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Wisznia, Roman. "Condition Monitoring of Offshore Wind Turbines." Thesis, KTH, Kraft- och värmeteknologi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-118455.
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The growing interest around offshore wind power, providing at the same time better wind conditions and fewer visual or environmental impacts, has lead many energy suppliers to consider the installation of offshore wind farms. However, the marine environment makes the installation and maintenance of wind turbines much more complicated, raising the capital and operation costs to an undesirable level and preventing the fast progression of this technology worldwide. Availability of offshore wind turbines varies between 65 and 90% depending on location, whereas onshore turbines range between 95 and 98% in most cases. In 2009, the ETI launched a research project aiming to improve economical efficiency of offshore wind farms by increasing their availability and decreasing their maintenance costs (partly through replacing corrective maintenance by preventive maintenance). This project named “Inflow” involves the development of a condition monitoring system, a system designed to monitor the state of different wind turbine components, and to analyze this data in order to determine the wind turbines overall condition at any given time, as well as its potential system ailments This paper describes two different approaches to perform the condition monitoring of offshore wind farms, the first one involves thresholds-based analysis, while the other involves pattern recognition.
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Kuiler, Ian Radcliffe. "Condition monitoring of squirrel cage induction generators in wind turbines." Thesis, Cape Peninsula University of Technology, 2017. http://hdl.handle.net/20.500.11838/2530.
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Thesis (MTech (Electrical Engineering))--Cape Peninsula University of Technology, 2017.Globally governments are faced with challenges in the energy sector which are exacerbated by uncertain financial markets and resource limitations. The over utilization of fossil fuels for electricity generation has had a profound impact on the climatic conditions on earth. Coal power stations release carbon dioxide (CO2) during the combustion process and studies show that concentrations have sharply risen in the atmosphere. Adverse environmental conditions like global warming exist as a result of high greenhouse gas (GHG) emissions in particular CO2. In 2015 Eskom constructed Sere Wind farm with a supply capability of 100 MW. Due to the lack of technical expertise and skills with regard to the new technology within Eskom, Siemens was offered a 5 year maintenance contract. Siemens also provides training on basic operation and maintenance (O&M) of the wind farm to Eskom staff. This excludes specialised training on Siemens Turbine Condition Monitoring (TCM) systems which is a critical part to develop optimum maintenance strategies. This shortage of specialised skills in the application of condition monitoring techniques within Eskom is a major concern. If the most cost effective maintenance strategies during the contract period are implemented, the long term plant health and design life of Sere wind farm will be reduced. There is a need to develop new condition monitoring techniques to complement or address the shortcomings of the existing systems. Developing these skills will increase the understanding of the technology and improve the operating and maintenance of Sere wind farm.
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Nilsson, Julia. "Maintenance management of wind power systems : Cost effect analysis of condition monitoring systems." Thesis, KTH, School of Electrical Engineering (EES), 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4124.
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The wind power industry has experienced a large growth the past years. The growth mainly focus on a growing market, better economical conditions for wind power because of political decisions and the development of large wind turbines and offshore farms. A goal is to increase reliability for turbines. The topic is even more important for offshore farms where service is difficult and expensive.
The answer for the wind power industry, for better maintenance management and increased reliability, could be Condition Monitoring Systems (CMS). Such systems are commonly used in other industries. They continuously monitor the performance of the wind turbine parts e.g. generator, gearbox and transformer, and help determine the best time for a specific maintenance work. How these systems could support the wind power user is investigated in this report.
The further step could be to implement CMS as a part of Reliability Centered Maintenance (RCM).RCM is a structured approach that focus on reliability aspects when determining maintenance plans, that is to find a balance between preventive- and corrective maintenance. Preventive maintenance is maintenance carried out before failures occur and corrective maintenance is maintenance carried out after failures occur.
Condition Monitoring can consist of e.g. vibration analysis and oil analysis. In these two different analyses there are several methods that can be used. The components that are of interest of condition monitoring are the gearbox, generator and the main shaft. The component of most interest, and that it has been shown is a critical component due to its impact on system availability, is the gearbox.
Life Cycle Cost (LCC) analyses have been made to calculate if it is profitable to implement CMS. The total cost, LCC including additional costs for implementing CMS, is compared for different alternative maintenance strategies. For a single turbine onshore versus an average turbine offshore in three strategies, and for a farm offshore where maintenance is planned using CMS in three strategies. The LCC without costs for CMS is called the basic case.
The first three strategies studied for the separate turbine onshore gave the following results when a CMS cost is added to the basic case; to compensate for the additional cost the preventive maintenance has to be decreased by 23 %. To compensate for the additional cost the preventive and corrective maintenance together have to be decreased by 3,5 %. The same results for the farm offshore, where an average turbine was observed, were 4,5 % and 2,5 % respectively. Decreased corrective maintenance is needed to motivate CMS, at least for the turbine onshore.
The following three strategies studied for the farm offshore gave the following results: a change from corrective maintenance to preventive maintenance with 47 % would be enough to make CMS profitable. The availability would not have to be increased with more than 0,43 % to get a reduction in cost for production loss that would cover the cost for CMS.
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Konaklieva, Syliva. "Power module condition monitoring for offshore wind applications with focus on the die attach degradation." Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/111772/.
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This thesis documents the research for a field-deployable on-line condition monitoring method which can be applied to the IGBT modules inside power electronic converters in offshore wind turbines. The main focus is on determining the health condition of the module packaging and on finding a viable means for tracking its gradual in-service degradation. Of the two main packaging degradation mechanisms - solder fatigue and bond-wire lift off - greater attention is paid to the former, although the researched condition monitoring method may also allow the detection of the latter. The signature considered here as most indicative of module degradation is the increase of its internal power loss for the same electrical operating point (defined by current level, power factor, switching frequency, etc.). Power loss and junction temperature affect each other in a circular fashion, progressively increasing, especially when the heat flow path to the outside cooling system is compromised by increasing levels of solder fatigue. The method explored here for assessing the device power losses in operation relies on external case and heat sink temperature signals and the novel use of ANNs in place of a thermal model. Although the explored concept is not yet ready for industrial use, it shows potential for further development. Power loss modelling with thermal feed-back is undertaken to develop a better understanding of the devices' operation. Special focus is paid to the current sharing and temperature profiles of paralleled chips inside the same packaging experiencing different degradation levels. High resolution scans of the die-attach solder layer of power cycled modules are also performed to gain understanding of their degradation.
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Qian, Peng. "Data-driven model-based approaches to condition monitoring and improving power output of wind turbines." Thesis, Lancaster University, 2017. http://eprints.lancs.ac.uk/89658/.
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The development of the wind farm has grown dramatically in worldwide over the past 20 years. In order to satisfy the reliability requirement of the power grid, the wind farm should generate sufficient active power to make the frequency stable. Consequently, many methods have been proposed to achieve optimizing wind farm active power dispatch strategy. In previous research, it assumed that each wind turbine has the same health condition in the wind farm, hence the power dispatch for healthy and sub-healthy wind turbines are treated equally. It will accelerate the sub-healthy wind turbines damage, which may leads to decrease generating efficiency and increases operating cost of the wind farm. Thus, a novel wind farm active power dispatch strategy considering the health condition of wind turbines and wind turbine health condition estimation method are the proposed. A modelbased CM approach for wind turbines based on the extreme learning machine (ELM) algorithm and analytic hierarchy process (AHP) are used to estimate health condition of the wind turbine. Essentially, the aim of the proposed method is to make the healthy wind turbines generate power as much as possible and reduce fatigue loads on the sub-healthy wind turbines. Compared with previous methods, the proposed methods is able to dramatically reduce the fatigue loads on subhealthy wind turbines under the condition of satisfying network operator active power demand and maximize the operation efficiency of those healthy turbines. Subsequently, shunt active power filters (SAPFs) are used to improve power quality of the grid by mitigating harmonics injected from nonlinear loads, which is further to increase the reliability of the wind turbine system.
