4,403 research outputs found
Condition Monitoring of Power Cables
A National Grid funded research project at Southampton has investigated possible methodologies for data acquisition, transmission and processing that will facilitate on-line continuous monitoring of partial discharges in high voltage polymeric cable systems. A method that only uses passive components at the measuring points has been developed and is outlined in this paper. More recent work, funded through the EPSRC Supergen V, UK Energy Infrastructure (AMPerES) grant in collaboration with UK electricity network operators has concentrated on the development of partial discharge data processing techniques that ultimately may allow continuous assessment of transmission asset health to be reliably determined
Time domain analysis of switching transient fields in high voltage substations
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
Discrimination of PD Signal using Wavelet Transform for Insulation Diagnosis of GIS under HVDC
중전기 산업에서 부분방전의 검출 및 분석 기술은 전력설비의 상태진단 및 자산관리를 위한 가장 효과적인 방법으로 간주되어 왔다. 그러나 검출의 감도 및 정확도는 현장 노이즈에 영향을 받아 위험도 평가, 결함 판별 또는 위치 추정의 오류를 유발한다. 교류전압에서 부분방전 신호의 노이즈 제거는 활발히 연구되었지만, 최근 이슈가 되고 있는 HVDC에서 관련 연구는 미흡한 실정이다. HVDC 기술이 급속히 발전되면서 관련 전력설비 진단을 위하여, HVDC에서 부분방전 신호의 노이즈를 제거할 필요가 있다. 이들 배경으로 본 논문에서는 HVDC 가스절연구조에서 절연진단의 감도 및 정확도를 향상할 목적으로 웨이블릿 변환을 이용하여 부분방전 신호를 식별하였다.
직류에서 부분방전 신호를 발생하기 위하여 실험계를 구축하였다. HVDC는 몰드변압기, 고압 다이오드 및 커패시터로 구성된 정류회로로 발생시켰다. 가스절연구조에서 발생하는 절연결함을 모의하기 위하여 도체돌출, 외함돌출, 자유입자 및 절연물 크랙 4종의 전극계를 제작하였다. 전극계는 SF6 가스를 0.5MPa로 충진하였으며, 차폐함을 사용하여 외부 노이즈의 영향을 최소화하였다.
4종의 모의결함에서 부분방전 단일펄스를 검출하여 HVDC에서 부분방전을 분석하기 위한 웨이블릿 변환 기술을 최적화하였다. 상관계수 및 동적시간워핑 법을 이용하여 부분방전 펄스와 다양한 모웨이블릿의 유사성을 비교하였다. 결과로부터 동적시간워핑 법에 의해 선정된 모웨이블릿 bior2.6이 HVDC에서 부분방전 신호 분석에 가장 적합하였다. 최적의 문턱함수 및 문턱값을 선정하기 위하여 감쇠 지수 펄스 및 감쇠 진동 펄스를 모의하였으며, 신호-잡음비, 상관계수, 크기 변화를 비교한 결과, 중간 문턱함수-자동 문턱값이 최적의 조합으로 선정되었다.
실제 부분방전 분석 및 평가 시 단일 펄스가 아닌 펄스 시퀀스가 사용되기 때문에, 최적화된 웨이블릿 변환 기술을 이용하여 모의결함으로부터 검출된 부분방전 신호를 식별하였으며, 그 효과를 고역 통과 필터와 비교하였다. 결과로부터, 부분방전 신호 식별 시 고역통과필터에 비해 웨이블리 기술이 잡음 감소와 상관계수가 높게, 크기 변화가 낮게 나타났다. 뿐만 아니라 웨이블릿 방법은 배경 잡음, 진폭 변조 전파 장해, 비정현 잡음 및 스위칭 임펄스로 간섭된 부분방전 신호를 식별하는 데 효과적이었다.
