Browsing by Author "Razavi Borghei, Seyyed Moein"

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    The Modeling of Partial Discharge under Fast, Repetitive Voltage Pulses Using Finite-Element Analysis
    Razavi Borghei, Seyyed Moein (Virginia Tech, 2020-04)
    By 2030, it is expected that 80% of all electric power will flow through power electronics systems. Wide bandgap power modules that can tolerate higher voltages and currents than silicon-based modules are the most promising solution to reducing the size and weight of power electronics systems. These wide-bandgap power modules constitute powerful building blocks for power electronics systems, and wide bandgap-based converter/power electronics building blocks are envisaged to be widely used in power grids in low- and medium-voltage applications and possibly in high-voltage applications for high-voltage direct current and flexible alternating current transmission systems. One of the merits of wide bandgap devices is that their slew rates and switching frequencies are much higher than silicon-based devices. However, from the insulation side, frequency and slew rate are two of the most critical factors of a voltage pulse, influencing the level of degradation of the insulation systems that are exposed to such voltage pulses. The shorter the rise time, the shorter the lifetime. Furthermore, lifetime dramatically decreases with increasing frequency. Thus, although wide bandgap devices are revolutionizing power electronics, electrical insulating systems are not prepared for such a revolution; without addressing insulation issues, the electronic power revolution will fail due to dramatically increased failure rates of electrification components. In this regard, internal partial discharges (PDs) have the most effect on insulation degradation. Internal PDs which occur in air-filled cavities or voids are localized electrical discharges that only partially bridge the insulation between conductors. Voids in solid or gel dielectrics are challenging to eliminate entirely and may result simply during manufacturing process. The objective of this study is to develop a Finite-Element Analysis (FEA) PD model under fast, repetitive voltage pulses, which has been done for the first time. The model is coded and implemented in COMSOL Multiphysics linked with MATLAB, and its simulation results are validated with experimental tests. Using the model, the influence of different parameters including void shape, void size, and void air pressure on PD parameters are studied.
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    Partial Discharges: Experimental Investigation, Model Development, and Data Analytics
    Razavi Borghei, Seyyed Moein (Virginia Tech, 2022-02-11)
    Insulation system is an inseparable part of electrical equipment. In this study, one of the most important aging factors in insulation systems known as partial discharge (PD) is targeted. PD phenomenon has been studied for more than a century and yet new technologies still demand the investigation of PD impact. Nowadays, electrification is penetrating into various fossil-fuel-based industries such as transportation system that demands the reliability of electrical equipment under various harsh environmental conditions. Due to the lack of knowledge on the behavior of insulation systems, research in this area is intensively needed. The current study probes into the partial discharge phenomenon from two aspects and the groundwork for both aspects are provided by experimentation of multiple PD types. In the first goal, a finite-element analysis (FEA) approach is developed based on measurement data to estimate electric field distribution. The FEA model is coupled with a programming scheme to evaluate PD conditions, calculate PD metrics, and perform statistical analysis of the results. For the second target, it is aimed to use deep neural networks to identify and discriminate different sources of PD. The measurement data are used to generate thousands of phase-resolved PD (PRPD) images that will be used for training deep learning models. To meet the characteristics of the dataset, a deep residual neural network is designed and optimized to discriminate PD sources in an accurate, stable, and time-efficient way. The outcome of this research enhances the reliability of electrical apparatus through a better understanding of the PD behavior and lays a foundation for automatic monitoring of PD sources.