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Dallyn, Paul A. "Advances in foundation design and assessment for strategic renewable energy." Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/24100.
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In order to meet EU legislation on emissions, significant effort is being invested into the development of cost-effective renewable power generation technologies. The two leading technologies are solar and wind power because of their potential for the lowest levelised cost of energy and for showing a growth in installed capacity and technological development. Various research findings have suggested that significant cost savings in the capital expenditure of renewable energy projects can be made through the optimisation of their support foundations, the understanding of which has formed the main goal of the research.
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Crabtree, Christopher James. "Condition monitoring techniques for wind turbines." Thesis, Durham University, 2011. http://etheses.dur.ac.uk/652/.
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This thesis focuses on practical condition monitoring of wind turbines. With offshore wind playing an increasing part in UK electricity generation, prompt fault detection leading to preventative maintenance is gaining in importance. This work describes the development of a condition monitoring test rig and the innovation and application of signal processing techniques for the detection of faults in non-stationary signals. Work is supported throughout by information from wind turbine operators and their experiences of variable speed, variable load wind turbines in the field. Experimental work is carried out on a condition monitoring test rig comprising a wound rotor induction generator, gearbox and DC driving motor. The test rig operates at variable speed and allows the implementation of a number of fault-like conditions including rotor electrical asymmetry, shaft mass unbalance and gear tooth failure. Test rig instrumentation was significantly developed during this research and both electrical and mechanical condition signals are monitored. A signal processing algorithm was developed based on experience with analysis techniques and their relationship with the characteristics of a wind turbine. The algorithm is based on Fourier analysis and allows the analysis of fault-related speed-dependent frequencies within non-stationary signals such as those encountered on a wind turbine. The detection of different faults is discussed and conclusions drawn on the applicability of frequency tracking algorithms. The newly developed algorithm is compared with a published method to establish its advantages and limitations.
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Wilkinson, Michael Richard. "Condition Monitoring for Offshore Wind Turbines." Thesis, University of Newcastle Upon Tyne, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492117.
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Hajiabady, Siavash. "Integrated condition monitoring of industrial wind turbines." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8121/.
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The continuous growth in wind turbine power ratings and numbers has led to increased demands in inspection and maintenance due to the more significant financial and operational consequences of unexpected wind turbine failure. The fact that wind farms are commonly located at remote sites with potentially poor accessibility means it is necessary to reduce the need for corrective maintenance through evolution to preventive and prognostic maintenance activities. Prognostic repair schedules can be employed in order to optimise maintenance and contribute to the minimisation of the overall operational costs of wind farms. The present study presents the development and qualitative evaluation of remote condition monitoring methodologies for the evaluation of the wind turbine power electronics and gearboxes. The failures of power converter and gearbox components result in significant wind turbine downtime and associated repair costs. Effective condition monitoring can enable the timely diagnosis of faults in order to prevent unexpected failures and loss of electricity production, contributing towards a noteworthy increase the reliability, availability, maintainability and safety (RAMS) of wind farms. Within this study two customised test rigs have been employed to simulate various of faults and assess the capability of RCM in diagnosing this fault effectively. In addition, field measurements have been carried out and correlated to the findings of the test rig experiments. In this study, it has been possible to identify these variables qualitatively, but the quantitative investigation is still pending and will be most likely the subject of several future studies in this field. The present thesis provides a compact summary of the analysis of the key findings of the experimental work performed within the context of the OPTIMUS FP7 European collaborative project.
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Zaher, Ammar S. "Automated fault detection for wind farm condition monitoring." Thesis, University of Strathclyde, 2010. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=17689.
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Wang, Yue. "Wind turbine condition monitoring based on SCADA data." Thesis, University of Strathclyde, 2014. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=23191.
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Wind energy has an increasingly essential role in meeting electrical power demand and achieving environmental sustainability. The excellent offshore wind resource and the need to reduce carbon emissions from electricity generation are driving policy to increase offshore wind generation capacity in UK waters. Access and maintenance offshore can be difficult and will be more costly than onshore and availability correspondingly lower and as a result there is a growing interest in wind turbine condition monitoring allowing condition based, rather than responsive or scheduled, maintenance. Existing wind turbine condition monitoring methods, such as vibration analysis and oil debris detection, require expensive sensors. The additional costs can be substantial considering the number of turbines typically deployed in offshore wind farms and in addition, costly expertise is generally required to interpret the results. In contrast, the potential to extend the Supervisory Control and Data Acq uisition (SCADA) data based analysis approach is considerable and could add real value to the condition monitoring with little or no cost to the wind farm operator. This thesis focuses on wind turbine condition monitoring that utilises exclusively data from SCADA systems. The aim is to detect incipient wind turbine operational faults or failures before they evolve to catastrophic failures, so that preventative maintenance or corrective action can be scheduled in time, hence reducing downtime and potentially preventing wider damage. Useful component condition indicators are derived by comparing incoming operational SCADA data with the results for relevant variables, like component temperature that reflect component condition, derived from relevant models trained on SCADA data from a healthy wind turbine. Incipient failures are identified through anomalous behaviour in the variables of interest manifest in the SCADA data. This approach is first applied to individual wind turbines, but then extended to include other wind turbines operating under similar conditions to derive component condition indicators through inter-machine comparison. This is demonstrated to facilitate significant savings in computational effort and model complexity compared to the repetitive development of individual turbine models. In addition, a real time wind turbine power curve is implemented based on SCADA data, and compared with a reference power curve to identify anomalous behaviour, through minor changes in the power curve, in a timely manner.
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Zappala, Donatella. "Advanced algorithms for automatic wind turbine condition monitoring." Thesis, Durham University, 2014. http://etheses.dur.ac.uk/11059/.
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Reliable and efficient condition monitoring (CM) techniques play a crucial role in minimising wind turbine (WT) operations and maintenance (O&M) costs for a competitive development of wind energy, especially offshore. Although all new turbines are now fitted with some form of condition monitoring system (CMS), very few operators make use of the available monitoring information for maintenance purposes because of the volume and the complexity of the data. This Thesis is concerned with the development of advanced automatic fault detection techniques so that high on-line diagnostic accuracy for important WT drive train mechanical and electrical CM signals is achieved. Experimental work on small scale WT test rigs is described. Seeded fault tests were performed to investigate gear tooth damage, rotor electrical asymmetry and generator bearing failures. Test rig data were processed by using commercial WT CMSs. Based on the experimental evidence, three algorithms were proposed to aid in the automatic damage detection and diagnosis during WT non-stationary load and speed operating conditions. Uncertainty involved in analysing CM signals with field fitted equipment was reduced, and enhanced detection sensitivity was achieved, by identifying and collating characteristic fault frequencies in CM signals which could be tracked as the WT speed varies. The performance of the gearbox algorithm was validated against datasets of a full-size WT gearbox, that had sustained gear damage, from the National Renewable Energy Laboratory (NREL) WT Gearbox Condition Monitoring Round Robin project. The fault detection sensitivity of the proposed algorithms was assessed and quantified leading to conclusions about their applicability to operating WTs.
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Esu, Ozak O. "Vibration-based condition monitoring of wind turbine blades." Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/21679.
