Journal articles: 'Wind power condition monitoring'

1

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Qian, Peng, Xiandong Ma, Dahai Zhang, and Junheng Wang. "Data-Driven Condition Monitoring Approaches to Improving Power Output of Wind Turbines." IEEE Transactions on Industrial Electronics 66, no. 8 (August 2019): 6012–20. http://dx.doi.org/10.1109/tie.2018.2873519.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Watson, Simon Jonathan, Beth J. Xiang, Wenxian Yang, Peter J. Tavner, and Christopher J. Crabtree. "Condition Monitoring of the Power Output of Wind Turbine Generators Using Wavelets." IEEE Transactions on Energy Conversion 25, no. 3 (September 2010): 715–21. http://dx.doi.org/10.1109/tec.2010.2040083.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Antoniadou, I., N. Dervilis, E. Papatheou, A. E. Maguire, and K. Worden. "Aspects of structural health and condition monitoring of offshore wind turbines." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373, no. 2035 (February 28, 2015): 20140075. http://dx.doi.org/10.1098/rsta.2014.0075.

Full text

Abstract:

Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector.

APA, Harvard, Vancouver, ISO, and other styles

14

Wang, Meng-Hui, and Hung-Cheng Chen. "Application of ENN-1 for Fault Diagnosis of Wind Power Systems." Mathematical Problems in Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/194091.

Full text

Abstract:

Maintaining a wind turbine and ensuring secure is not easy because of long-term exposure to the environment and high installation locations. Wind turbines need fully functional condition-monitoring and fault diagnosis systems that prevent accidents and reduce maintenance costs. This paper presents a simulator design for fault diagnosis of wind power systems and further proposes some fault diagnosis technologies such as signal analysis, feature selecting, and diagnosis methods. First, this paper uses a wind power simulator to produce fault conditions and features from the monitoring sensors. Then an extension neural network type-1- (ENN-1-) based method is proposed to develop the core of the fault diagnosis system. The proposed system will benefit the development of real fault diagnosis systems with testing models that demonstrate satisfactory results.

APA, Harvard, Vancouver, ISO, and other styles

15

Jeffries, W. Q., J. A. Chambers, and D. G. Infield. "Experience with bicoherence of electrical power for condition monitoring of wind turbine blades." IEE Proceedings - Vision, Image, and Signal Processing 145, no. 3 (1998): 141. http://dx.doi.org/10.1049/ip-vis:19982013.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Barton, John P., and Simon J. Watson. "Analysis of electrical power data for condition monitoring of a small wind turbine." IET Renewable Power Generation 7, no. 4 (July 2013): 341–49. http://dx.doi.org/10.1049/iet-rpg.2012.0326.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Lou, Jianlou, Kai Shan, and Jia Xu. "A New Condition Monitoring Method for Wind Turbines Based on Power Curve Model." International Journal of Control and Automation 9, no. 3 (March 31, 2016): 393–408. http://dx.doi.org/10.14257/ijca.2016.9.3.37.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Gao, Jian Tao, Jian Ding, Xiao Rong Zhu, Tian Tang, Zhang Sui Lin, Yi Lin, Xiao Dong Yang, Yun Ting Song, and Xiao Jun Li. "Overview on Integration Characteristics of Offshore Wind Power." Applied Mechanics and Materials 521 (February 2014): 128–34. http://dx.doi.org/10.4028/www.scientific.net/amm.521.128.

Full text

Abstract:

As onshore quality wind resource is drained, it has come into the era that offshore wind power is the most promising field in wind power industry. The key technology and development trend of power prediction, condition monitoring, integration mode, system characteristic and other aspects, is summarized in the paper. The latest technology of integration topology and the unique technology of offshore wind power are focused on, and the development prospect and technology trend about offshore wind power are discussed and expected.

APA, Harvard, Vancouver, ISO, and other styles

19

Asmussen, Magnus F., Jesper Liniger, and Henrik C. Pedersen. "Fault Detection and Diagnosis Methods for Fluid Power Pitch System Components—A Review." Energies 14, no. 5 (February 27, 2021): 1305. http://dx.doi.org/10.3390/en14051305.

