2,934 research outputs found

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

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    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

    Time domain analysis of switching transient fields in high voltage substations

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    Switching operations of circuit breakers and disconnect switches generate transient currents propagating along the substation busbars. At the moment of switching, the busbars temporarily acts as antennae radiating transient electromagnetic fields within the substations. The radiated fields may interfere and disrupt normal operations of electronic equipment used within the substation for measurement, control and communication purposes. Hence there is the need to fully characterise the substation electromagnetic environment as early as the design stage of substation planning and operation to ensure safe operations of the electronic equipment. This paper deals with the computation of transient electromagnetic fields due to switching within a high voltage air-insulated substation (AIS) using the finite difference time domain (FDTD) metho

    Automatic Tuning of Silicon Photonics Millimeter-Wave Transceivers Building Blocks

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    Today, continuously growing wireless traffic have guided the progress in the wireless communication systems. Now, evolution towards next generation (5G) wireless communication systems are actively researched to accommodate expanding future data traffic. As one of the most promising candidates, integrating photonic devices in to the existing wireless system is considered to improve the performance of the systems. Emerging silicon photonic integrated circuits lead this integration more practically, and open new possibilities to the future communication systems. In this dissertation, the development of the electrical wireless communication systems are briefly explained. Also, development of the microwave photonics and silicon photonics are described to understand the possibility of the hybrid SiP integrated wireless communication systems. A limitation of the current electrical wireless systems are addressed, and hybrid integrated mm-wave silicon photonic receiver, and silicon photonic beamforming transmitter are proposed and analyzed in system level. In the proposed mm-wave silicon photonic receiver has 4th order pole-zero silicon photonic filter in the system. Photonic devices are vulnerable to the process and temperature variations. It requires manual calibration, which is expensive, time consuming, and prone to human errors. Therefore, precise automatic calibration solution with modified silicon photonic filter structure is proposed and demonstrated. This dissertation demonstrates fully automatic tuning of silicon photonic all-pass filter (APF)-based pole/zero filters using a monitor-based tuning method that calibrates the initial response by controlling each pole and zero individually via micro-heaters. The proposed tuning approach calibrates severely degraded initial responses to the designed elliptic filter shapes and allows for automatic bandwidth and center frequency reconfiguration of these filters. This algorithm is demonstrated on 2nd- and 4th-order filters fabricated in a standard silicon photonics foundry process. After the initial calibration, only 300ms is required to reconfigure a filter to a different center frequency. Thermal crosstalk between the micro-heaters is investigated, with substrate thinning demonstrated to suppress this effect and reduce filter calibration to less than half of the original thick substrate times. This fully automatic tuning approach opens the possibility of employing silicon photonic filters in real communication systems. Also, in the proposed beamforming transmitter, true-time delay ring resonator based 1x4 beamforming network is imbedded. A proposed monitor-based tuning method compensates fabrication variations and thermal crosstalk by controlling micro-heaters individually using electrical monitors. The proposed tuning approach successfully demonstrated calibration of OBFN from severely degraded initial responses to well-defined group delay response required for the targeted radiating angle with a range of 60â—¦ (-30â—¦ to 30â—¦ ) in a linear beamforming antenna array. This algorithm is demonstrated on OBFN fabricated in a standard silicon photonics foundry process. The calibrated OBFN operates at 30GHz and provide 2GHz bandwidth. This fully automatic tuning approach opens the possibility of employing silicon OBFN in real wideband mm-wave wireless communication systems by providing robust operating solutions. All the proposed photonic circuits are implemented using the standard silicon photonic technologies, and resulted in several publications in IEEE/OSA Journals and Conferences

    A Continuous and Static Water Contaminant Detection System Using RF Microwave Principles

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    A continuous, static, and non-interfering water contaminant detection method is presented to measure specific water contaminants (NaCl, MgCl2, and mixture of NaCl and MgCl2) using RF microwave principles. A coil is mounted on the surface of a glass tube and the liquid sample is placed inside of the tube. An external magnetic field generated by the coil continuously measures changes in radio frequency energy. The non-contact feature of the device allows a long sensor lifetime with high sensitivity for real-time measurements. The measurement parameter is reflection coefficient (S11) and the operating frequency is 10 MHz – 5 GHz. For NaCl and MgCl2, 11 different concentrations (1000 ppm – 400 ppb) liquid solutions are prepared. Amplitude changes and frequency shifts are noticeable among different materials and concentrations. Different test materials have different radio frequencies at which they undergo excitation and the responses are identified in S11 measurement. A machine learning algorithm is introduced to analyze the measured S11 data. A support vector regressor (SVR) model is trained using the measured data of various salt samples. The training data is constructed by concatenating the 20,000 amplitudes and 20,000 phase values from the measured S11 data. The hyperparameters of the SVR are optimized using 10-fold cross-validation method. Based on the trained model, the algorithm predicts the concentrations of the liquid samples. The experimental results indicate that the device can detect concentrations as low as 400 ppb with high accuracy

