Underground distribution cable incipient fault diagnosis system

This dissertation presents a methodology for an efficient, non-destructive, and online incipient fault diagnosis system (IFDS) to detect underground cable incipient faults before they become catastrophic. The system provides vital information to help the operator with the decision-making process regarding the condition assessment of the underground cable. It incorporates advanced digital signal processing and pattern recognition methods to classify recorded data into designated classes. Additionally, the IFDS utilizes novel detection methodologies to detect when the cable is near failure. The classification functionality is achieved through employing an ensemble of rule-based and supervised classifiers. The Support Vector Machines, designed and used as a supervised classifier, was found to perform superior. In addition to the normalized energy features computed from wavelet packet analysis, two new features, namely Horizontal Severity Index, and Vertical Severity Index are defined and used in the classification problem. The detection functionality of the IFDS is achieved through incorporating a temporal severity measure and a detection method. The novel severity measure is based on the temporal analysis of arrival times of incipient abnormalities, which gives rise to a numeric index called the Global Severity Index (GSI). This index portrays the progressive degradation path of underground cable as catastrophic failure time approaches. The detection approach utilizes the numerical modeling capabilities of SOM as well as statistical change detection techniques. The natural logarithm of the chronologically ordered minimum modeling errors, computed from exposing feature vectors to a trained SOM, is used as the detection index. Three modified change detection algorithms, namely Cumulative Sum, Exponentially Weighted Moving Averages, and Generalized Likelihood Ratio, are introduced and applied to this application. These algorithms determine the change point or near failure time of cable from the instantaneous values of the detection index. Performance studies using field recorded data were conducted at three warning levels to assess the capability of the IFDS in predicting the faults that actually occurred in the monitored underground cable. The IFDS presents a high classification rate and satisfactory detection capability at each warning level. Specifically, it demonstrates that at least one detection technique successfully provides an early warning that a fault is imminent

Noise reduction and source recognition of partial discharge signals in gas-insulated substation

Ph.DDOCTOR OF PHILOSOPH

The applications of near infra-red fibre bragg grating sensors for wave propagation based structural health monitoring of thin laminated composite plates

This thesis contributes to the research and development towards achieving better structural health monitoring (SHM) system for composite structures. Composites are widely used in critical engineering applications due to the advantage of higher specific strength and stiffness compared to other conventional materials. However, composite laminates have a very high probability of unexpected damage development during service. This study uses fiber Bragg grating (FBG) sensor to create a practical and robust SHM tool based on monitoring the acoustic emission, in order to provide continuous information of the structure's condition. The remarkable capability of using the FBG sensors for dynamic sensing has been demonstrated, in particular for the wave propagation based SHM. Combined with FBG sensor technologies, the wave propagation based SHM such as acoustic emission (AE), ultrasonic evaluation and acousto-ultrasonics becomes more exciting. The FBG sensor has the ability of acquiring both static and dynamic strains with a single sensor. Besides, the physical size of FBG sensor provides greater access to embed them in composite structures without significantly affecting its structural properties. This study also emphasizes some drawbacks in the use of piezoelectric sensors in the wave propagation based SHM of composite structures, specifically in the AE applications. In most optical fiber based SHM applications to date, people have used only FBG sensors with wavelength 1550 nm. The FBG sensors with this wavelength are commonly used in industries such as telecommunications and health. However, there is an option of using near infra-red (NIR) FBG range which is comparably cheap in terms of total system design, yet offers the same performance of a conventional 1550 nm range FBGs. This research work presents the NIR FBG dynamic sensing system, as a wave propagation-based SHM system for monitoring the damages in thin glass fiber reinforced composite plates. The NIR-FBG sensor system has been validated successfully, in particular for thin composite plate's applications. The sensor system has shown its unique capability whereby it can be applied in the area which cannot be accessed by standard piezoelectric based system. The developed NIR FBG sensor system has shown its competitiveness and ability to replace the piezoelectric sensors in the 'wave propagation based SHM' of laminated composite plates

Development, Implementation, and Validation of an Acoustic Emission-based Structural Health Monitoring System