Full text

Abstract:

Wind turbines have become a significant part of the global power production and are still increasing in capacity. Pitch systems are an important part of modern wind turbines where they are used to apply aerodynamic braking for power regulation and emergency shutdowns. Studies have shown that the pitch system is responsible for up to 20% of the total down time of a wind turbine. Reducing the down time is an important factor for decreasing the total cost of energy of wind energy in order to make wind energy more competitive. Due to this, attention has come to condition monitoring and fault detection of such systems as an attempt to increase the reliability and availability, hereby the reducing the turbine downtime. Some methods for fault detection and condition monitoring of fluid power systems do exists, though not many are used in today’s pitch systems. This paper gives an overview of fault detection and condition monitoring methods of fluid power systems similar to fluid power pitch systems in wind turbines and discuss their applicability in relation to pitch systems. The purpose is to give an overview of which methods that exist and to find areas where new methods need to be developed or existing need to be modified. The paper goes through the most important components of a pitch system and discuss the existing methods related to each type of component. Furthermore, it is considered if existing methods can be used for fluid power pitch systems for wind turbine.

APA, Harvard, Vancouver, ISO, and other styles

20

Caselitz, Peter, and Jochen Giebhardt. "Rotor Condition Monitoring for Improved Operational Safety of Offshore Wind Energy Converters." Journal of Solar Energy Engineering 127, no. 2 (April 25, 2005): 253–61. http://dx.doi.org/10.1115/1.1850485.

Full text

Abstract:

Background: Due to cost effectiveness and operational safety online monitoring of rotor blades is recommended, especially for offshore wind energy converters. Method of Approach: Statistic evaluation of wind speed and power output of a wind energy converter is used to monitor the overall rotor performance including increased blade surface roughness. Nacelle oscillation spectral analysis methods are applied to monitor the rotor faults mass imbalance and aerodynamic asymmetry. Results: Results of ISET’s research work related to online rotor condition monitoring are presented. A description of the fault effects on the rotor, the sensor and data acquisition equipment and a description of the developed signal processing and fault prediction algorithms are given. The paper also presents results from experiments and field tests. Conclusions: The developed algorithms have been verified due to their monitoring capabilities and suitability in commercial online condition monitoring systems.

APA, Harvard, Vancouver, ISO, and other styles

21

Ling, Hao Yi, Ling Mei Wang, and Xing Yong Zhao. "Transmission Network Status Monitoring Overview." Applied Mechanics and Materials 325-326 (June 2013): 599–603. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.599.

Full text

Abstract:

Transmission lines is an important part of the power system. Transmission line condition monitoring system can enhance the operational reliability of the grid line level of safety, at the same time lay the foundation for intelligent transmission line. Insulator contamination monitoring , lightning monitoring, environmental monitoring, wire breeze vibration monitoring online monitoring technology on the existing transmission line condition monitoring technologies , including comparative analysis of the far-reaching. It can reduce the workload of the artificial line inspection , to reduce the occurrence of pollution flashover to improve power supply reliability. To reduce the pollution flashover occurred to improve the reliability of power supply. Condition based maintenance decision support and sharing of information with other systems. HOMER and MATLAB simulation software , simulation , historical data analysis . Export real - time wind speed data, provide data to support the conductor galloping and aeolian vibration of monitoring and environmental monitoring.

APA, Harvard, Vancouver, ISO, and other styles

22

Parajuli, Amrit, Mohammadreza R. Barzegaran, and Osama A. Mohammed. "Wide area condition monitoring of power electric drives in wind power generation system using radiated electromagnetic fields." IET Power Electronics 11, no. 5 (May 2018): 876–83. http://dx.doi.org/10.1049/iet-pel.2017.0053.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Romero, Antonio, Slim Soua, Tat-Hean Gan, and Bin Wang. "Condition monitoring of a wind turbine drive train based on its power dependant vibrations." Renewable Energy 123 (August 2018): 817–27. http://dx.doi.org/10.1016/j.renene.2017.07.086.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Wiese, Benedikt, Niels L. Pedersen, Esmaeil S. Nadimi, and Jürgen Herp. "Estimating the Remaining Power Generation of Wind Turbines—An Exploratory Study for Main Bearing Failures." Energies 13, no. 13 (July 2, 2020): 3406. http://dx.doi.org/10.3390/en13133406.