    Recent Advances and Future Trends in Nanophotonics

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    Nanophotonics has emerged as a multidisciplinary frontier of science and engineering. Due to its high potential to contribute to breakthroughs in many areas of technology, nanophotonics is capturing the interest of many researchers from different fields. This Special Issue of Applied Sciences on “Recent advances and future trends in nanophotonics” aims to give an overview on the latest developments in nanophotonics and its roles in different application domains. Topics of discussion include, but are not limited to, the exploration of new directions of nanophotonic science and technology that enable technological breakthroughs in high-impact areas mainly regarding diffraction elements, detection, imaging, spectroscopy, optical communications, and computing

    Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

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    INE/AUTC 10.0

    A framework for developing a prognostic model using partial discharge data from electrical trees

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    Insulation breakdown is a key failure mode of high voltage (HV) equipment, with progressive faults such as electrical treeing leading to potentially catastrophic failure. Electrical treeing proceeds from defects in solid insulation, and cables are particularly affected. Research has shown that diagnosis of the fault can be achieved based on partial discharge (PD) analysis. Nonetheless, after diagnosis of a defect, engineers need to know how long they have to take action. This requires prognosis of remaining insulation life. The progression of a defect is far less well understood than diagnosis, making prognosis a key challenge requiring new approaches to defect modelling. The practical deployment of prognostics for cable monitoring is not currently feasible, due to the lack of understanding of degradation mechanisms and limited data relating defect inception to plant failure. However, this thesis advances the academic state of the art, with an eye towards practical deployment in the future. The expected beneficiaries of this work are therefore researchers in the field of HV condition monitoring in general, and electrical treeing within cables in particular. This research work develops a prognostic model of insulation failure due to the electrical treeing phenomenon by utilising the associated PD data from previous experiment. Both phase-resolved and pulse sequence approaches were employed for PD features extraction. The performance of the PD features as prognostic parameters were evaluated using three metrics, monotonicity, prognosability and trendability. The analysis revealed that features from pulse sequence approach are better than phase-resolved approach in terms of monotonicity and prognosability. The key contributions to knowledge of this work are three-fold: the selection of the most appropriate prognostic parameter for PD in electrical trees, through thorough analysis of the behaviour of a number of candidate parameters; a prognostic modelling approach for this parameter based on curve-fitting; and a generalised framework for prognostic modelling using data-driven techniques.Insulation breakdown is a key failure mode of high voltage (HV) equipment, with progressive faults such as electrical treeing leading to potentially catastrophic failure. Electrical treeing proceeds from defects in solid insulation, and cables are particularly affected. Research has shown that diagnosis of the fault can be achieved based on partial discharge (PD) analysis. Nonetheless, after diagnosis of a defect, engineers need to know how long they have to take action. This requires prognosis of remaining insulation life. The progression of a defect is far less well understood than diagnosis, making prognosis a key challenge requiring new approaches to defect modelling. The practical deployment of prognostics for cable monitoring is not currently feasible, due to the lack of understanding of degradation mechanisms and limited data relating defect inception to plant failure. However, this thesis advances the academic state of the art, with an eye towards practical deployment in the future. The expected beneficiaries of this work are therefore researchers in the field of HV condition monitoring in general, and electrical treeing within cables in particular. This research work develops a prognostic model of insulation failure due to the electrical treeing phenomenon by utilising the associated PD data from previous experiment. Both phase-resolved and pulse sequence approaches were employed for PD features extraction. The performance of the PD features as prognostic parameters were evaluated using three metrics, monotonicity, prognosability and trendability. The analysis revealed that features from pulse sequence approach are better than phase-resolved approach in terms of monotonicity and prognosability. The key contributions to knowledge of this work are three-fold: the selection of the most appropriate prognostic parameter for PD in electrical trees, through thorough analysis of the behaviour of a number of candidate parameters; a prognostic modelling approach for this parameter based on curve-fitting; and a generalised framework for prognostic modelling using data-driven techniques
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