Relevant bibliographies by topics / Wind power condition monitoring

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Wind power condition monitoring'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Wind power condition monitoring"

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Benbouzid, Mohamed, Tarek Berghout, Nur Sarma, Siniša Djurović, Yueqi Wu, and Xiandong Ma. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review." Energies 14, no. 18 (September 20, 2021): 5967. http://dx.doi.org/10.3390/en14185967.

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Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.
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Moeini, R., M. Entezami, M. Ratkovac, P. Tricoli, H. Hemida, R. Hoeffer, and C. Baniotopoulos. "Perspectives on condition monitoring techniques of wind turbines." Wind Engineering 43, no. 5 (November 28, 2018): 539–55. http://dx.doi.org/10.1177/0309524x18807028.

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The ever-increasing development of wind power plants has raised awareness that an appropriate condition monitoring system is required to achieve high reliability of wind turbines. In order to develop an efficient, accurate and reliable condition monitoring system, the operations of wind turbines need to be fully understood. This article focuses on the online condition monitoring of electrical, mechanical and structural components of a wind turbine to diminish downtime due to maintenance. Failure mechanisms of the most vulnerable parts of wind turbines and their root causes are discussed. State-of-the-art condition monitoring methods of the different parts of wind turbine such as generators, power converters, DC-links, bearings, gearboxes, brake systems and tower structure are reviewed. This article addresses the existing problems in some areas of condition monitoring systems and provides a novel method to overcome these problems. In this article, a comparison between existing condition monitoring techniques is carried out and recommendations on appropriate methods are provided. In the analysis of the technical literature, it is noted that the effect of wind speed variation is not considered for traditional condition monitoring schemes.
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Baygildina, Elvira, Liudmila Smirnova, Raimo Juntunen, Kirill Murashko, Andrey V. Mityakov, Mikko Kuisma, Olli Pyrhönen, et al. "Condition Monitoring of Wind Power Converters Using Heat Flux Sensor." International Review of Electrical Engineering (IREE) 11, no. 3 (June 30, 2016): 239. http://dx.doi.org/10.15866/iree.v11i3.8404.

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Entezami, M., S. Hillmansen, P. Weston, and M. Ph Papaelias. "Condition monitoring of hydraulic power units in industrial wind turbines." International Journal of Condition Monitoring 3, no. 2 (October 1, 2013): 47–52. http://dx.doi.org/10.1784/204764213808146635.

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Kang, Jian She, Xing Hui Zhang, Lei Xiao, and Xiu Ai Zhang. "Condition Monitoring System of Repaired Gearboxes of Wind Turbine." Applied Mechanics and Materials 556-562 (May 2014): 2970–73. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2970.

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Wind power becomes one of the most important cleaner energy in the world. The maintenance problem becomes the main factor preventing the appropriate power price which can be accepted by people. So, many researches contribute to improve the availability of wind farms. Traditionally, most researchers paid attention to the fault diagnosis, prognosis and maintenance strategies optimization to short the down time and the maintenance cost. But they all neglect the condition monitoring work of repaired gearbox of wind turbine before it is delivered to the customer from maintenance center to the wind farm. The life of repaired gearbox is also a very critical factor which influences the availability of wind farm. Good checking methods and good standard can provide a good support of good performance after the gearbox installed on the up tower. So, this paper proposed how to address these issues.
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Zhang, Jun, Xiong Du, Cheng Qian, and Heng-Ming Tai. "A quasi-online condition monitoring technique for the wind power converter." International Journal of Electrical Power & Energy Systems 130 (September 2021): 106971. http://dx.doi.org/10.1016/j.ijepes.2021.106971.

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Huang, Zhongshan, Ling Tian, Dong Xiang, Sichao Liu, and Yaozhong Wei. "Condition Monitoring of Wind Turbine Based on Copula Function and Autoregressive Neural Network." MATEC Web of Conferences 198 (2018): 04008. http://dx.doi.org/10.1051/matecconf/201819804008.

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The traditional wind turbine fault monitoring is often based on a single monitoring signal without considering the overall correlation between signals. A global condition monitoring method based on Copula function and autoregressive neural network is proposed for this problem. Firstly, the Copula function was used to construct the binary joint probability density function of the power and wind speed in the fault-free state of the wind turbine. The function was used as the data fusion model to output the fusion data, and a fault-free condition monitoring model based on the auto-regressive neural network in the faultless state was established. The monitoring model makes a single-step prediction of wind speed and power, and statistical analysis of the residual values of the prediction determines whether the value is abnormal, and then establishes a fault warning mechanism. The experimental results show that this method can provide early warning and effectively realize the monitoring of wind turbine condition.
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Li, Suo, Ling-ling Huang, Yang Liu, and Meng-yao Zhang. "Modeling of Ultra-Short Term Offshore Wind Power Prediction Based on Condition-Assessment of Wind Turbines." Energies 14, no. 4 (February 9, 2021): 891. http://dx.doi.org/10.3390/en14040891.