Full text

Abstract:

Condition monitoring for wind turbines is tailored to predict failure and aid in making better operation and maintenance (O&M) decisions. Typically the condition monitoring approaches are concerned with predicting the remaining useful lifetime (RUL) of assets or a component. As the time-based measures can be rendered absolute when changing the operational set-point of a wind turbine, we propose an alternative in a power-based condition monitoring framework for wind turbines, i.e., the remaining power generation (RPG) before a main bearing failure. The proposed model utilizes historic wind turbine data, from both run-to-failure and non run-to-failure turbines. Comprised of a recurrent neural network with gated recurrent units, the model is constructed around a censored and uncensored data-based cost function. We infer a Weibull distribution over the RPG, which gives an operator a measure of how certain any given prediction is. As part of the model evaluation, we present the hyper-parameter selection, as well as modeling error in detail, including an analysis of the driving features. During the application on wind turbine main bearing failures, we achieve prediction in the magnitude of 1 to 2 GWh before the failure. When converting to RUL this corresponds to predicting the failure, on average, 81 days beforehand, which is comparable to the state-of-the-art’s 94 days predictive horizon in a similar feature space.

APA, Harvard, Vancouver, ISO, and other styles

25

Mittelmeier, Niko, Tomas Blodau, and Martin Kühn. "Monitoring offshore wind farm power performance with SCADA data and an advanced wake model." Wind Energy Science 2, no. 1 (March 28, 2017): 175–87. http://dx.doi.org/10.5194/wes-2-175-2017.

Full text

Abstract:

Abstract. Wind farm underperformance can lead to significant losses in revenues. The efficient detection of wind turbines operating below their expected power output and immediate corrections help maximize asset value. The method, presented in this paper, estimates the environmental conditions from turbine states and uses pre-calculated lookup tables from a numeric wake model to predict the expected power output. Deviations between the expected and the measured power output ratio between two turbines are an indication of underperformance. The confidence of detected underperformance is estimated by a detailed analysis of the uncertainties of the method. Power normalization with reference turbines and averaging several measures performed by devices of the same type can reduce uncertainties for estimating the expected power. A demonstration of the method's ability to detect underperformance in the form of degradation and curtailment is given. An underperformance of 8 % could be detected in a triple-wake condition.

APA, Harvard, Vancouver, ISO, and other styles

26

Sanati, Hadi, David Wood, and Qiao Sun. "Condition Monitoring of Wind Turbine Blades Using Active and Passive Thermography." Applied Sciences 8, no. 10 (October 22, 2018): 2004. http://dx.doi.org/10.3390/app8102004.

Full text

Abstract:

The failure of wind turbine blades is a major concern in the wind power industry due to the resulting high cost. It is, therefore, crucial to develop methods to monitor the integrity of wind turbine blades. Different methods are available to detect subsurface damage but most require close proximity between the sensor and the blade. Thermography, as a non-contact method, may avoid this problem. Both passive and active pulsed and step heating and cooling thermography techniques were investigated for different purposes. A section of a severely damaged blade and a small “plate” cut from the undamaged laminate section of the blade with holes of varying diameter and depth drilled from the rear to provide “known” defects were monitored. The raw thermal images captured by both active and passive thermography demonstrated that image processing was required to improve the quality of the thermal data. Different image processing algorithms were used to increase the thermal contrasts of subsurface defects in thermal images obtained by active thermography. A method called “Step Phase and Amplitude Thermography”, which applies a transform-based algorithm to step heating and cooling data was used. This method was also applied, for the first time, to the passive thermography results. The outcomes of the image processing on both active and passive thermography indicated that the techniques employed could considerably increase the quality of the images and the visibility of internal defects. The signal-to-noise ratio of raw and processed images was calculated to quantitatively show that image processing methods considerably improve the ratios.