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Significant advances in wind turbine technology have increased the need for maintenance through condition monitoring. Indeed condition monitoring techniques exist and are deployed on wind turbines across Europe and America but are limited in scope. The sensors and monitoring devices used can be very expensive to deploy, further increasing costs within the wind industry. The work outlined in this thesis primarily investigates potential low-cost alternatives in the laboratory environment using vibration-based and modal testing techniques that could be used to monitor the condition of wind turbine blades. The main contributions of this thesis are: (1) the review of vibration-based condition monitoring for changing natural frequency identification; (2) the application of low-cost piezoelectric sounders with proof mass for sensing and measuring vibrations which provide information on structural health; (3) the application of low-cost miniature Micro-Electro-Mechanical Systems (MEMS) accelerometers for detecting and measuring defects in micro wind turbine blades in laboratory experiments; (4) development of an in-service calibration technique for arbitrarily positioned MEMS accelerometers on a medium-sized wind turbine blade. This allowed for easier aligning of coordinate systems and setting the accelerometer calibration values using samples taken over a period of time; (5) laboratory validation of low-cost modal analysis techniques on a medium-sized wind turbine blade; (6) mimicked ice-loading and laboratory measurement of vibration characteristics using MEMS accelerometers on a real wind turbine blade and (7) conceptualisation and systems design of a novel embedded monitoring system that can be installed at manufacture, is self-powered, has signal processing capability and can operate remotely. By applying the conclusions of this work, which demonstrates that low-cost consumer electronics specifically MEMS accelerometers can measure the vibration characteristics of wind turbine blades, the implementation and deployment of these devices can contribute towards reducing the rising costs of condition monitoring within the wind industry.
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Cablea, Georgia. "„Three-phase signals analysis for condition monitoring of electromechanical systems : application to wind turbine condition monitoring”." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT073/document.
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Cette thèse propose une méthode d'analyse des signaux triphasés pour la surveillance d'état des systèmes électromécaniques. La méthode proposée repose sur l'utilisation de la transformée en composantes symétriques instantanées et d'outils simples de traitement du signal pour détecter les défauts électriques et mécaniques dans de tels systèmes. Les avantages de cette approche triphasée par rapport à une approche monophasée pour la surveillance d'état sont étudiés en détail. Tout d'abord, pour les défauts électriques, l'utilisation de la transformée triphasée permet de séparer les composantes symétriques et asymétriques, et facilite ainsi la détection d'un déséquilibre électrique. Ensuite, pour les défauts mécaniques, l'approche par transformée en composantes symétriques permet de travailler dans des espaces avec un meilleur rapport signal à bruit. En effet, en appliquant le même traitement à la fois en monophasé et en triphasé sur les composantes symétriques, on observe que certains défauts mécaniques ne sont détectables qu’en utilisant la séquence positive des composantes symétriques. La méthodologie complète et les algorithmes pour calculer les indicateurs de défaut pour les défauts électriques et mécaniques sont donnés et les résultats sont validés sur signaux synthétiques et expérimentaux. En termes d'application, l'accent est mis sur la surveillance d'état des composants de turbines éoliennes. Toutefois, le procédé proposé peut être appliqué à des systèmes électromécaniques en général et peut facilement être étendu à des systèmes polyphasésThis thesis proposes a three-phase electrical signals analysis method for condition monitoring of electromechanical systems. The proposed method relies on the use of instantaneous symmetrical components (ISCs) transform and simple signal processing tools to detect both electrical and mechanical faults in such systems. The advantages of using this three-phase approach for condition monitoring instead of single-phase ones are thoroughly detailed. Firstly, for electrical faults the use of the three-phase transform separates the balanced and unbalanced components thus making electrical unbalance detection easier. Secondly, for mechanical faults the ISCs approach has better signal-to-noise ratio (SNR). Indeed, by applying the same processing to both single-phase and ISCs, some mechanical faults are only detectable using the positive-sequence ISC. The complete methodology and algorithms to compute fault indicators for both electrical and mechanical faults are given and the results are validated using synthetic and experimental signals. In terms of application, the focus was on condition monitoring of wind turbine components. However, the proposed method can be applied on electromechanical systems in general and can easily be extended to poly-phase systems
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Strömbergsson, Daniel. "Condition monitoring of wind turbine drivetrains using wavelet analysis." Licentiate thesis, Luleå tekniska universitet, Maskinelement, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-67337.
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Ferguson, David. "Increasing the reliability of wind turbine condition monitoring systems." Thesis, University of Strathclyde, 2017. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=28417.
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Wind turbines are leading the way in helping to reduce the dependency on fossil fuel energy sources. However to compete with other energy sources there is a need to reduce the cost of energy from wind turbines. It has been shown in the literature that as wind turbines increase in size their reliability decreases. As wind turbines move further offshore and into deeper water this becomes more of an issue as carrying out maintenance becomes more challenging and costly. One way of improving the reliability of wind turbines is through the use of condition monitoring systems (CMS) which can continually monitor the health of the machine and allow more optimised maintenance and repair scheduling. Although the benefits of using a CMS may seem evident, operators have been slow in the uptake of such systems. One reason for this is due to issues with the reliability of CMS themselves. As stated in the literature, CMS must accurately detect 60-80% of faults to be economically justifiable. Not detecting faults or the occurrence of false alarms is detrimental to the effectiveness of CMS. The work presented in this thesis aims to address the issue of CMS reliability. Through the installation of two CMS in operational wind turbines the author of this thesis has gained valuable insight into the design, build and installation of CMS which has facilitated the novel contributions from this work. The first contribution comes from the formulation of an engineering design process which incorporates five categories of robustness which were identified by the author through Failure-Mode Effects Analysis on a wind turbine CMS that was installed in an operational wind turbine. The engineering design process incorporating the robustness categories will allow wind turbine CMS to be designed which are capable of operating reliably in the harsh environment they are subjected to. The second contribution comes from the development of three techniques which will increase CMS reliability by reducing false alarms and introducing the ability to detect erroneous data.
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Zaggout, Mahmoud Nouh. "Wind turbine generator condition monitoring via the generator control loop." Thesis, Durham University, 2013. http://etheses.dur.ac.uk/9383/.
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This thesis focuses on the development of condition monitoring techniques for application in wind turbines, particularly for offshore wind turbine driven doubly fed induction generators. The work describes the significant development of a physical condition monitoring Test Rig and its MATLAB Simulink model to represent modern variable speed wind turbine and the innovation and application of the rotor side control signals for the generator fault detection. Work has been carried out to develop a physical condition monitoring Test Rig from open loop control, with a wound rotor induction generator, into closed loop control with a doubly fed induction generator. This included designing and building the rotor side converter, installing the back-to-back converter and other new instrumentation. Moreover, the MATLAB Simulink model of the Test Rig has been developed to represent the closed loop control, with more detailed information on the Rig components and instrumentation and has been validated against the physical system in the time and frequency domains. A fault detection technique has been proposed by the author based on frequency analysis of the rotor-side control signals, namely; d-rotor current error, q-rotor current error and q-rotor current, for wind turbine generator fault detection. This technique has been investigated for rotor electrical asymmetry on the physical Test Rig and its MATLAB Simulink model at different fixed and variable speed conditions. The sensitivity of the each proposed signal has been studied under different operating conditions. Measured and simulated results are presented, a comparison with the results from using stator current and total power has been addressed and the improvement in condition monitoring detection performance has been demonstrated in comparison with previous methods, looking at current, power and vibration analysis.
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Entezami, Mani. "Novel operational condition monitoring techniques for wind turbine brake systems." Thesis, University of Birmingham, 2013. http://etheses.bham.ac.uk//id/eprint/4719/.