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More accurate wind power prediction (WPP) is of great significance for the operation of electrical power systems, as offshore wind power penetration increases continuously. As the offshore wind turbines (OWT) are a key system in converting offshore wind power into electrical power, maintaining their condition plays a pivotal role in WPP. However, it is seldom considered in traditional WPP. This paper proposes an ultra-short term offshore WPP methodology based on the condition assessment (CA) of OWTs. Firstly, a modified fuzzy comprehensive evaluation (MFCE) based CA of the OWT is presented with a new defined deterioration of indicators calculated by the relative errors. Long short-term memory (LSTM) neural network is introduced to deal with the complicated interactions between the various monitoring data of an OWT and the dynamic marine environment. Then, with the classifications of the health conditions of the OWT, the historical operation data is classified accordingly. An OWT-condition based WPP with a backpropagation (BP) neural network is developed to deal with the non-linear mapping relations between the numerical weather prediction (NWP) information, health conditions of OWT, and the output power. The results of the case study show the influences of the OWT health conditions to its output power and verifies the effectiveness and higher accuracy of the proposed method.
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Zhong, Xian You, Liang Cai Zeng, Chun Hua Zhao, Jin Zhang, and Shi Qing Wan. "Research of Condition Monitoring and Fault Diagnosis Techniques for Wind Turbine Gearbox." Applied Mechanics and Materials 197 (September 2012): 206–10. http://dx.doi.org/10.4028/www.scientific.net/amm.197.206.

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Wind power industry enormously expanded during the last several years. However, wind turbines are subjected to different sorts of failures, which lead to the increasement of the cost. The wind turbine gearbox is the most critical component in terms of high failure rates and long time to repair. This paper described common failures and root causes of wind turbine gearboxes. Then it focused on fault diagnosis and monitoring techniques for the wind turbine gearbox. The challenges and future research directions were presented, and the simulator rig of wind turbine gearbox was designed to develop condition monitoring and fault diagnosis techniques for wind turbine gearbox.
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Tian, Zhigang, Tongdan Jin, Bairong Wu, and Fangfang Ding. "Condition based maintenance optimization for wind power generation systems under continuous monitoring." Renewable Energy 36, no. 5 (May 2011): 1502–9. http://dx.doi.org/10.1016/j.renene.2010.10.028.

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Dissertations / Theses on the topic "Wind power condition monitoring"

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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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Books on the topic "Wind power condition monitoring"

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Barszcz, Tomasz. Vibration-Based Condition Monitoring of Wind Turbines. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05971-2.

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Tang, W. H. Condition monitoring and assessment of power transformers using computational intelligence. London: Springer, 2011.

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Bathe, Martin J. On-line condition monitoring of power press tooling using ultrasonics. Birmingham: University of Central England, 1992.

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Tang, W. H., and Q. H. Wu. Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-052-6.

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Graham, R. Electrical demand profiles at six wind monitoring sites. Edinburgh: Scottish Agricultural College, Engineering Dept., 1999.

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Dunleavy, Padraig. Condition monitoring in a gas-fired combined cycle generating station. Dublin: University College Dublin, 1996.

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Ahola, Jero. Applicability of power-line communications to data transfer on on-line condition monitoring of electrical drives. Lappeenranta: Lappeenranta University of Technology, 2003.

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Davies, M. S. The development of an expert system for condition monitoring of the preheating circuit in a power plant. Manchester: UMIST, 1993.

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National Seminar, Large Power Transformers - Modern Trends in Aplication, Testing, and Condition Monitoring (2002 New Delhi, India). National Seminar, Large Power Transformers - Modern Trends in Application, Testing, and Condition Monitoring, 14-15 November, 2002, New Delhi: Proceedings. Edited by Mathur G. N, Narasimhan S. L, Prasher V. K, and India. Central Board of Irrigation and Power. New Delhi: Central Board of Irrigation and Power, 2002.

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Abu-Siada, Ahmed, ed. Power Transformer Condition Monitoring and Diagnosis. Institution of Engineering and Technology, 2018. http://dx.doi.org/10.1049/pbpo104e.