Entwicklung, Implementierung und Validierung eines schallemissionsbasierten Strukturüberwachungssystems Die Strukturüberwachung eng. Structural Health Monitoring (SHM) ist ein grundlegender Prozess für die Kontrolle der Betriebssicherheit und Zuverlässigkeit von Strukturen und Bauteilen während des Betriebs. Ein Überwachungssystem soll die Strukturdegradation in einer frühen Phase erkennen und quantifizieren, um den Totalausfall zu verhindern und somit menschliche und finanzielle Verluste zu vermeiden. Mit der wachsenden Nachfrage nach kosteneffizienten und robusten Produkten ist SHM mit besonders hohen Anforderungen konfrontiert. Diese Arbeit befasst sich mit der Entwicklung, Implementierung und experimenteller Validierung eines innovativen SHM-Systems, das auf umfassende Weise Schädigungsmechanismen von unterschiedlichen Materialen erkennt, identifiziert und klassifiziert. Für in-situ-Strukturüberwachung können verschiedene Methoden angewendet werden. Hier wird die Schallemissionsanalyse eng. Acoustic Emission Technik (AET) eingesetzt. Acoustic Emission ist eine passive zerstörungsfreie Prüfund Überwachungsmethode. Sie basiert auf der Analyse elastischer Wellen, die durch freigesetzte Energie während mikrostrukturelle Änderungen wie z. B. Risse, Brüche, und Verschleiß entstehen. Unter Verwendung geeigneter Hardware und fortgeschrittener Signalverarbeitungsverfahren können diese Wellen kontinuierlich und in Echtzeit erfasst und analysiert werden. Die Leistungsfähigkeit und Zuverlässigkeit einer AE-basierten Schadensdiagnose sind stark abhängig von Material/Werkstoff, Konstruktion und möglichen Schadensszenarien. Der Fokus dieser Arbeit liegt daher auf der Entwicklung einer hocheffizienten und leicht anpassbaren Field Programmable Gate Array (FPGA)–basierten Messkette zum Abtasten und Erfassen der erzeugten AE-Signale. Neben der Verwendung von sehr leistungsfähiger Hardware ist eine zuverlässige Interpretation der AE Signale von zentraler Bedeutung. Deswegen erfordern die Entwicklung und Umsetzung von Multi-Level-Signalverarbeitungsansätzen und Mustererkennungsverfahren eine besondere Beachtung. Die experimentelle Validierung des entwickelten Systems erfolgt durch die Untersuchungen von drei verschiedenen Materialien/Strukturen: Verschleißfeste Metallbleche, Faserverbundwerkstoff Platten und elektrochemische Zelle. Aufgrund der Diversität der untersuchten Strukturen werden drei Verarbeitungsprozesse entwickelt. Die implementierten Algorithmen können AE-Signale erkennen, quantifizieren und qualifizieren, so dass AE-basierte Eigenschaften identifiziert und mit den entsprechenden AE-Quellen korreliert sind. Die Diagnose konzentriert sich hauptsächlich auf die Schadenserkennung (Merkmalsextraktion), Schadensabschätzung (Merkmalsauswahl) und Schadensklassifizierung unter Anwendung von Zeit-Frequenz-Analyse, statistischen Ansätzen und überwachten Klassifikationsverfahren. Die gewonnenen Ergebnisse zeigen eine bemerkbare Verbesserung der Identifizierung und Klassifizierung von Schadensmechanismen und beweisen die Effizienz des angewandten Multi-Level-Verarbeitungsansätze. Die vorgestellte Methodik ermöglicht eine automatisierte Zustandsüberwachung und stellt daher einen wichtigen Schritt in der Entwicklung von sicheren und zuverlässigen Strukturen dar.In engineering, Structural Health Monitoring (SHM) is an important field of study representing a fundamental process to control the longevity and reliability of structures during service. The objective of an SHM is to detect and quantify the structure degradation at an earlier stage. The acquisition of such information can contribute to prevention of total failure and hence avoiding human and financial losses becomes more possible. With the growing demands for cost-efficient and robust products, SHM is facing particularly high requirements. This thesis focuses on the development, implementation, and experimental validation of an innovative SHM system able to detect, identify, and classify in an extensive way damage mechanisms occurring in different materials. Several techniques can be applied for in situ health monitoring. In this work, Acoustic Emission Technique (AET) is used. Acoustic Emission is a passive nondestructive evaluation technique referring to the elastic waves generated by energy release during microstructural changes in the material. Those changes arise as a result of mechanical and environmental stresses. Monitoring of such a conversion can be continuously done in real-time using suitable hardware and advanced signal processing methods. The performance and reliability of an AE-based damage diagnosis approach are highly dependent on material, structure design and the damage scenarios. Therefore, a Field Programmable Gate Array (FPGA)-based measurement chains developed for sensing and acquiring the generated AE signals. This chain is easily adaptable to different structures and materials. It was therefore kept so far constant as possible throughout all tests conducted. Additionally to the use of highly efficient hardware that enhance the sensing quality and the data acquisition speed, the implementation of advanced filtering techniques with high processing accuracy is of central importance. The main objective of this thesis is to prove the function of the system developed to analyze AE waves under different damage scenarios. For this purpose, three different materials namely wear resistant plates, laminated composite plates, and electrochemical cells are investigated. Owing to the diversity of the studied materials, special attention is paid to the development and implementation of multilevel signal processing approach and pattern recognition methods. The processing chains are capable to detect, quantify and qualify the AE data, whereby AE-based characteristics are identified and correlated with the corresponding AE sources. The designed diagnosis methodology concentrates/focuses on damage detection (feature extraction), damage estimation (feature selection), and damage classification by using time-frequency analysis, multilevel statistical approaches, and supervised classification methods. The results obtained show a noticeable/remarkable enhancement of the identification and classification of damage mechanisms. The efficiency of applying multilevel processing approach is/(could be) thus proved. The methodology presented here, allows an automated structural health monitoring. Hereby, an important step forward in future development of safe and reliable structures is represented