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In the event of important component failure or during high wind speeds, it is crucial for wind turbines to stop operating. A failure in braking systems may result in loss of the whole structure. This thesis addresses the problem of automatic detection and diagnosis of faults within wind turbine brake systems. The aim is to develop a cost effective and non-intrusive condition monitoring solution for brake systems. This can then be used to either enhance safety or reduce costs to maintain current levels of safety. The use of an induction motor as a sensor to identify faults within the hydraulic unit of the brake system has been explored. The braking event was also used as an input to analysis which identified faults within blade systems. The use and further development of appropriate fault detection and diagnosis methods within electrical machines using several detailed case studies to monitor the condition of wind turbine brake systems has been demonstrated. In conclusion, it is shown that the results of the analyses of laboratory and field trial experiments with the proposed approaches and simulations have the potential to develop a comprehensive commercialised condition monitoring application for wind turbine brakes.
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May, Allan. "Operational expenditure optimisation utilising condition monitoring for offshore wind parks." Thesis, University of Strathclyde, 2016. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=26907.
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There is a strong desire to increase the penetration of renewable energy sources in the UK electricity market. Offshore wind energy could be a method to achieve this. However, there are still issues, both technical and economical, that hinder the development and exploitation of this energy source. A condition based maintenance plan that relies on fully integrating the input from condition monitoring and structural health monitoring systems could be the method to solve many of these issues. Improved maintenance scheduling has the potential to reduce maintenance costs, increase energy production and reduce the overall cost of energy. While condition monitoring systems for gearboxes, generators and main bearings have become common place over the last few years, the deployment of other monitoring systems has been slower. This could be due to the expense and complication of monitoring an entire wind farm. Wind park operators, correctly, would like to see proof that their investment will be prudent. To assist wind park operators and owners with this decision, an offshore wind operations and maintenance model that attempts to model the impacts of using monitoring systems has been developed. The development of the model is shown in this analysis: multiple methodologies are used to capture deterioration and the abilities of monitoring systems. At each stage benchmarks are shown against other models and available data. This analysis has a breadth and scope not currently addressed in literature and attempts to give insight to industry that was previously unavailable.
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Tang, Wenhu. "Intelligent condition monitoring and assessment for power transformers." Thesis, University of Liverpool, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400134.
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Guo, Dongsheng. "Power transformer condition monitoring with partial discharge measurement." Thesis, Glasgow Caledonian University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.443183.
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Tautz-Weinert, Jannis. "Improved wind turbine monitoring using operational data." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/36199.
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With wind energy becoming a major source of energy, there is a pressing need to reduce all associated costs to be competitive in a market that might be fully subsidy-free in the near future. Before thousands of wind turbines were installed all over the world, research in e.g. understanding aerodynamics, developing new materials, designing better gearboxes, improving power electronics etc., helped to cut down wind turbine manufacturing costs. It might be assumed, that this would be sufficient to reduce the costs of wind energy as the resource, the wind itself, is free of costs. However, it has become clear that the operation and maintenance of wind turbines contributes significantly to the overall cost of energy. Harsh environmental conditions and the frequently remote locations of the turbines makes maintenance of wind turbines challenging. Just recently, the industry realised that a move from reactive and scheduled maintenance towards preventative or condition-based maintenance will be crucial to further reduce costs. Knowing the condition of the wind turbine is key for any optimisation of operation and maintenance. There are various possibilities to install advanced sensors and monitoring systems developed in recent years. However, these will inevitably incur new costs that need to be worthwhile and retro-fits to existing turbines might not always be feasible. In contrast, this work focuses on ways to use operational data as recorded by the turbine's Supervisory Control And Data Acquisition (SCADA) system, which is installed in all modern wind turbines for operating purposes -- without additional costs. SCADA data usually contain information about the environmental conditions (e.g. wind speed, ambient temperature), the operation of the turbine (power production, rotational speed, pitch angle) and potentially the system's health status (temperatures, vibration). These measurements are commonly recorded in ten-minutely averages and might be seen as indirect and top-level information about the turbine's condition. Firstly, this thesis discusses the use of operational data to monitor the power performance to assess the overall efficiency of wind turbines and to analyse and optimise maintenance. In a sensitivity study, the financial consequences of imperfect maintenance are evaluated based on case study data and compared with environmental effects such as blade icing. It is shown how decision-making of wind farm operators could be supported with detailed `what-if' scenario analyses. Secondly, model-based monitoring of SCADA temperatures is investigated. This approach tries to identify hidden changes in the load-dependent fluctuations of drivetrain temperatures that can potentially reveal increased degradation and possible imminent failure. A detailed comparison of machine learning regression techniques and model configurations is conducted based on data from four wind farms with varying properties. The results indicate that the detailed setup of the model is very important while the selection of the modelling technique might be less relevant than expected. Ways to establish reliable failure detection are discussed and a condition index is developed based on an ensemble of different models and anomaly measures. However, the findings also highlight that better documentation of maintenance is required to further improve data-driven condition monitoring approaches. In the next part, the capabilities of operational data are explored in a study with data from both the SCADA system and a Condition Monitoring System (CMS) based on drivetrain vibrations. Analyses of signal similarity and data clusters reveal signal relationships and potential for synergistic effects of the different data sources. An application of machine learning techniques demonstrates that the alarms of the commercial CMS can be predicted in certain cases with SCADA data alone. Finally, the benefits of having wind turbines in farms are investigated in the context of condition monitoring. Several approaches are developed to improve failure detection based on operational statistics, CMS vibrations or SCADA temperatures. It is demonstrated that utilising comparisons with neighbouring turbines might be beneficial to get earlier and more reliable warnings of imminent failures. This work has been part of the Advanced Wind Energy Systems Operation and Maintenance Expertise (AWESOME) project, a European consortium with companies, universities and research centres in the wind energy sector from Spain, Italy, Germany, Denmark, Norway and UK. Parts of this work were developed in collaboration with other fellows in the project (as marked and explained in footnotes).
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Wang, Yifei. "Variable selection for wind turbine condition monitoring and fault detection system." Thesis, Lancaster University, 2016. http://eprints.lancs.ac.uk/79827/.
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With the fast growth in wind energy, the performance and reliability of the wind power generation system has become a major issue in order to achieve cost-effective generation. Integration of condition monitoring system (CMS) in the wind turbine has been considered as the most viable solution, which enhances maintenance scheduling and achieving a more reliable system. However, for an effective CMS, large number of sensors and high sampling frequency are required, resulting in a large amount of data to be generated. This has become a burden for the CMS and the fault detection system. This thesis focuses on the development of variable selection algorithm, such that the dimensionality of the monitoring data can be reduced, while useful information in relation to the later fault diagnosis and prognosis is preserved. The research started with a background and review of the current status of CMS in wind energy. Then, simulation of the wind turbine systems is carried out in order to generate useful monitoring data, including both healthy and faulty conditions. Variable selection algorithms based on multivariate principal component analysis are proposed at the system level. The proposed method is then further extended by introducing additional criterion during the selection process, where the retained variables are targeted to a specific fault. Further analyses of the retained variables are carried out, and it has shown that fault features are present in the dataset with reduced dimensionality. Two detection algorithms are then proposed utilising the datasets obtained from the selection algorithm. The algorithms allow accurate detection, identification and severity estimation of anomalies from simulation data and supervisory control and data acquisition data from an operational wind farm. Finally an experimental wind turbine test rig is designed and constructed. Experimental monitoring data under healthy and faulty conditions is obtained to further validate the proposed detection algorithms.
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Zhan, Ryan A. "Development of Novel Hardware and Software for Wind Turbine Condition Monitoring." DigitalCommons@CalPoly, 2021. https://digitalcommons.calpoly.edu/theses/2268.
