, and electromagnetic time reversal (EMTR). Additionally, it gathers circuit modeling for various types of PD along cables to improve detection accuracy. It is evident that all time-dependent methods, despite their simplicity and requiring only a small number of components, face challenges when applied to long cables. This is primarily due to their reliance on signal propagation time. The authors provide profound insights into suggestions for future study areas. This review will provide essential insights and serve as a foundation for researchers to develop more effective methods for detecting PD in cables.

Silicon carbide (SiC) power modules are increasingly being used in high-voltage and high-frequency applications due to their superior electrical and thermal qualities. However, the issue of the partial discharge (PD) phenomenon raises serious reliability difficulties resulting in insulation failure, performance degradation, and potential device collapse. This paper provides a thorough assessment of the current PD detection strategies in SiC power modules. The issues provided by SiC devices’ distinct operational features, such as high switching frequencies and higher voltage stresses, which hinder PD detection and mitigation, are widely investigated. This review compares the effectiveness, benefits, and limitations of various detection methods, emphasizing the need for better strategies to ensure long-term reliability and performance. This study gives an in-depth overview of the numerous forms of PD phenomena that occur in power modules, including internal and surface discharges, as well as how they appear under various detection systems. It examines the performance of several methods for power module technologies such as SiC. To address these PD issues, this article proposes ways to improve reliability and detection accuracy.

To date, one of the main tools for evaluating the reliability of an insulation system is the continuous monitoring of those phenomena which, by interacting with the elements of the system, can induce aging processes or failures. For power grids, a signal that identifies possible aging or improper use of the component is Partial Discharge (PD) activity. Generally, the evaluation of the PD phenomenon is carried out through a two-step procedure: measurement and data analysis. To optimize the PD analysis process, increasingly sophisticated PD separation/classification algorithms are needed. Especially for the measurements carried out in HVDC systems for which the absence of a phase reference makes more difficult to identify the different types of discharge. The purpose of this article is to investigate the possibility of optimizing the input data to a hierarchical clustering algorithm in order to obtain a subdivision of the dataset more faithful to the real behavior of the phenomena. Specifically, the proposed approach is based on the use of the cross-correlation matrix to carry out the clustering operation. This matrix replaces the matrix of the distances among the points distributed in the map used for the representation of the data. Results show that with this modification it is possible to separate phenomena that present partially or completely overlapping patterns. Moreover, the algorithm turns out to be automatic and does not require the choice of references or thresholds to define the similarity among pulses.

To date, continuous monitoring of power grid elements susceptible to aging processes is essential to increase the reliability of these systems and avoid failures. In the field of energy transmission, an index of the state of a cable insulation system is partial discharge activity, which is influenced by the presence of structural defects (e.g. voids within the material). In High-Voltage-Direct-Current applications, given the dependence of dielectric conductivity on temperature, this phenomenon is also influenced by the load. Thus, partial discharge activity is strongly related to the operating condition of the line and not only by the aging of materials. Given the importance of partial discharges monitoring, it is necessary to develop techniques for analyzing them that allow the identification of different types of discharges. The lack of a reference standard prompts researchers to propose different approaches to achieve these goals. This paper describes a comparison of two separation algorithms both based on cross-correlation. Data used for comparison have been obtained through a partial discharges measurement, under DC voltage stress, on XLPE model cable. The first algorithm evaluates the similarity among the pulses and separates them into clusters following the acquisition order. The second one, on the other hand, uses the correlation matrix as input data, which must be calculated before running the algorithm. The results show that both algorithms succeed in identifying the same phenomena. The former with less accuracy but employing less computation time than the latter. The latter, on the other hand, provides higher accuracy but requires longer computation time. The choice in using either algorithm can thus be traced back to the desired accuracy/time ratio.

This work is aimed at characterizing the main partial discharges phenomena under HVDC stress and with the presence of thermal gradient. Specifically, the increase in discharge activity without changes in applied voltage is investigated. Even the supply and disconnection phases are excluded from the analyzed acquisition range. In this way, any variation in discharge activity can be attributed only to thermal effects. A setup consisting of a pair of specimens connected in series and supplied with a voltage of 20 kV DC has been used for the tests. One specimen is defect-free and immersed in a tank containing silicone oil. The other is designed to give rise to certain discharge phenomenon. Three tests were carried out using specimens for internal, surface, and corona discharges. For each test, two acquisitions, lasting 30 minutes, have been made. One with both specimens at room temperature and another with the healthy specimen heated through a resistive element in order to obtain a discrete thermal gradient. Thus, a total of 6 acquisitions have been made and analyzed. The results show that for all three specimens the discharge activity undergoes a significant increase in the transition from the room-temperature condition to the thermal gradient condition. The increase in the amount of detected discharge is greater in tests with corona and surface specimens. In general, an increase in average discharge amplitude is also observed. The observed changes can be attributed to a different distribution of the applied voltage between the two specimens due to changes in the conductivity of the defect-free specimen caused by heating. This behavior is also found in variations in the electric field profile in HVDC applications due to the presence of heat sources that generate thermal gradients. For example, load currents for cables.