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

Effect of water on electrical properties of Refined, Bleached, and Deodorized Palm Oil (RBDPO) as electrical insulating material

This paper describes the properties of refined, bleached, deodorized palm oil (RBDPO) as having the potential to be used as insulating liquid. There are several important properties such as electrical breakdown, dielectric dissipation factor, specific gravity, flash point, viscosity and pour point of RBDPO that was measured and compared to commercial mineral oil which is largely in current use as insulating liquid in power transformers. Experimental results of the electrical properties revealed that the average breakdown voltage of the RBDPO sample, without the addition of water at room temperature, is 13.368 kV. The result also revealed that due to effect of water, the breakdown voltage is lower than that of commercial mineral oil (Hyrax). However, the flash point and the pour point of RBDPO is very high compared to mineral oil thus giving it advantageous possibility to be used safely as insulating liquid. The results showed that RBDPO is greatly influenced by water, causing the breakdown voltage to decrease and the dissipation factor to increase; this is attributable to the high amounts of dissolved water

Partial discharges studied by dielectric response method

The increasing demand of integrating various renewable energy recourses in power system requires extensive use of power electronic solutions, which allows energy conversion between different frequencies and stabilizes the system. Consequently, other than the traditional 50/60 Hz sinusoidal voltage stresses act on the high voltage insulation systems. Therefore a need for elaborating fast and accurate characterization methods arises for facilitating studies of the different types of voltage waveforms on the behaviour of insulation materials and systems. Two commonly applied non-destructive insulation characterization techniques, dielectric response and partial discharge (PD) measurements, are addressed in the project. Several methods based on the so called Arbitrary Waveform Impedance Spectroscopy (AWIS) technique have been developed to enable fast and accurate characterization of dielectric material frequency response. This approach was further adopted to study the behaviour of PDs in various types of test objects, including needle-plate electrode arrangement, twisted pair enamel wires and dielectrically insulated cavities, by simultaneously applying the dielectric response measurements and the stochastic PD detection. Various experiments, involving occasionally changing voltage level, circulating air around a specimen, and modifying conductivity of cavity walls, were performed and allowed identifying additional PD current components in the total current response, which are in the following named as excess currents. It is shown among others, by comparing the excess currents with simultaneously detected PD pulses, that contributions from weak discharges lying below the conventional PD detection threshold as well as slow contributions to the current caused by charge movements within the partial discharge area can be identified and evaluated. An important component of the excess current is a non-PD excess current that repeatedly appears in all studied types of objects and causes a decay or even disappearance of PD activity with time. At longer exposures of the dielectrically isolated cavities, it also yields oscillating interchanges between PD activity and the excess current

Accurate Classification of Partial Discharge Phenomena in Power Transformers in the Presence of Noise

The objective of this research is to accurately classify different types of Partial Discharge (PD) phenomenon that occurs in transformers in the presence of noise. A PD is an electrical discharge or spark that bridges a small portion of the insulation in electrical equipment, which causes progressive deterioration of high voltage equipment and could potentially lead to flashover. The data for the study is generated from a laboratory setup and it is 300 time series signals each with 2016 attributes corresponding to 3 types of PDs; namely: Porcelain, Cable and Corona. The data is collected from two sensors with different bandwidths, in which Channel A signals refer to the data collected from the higher frequency sensor and signals from Channel B refer to data of the lower frequency sensor. Different feature engineering approaches are investigated in order to find the set of the most discriminant features which help to achieve high levels of classification accuracy for Channel A and Channel B signals. First, features that describe the shape and pulse of signals in the time domain are extracted. Then frequency domain based statistical features are generated. In comparison with classification accuracies using frequency domain features, time domain based features gave higher accuracy of more than 90% on average for both channels in the absence of noise while frequency domain features allowed classification accuracy up to 80% on average. However, in the presence of noise, both methods degraded. To overcome this, Regularization techniques were applied on the features from the frequency domain which helped to maintain classification accuracy even in the presence of high levels of noise

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

Micro-manufactured Rogowski coils for fault detection of aircraft electrical wiring and interconnection systems (EWIS)