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With the increased use of wind turbines as sources of energy, maintenance of these devices becomes more and more important. Utility scale wind turbines can be time consuming and expensive to repair so an intelligent method of monitoring these devices is important. Commercial solutions for condition monitoring exist but are expensive and can be difficult to implement. In this project a novel condition monitoring system is developed. The priority of this system, dubbed the LifeLine, is to provide reliable condition monitoring through an easy-to-install and low-cost system. This system utilizes a microcontroller to collect acceleration data to detect imbalances on turbines blades. Two graphical user interfaces are created. One improves control with a small wind turbine while the other interfaces with the LifeLine. A custom PCB is designed for the LifeLine and additional rotor speed, current, and voltage sensors are incorporated into the LifeLine system. Future improvements to this system are also discussed.
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Imam, Afroz M. "Condition Monitoring of Electrolytic Capacitors for Power Electronics Applications." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/14472.
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The objective of this research is to advance the field of condition monitoring of electrolytic capacitors used in power electronics circuits.
The construction process of an electrolytic capacitor is presented. Descriptions of various kinds of faults that can occur in an electrolytic capacitor are discussed. The methods available to detect electrolytic capacitor faults are discussed. The effects of the capacitor faults on the capacitor voltage and current waveforms are investigated through experiments. It is also experimentally demonstrated that faults in the capacitor can be detected by monitoring the capacitor voltage and current.
Various ESR estimation based detection techniques available to detect capacitor failures in power electronics circuits are reviewed. Three algorithms are proposed to track and detect capacitor failures: an FFT based algorithm, a system modeling based detection scheme, and finally a parameter estimation based algorithm. The parameter estimation based algorithm is a low-cost real-time scheme, and it is inexpensive to implement.
Finally, a detailed study is carried out to understand the failure mechanism of an electrolytic capacitor due to inrush current.
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Bish, Nigel B. "Dielectric condition monitoring using intelligent partial discharge analysis." Thesis, University of Brighton, 2003. https://research.brighton.ac.uk/en/studentTheses/5dfeb20c-6066-41b8-ba10-59ae9a7877af.
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Abouhnik, Abdelnasser Abouzid. "An investigation into vibration based techniques for wind turbine blades condition monitoring." Thesis, Manchester Metropolitan University, 2012. http://e-space.mmu.ac.uk/313141/.
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The rapid expansion of wind power has been accompanied by reported reliability problems and the aim is to provide a means of increasing wind turbine reliability, prevent break downs, increase availability and reduce maintenance costs and power outages. This research work reports the development of condition monitoring (CM) for early fault detection in wind turbine blades based on vibration measurements. The research started with a background and a survey of methods used for monitoring wind turbines. Then, finite element modelling (FEM) of three bladed horizontal axis wind turbine (HAWT) was developed to understand the nature and mechanism of the induced vibration. A HAWT test rig was constructed and equipped with computerised vibration measuring system for model verification. Statistical and spectral processing parameters then were used to analyse vibration signals that collected in healthy and faulty cases. Results obtained using time and frequency based techniques are not suitable for extracting blades condition related information. Consequently, empirical mode decomposition method (EMD), principal component analysis method (PCA) and continuous wavelet transform (CWT) are applied for extraction blade condition related features from the measured vibration. The result showed that although these methods generally proved their success in other fields, they have failed to detect small faults or changes in blade structure. Therefore, new techniques were developed using the above mentioned methods combined with feature intensity level (FIL) and crest factor. Namely, those are EDFIL, RMPCA and wavelet based FIL. The new techniques are found to be reliable, robust and sensitive to the severity of faults. Those analysis techniques are suitable to be the detection tool for an integrated wind turbine condition monitoring system. Directions for future work are also given at the end of the thesis.
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Antoniadou, Ifigeneia. "Accounting for non-stationarity in the condition monitoring of wind turbine gearboxes." Thesis, University of Sheffield, 2013. http://etheses.whiterose.ac.uk/4838/.
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Increasing growth of wind turbine systems suggests a more systematic research around their design, operation and maintenance is needed. These systems operate under challenging enviromental conditions and failure of some of their parts, for the time being, is frequent, although undesirable. Wind turbine gearboxes, more particularly, seem to be so problematic that some wind turbine designs avoid including them. Structural health monitoring and condition monitoring of wind turbines appear to be necessary in order to determine the condition and lifespan of the wind turbine components and the drivetrain respectively. In this way reparative actions could be taken whenever needed resulting in reduction of maintenance costs. This thesis focuses on the condition monitoring of wind turbine gearboxes, taking into account the varying loads that they endure. Currently, the vibration-based damage detection methods used in real life wind turbine condition monitoring systems are based on conventional methods that generally fail to detect damage at its early stage under the operational conditions observed in wind turbines. Load and speed variations of the drivetrain that are observed commonly in wind turbines influence the vibration signals and can possibly affect potential damage features. This shows a demand for effective methods for early damage detection. Developments in the area of advanced signal processing should be examined and applied in damage detection of wind turbine gearboxes. Methods from time-frequency analysis, time-scale analysis, pattern recognition, multivariate statistics and econometrics are examined in this study in a condition monitoring context. One important part of the work presented is the development of a simple gearbox model interfaced with realistic wind loading, a model feature that appears to be novel. Other interesting aspects of this thesis are related to the use of the empirical mode decomposition method for time-frequency analysis. The use of Teager-Kaiser energy operator as an alternative technique to Hilbert transform for the estimation of the instantaneous characteristics of the decomposed signals is one of these aspects. The study showed that for some cases and under certain conditions this operator could help to improve the time-frequency analysis. Another aspect is the observation of the change of the number of the intrinsic mode functions produced, for the different load and damage cases, during the decomposition process. This observation was connected theoretically with what is known as the mode mixing problem of the empirical mode decomposition method. For the feature discrimination part of this work, the simplest novelty detection method, outlier analysis, was used in a slightly different manner than in previous studies and the results obtained were compared with a novel adaptive thresholding technique, the 3D phase-space thresholding method. The previously described approaches were applied on the simulated gearbox data but also on real wind turbine gearbox data. Finally, cointegration analysis was proposed as a potential method for removing the effects of the gearbox load variations. This is a novel concept for the condition monitoring of wind turbine gearboxes. An approach which makes it possible to use data from just a single sensor in order to perform cointegration analysis was developed and the process for applying multiscale cointegration using either wavelets or the empirical mode decomposition method was discussed. This final part of the work is an initial step towards applying cointegration to condition monitoring data.
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Zachrison, Anders. "Fluid Power Applications Using Self-Organising Maps in Condition Monitoring." Doctoral thesis, Linköping : Department of Management and Engineering, Linköping University, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11127.
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Hsu, Cheng-Yu. "Condition monitoring of fluid power systems using artificial neural networks." Thesis, University of Bath, 1995. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295443.
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Palmer, Iain Alastair. "Condition monitoring and optical strain measurement for power industry components." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/9195.
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Condition monitoring and life extension of components is vital to reducing risk of failure and operational costs in the power industry. Optical strain measurement techniques have been studied and developed for use in condition monitoring of power plant steam pipes and wind turbine components. In addition, these techniques have been used to assist evaluation of damage mechanisms in these components. Power plant steam pipes operate at high temperature (approximately 600°C) and pressure, and as a result undergo large creep deformations. Monitoring creep strain is a key factor in assessing remaining life of important components. An optical strain measurement system developed by EON, Automatic Reference Creep Measurement And Control (ARCMAC) has been researched as to its combined effectiveness with Digital Image Correlation (DIC) in obtaining accurate and reliable strain measurement for high temperature components. DIC has also been used to monitor and evaluate damage in composite wind turbine blade components. The use of this optical strain technique has allowed comparison of experimentally-derived full-field strain maps to be compared with finite element analysis (FEA) results. Additionally, the use of acoustic emission (AE) as a condition monitoring technique for wind turbine blades has been investigated. Use of these techniques has given greater understanding of failure mechanisms in wind turbine components; in particular, transverse tensile damage and delamination have been investigated. The influence of the Brazier effect upon wind turbine blade failure has also been researched. Results of this research have evaluated accuracy of using optical strain measurement techniques as well as their ability to effectively measure strain in particular regions of interest. The application of such techniques is an important requirement for both power plant and wind turbine components. Finally, studies into the use of optical strain measurement techniques as lab-based tools to study failure mechanisms have been performed.
