2,291 research outputs found

    Partial discharge feature extraction based on ensemble empirical mode decomposition and sample entropy

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    Partial Discharge (PD) pattern recognition plays an important part in electrical equipment fault diagnosis and maintenance. Feature extraction could greatly affect recognition results. Traditional PD feature extraction methods suffer from high-dimension calculation and signal attenuation. In this study, a novel feature extraction method based on Ensemble Empirical Mode Decomposition (EEMD) and Sample Entropy (SamEn) is proposed. In order to reduce the influence of noise, a wavelet method is applied to PD de-noising. Noise Rejection Ratio (NRR) and Mean Square Error (MSE) are adopted as the de-noising indexes. With EEMD, the de-noised signal is decomposed into a finite number of Intrinsic Mode Functions (IMFs). The IMFs, which contain the dominant information of PD, are selected using a correlation coefficient method. From that, the SamEn of selected IMFs are extracted as PD features. Finally, a Relevance Vector Machine (RVM) is utilized for pattern recognition using the features extracted. Experimental results demonstrate that the proposed method combines excellent properties of both EEMD and SamEn. The recognition results are encouraging with satisfactory accuracy

    Study of 42 and 85 GHz coupled cavity traveling-wave tubes for space use

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    Designs were formulated for four CW, millimeter wavelength traveling-wave tubes having high efficiency and long life. Three of these tubes, in the 42 to 44 GHz frequency region, develop power outputs of 100 to 300 watts with overall efficiencies of typically 45 percent. Another tube, which covers the frequency range of 84 to 86 GHz, provides a power output of 200 watts at 25 percent efficiency. The cathode current density in each design was 1A/sq cm. Each tube includes: metal-ceramic construction, periodic permanent magnet focusing, a two step velocity taper, an electron beam refocusing section, and a radiation cooled three-stage depressed collector. The electrical and mechanical design for each tube type is discussed in detail. The results of thermal and mechanical analyses are presented

    Reducing sensor complexity for monitoring wind turbine performance using principal component analysis

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    Availability and reliability are among the priority concerns for deployment of distributed generation (DG) systems, particularly when operating in a harsh environment. Condition monitoring (CM) can meet the requirement but has been challenged by large amounts of data needing to be processed in real time due to the large number of sensors being deployed. This paper proposes an optimal sensor selection method based on principal component analysis (PCA) for condition monitoring of a DG system oriented to wind turbines. The research was motivated by the fact that salient patterns in multivariable datasets can be extracted by PCA in order to identify monitoring parameters that contribute the most to the system variation. The proposed method is able to correlate the particular principal component to the corresponding monitoring variable, and hence facilitate the right sensor selection for the first time for the condition monitoring of wind turbines. The algorithms are examined with simulation data from PSCAD/EMTDC and SCADA data from an operational wind farm in the time, frequency, and instantaneous frequency domains. The results have shown that the proposed technique can reduce the number of monitoring variables whilst still maintaining sufficient information to detect the faults and hence assess the system’s conditions

    Applications of Fiber-Optic Interferometry Technology in Sensor Fields

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    Optical interferometry as a precision metrology has been widely employed in many aspects for accurate measurements of various physical quantities. As an important branch of measurement technology, now the fiber-optic interferometry technology, based on fiber-optic and laser technologies, has been developed and widely applied in sensor fields for detections of various unknown or uncontrolled physical parameters. In this chapter, basic concepts of fiber-optic interferometry are presented and clarified. Also, three novel fiber-optic sensors, based on the optical interferometry technology and mainly used in the power industry for equipment failure monitoring, are demonstrated

    Wind turbine condition monitoring : technical and commercial challenges.