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Book chapters on the topic "Wind power condition monitoring"

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Thöns, Sebastian, and David McMillan. "Condition Monitoring Benefit for Operation Support of Offshore Wind Turbines." In Reliability Modeling and Analysis of Smart Power Systems, 169–82. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1798-5_11.

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Al-Habaibeh, Amin, Ampea Boateng, and Hyunjoo Lee. "Innovative Strategy for Addressing the Challenges of Monitoring Off-Shore Wind Turbines for Condition-Based Maintenance." In Springer Proceedings in Energy, 189–96. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63916-7_24.

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AbstractOff-shore wind energy technology is considered to be one of the most important renewable energy source in the 21st century towards reducing carbon emission and providing the electricity needed to power our cities. However, due to being installed away from the shore, ensuring availability and performing maintenance procedures could be an expensive and time consuming task. Condition Based Maintenance (CBM) could play an important role in enhancing the payback period on investment and avoiding unexpected failures that could reduce the available capacity and increase maintenance costs. Due to being at distance from the shore, it is difficult to transfer high frequency data in real time and because of this data transferring issue, only low frequency-average SCADA data (Supervisory Control And Data Acquisition) is available for condition monitoring. Another problem when monitoring wind energy is the massive variation in weather conditions (e.g. wind speed and direction), which could produce a wide range of operational alerts and warnings. This paper presents a novel case study of integrated event-based wind turbine alerts with time-based sensory data from the SCADA system to perform a condition monitoring strategy to categorise health conditions. The initial results presented in this paper, using vibration levels of the drive train, indicate that the suggested monitoring strategy could be implemented to develop an effective condition monitoring system.
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Driss, Zied, Tarek Chelbi, Ahmed Kaffel, and Mohamed Salah Abid. "Experimental Characterization of a NACA2415 Airfoil Wind Turbine." In Applied Condition Monitoring, 111–20. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14532-7_12.

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Lebranchu, Alexis, Sylvie Charbonnier, Christophe Berenguer, and Frédéric Prevost. "Using SCADA Data for Fault Detection in Wind Turbines: Local Internal Model Versus Distance to a Wind Farm Reference." In Applied Condition Monitoring, 225–36. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20463-5_17.

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Dao, Phong B., Wieslaw J. Staszewski, and Tadeusz Uhl. "Operational Condition Monitoring of Wind Turbines Using Cointegration Method." In Applied Condition Monitoring, 223–33. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61927-9_21.

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Castellani, Francesco, Davide Astolfi, and Ludovico Terzi. "Analyzing State Dynamics of Wind Turbines Through SCADA Data Mining." In Applied Condition Monitoring, 213–23. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20463-5_16.

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Yu, Guoqing, Zhitao Yan, and Xinpeng Liu. "Nonlinear Wind-Induced Response Analysis of Substation Down-Conductor System." In Applied Condition Monitoring, 35–43. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76517-0_5.

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Thomas, R. A., and C. Davies. "Efficient Power Generation through Predictive Maintenance." In Profitable Condition Monitoring, 133–43. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1616-9_12.

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Barszcz, Tomasz, Rafał Gawarkiewicz, Adam Jabłoński, Michał Sękal, and Michał Wasilczuk. "Knocking Sounds in the Wind Turbine Gearbox During Slowing Down—Case Study." In Applied Condition Monitoring, 235–44. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61927-9_22.

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Yangui, Majdi, Salma Thabti, Slim Bouaziz, and Mohamed Haddar. "Numerical Assessment of the Structural Performance of a Segmented Wind Turbine Blade." In Applied Condition Monitoring, 1–7. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85584-0_1.

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Conference papers on the topic "Wind power condition monitoring"

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Garg, Himani, and Ratna Dahiya. "Wind Turbine Condition Monitoring for Rotor Faults using MCSA." In 2018 IEEE 8th Power India International Conference (PIICON). IEEE, 2018. http://dx.doi.org/10.1109/poweri.2018.8704387.

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Tian, S. S., Z. Qian, and L. X. Cao. "Wind turbine power generation performance evaluation under faults condition." In 2016 International Conference on Condition Monitoring and Diagnosis (CMD). IEEE, 2016. http://dx.doi.org/10.1109/cmd.2016.7757880.

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Papadopoulos, P., and L. Cipcigan. "Wind turbines' condition monitoring: an ontology model." In 2009 International Conference on Sustainable Power Generation and Supply. SUPERGEN 2009. IEEE, 2009. http://dx.doi.org/10.1109/supergen.2009.5430854.

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Hajiabady, S., M. Papaelias, S. Kerkyras, P. Tricoli, and S. Hillmansen. "Efficient diagnostic condition monitoring for industrial wind turbines." In 3rd Renewable Power Generation Conference (RPG 2014). Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/cp.2014.0932.