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Bathe, Martin J. "On-line condition monitoring of power press tooling using ultrasonics." Thesis, Birmingham City University, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316614.
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The principal objective of the research programme was to develop a technique for monitoring the condition of power-press cutting tools using ultrasonics. The principle of the technique was based upon the reflection of Surface Acoustic Waves (SAWs) from the tooling's cutting edges, and the main aim of the research was to establish the relationship between reflected SAW amplitude and tool edge condition. Three approaches were used to establish the relationship, namely on-line experiments, off-line bench-top experiments, and mathematical modelling. The modelling work involved using Transmission Line Modelling. Three types of wear were observed on the punch: i. The formation of a wear radius on the cutting edge of the punch. ii. The beating back of the bottom face of the punch adjacent to the cutting edge. iii. The formation of an abraded region on the flank of the punch adjacent to the cutting edge. The research has shown that it is possible to monitor the radius that forms on the cutting edge of the punch using the amplitude of SAW pulses reflected from the edge. Also the work has indicated that the rate of radius formation is a function of the clearance between the punch and the die. However, the effects of the change in the cutting edge's condition on the propagation delay of the SAWs, which was also investigated, was not found to be significant. Whilst in principle it should be possible to automatically monitor the condition and predict the wearing out of press-tools, the development of a working tool-moni toring system capable of monitoring the complete cutting edge of a press-tool is presently being hindered due to the lack of a suitable transducer.
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Halvorson, Hans Lavoll. "Condition Assessment of Wind Farm Medium Voltage Cable Joints." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elkraftteknikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18884.
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Rapid aging and failure in wind-farm collector systems has become an issue for grid owners. PD measurements of the cable systems can give good diagnostics, detect aging and pinpoint faults. Different PD measuring techniques are used today and the results will vary with applied voltage and frequency. This master thesis deals with preparation, electrothermal aging and characterization of XLPE cable joints. Dissection of the test objects was performed after aging and characterization. For characterization a variable frequency PD measurement setup has been built. The setup is defined by being able to detect PD with high accuracy in test objects at ± 17 kVpeak using Omicron MPD600 measuring equipment at variable frequencies between 10 mHz and 100 Hz. A PD-free low-pass RLC filter had to be used in the setup to reduce noise from the high-voltage source. The most important findings are that PD measurement results will vary significantly with regards to measuring technique. Aging may not be detected when measuring with too low frequency or voltage.
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Van, Niekerk Paul. "Development of a wind turbine condition monitoring facility for drivetrain torsional dynamics investigations." Diss., University of Pretoria, 2018. http://hdl.handle.net/2263/66388.
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Maintenance can be performed according to one of two strategies, failure based or condition based. In most cases, where large and expensive assets such as wind turbines are operated on a continuous basis, condition based maintenance is preferred. However, condition based maintenance relies on the continuous and accurate gathering of condition-information of the particular machine and its various components.
This dissertation reports the experimental and numerical work performed as part of the development of an experimental facility that will allow the development of condition monitoring techniques for wind turbines. This work is focussed on the torsional dynamics of a wind turbine setup. A physical setup, consisting of a 1.6 m diameter turbine, a 1:1. ̇ speed-multiplication gearbox, and a 24 Volt direct current generator is built. All of it is mounted within an open-return wind tunnel, which is also designed and built as part of this work. The following two cost-effective experimental techniques are used to measure the torsional natural frequencies: a shaft encoder tachometer from which instantaneous rotational frequency is obtained, and power signal analysis, where the generated voltage is recorded and analysed. It is shown how an algorithm developed by Diamond et al. (2016) is used for the shaft encoder geometry compensation. Frequency spectra based on Fourier transforms and short time Fourier transforms are used to identify harmonic frequencies. Both measurement techniques proves useful to identify not only natural frequencies of torsional vibration, but also various characteristic frequencies of the drivetrain such as shaft rotation, blade pass, gear mesh and generator armature. It is found that power signal analysis is more useful to identify the characteristic frequencies.
Torsional dynamics of the drivetrain and its components are also investigated with the following two numerical methods: an eight-degree-of-freedom torsional Lumped Mass Model (LMM), and a three-dimensional Finite Element Model (FEM). Torsional mode shapes and frequencies are calculated with both methods and a good agreement is found in the lower four modes. Numerical results are then compared with the experimental results, where there is also good agreement in the lower four modes. Model updating is performed on the FEM and by changing the torsional stiffness of the flexible couplings, the difference between measured and calculated natural frequencies are reduced to less than 6 %. It is concluded that future models should address lateral vibration of the drivetrain and the support structure.
From this study the following is contributed to the wind turbine condition monitoring field: considerations for the design and a working example of an experimental facility for investigating torsional dynamics, illustration of two measurement techniques, and two types of validated numerical models.Dissertation (MEng)--University of Pretoria, 2018.
Mechanical and Aeronautical Engineering
MEng
Unrestricted
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Kim, Taesic. "Model-Based Condition Monitoring and Power Management for Rechargeable Electrochemical Batteries." Thesis, The University of Nebraska - Lincoln, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3700290.
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Rechargeable multicell batteries have been used in various electrical and electronic systems, e.g., renewable energy systems, electric-drive vehicles, commercial electronics, etc. However, there are still concerns about the reliability and performance degradation of rechargeable batteries caused by low thermal stability and the aging process. A properly designed battery management system (BMS) is required for condition monitoring and control of multicell batteries to ensure their safety, reliability, and optimal performance. The goal of this dissertation research was to develop a novel BMS for rechargeable multicell batteries.
First, this research developed high-fidelity battery models for online condition monitoring and power management of battery cells. The battery models were capable of capturing the dynamic circuit characteristics, nonlinear capacity and nonlinear open-circuit voltage effects, hysteresis effect, and temperature effect of the battery cells.
Second, this research developed a novel self-X, multicell battery design. The proposed multicell battery can automatically configure itself according to the dynamic load/storage demand and the condition of each cell. The proposed battery can self-heal from failure or abnormal operation of single or multiple cells, self-balance from cell state imbalances, and self-optimize to improve energy conversion efficiency. These features were achieved by a highly efficient cell switching circuit and a high-performance condition monitoring and control system.
Moreover, this research developed several model-based condition monitoring algorithms based on the proposed battery models. First, a particle swarm optimization-based parameter identification algorithm was developed to estimate the impedance and state of charge (SOC) of batteries using the proposed hybrid battery model. Second, an algorithm combining a regression method for parameter identification, a sliding-mode observer for SOC estimation, and a two-point capacity estimation method were proposed. In addition, an electrical circuit with hysteresis model-based condition monitoring algorithm was proposed. It systematically integrates: a fast upper-triangular and diagonal recursive least square for online parameter identification, a smooth variable structure filter for SOC estimation, and a recursive total least square for maximum capacity and state of health estimation. These algorithms provided accurate, robust condition monitoring for lithium-ion batteries. Due to the low complexity, the proposed second and third algorithms are suitable for the embedded BMS applications.
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Hologne, Malorie. "Contribution to condition monitoring of Silicon Carbide MOSFET based Power Module." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE1317/document.