APA, Harvard, Vancouver, ISO, and other styles

27

Hassan, Yasser Falah, Yasir Ghazy Rashid, and Firas Mohammed Tuamiah. "Demand Priority in a Power System With Wind Power Contribution Load Shedding Scheme Based." Journal of Engineering 25, no. 11 (October 29, 2019): 92–110. http://dx.doi.org/10.31026/j.eng.2019.11.08.

Full text

Abstract:

The load shedding scheme has been extensively implemented as a fast solution for unbalance conditions. Therefore, it's crucial to investigate supply-demand balancing in order to protect the network from collapsing and to sustain stability as possible, however its implementation is mostly undesirable. One of the solutions to minimize the amount of load shedding is the integration renewable energy resources, such as wind power, in the electric power generation could contribute significantly to minimizing power cuts as it is ability to positively improving the stability of the electric grid. In this paper propose a method for shedding the load base on the priority demands with incorporating the wind power generated. The higher priority demands are fed with a reliable wind energy resource in order to protect them from shedding under contingency condition such as high overloading by the real time monitoring of the network accompanied with power reducing for the lower priority demands. The simulation results prove effectiveness and practicality of the applied method paving the way for possible applications in power systems.

APA, Harvard, Vancouver, ISO, and other styles

28

Ogidi, Oladapo Omotade, Paul S. Barendse, and Mohamed A. Khan. "Fault diagnosis and condition monitoring of axial-flux permanent magnet wind generators." Electric Power Systems Research 136 (July 2016): 1–7. http://dx.doi.org/10.1016/j.epsr.2016.01.018.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Zhai, Guodong, Xujie Qin, and Xing Yang. "Research on Real Working Condition Simulation and Performance Test of Wind Power Main Bearing Based on Test Bench." Mathematical Problems in Engineering 2021 (April 16, 2021): 1–13. http://dx.doi.org/10.1155/2021/6623988.

Full text

Abstract:

As a renewable energy source, wind energy has received more and more attention, and the wind power industry has also been advocated and developed by countries all over the world. In the production and use of wind turbines, the design and manufacturing technology of wind turbine bearings is very important. In order to ensure the reliable operation of the wind power main bearing after installation and realize the longest life of it, this paper designs a bearing test bench that can test the performance of the wind power main bearing. It can analyze the temperature, displacement, load, and moment of the key parts of the 5 MW wind power main shaft bearing. The solid modeling of the experimental platform was carried out using the 3D modeling software SolidWorks. Hydraulic loading system and test monitoring system are designed to realize the drive and control of the test bench. Through the established mathematical model, the central load of the hub is converted into the axial cylinder load and the radial cylinder load of the test bench to simulate the actual working conditions of the tested bearing. The test results show that the test bench meets various loading requirements and can reliably complete the task of testing wind power main bearings.

APA, Harvard, Vancouver, ISO, and other styles

30

Zhang, Pinjia, and Delong Lu. "A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines." Energies 12, no. 14 (July 20, 2019): 2801. http://dx.doi.org/10.3390/en12142801.

Full text

Abstract:

Wind power, as a renewable energy for coping with global climate change challenge, has achieved rapid development in recent years. The breakdown of wind turbines (WTs) not only leads to high repair expenses but also may threaten the stability of the whole power grid. How to reduce the operation and the maintenance (O&M) cost of wind farms is an obstacle to its further promotion and application. To provide reliable condition monitoring and fault diagnosis (CMFD) for WTs, this paper presents a comprehensive survey of the existing CMFD methods in the following three aspects: energy flow, information flow, and integrated O&M system. Energy flow mainly analyzes the characteristics of each component from the angle of energy conversion of WTs. Information flow is the carrier of fault and control information of WT. At the end of this paper, an integrated WT O&M system based on electrical signals is proposed.

