Charge transport and breakdown physics in liquid/solid insulation systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 223-230).Liquid dielectrics provide superior electrical breakdown strength and heat transfer capability, especially when used in combination with liquid-immersed solid dielectrics. Over the past half-century, there has been extensive research characterizing "streamers" in order to prevent them, as they are the main origins of electrical breakdown in liquid dielectrics. Streamers are conductive structures that form in regions of liquid dielectrics that are over-stressed by intense electric fields. Streamers can transform to surface flashovers when they reach any liquid-immersed solid insulation. Surface flashovers usually propagate faster and further than streamers in similar electric field intensity. Charge generation and transport is crucially important in liquid dielectric breakdown, since without the presence of the electric charge and its ability to migrate in the liquid dielectric volume and on the interface of liquid/solid dielectrics, streamers and surface flashovers are unable to develop. In this thesis, we develop a finite element method transport model in one, two and threedimensional geometries to help understand the complicated dynamics of electric charge transport and streamer breakdown in liquid dielectrics. This electrohydrodynamic model clarifies many of the mechanisms behind streamer/surface flashover formation, propagation and branching in typical liquid/solid dielectric composite systems. Several key mechanisms have been identified and added to the transport model of streamers, such as effects of electric field intensity on the ionization potential of liquid dielectric molecules and electron velocity saturation, which make the modeling results more realistic. In addition to improving the understanding of electrical breakdown physics in liquid-based insulation systems, a significant effort is made throughout this thesis research to enhance the stability, convergence, speed and accuracy of the model, making it a convenient and reliable tool for designing high voltage components that contain pure liquid dielectrics, nanofluids and liquid immersed insulation systems. This model, for the first time, is able to treat any given electrode shape and gap distance as well as any applied voltage waveform with accurate results, which provides a convenient preliminary way to verify the performance of an insulation system in terms of breakdown voltage, time to breakdown, electric field intensity distribution and ionization level. The model precision is validated through experimental records, analytical solutions and alternative modeling approaches wherever available. Specifically, we verify our one-dimensional numerical results with exact analytical solutions, and our two and three-dimensional modeling results with experimental data found in the literature or provided by ABB Corporate Research, Sweden. The streamer initiation voltages, number of streamer branches, breakdown voltages and currents are in excellent agreement with the experimental data compared to the prior theoretical research on liquid breakdown physics. Identical results obtained using a finite volume method also confirm the correctness of the finite element approach used in this thesis. The presented model can be employed to search for novel configurations of liquid immersed insulation systems including nanofluids and liquid/solid composite systems.by Jouya Jadidian.Ph.D

High Voltage Insulating Materials-Current State and Prospects

Studies on new solutions in the field of high-voltage insulating materials are presented in this book. Most of these works concern liquid insulation, especially biodegradable ester fluids; however, in a few cases, gaseous and solid insulation are also considered. Both fundamental research as well as research related to industrial applications are described. In addition, experimental techniques aimed at possibly finding new ways of analysing the experimental data are proposed to test dielectrics

Selected Papers from 2020 IEEE International Conference on High Voltage Engineering (ICHVE 2020)

The 2020 IEEE International Conference on High Voltage Engineering (ICHVE 2020) was held on 6–10 September 2020 in Beijing, China. The conference was organized by the Tsinghua University, China, and endorsed by the IEEE Dielectrics and Electrical Insulation Society. This conference has attracted a great deal of attention from researchers around the world in the field of high voltage engineering. The forum offered the opportunity to present the latest developments and different emerging challenges in high voltage engineering, including the topics of ultra-high voltage, smart grids, and insulating materials

Development of a quantitative health index and diagnostic method for efficient asset management of power transformers

Power transformers play a very important role in electrical power networks and are frequently operated longer than their expected design life. Therefore, to ensure their best operating performance in a transmission network, the fault condition of each transformer must be assessed regularly. For an accurate fault diagnosis, it is important to have maximum information about an individual transformer based on unbiased measurements. This can best be achieved using artificial intelligence (AI) that can systematically analyse the complex features of diagnostic measurements. Clustering techniques are a form of AI that is particularly well suited to fault diagnosis. To provide an assessment of transformers, a hybrid k-means algorithm, and probabilistic Parzen window estimation are used in this research. The clusters they form are representative of a single or multiple fault categories. The proposed technique computes the maximum probability of transformers in each cluster to determine their fault categories. The main focus of this research is to determine a quantitative health index (HI) to characterize the operating condition of transformers. Condition assessment tries to detect incipient faults before they become too serious, which requires a sensitive and quantified approach. Therefore, the HI needs to come from a proportionate system that can estimate health condition of transformers over time. To quantify this condition, the General Regression Neural Network (GRNN), a type of AI, has been chosen in this research. The GRNN works well with small sets of training data and avoids the needs to estimate large sets of model parameters, following a largely non-parametric approach. The methodology used here regards transformers as a collection of subsystems and summarizes their individual condition into a quantified HI based on the existing agreed benchmarks drawn from IEEE and CIGRE standards. To better calibrate the HI, it may be mapped to a failure probability estimate for each transformer over the coming year. Experimental results of the research show that the proposed methods are more effective than previously published approaches when diagnosing critical faults. Moreover, this novel HI approach can provide a comprehensive assessment of transformers based on the actual condition of their individual subsystems