Partial Discharge Detection and localization Using Software Defined Radio in the future smart grid

Partial discharge (PD) occurs if a high voltage is applied to insulation that contains voids. PD is one of the predominant factors to be controlled to ensure reliability and undisrupted functions of power generators, motors, Gas Insulated Switchgear (GIS) and grid connected power distribution equipment. PD can degrade insulation and if left untreated can cause catastrophic insulation failure. However, PD pulse monitoring and detection can save cost and life prior to plant failure. PD is detected using traditional methods such as galvanic contact methods or UHF PD detection methods. Recently, an alternative method for PD detection and monitoring using wireless technology has become possible. Software Defined Radio has opened new opportunities to detect and monitor PD activity. This research makes use of SDR technology for PD detection and monitoring. The main advantages of SDR technology are that it is cost-effective and it is relatively immune against environmental noise. This is because the noise at electrical power stations is from around a few KHz to a few MHz and this is well below the SDR frequency range and PD frequency band (50-800 MHz). However, noise or interference also exists in the PD frequency band. These interferences are narrow band and mainly from FM, TV broadcasting and mobile telephony signals whose frequencies are well known, thus these interferences can be possibly processed and removed. In this research two SDR products (Realtek software defined radio RTL-SDR/Universal software radio peripheral USRP N200) are used to detect PD signals emitted by a PD source that was located at a distance of 1 m in case of RTL-SDR device while in case of USRP N200 the PD source was located at a distance of 3 m. These PD signals once received by an SDR device are recorded and processed offline in order to localize the PD source. The detected PD signal was around 20 dB above background noise in case of the RTL-SDR device and 25 dB above background noise in case of using the USRP N200. Selecting the appropriate SDR device depends on factors such as high sensitivity and selectivity. Furthermore, although USRP N200 is more expensive than RTL-SDR dongles, USRP N200 was preferred over RTL-SDR as it demonstrates higher sensitivity and overall better results. PD detection using SDR devices was conducted in the frequency domain. These result were validated using a high-end costly device, i.e. spectrum analyzer. Generally, SDR devices demonstrate satisfactory results when compared to spectrum analyzers. Considering that spectrum analyzers cost around £10,000, while a USRP N200 SRD device costs less than £1000, SDR technology seems to be cost-effective. Following PD detection, PD localization was performed using USRP N200 results, and a localization algorithm based on Received Signal Strength (RSS) was adopted. The localization result was within a 1.3-meter accuracy and this can be considered as a relatively good result. In addition, and for the purpose of evaluating the proposed scheme, more experiments were conducted using another system that is based on radiometric sensors which is WSN PD system. The estimated error was 1m in case of using the SDR-USRP N200 system and 0.8 m in case of using the WSN PD system. Results of both systems were very satisfactory, although some results at the corners of the detection grid were not good and the error was higher than 3 meters due to the fact that the RSS algorithm performs poorly at corners. These experiments were used to validate both systems for PD detection and localization in industrial environments