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    Deployment of larger scale wind turbine systems, particularly offshore, requires more organized operation and maintenance strategies to ensure systems are safe, profitable and cost-effective. Among existing maintenance strategies, reliability centred maintenance is regarded as best for offshore wind turbines, delivering corrective and proactive (i.e. preventive and predictive) maintenance techniques enabling wind turbines to achieve high availability and low cost of energy. Reliability centred maintenance analysis may demonstrate that an accurate and reliable condition monitoring system is one method to increase availability and decrease the cost of energy from wind. In recent years, efforts have been made to develop efficient and cost-effective condition monitoring techniques for wind turbines. A number of commercial wind turbine monitoring systems are available in the market, most based on existing techniques from other rotating machine industries. Other wind turbine condition monitoring reviews have been published but have not addressed the technical and commercial challenges, in particular, reliability and value for money. The purpose of this paper is to fill this gap and present the wind industry with a detailed analysis of the current practical challenges with existing wind turbine condition monitoring technology

    Generator Insulation-Aging On-Line Monitoring Technique Based on Fiber Optic Detecting Technology

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    The relationship between insulation aging and generator lifespan using fiber optic sensors (FOSs) is explored to ultimately improve asset lifespan through smart choices in running conditions and maintenance. Insulation aging is a major factor that causes generator failure. FOS provides the rare opportunity of being installed up close to the insulation, monitoring degradations that are otherwise difficult to detect. FOSs, unlike purely electrical transducers, are immune to high voltage (HV) and strong electromagnetic (EM) fields. They are small and have a proven long life by their deployment in the Telecom industry. The proposed FOS is a Fabry-Perot cavity made up of two identical fiber Bragg gratings (FBGs) using light wave interference as the working principle. Such architecture delivers simultaneous vibration (10 Hz–1 kHz) and temperature (0.1°C resolution) monitoring, both helping to spot irregular vibration patterns (signatures) and hot-spots inside the generator stator slots. The signal processing unit equipped with a gateway device can help to connect the large volume of sensor data, allowing correlation with the supervisory control and data acquisition (SCADA) system data of the plant. This chapter also elaborates on the field test jointly conducted with Calpine Corporation and Oz Optics, Ltd. (Ottawa, Ontario, Canada)

    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

    Advances in Sensors and Sensing for Technical Condition Assessment and NDT

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    The adequate assessment of key apparatus conditions is a hot topic in all branches of industry. Various online and offline diagnostic methods are widely applied to provide early detections of any abnormality in exploitation. Furthermore, different sensors may also be applied to capture selected physical quantities that may be used to indicate the type of potential fault. The essential steps of the signal analysis regarding the technical condition assessment process may be listed as: signal measurement (using relevant sensors), processing, modelling, and classification. In the Special Issue entitled “Advances in Sensors and Sensing for Technical Condition Assessment and NDT”, we present the latest research in various areas of technology

    Performance optimization of lateral-mode thin-film piezoelectric-on-substrate resonant systems

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    The main focus of this dissertation is to characterize and improve the performance of thin-film piezoelectric-on-substrate (TPoS) lateral-mode resonators and filters. TPoS is a class of piezoelectric MEMS devices which benefits from the high coupling coefficient of the piezoelectric transduction mechanism while taking advantage of superior acoustic properties of a substrate. The use of lateral-mode TPoS designs allows for fabrication of dispersed-frequency filters on a single substrate, thus significantly reducing the size and manufacturing cost of devices. TPoS filters also offer a lower temperature coefficient of frequency, and better power handling capability compared to rival technologies all in a very small footprint. Design and fabrication process of the TPoS devices is discussed. Both silicon and diamond substrates are utilized for fabrication of TPoS devices and results are compared. Specifically, the superior acoustic properties of nanocrystalline diamond in scaling the frequency and energy density of the resonators is highlighted in comparison with silicon. The performance of TPoS devices in a variety of applications is reported. These applications include lateral-mode TPoS filters with record low IL values (as low as 2dB) and fractional bandwidth up to 1%, impedance transformers, very low phase noise oscillators, and passive wireless temperature sensors
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