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L’avion plus électrique demande des modules de puissances de plus en plus performants dans les domaines de la fiabilité et de la maîtrise de la durée de vie restante. Le remplacement des systèmes hydrauliques et pneumatiques par des actionneurs électriques et leurs convertisseurs associés est, aujourd’hui, un moyen efficace de réduire les coûts de maintenance et la consommation de carburant. L’ajout de composantes électriques est également un bon moyen d’augmenter la fiabilité des systèmes. La fiabilité est toujours étudiée à partir de contraintes cycliques accélérées. La tendance actuelle est d’embarquer des fonctions de suivi de l’état de santé dans les modules de puissance pour permettre la prédiction de la durée de vie restante. Cette approche implique des modifications du circuit afin de mettre en place des capteurs et est souvent dédiée à un mode de défaillance en particulier. Cette thèse propose une approche par apprentissage du suivi de l’état de santé de modules de puissance à base de MOSFET en carbure de silicium. Une large étude bibliographique a permis de créer et de réaliser un banc de test instrumenté permettant de mettre en œuvre des défaillances attendues dans les modules de puissance mais aussi d’enregistrer un grand nombre de paramètres électriques au cours de la vie du module. Ces paramètres montrent une évolution au cours du vieillissement du module en fonction des modes de défaillances. Un modèle de réseaux neuronaux s’appuie sur la dérive de ces paramètres pour établir le pronostic de durée de vie restante d’un module de puissance à chaque instant de son utilisation normaleMore electrical aircraft requires power modules of higher performances, especially in terms of reliability with a control of lifetime. The replacement of hydraulic and pneumatic systems by electric actuators and their associated converters is the present trend to reduce maintenance cost and fuel consumption. Adding more electric components is also thought as a good way to increase reliability in systems. Reliability is still analysed from accelerated stress cycles. A large volume of data must be obtained in various conditions to assert a pertinent extrapolation of remaining lifetime during operation. A trend is to embed some condition monitoring functions in power modules to help predict the remaining lifetime. This approach is the field of hardware developments with respect to sensors and decorrelation methods but mainly dedicated to one particular failure. This thesis presents a learning approach of silicon carbide MOSFET based power modules condition monitoring. A large literature study has led to the elaboration of a test plan and an instrumented test bench. This test bench allows an accelerated lifespan of power module and an on-line recording of several electrical parameters. These parameters shows a drift according to the power module ageing. A neural network model based on these parameters drifts has been constructed to estimate the remaining useful lifetime of a power module in normal operation
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Aburaghiega, Ehnaish Ali F. "Condition monitoring and evaluation techniques for extending power transformers' life cycle." Thesis, Glasgow Caledonian University, 2017. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.743901.
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Rylands, Naasef. "Condition monitoring of induction motors in the nuclear power station environment." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29686.
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The induction motor is a highly utilised electrical machine in industry, with the nuclear industry being no exception. A typical nuclear power station usually contains more than 1000 motors, where they are used in safety and non-safety application. The efficient and fault-free operation of this machine is critical to the safe and economical operation of any plant, including nuclear power stations. A comprehensive literature review was conducted that covered the functioning of the induction machine, its common faults and methods of detecting these faults. The Condition Based Maintenance framework was introduced in which condition monitoring of induction machines is an essential component. The main condition monitoring methods were explained with the main focus being on Motor Current Signature Analysis (MCSA) and the various methods associated with it. Three analysis methods were selected for further study, namely, Current Signature Analysis, Instantaneous Power Signature Analysis (IPSA) and Motor Square Current Signature Analysis (MSCSA). Essentially, the methodology used in this dissertation was to study the three common motor faults (bearings, stator and rotor cage) in isolation and compare the results to that of the healthy motor of the same type. The test loads as well as fault severity were varied where possible to investigate its effect on the fault detection scheme. The data was processed using an FFT based algorithm programed in MATLAB. The results of the study of the three spectral analysis techniques showed that no single technique is able to detect motor faults under all tested circumstances. The MCSA technique proved the most capable of the three techniques as it was able to detect faults under most conditions, but generally suffered poor results in inverter driven motor applications. The IPSA and MSCSA techniques performed selectively when compared to MCSA and were relatively successful when detecting the mechanical faults. The fact that the former techniques produce results at unique points in the spectrum would suggest that they are more suitable for verifying results. As part of a comprehensive condition monitoring scheme, as required by a large population of the motors on a nuclear power station, the three techniques presented in this study could readily be incorporated into the Condition Based Maintenance framework where the strengths of each could be exploited.
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Ortiz, Gonzalez Jose Angel. "Electrothermal characterisation of silicon and silicon carbide power devices for condition monitoring." Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/99636/.
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Condition monitoring in power electronics is increasingly becoming a critical component of reliable power electronics both in traditional discrete and DBC packages as well as in pressure-packages. Condition monitoring involves on and off-line assessment of the state of health of the power module in an effort benchmark the reliability performance of the module (if off-line) or to prolong its useful operating life (if on-line). One widely acknowledged method of condition monitoring involves the use of temperature sensitive electrical parameters (TSEPs) to estimate the junction temperature and hence, the junction to case thermal impedance as well as on-state electrical resistance. However, for TSEP based condition monitoring to become reality, a significant amount of electrothermal characterisation of modern power devices is necessary and that is where this thesis makes a valuable contribution, especially for new SiC power devices with relatively unknown thermal characteristics in alternative packages like press-packs. TSEPs including diode/transistor on-state voltage drop, turn-on current switching rate, gate current/voltage switching transients etc., depend on the physics of the power devices and can vary from device to device depending on whether they are bipolar, unipolar, silicon or SiC. The on-state voltage drop of some devices exhibits a negative temperature coefficient, while others exhibit a positive one depending on the value of the Zero Temperature Coefficient (ZTC) point exhibited in the forward characteristics. The Zero temperature coefficient results from the interaction between junction voltages which reduce with temperature (due to increased intrinsic carrier concentration from bandgap narrowing) and parasitic series resistances which increase with temperature (due to mobility and ambipolar diffusion length reduction with temperature). This thesis shows that SiC power MOSFETs have the unique property of switching on faster at higher temperatures whereas the converse is true for silicon MOSFETs and IGBTs. Hence, the turn-on current switching rate has been proposed as a TSEP in SiC MOSFETs. The impact of parasitic inductance on the temperature sensitivity of the turn-on switching rate is also investigated and recommendations are made for the use of intelligent gate drivers with alterable gate driver impedances for implementing condition monitoring. This thesis also investigates the impact of junction temperature variation in parallel connected power devices on the accuracy of the TSEP for the entire module and how the gate driver could be used to improve the temperature sensitivity of determined electrical parameters. In the context of pressure-packaged assemblies, where the thermal impedance is inextricably linked with the electrical impedance, this thesis presents the electrothermal characterisation of SiC Schottky diodes. Both a single chip and a multichip press-pack prototypes have been designed and tested, including the evaluation of different intermediate contact materials, namely Aluminium Graphite and molybdenum. The impact of pressure imbalance and device temperature characteristics (ZTC point) on electrothermal stability of parallel devices are explored in this thesis.
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Habtemariam, Filmon A. "HIGH-FREQUENCY IMPEDANCE CHARACTERISTICS AND HEALTH CONDITION MONITORING OF OVERHEAD POWER LINES." University of Akron / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=akron1472735633.
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Laube, Martin, and Steffen Haack. "Condition Monitoring for hydraulic Power Units – user-oriented entry in Industry 4.0." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-200244.