본 논문에서 제안한 웨이블릿 변환 기술은 현장의 노이즈로부터 부분방전 신호를 성공적으로 식별하였다. 향후 HVDC에서 가스절연구조의 부분방전 검출 및 분석에 적용될 것으로 예상되며, 부분방전 검출, 위험도 평가, 결함 판별 및 위치 측정의 정확도가 향상될 수 있을 것으로 기대된다.Contents ⅰ
Lists of Figures and Tables ⅲ
Abstract ⅵ
Chapter 1 Introduction 1
1.1 Research Background 1
1.2 Dissertation Outline 5
Chapter 2 Partial Discharge Review 7
2.1 Mechanism and Recurrence 7
2.2 Detection and Measurement 12
2.3 Analysis Methods 23
Chapter 3 Experiment and Optimization 45
3.1 Experimental Setup 45
3.2 Optimization of Wavelet Transform 49
Chapter 4 Discrimination of PD Sequences 66
4.1 DEP-type Pulse Sequence 70
4.2 DOP-type Pulse Sequence 79
Chapter 5 Conclusions 89Docto
Imaging time series for the classification of EMI discharge sources
In this work, we aim to classify a wider range of Electromagnetic Interference (EMI) discharge sources collected from new power plant sites across multiple assets. This engenders a more complex and challenging classification task. The study involves an investigation and development of new and improved feature extraction and data dimension reduction algorithms based on image processing techniques. The approach is to exploit the Gramian Angular Field technique to map the measured EMI time signals to an image, from which the significant information is extracted while removing redundancy. The image of each discharge type contains a unique fingerprint. Two feature reduction methods called the Local Binary Pattern (LBP) and the Local Phase Quantisation (LPQ) are then used within the mapped images. This provides feature vectors that can be implemented into a Random Forest (RF) classifier. The performance of a previous and the two new proposed methods, on the new database set, is compared in terms of classification accuracy, precision, recall, and F-measure. Results show that the new methods have a higher performance than the previous one, where LBP features achieve the best outcome
Partial discharge denoising for power cables
Partial discharge (PD) diagnostics is considered a major and effective tool for the monitoring of insulating conditions of power cables. As such, a large amount of off-line or online PD measurements have been deployed in power cables during the past decades. However, challenges still exist in PD diagnostics for power cables. Noise is one of the challenges involved in PD measurement. This thesis develops new algorithms based on the characteristics of both PD signals and noise to improve the effectiveness of wavelet-based PD denoising. In the meantime, it presents new findings in the application of empirical mode decomposition (EMD) in PD denoising. Wavelet-based technique has received high attention in the area of PD denoising, it still faces challenges, however, in wavelet selection, decomposition scale determination, and noise estimation. It is therefore the first area of interest in this thesis to improve the effectiveness of existing wavelet-based technique in PD detection by incorporating proposed algorithms. These new algorithms were developed based on the difference of entropy between transformed PD signals and noise, and the sparsity of transformed PD signals corrupted by noise. One concern commonly expressed by critics of wavelet-based technique is a pre-defined wavelet is applied in wavelet-based technique. EMD is an algorithm that can decompose a signal based on the signal itself. Thus, the second area of interest in this thesis is to further investigate the application of EMD in PD denoising; a technique that does not require the selection of a pre-defined signal to represent the "unknown" signal of interest. A new method for relative mode selection (RMS) was proposed based on the entropy of each intrinsic mode function (IMF). Although this new method cannot outperform the existing ones, it reveals that RMS is not as important as claimed in the application of EMD in signal denoising. Also, PD signals, especially those with lower magnitudes, can receive serious distortion through EMD-based denoising. Finally, comparisons between wavelet-based and EMD-based denoising were implemented in the following aspects, i.e., executing time, distortion, effectiveness, adaptivity and robustness. Results unveil that improved wavelet-based technique is more preferable as it can present better performance in PD denoising.Partial discharge (PD) diagnostics is considered a major and effective tool for the monitoring of insulating conditions of power cables. As such, a large amount of off-line or online PD measurements have been deployed in power cables during the past decades. However, challenges still exist in PD diagnostics for power cables. Noise is one of the challenges involved in PD measurement. This thesis develops new algorithms based on the characteristics of both PD signals and noise to improve the effectiveness of wavelet-based PD denoising. In the meantime, it presents new findings in the application of empirical mode decomposition (EMD) in PD denoising. Wavelet-based technique has received high attention in the