APA, Harvard, Vancouver, ISO, and other styles

31

Mauntz, M. R., J. Gegner, U. Kuipers, and S. Klingauf. "Sustainable wind power generation by online sensor oil condition monitoring against premature gearbox bearing failures." IFAC Proceedings Volumes 46, no. 16 (2013): 425–29. http://dx.doi.org/10.3182/20130825-4-us-2038.00035.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Mazidi, Peyman, Mian Du, Lina Bertling Tjernberg, and Miguel A. Sanz Bobi. "A health condition model for wind turbine monitoring through neural networks and proportional hazard models." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 231, no. 5 (May 4, 2017): 481–94. http://dx.doi.org/10.1177/1748006x17707902.

Full text

Abstract:

In this article, a parametric model for health condition monitoring of wind turbines is developed. The study is based on the assumption that a wind turbine’s health condition can be modeled through three features: rotor speed, gearbox temperature and generator winding temperature. At first, three neural network models are created to simulate normal behavior of each feature. Deviation signals are then defined and calculated as accumulated time-series of differences between neural network predictions and actual measurements. These cumulative signals carry health condition–related information. Next, through nonlinear regression technique, the signals are used to produce individual models for considered features, which mathematically have the form of proportional hazard models. Finally, they are combined to construct an overall parametric health condition model which partially represents health condition of the wind turbine. In addition, a dynamic threshold for the model is developed to facilitate and add more insight in performance monitoring aspect. The health condition monitoring of wind turbine model has capability of evaluating real-time and overall health condition of a wind turbine which can also be used with regard to maintenance in electricity generation in electric power systems. The model also has flexibility to overcome current challenges such as scalability and adaptability. The model is verified in illustrating changes in real-time and overall health condition with respect to considered anomalies by testing through actual and artificial data.

APA, Harvard, Vancouver, ISO, and other styles

33

Guo, Peng, Jian Fu, and XiYun Yang. "Condition Monitoring and Fault Diagnosis of Wind Turbines Gearbox Bearing Temperature Based on Kolmogorov-Smirnov Test and Convolutional Neural Network Model." Energies 11, no. 9 (August 27, 2018): 2248. http://dx.doi.org/10.3390/en11092248.

Full text

Abstract:

Wind turbine condition-monitoring and fault diagnosis have important practical value for wind farms to reduce maintenance cost and improve operating level. Due to the special distribution law of the operating parameters of similar turbines, this paper compares the instantaneous operation parameters of four 1.5 MW turbines with strong correlation of a wind farm. The temperature-power distribution of the gearbox bearings is analyzed to find out the main trend of the turbines and the deviations of individual turbine parameters. At the same time, for the huge amount of data caused by the increase of turbines number and monitoring parameters, this paper uses the huge neural network and multi-hidden layer of a convolutional neural network to model historical data. Finally, the rapid warning and judgment of gearbox bearing over-temperature faults proves that the monitoring method is of great significance for large-scale wind farms.

APA, Harvard, Vancouver, ISO, and other styles

34

Bian, Zhong Guo, Shu Qin Liu, and Hong Tao Yang. "Wind Turbine On-Line Monitoring System Based on Vibration Mechanics." Applied Mechanics and Materials 252 (December 2012): 181–84. http://dx.doi.org/10.4028/www.scientific.net/amm.252.181.

Full text

Abstract:

Wind turbine in the run-time under natural wind conditions, it will cause the turbine vibration. The vibration will cause the body of material fatigue, will increase the probability of the unit appears damaged, and will cause the turbine speed dropped significantly reduce the output power. This paper introduced a wind turbine monitoring system based on vibration Mechanics, which included the hardware test platform, remote transmission and monitoring software platform based on LabVIEW. Hardware monitoring used the computer as the control centre, according to the operating parameters of wind power generation system which included the wind speed, the generator voltage and current, the charging voltage, current and power, the vibration quantity of the wind turbine, the working state of the energy storage devices and the load, displayed and stored the data and curve through the sensors and signal conditioning circuit. Senseless monitoring was used to monitor the speed of generator. This monitoring system provided the basis for the match of the engine power and efficiency of wind turbine and generator.