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One of Bosch Rexroth’s newest developments is the ABPAC power unit, which is both modular and configurable. The modular design of the ABPAC is enhanced by a selfcontained Condition Monitoring System (CMS), which can also be used to retrofit existing designs. This dissertation shows how Industry 4.0-Technology provides special advantages for the diverse user profiles. Today, Hydraulic Power Units have either scheduled intervals for preventive maintenance or are repaired in case of component failures. Preventive maintenance concepts, until now, did not fully utilize the entire life expectancy of the components, causing higher maintenance costs and prolonged downtimes. Risk of unscheduled downtime forces the customer to stock an array of spare parts leading to higher inventory costs or in the event a spare is not readily available, the customer may encounter long delivery times and extended downtime. Bearing this in mind, we’ve conceived the idea of a self-contained intelligent Condition Monitoring System including a predictive maintenance concept, which is explained in the following.
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Hamilton, Andrew. "Development of novel gearbox lubrication condition monitoring sensors in the context of wind turbine gearboxes." Thesis, University of Strathclyde, 2015. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=25910.
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Wind power has become established as an alternative power source that forms a significant proportion of national energy generation. An increasing proportion of turbines is being constructed offshore to exploit higher average wind speeds and to avoid development issues associated with onshore wind farms. Isolated locations and unpredictable weather conditions lead to increased access costs for operators when conducting scheduled and unscheduled maintenance and repairs. This has increased interest in condition monitoring systems which can track the current state of components within a wind turbine and provide operators with predicted future trends. Asset management can be improved through condition based maintenance regimes and preventative repairs. Development of novel condition monitoring systems that can accurately predict incipient damage can optimise operational performance and reduce the overall level of wind turbine generation costs. The work described in this thesis presents the development of novel sensors that may be applied to monitor wind turbine gearboxes, a component that experiences relatively high failure rates and causes considerable turbine downtime. Current systems and technology that may be adapted for use in wind turbine condition monitoring are evaluated. Lubrication related monitoring systems have been identified as an area that could be improved and are divided into those that track liberated wear material suspended in the lubricant and those that assess the state of the lubricant itself. This study presents two novel lubrication based gearbox monitoring sensors that potentially offer a low cost solution for continuous data capture. The first demonstrates the potential for active pixel sensors such as those found in digital cameras to capture images of wear particles within gearbox lubricants. Particle morphology was tracked in this system, allowing the type of particles to be correlated with the type of wear that is generated and a potential source. The second sensor uses a targeted form of infra-red absorption spectroscopy to track changes in the lubricant chemistry due to the increase in acidity. Ensuring the lubricant is functioning correctly decreases component stress and fatigue, reducing maintenance requirements.
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Benjanirat, Sarun. "Computational studies of the horizontal axis wind turbines in high wind speed condition using advanced turbulence models." Diss., Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-08222006-145334/.
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Thesis (Ph. D.)--Aerospace Engineering, Georgia Institute of Technology, 2007.Samual V. Shelton, Committee Member ; P.K. Yeung, Committee Member ; Lakshmi N. Sankar, Committee Chair ; Stephen Ruffin, Committee Member ; Marilyn Smith, Committee Member.
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Buse, D. P. "Information management, condition monitoring and control of power systems over internet protocol networks." Thesis, University of Liverpool, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.402263.
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Pollmeier, Klemens. "Parallel computing for real-time simulation and condition monitoring of fluid power systems." Thesis, University of Bath, 1997. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388563.
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Helwig, Nikolai, and Andreas Schütze. "Data-based condition monitoring of a fluid power system with varying oil parameters." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-200657.
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In this work, an automated statistical approach for the condition monitoring of a fluid power system based on a process sensor network is presented. In a multistep process, raw sensor data are processed by feature extraction, selection and dimensional reduction and finally mapped to discriminant functions which allow the detection and quantification of fault conditions. Experimentally obtained training data are used to evaluate the impact of temperature and different aeration levels of the hydraulic fluid on the detection of pump leakage and a degraded directional valve switching behavior. Furthermore, a robust detection of the loading state of the installed filter element and an estimation of the particle contamination level is proposed based on the same analysis concept.
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Yu, Xi. "Modelling offshore wind farm operation and maintenance with view to estimating the benefits of condition monitoring." Thesis, University of Strathclyde, 2016. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=27387.
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Offshore wind energy is progressing rapidly and playing an increasingly important role in electricity generation. Since the Kyoto Protocol in February 2005, Europe has been substantially increasing its installed wind capacity. Compared to onshore wind, offshore wind allows the installation of larger turbines, more extensive sites, and encounters higher wind speed with lower turbulence. On the other hand, harsh marine conditions and the limited access to the turbines are expected to increase the cost of operation and maintenance (O&M costs presently make up approximately 20-25% of the levelised total lifetime cost of a wind turbine). Efficient condition monitoring has the potential to reduce O&M costs. In the analysis of the cost effectiveness of condition monitoring, cost and operational data are crucial. Regrettably, wind farm operational data are generally kept confidential by manufacturers and wind farm operators, especially for the offshore ones. To facilitate progress, this thesis has investigated accessible SCADA and failure data from a large onshore wind farm and created a series of indirect analysis methods to overcome the data shortage including an onshore/offshore failure rate translator and a series of methods to distinguish yawing errors from wind turbine nacelle direction sensor errors. Wind turbine component reliability has been investigated by using this innovative component failure rate translation from onshore to offshore, and applies the translation technique to Failure Mode and Effect Analysis for offshore wind. An existing O&M cost model has been further developed and then compared to other available cost models. It is demonstrated that the improvements made to the model (including the data translation approach) have improved the applicability and reliability of the model. The extended cost model (called StraPCost+) has been used to establish a relationship between the effectiveness of reactive and condition-based maintenance strategies. The benchmarked cost model has then been applied to assess the O&M cost effectiveness for three offshore wind farms at different operational phases. Apart from the innovative methodologies developed, this thesis also provides detailed background and understanding of the state of the art for offshore wind technology, condition monitoring technology. The methodology of cost model developed in this thesis is presented in detail and compared with other cost models in both commercial and research domains.
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Djekic, Zarko. "Online circuit breaker monitoring system." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-2060.
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Bi, Ran. "Interpretation to wind turbine generator faults and an improved condition monitoring technique based on normal behaviour models for wind turbine generator systems." Thesis, Glasgow Caledonian University, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.700993.
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Novanda, Happy. "Monitoring of power quality indices and assessment of signal distortions in wind farms." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/monitoring-of-power-quality-indices-and-assessment-of-signal-distortions-in-wind-farms(403a470c-279a-4b00-94dc-eaa2507dc579).html.
Full textAbstract:
Power quality has become one of major concerns in the power industry. It can be described as the reliability of the electric power to maintain continuity operation of end-use equipment. Power quality problems are defined as deviation of voltage or current waveforms from the ideal value. The expansion plan of wind power generation has raised concern regarding how it influences the voltage and current signals. The variability nature of wind energy and the requirements of wind power generation increase the potential problems such as frequency and harmonic distortions. In order to analyze and mitigate problems in wind power generation, it is important to monitor power quality in wind farm. Therefore, the more accurate and reliable parameter estimation methods suitable for wind power generation are needed. Three parameter estimation methods are proposed in this thesis to estimate the unknown parameters, i.e. amplitude and phase angle of fundamental and harmonic components, DC component and system frequency, during the dynamic change in wind farm. In the first method, a self-tuning procedure is introduced to least square method to increase the immunity of the algorithm to noise. In the second method, nonrecursive Newton Type Algorithm is utilised to estimate the unknown parameters by obtaining the left pseudoinverse of Jacobian matrix. In the last technique, unscented transformation is used to replace the linearization procedure to obtain mean and covariance which will be used in Kalman filter method. All of the proposed methods have been tested rigorously using computer simulated data and have shown their capability to track the unknown parameters under extreme distortions. The performances of proposed methods have also been compared using real recorded data from several wind farms in Europe and have demonstrated high correlation. This comparison has verified that UKF requires the shortest processing time and STLS requires the longest.