area of PD denoising, it still faces challenges, however, in wavelet selection, decomposition scale determination, and noise estimation. It is therefore the first area of interest in this thesis to improve the effectiveness of existing wavelet-based technique in PD detection by incorporating proposed algorithms. These new algorithms were developed based on the difference of entropy between transformed PD signals and noise, and the sparsity of transformed PD signals corrupted by noise. One concern commonly expressed by critics of wavelet-based technique is a pre-defined wavelet is applied in wavelet-based technique. EMD is an algorithm that can decompose a signal based on the signal itself. Thus, the second area of interest in this thesis is to further investigate the application of EMD in PD denoising; a technique that does not require the selection of a pre-defined signal to represent the "unknown" signal of interest. A new method for relative mode selection (RMS) was proposed based on the entropy of each intrinsic mode function (IMF). Although this new method cannot outperform the existing ones, it reveals that RMS is not as important as claimed in the application of EMD in signal denoising. Also, PD signals, especially those with lower magnitudes, can receive serious distortion through EMD-based denoising. Finally, comparisons between wavelet-based and EMD-based denoising were implemented in the following aspects, i.e., executing time, distortion, effectiveness, adaptivity and robustness. Results unveil that improved wavelet-based technique is more preferable as it can present better performance in PD denoising
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A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure
Hypertension or high blood pressure is a leading cause of death throughout the world and a critical factor for increasing the risk of serious diseases, including cardiovascular diseases such as stroke and heart failure. Blood pressure is a primary vital sign that must be monitored regularly for the early detection, prevention and treatment of cardiovascular diseases. Traditional blood pressure measurement techniques are either invasive or cuff-based, which are impractical, intermittent, and uncomfortable for patients. Over the past few decades, several indirect approaches using photoplethysmogram (PPG) have been investigated, namely, pulse transit time, pulse wave velocity, pulse arrival time and pulse wave analysis, in an effort to utilise PPG for estimating blood pressure. Recent advancements in signal processing techniques, including machine learning and artificial intelligence, have also opened up exciting new horizons for PPG-based cuff less and continuous monitoring of blood pressure. Such a device will have a significant and transformative impact in monitoring patients’ vital signs, especially those at risk of cardiovascular disease. This paper provides a comprehensive review for non-invasive cuff-less blood pressure estimation using the PPG approach along with their challenges and limitations
A Partial Discharge Measurement Technique for Applied Square Pulse Voltage with 50 NS Rise Times
During the fabrication of solid electrical insulation, small cavities known as micro voids may form in the material. As electrical stress increases in this micro void, the breakdown probability also increases. This type of electrical breakdown is commonly known as partial discharge. Magnitudes of partial discharge currents are typically small but enough to cause degradation of the electrical insulation. To study degradation for fast-rise time voltage square pulse train, partial discharge measurement is needed. In current studies, partial discharge pulse widths have been measured in the range of nanoseconds. The best approach for measurement at ultra wide band frequencies is a bridge type measurement system, to reduce external noise and improve sensitivity to PD currents. The bridge configuration can be used with samples instead of one sample and one coupling capacitor. Identically created samples will have a close match for impedance and frequency response. This type of bridge also helps to reduce other sources of measured current such as the high displacement currents due to fast rise time square pulse voltage on the samples. Further improvement includes simultaneous measurements using a “linked” bridge configuration, where bridges share a common sample. A directly connected measurement current shunt should be used for high sensitivity with a uniform ultra wide band frequency response. Post-measurement digital signal processing (DSP) algorithms will perform the task of pulse discrimination and time delay from the pulse front. This research presents a method to improve the measurement of partial discharge when applied voltage is non-sinusoidal, with high frequency components. The improvements are apparent when square pulse voltage rise times are less than 50 ns. Ultra wide band measurements of physical samples will be performed for short time duration with a digital storage oscilloscope. A DSP algorithm is used to filter residual noise from the partial discharge current. The presented measurement technique for samples for this study is an original approach. Sample results demonstrate the effectiveness of the technique