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Crabtree, C. J., and P. J. Tavner. "Condition monitoring algorithm suitable for wind turbine use." In IET Conference on Renewable Power Generation (RPG 2011). IET, 2011. http://dx.doi.org/10.1049/cp.2011.0163.

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Brandao, R. F. Mesquita, J. A. Beleza Carvalho, and F. P. Maciel Barbosa. "Condition monitoring of the wind turbine generator slip ring." In 2012 47th International Universities Power Engineering Conference (UPEC). IEEE, 2012. http://dx.doi.org/10.1109/upec.2012.6398673.

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Nilsson, Julia, and Lina Bertling. "Maintenance management of wind power systems using Condition Monitoring Systems." In 2007 IEEE Power Engineering Society General Meeting. IEEE, 2007. http://dx.doi.org/10.1109/pes.2007.385616.

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Feng Yanhui, Qiu Yingning, D. Infield, Li Jiawei, and Yang Wenxian. "Study on Order Analysis for Condition Monitoring Wind Turbine Gearbox." In 3rd Renewable Power Generation Conference (RPG 2014). Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/cp.2014.0904.

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Zaggout, M. N., P. J. Tavner, and L. Ran. "Wind turbine condition monitoring using generator control loop signals." In 6th IET International Conference on Power Electronics, Machines and Drives (PEMD 2012). IET, 2012. http://dx.doi.org/10.1049/cp.2012.0292.

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Zhou, Xiangyu, Xin Huang, Zhulin Dong, Weizhi Li, and Minghao Jiang. "Wind Turbine Performance Assessment and Condition Monitoring with Performance Index." In 2019 IEEE Sustainable Power and Energy Conference (iSPEC). IEEE, 2019. http://dx.doi.org/10.1109/ispec48194.2019.8975081.

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Reports on the topic "Wind power condition monitoring"

1

Sheng, Shuangwen. Wind Turbine Gearbox Condition Monitoring Round Robin Study - Vibration Analysis. Office of Scientific and Technical Information (OSTI), July 2012. http://dx.doi.org/10.2172/1048981.

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Wan, Y. Wind Power Plant Monitoring Project Annual Report. Office of Scientific and Technical Information (OSTI), July 2001. http://dx.doi.org/10.2172/786384.

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Sheng, Shuangwen, Hal Link, William LaCava, Jeroen van Dam, Brian McNiff, Paul Veers, Jonathan Keller, Sandy Butterfield, and Francisco Oyague. Wind Turbine Drivetrain Condition Monitoring During GRC Phase 1 and Phase 2 Testing. Office of Scientific and Technical Information (OSTI), October 2011. http://dx.doi.org/10.2172/1027157.

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Pereira da Cunha, Mauricio. Wireless microwave acoustic sensor system for condition monitoring in power plant environments. Office of Scientific and Technical Information (OSTI), March 2017. http://dx.doi.org/10.2172/1406890.

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Qiao, Wei. Final Technical Report Recovery Act: Online Nonintrusive Condition Monitoring and Fault Detection for Wind Turbines. Office of Scientific and Technical Information (OSTI), May 2012. http://dx.doi.org/10.2172/1043180.

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Schulz, M. J., and M. J. Sundaresan. Smart Sensor System for Structural Condition Monitoring of Wind Turbines: 30 May 2002--30 April 2006. Office of Scientific and Technical Information (OSTI), August 2006. http://dx.doi.org/10.2172/891105.

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Joshi, N. R., A. D. Ramirez, D. W. Brock, and S. D. Russell. Monitoring of High-Power Microwave Tube Systems Using the Integrated Condition Assessment System (ICAS). Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada406339.

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Sexton, Casey, Gary Harmon, and Trevor Toll. Development of an In-Situ Method for Cable Condition Monitoring in Nuclear Power Plants. Office of Scientific and Technical Information (OSTI), July 2020. http://dx.doi.org/10.2172/1637946.

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Loparo, Kenneth, Richard Kolacinski, Wanchat Threeanaew, and Hanieh Agharazi. An Information Theoretic Framework and Self-organizing Agent- based Sensor Network Architecture for Power Plant Condition Monitoring. Office of Scientific and Technical Information (OSTI), January 2017. http://dx.doi.org/10.2172/1345557.

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Gevorgian, V. Wind Farm Monitoring at Lake Benton II Wind Power Project - Equipment Only: Cooperative Research and Development Final Report, CRADA Number CRD-08-275. Office of Scientific and Technical Information (OSTI), June 2014. http://dx.doi.org/10.2172/1134503.

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