APA, Harvard, Vancouver, ISO, and other styles

35

Ding, Fangfang, and Zhigang Tian. "Integrated Prognosis for Wind Turbine Gearbox Condition-Based Maintenance Considering Time-Varying Load and Crack Initiation Time Uncertainty." International Journal of Reliability, Quality and Safety Engineering 28, no. 04 (February 23, 2021): 2150024. http://dx.doi.org/10.1142/s0218539321500248.

Full text

Abstract:

Maintenance management in wind energy industry has great impact on the overall wind power cost. Maintenance services are either supported by wind turbine manufacturers within warranty period, or managed by wind farm owners. With condition-based maintenance (CBM) strategy, maintenance activities are scheduled based on the predicted health conditions of wind turbine components, and accurate prognostics methods are critical for effective CBM. The reported studies on integrated health prognostics considered the uncertainty in crack initiation time (CIT) uncertainty, but did not incorporate time-varying loading conditions, which could also have a significant impact on future health condition and remaining useful life (RUL) prediction. Constant loads were generally used to approximate the actual time-varying loading conditions. In this paper, an integrated prognostics method is proposed for wind turbine gearboxes considering both time-varying loading conditions and CIT uncertainty. As new condition monitoring observations are available, the distributions of both material model parameter and CIT are updated via Bayesian inference, and the failure time prediction is updated accordingly. An example is provided to demonstrate that the proposed time-varying load approach presents more benefits considering the uncertainty of CIT, with significant accuracy improvement comparing to the constant-load approach.

APA, Harvard, Vancouver, ISO, and other styles

36

Cornel, Daniel, Francisco Gutiérrez Guzmán, Georg Jacobs, and Stephan Neumann. "Condition monitoring of roller bearings using acoustic emission." Wind Energy Science 6, no. 2 (March 5, 2021): 367–76. http://dx.doi.org/10.5194/wes-6-367-2021.

Full text

Abstract:

Abstract. Roller bearing failures in wind turbines' gearboxes lead to long downtimes and high repair costs, which could be reduced by the implementation of a predictive maintenance strategy. In this paper and within this context, an acoustic-emission-based condition monitoring system is applied to roller bearing test rigs with the aim of identifying critical operating conditions before bearing failures occurs. Furthermore, a comparison regarding detection times is carried out with traditional vibration-based condition monitoring systems, with a focus on premature bearing failures such as white etching cracks. The investigations show a sensitivity of the acoustic-emission system towards lubricating conditions. In addition, the system has shown that a damaged surface can be detected at least ∼ 4 % (8 h, regarding the time to failure) earlier than by using the vibration-based system. Furthermore, significant deviations from the average acoustic-emission signal were detected up to ∼ 50 % (130 h) before the test stop and are possibly related to sub-surface damage initiation and might result in an earlier damage detection in the future.

APA, Harvard, Vancouver, ISO, and other styles

37

Pandit, Ravi, and Athanasios Kolios. "SCADA Data-Based Support Vector Machine Wind Turbine Power Curve Uncertainty Estimation and Its Comparative Studies." Applied Sciences 10, no. 23 (December 4, 2020): 8685. http://dx.doi.org/10.3390/app10238685.