An improved machine learning pipeline for urinary volatiles disease detection:Diagnosing diabetes
Motivation The measurement of disease biomarkers in easily–obtained bodily fluids has opened the door to a new type of non–invasive medical diagnostics. New technologies are being developed and fine–tuned in order to make this possibility a reality. One such technology is Field Asymmetric Ion Mobility Spectrometry (FAIMS), which allows the measurement of volatile organic compounds (VOCs) in biological samples such as urine. These VOCs are known to contain a range of information on the relevant person’s metabolism and can in principle be used for disease diagnostic purposes. Key to the effective use of such data are well–developed data processing pipelines, which are necessary to extract the most useful data from the complex underlying biological structure. Results In this study, we present a new data analysis pipeline for FAIMS data, and demonstrate a number of improvements over previously used methods. We evaluate the effect of a series of candidate operational steps during data processing, such as the use of wavelet transforms, principal component analysis (PCA), and classifier ensembles. We also demonstrate the use of FAIMS data in our pipeline to diagnose diabetes on the basis of a simple urine sample using machine learning classifiers. We present results for data generated from a case-control study of 115 urine samples, collected from 72 type II diabetic patients, with 43 healthy volunteers as negative controls. The resulting pipeline combines the steps that resulted in the best classification model performance. These include the use of a two–dimensional discrete wavelet transform, and the Wilcoxon rank–sum test for feature selection. We are able to achieve a best ROC curve AUC of 0.825 (0.747–0.9, 95% CI) for classification of diabetes vs control. We also note that this result is robust to changes in the data pipeline and different analysis runs, with AUC > 0.80 achieved in a range of cases. This is a substantial improvement in performance over previously used data processing methods in this area. Our ability to make strong statements about FAIMS ability to diagnose diabetes is sadly limited, as we found confounding effects from the demographics when including these data in the pipeline. The demographics alone produced a best AUC of 0.87 (0.795–0.94, 95% CI). While the combination of the demographics and FAIMS data resulted in an improvement on the AUC (0.907; 0.848–0.97, 95% CI), it did not prove to be a significant difference. Nevertheless, the pipeline itself shows a significant improvement in performance over more basic methods which have been used with FAIMS data in the past
Wearable System for Biosignal Acquisition and Monitoring Based on Reconfigurable Technologies
Wearable monitoring devices are now a usual commodity in the market, especially for the
monitoring of sports and physical activity. However, specialized wearable devices remain an open
field for high-risk professionals, such as military personnel, fire and rescue, law enforcement, etc.
In this work, a prototype wearable instrument, based on reconfigurable technologies and capable
of monitoring electrocardiogram, oxygen saturation, and motion, is presented. This reconfigurable
device allows a wide range of applications in conjunction with mobile devices. As a proof-of-concept,
the reconfigurable instrument was been integrated into ad hoc glasses, in order to illustrate the
non-invasive monitoring of the user. The performance of the presented prototype was validated
against a commercial pulse oximeter, while several alternatives for QRS-complex detection were
tested. For this type of scenario, clustering-based classification was found to be a very robust option.This work was funded by Banco Santander and Centro Mixto UGR-MADOC through project SIMMA
(code 2/16). The contribution of Víctor Toral was funded by the University of Granada through a grant from the
“Iniciación a la investigación 2016” program. The contribution of Antonio García was partially funded by Spain’s
Ministerio de Educación, Cultura y Deporte (Programa Estatal de Promoción del Talento y su Empleabilidad
en I+D+i, Subprograma Estatal de Movilidad, within Plan Estatal de Investigación Científica y Técnica y de
Innovación 2013-2016) under a “Salvador de Madariaga” grant (PRX17/00287). The contribution of Francisco J.
Romero was funded by Spain’s Ministerio de Educación, Cultura y Deporte under a FPU grant (FPU16/01451).
The contribution of Francisco M. Gómez-Campos was funded by Spain’s Ministerio de Economía, Industria y
Competitividad under Project ENE2016_80944_R
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