Development of a quantitative health index and diagnostic method for efficient asset management of power transformers

Power transformers play a very important role in electrical power networks and are frequently operated longer than their expected design life. Therefore, to ensure their best operating performance in a transmission network, the fault condition of each transformer must be assessed regularly. For an accurate fault diagnosis, it is important to have maximum information about an individual transformer based on unbiased measurements. This can best be achieved using artificial intelligence (AI) that can systematically analyse the complex features of diagnostic measurements. Clustering techniques are a form of AI that is particularly well suited to fault diagnosis. To provide an assessment of transformers, a hybrid k-means algorithm, and probabilistic Parzen window estimation are used in this research. The clusters they form are representative of a single or multiple fault categories. The proposed technique computes the maximum probability of transformers in each cluster to determine their fault categories. The main focus of this research is to determine a quantitative health index (HI) to characterize the operating condition of transformers. Condition assessment tries to detect incipient faults before they become too serious, which requires a sensitive and quantified approach. Therefore, the HI needs to come from a proportionate system that can estimate health condition of transformers over time. To quantify this condition, the General Regression Neural Network (GRNN), a type of AI, has been chosen in this research. The GRNN works well with small sets of training data and avoids the needs to estimate large sets of model parameters, following a largely non-parametric approach. The methodology used here regards transformers as a collection of subsystems and summarizes their individual condition into a quantified HI based on the existing agreed benchmarks drawn from IEEE and CIGRE standards. To better calibrate the HI, it may be mapped to a failure probability estimate for each transformer over the coming year. Experimental results of the research show that the proposed methods are more effective than previously published approaches when diagnosing critical faults. Moreover, this novel HI approach can provide a comprehensive assessment of transformers based on the actual condition of their individual subsystems

Condition Monitoring of Subsea Connectors - Partial Discharge Detection Using a High Frequency Sensor

This thesis discusses possible causes of failure and potential condition indicators that may be used to monitor the condition of the insulation system of a subsea connector. The main focus is to describe how high frequency capacitive couplers can be used to detect the presence of partial discharges in subsea connectors. Fac- tors relevant to the sensitivity and implementation of high frequency capacitive couplers are discussed and an approximate model of the coupler is created. The experimental work were conducted at the high voltage laboratory at NTNU/Sintef Energy Research where capacitive couplers were constructed using copper foil before being mounted on the outer semi-conductive screen of a 12 kV medium voltage cable. The functionality and sensitivity of the couplers were investigated with both pulses from a calibrator and real partial discharges. The signal received from the capacitive couplers were analyzed using a spectrum ana- lyzer and the signal power were compared to the measured apparent charge from a conventional partial discharge measuring setup. The results show that by connecting a simple high frequency capacitive coupler, mounted on the outside of the outer semi-conductive layer to a spectrum analyzer, partial discharges can be detected. The experiments suggest that calibration sig- nals down to a magnitude equivalent of an apparent discharge of 2 pC can be detected, depending on the location of the discharge. The frequency spectrum of the partial discharge signal is shown to be dependent on the type of discharge and the measured signal magnitude increases with increasing partial discharge activity. Based on the results obtained it was concluded that high frequency capacitive couplers is a promising way of detecting and monitoring partial dis- charges for the purpose of on-line condition monitoring of high voltage subsea connectors

Conference Proceedings: 1st International Conference on Nanofluids (ICNf2019), 2nd European Symposium on Nanofluids (ESNf2019)

Conference proceedings of the 1st International Conference on Nanofluids (ICNf2019) and 2nd European Symposium on Nanofluids (ESNf2019), 26-28 June 2019 in Castelló (Spain), organized by Nanouptake Action (CA15119) and Universitat Jaume

Proceeding Of Mechanical Engineering Research Day 2016 (MERD’16)

This Open Access e-Proceeding contains a compilation of 105 selected papers from the Mechanical Engineering Research Day 2016 (MERD’16) event, which is held in Kampus Teknologi, Universiti Teknikal Malaysia Melaka (UTeM) - Melaka, Malaysia, on 31 March 2016. The theme chosen for this event is ‘IDEA. INSPIRE. INNOVATE’. It was gratifying to all of us when the response for MERD’16 is overwhelming as the technical committees received more than 200 submissions from various areas of mechanical engineering. After a peer-review process, the editors have accepted 105 papers for the e-proceeding that cover 7 main themes. This open access e-Proceeding can be viewed or downloaded at www3.utem.edu.my/care/proceedings. We hope that these proceeding will serve as a valuable reference for researchers. With the large number of submissions from the researchers in other faculties, the event has achieved its main objective which is to bring together educators, researchers and practitioners to share their findings and perhaps sustaining the research culture in the university. The topics of MERD’16 are based on a combination of fundamental researches, advanced research methodologies and application technologies. As the editor-in-chief, we would like to express our gratitude to the editorial board and fellow review members for their tireless effort in compiling and reviewing the selected papers for this proceeding. We would also like to extend our great appreciation to the members of the Publication Committee and Secretariat for their excellent cooperation in preparing the proceeding of MERD’16