Aircraft wiring failures have increased over the last few years resulting in arc faults and high-energy flashover on the wiring bundle, which can propagate down through aircraft Electrical Wiring and Interconnect Systems (EWIS). It is considered cost prohibitive to completely rewire a plane in terms of man hours and operational time lost to do this, and most faults are only detectable whilst the aircraft is in flight. Temperature, humidity and vibration all accelerate ageing and failure effects on EWIS. This research investigates methods of in-situ non-invasive testing of aircraft wiring during fight. Failure Mode Effects and Analysis (FMEA) was performed on legacy aircraft EWIS using data obtained from RAF Brize Norton. Micro-Electro-mechanical- Systems (MEMS) were evaluated for use in a wire monitoring system that measures the environmental parameters responsible for ageing and failure of EWIS. Such MEMS can be developed into a Health and Usage Monitoring MicroSystem (HUMMS) by incorporating advanced signal processing and prognostic software. Current and humidity sensors were chosen for further investigation in this thesis. These sensors can be positioned inside and outside cable connectors of EWIS so that arc faults can be reliably detected and located. This thesis presents the design, manufacture and test of micro-manufactured Rogowski sensors. The manufactured sensors were benchmarked against commercial high frequency current transformers (HFCT), as these devices can also detect high frequency current signature due to wire insulation failure. Results indicate that these sensors possess superior voltage output compared to the HFCT. The design, manufacture and test of a polymer capacitive humidity sensor is also presented. Two different types of polymer were reviewed as part of the evaluation. A feature of the sensor design is recovery from exposure to chemicals found on wiring bundles. Current and humidity sensors were demonstrated to be suitable for integrating onto a common substrate with accelerometers, temperature sensors and pressure sensors for health monitoring and prognostics of aircraft EWIS.Engineering and Physical Sciences Research Council (EPSRC

Micro-manufactured Rogowski coils for fault detection of aircraft electrical wiring and interconnect systems (EWIS)

Aircraft wiring failures have increased over the last few years resulting in arc faults and high-energy flashover on the wiring bundle, which can propagate down through aircraft Electrical Wiring and Interconnect Systems (EWIS). It is considered cost prohibitive to completely rewire a plane in terms of man hours and operational time lost to do this, and most faults are only detectable whilst the aircraft is in flight. Temperature, humidity and vibration all accelerate ageing and failure effects on EWIS. This research investigates methods of in-situ non-invasive testing of aircraft wiring during fight. Failure Mode Effects and Analysis (FMEA) was performed on legacy aircraft EWIS using data obtained from RAF Brize Norton. Micro-Electro-mechanical- Systems (MEMS) were evaluated for use in a wire monitoring system that measures the environmental parameters responsible for ageing and failure of EWIS. Such MEMS can be developed into a Health and Usage Monitoring MicroSystem (HUMMS) by incorporating advanced signal processing and prognostic software. Current and humidity sensors were chosen for further investigation in this thesis. These sensors can be positioned inside and outside cable connectors of EWIS so that arc faults can be reliably detected and located. This thesis presents the design, manufacture and test of micro-manufactured Rogowski sensors. The manufactured sensors were benchmarked against commercial high frequency current transformers (HFCT), as these devices can also detect high frequency current signature due to wire insulation failure. Results indicate that these sensors possess superior voltage output compared to the HFCT. The design, manufacture and test of a polymer capacitive humidity sensor is also presented. Two different types of polymer were reviewed as part of the evaluation. A feature of the sensor design is recovery from exposure to chemicals found on wiring bundles. Current and humidity sensors were demonstrated to be suitable for integrating onto a common substrate with accelerometers, temperature sensors and pressure sensors for health monitoring and prognostics of aircraft EWIS

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

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

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

Robust Condition Assessment of Electrical Equipment with One Class Support Vector Machines Based on the Measurement of Partial Discharges

This paper presents a system for the detection of partial discharges (PD) in industrial applications based on One Class Support Vector Machines (OCSVM). The study stresses the detection of Partial Discharges (PD) as they represent a major source of information related to degradation in the equipment. PD measurement is a widely extended technique for condition monitoring of electrical machines and power cables to avoid catastrophic failures and the consequent blackouts. One of the most important keystones in the interpretation of partial discharges is their separation from other signals considered as not-PD especially in low SNR measurements. In this sense, the OCSVM is an interesting alternative to binary SVMs since it does not need a training set with examples of all the output classes correctly labelled. On the contrary, the OCSVM learns a model of the signals acquired when the equipment is in PD-free mode, defined as a state where no degradation mechanism is active, so one only needs to make sure that the training signals were recorded under this setting. These default mode signals are easier to characterize and acquire in industrial environments than PD and lead to more robust detectors that practically do not need domain adaptation to perform in scenarios prone to different types of PD. In fact, the experimental results show that the performance of the OCSVM is comparable to that achieved by a binary SVM trained using both noise and PD pulses. Finally, the method is successfully applied to a more realistic scenario involving the detection of PD in a damaged distribution power cable.Tests were conducted at the High Voltage Research and Testing Laboratory (LINEALT) of Universidad Carlos III de Madrid. This work has been funded by the Spanish Government through project SI-DP (DPI2015-66478-C2-1 MINECO/FEDER, UE) and the Chilean Research Council (CONICYT), under the project Fondecyt 11160115