Full text

Abstract:

Power curves, supplied by turbine manufacturers, are extensively used in condition monitoring, energy estimation, and improving operational efficiency. However, there is substantial uncertainty linked to power curve measurements as they usually take place only at hub height. Data-driven model accuracy is significantly affected by uncertainty. Therefore, an accurate estimation of uncertainty gives the confidence to wind farm operators for improving performance/condition monitoring and energy forecasting activities that are based on data-driven methods. The support vector machine (SVM) is a data-driven, machine learning approach, widely used in solving problems related to classification and regression. The uncertainty associated with models is quantified using confidence intervals (CIs), which are themselves estimated. This study proposes two approaches, namely, pointwise CIs and simultaneous CIs, to measure the uncertainty associated with an SVM-based power curve model. A radial basis function is taken as the kernel function to improve the accuracy of the SVM models. The proposed techniques are then verified by extensive 10 min average supervisory control and data acquisition (SCADA) data, obtained from pitch-controlled wind turbines. The results suggest that both proposed techniques are effective in measuring SVM power curve uncertainty, out of which, pointwise CIs are found to be the most accurate because they produce relatively smaller CIs. Thus, pointwise CIs have better ability to reject faulty data if fault detection algorithms were constructed based on SVM power curve and pointwise CIs. The full paper will explain the merits and demerits of the proposed research in detail and lay out a foundation regarding how this can be used for offshore wind turbine conditions and/or performance monitoring activities.

APA, Harvard, Vancouver, ISO, and other styles

38

Puruncajas, B., W. Alava, Encalada Dávila, C. Tutivén, and Y. Vidal. "Convolutional Neural Network for Wind Turbine Failure Classification Based on SCADA Data." Renewable Energy and Power Quality Journal 19 (September 2021): 447–51. http://dx.doi.org/10.24084/repqj19.316.

Full text

Abstract:

As a renewable energy source and an alternative to fossil fuels, the wind power industry is growing rapidly. However, due to harsh weather conditions, wind turbines (WT) still face many failures that raise the price of energy produced and reduce the reliability of wind energy. Hence, the use of reliable monitoring and diagnostic systems of WTs is of great importance. Operation and maintenance expenses represent 30% of the total cost of large wind farms. The installation of offshore and remote wind farms has increased the need for efficient fault detection and condition monitoring systems. In this work, without using specific custom devices for monitoring conditions, but only increasing the sampling frequency in the sensors already available (in all commercial WT) of the supervisory control and data acquisition system (SCADA), datadriven multiple fault detection is performed, and a classification strategy is developed. The data is processed, and subsequently, using a convolutional neural network (CNN), six faults are classified and evaluated with different metrics. Finally, it should be noted that the classification speed allows the implementation of this strategy to monitor conditions online in real under-production WTs.

APA, Harvard, Vancouver, ISO, and other styles

39

Borowski, Sylwester, Mirosław Szubartowski, Leszek Knopik, and Klaudiusz Migawa. "Wind turbine condition monitoring system as a source of diagnostic information." MATEC Web of Conferences 182 (2018): 01015. http://dx.doi.org/10.1051/matecconf/201818201015.

Full text

Abstract:

The issues relating to the necessity of monitoring the wind turbines’ condition and operation are presented in the study. The wind turbines of high powers, are very expensive and complicated machines requiring appropriate control and high quality technical service. The idea of appropriate technical turbine’s maintaining, combines its high availability and productivity, as well as minimization of the costs related to failures and unexpected damages. Remote diagnostic systems allow obtaining the basic information, making it possible to maintain and appropriate control, use and high quality technical service.

APA, Harvard, Vancouver, ISO, and other styles

40

R. KARTHIKEYAN, S. KALAIVANI, C. THARINI, A. M. AZARUDEEN,. "IoT BASED SMART AND EFFICIENT WIND TURBINE MONITORING SYSTEM." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 18, 2021): 1205–12. http://dx.doi.org/10.17762/itii.v9i1.256.

Full text

Abstract:

Wind Turbine industry has the improved latest generation of Wind turbines with bigger flexible blades, high tower, Good efficiency & low cost repairing in all platforms of wind turbines from Small wind mills to Ocean wind turbines. The Control centre is responsible for Monitoring and Controlling wind turbines in wind power farms. Various parameters like Oil level, Gas leakage, air pressure, vibrations & linear velocity, environmental condition like rain & humidity are to be monitored and controlled for proper working of the wind turbines. In the proposed work, smart and efficient turbine network architecture is designed to automate this process. The aim of the proposed work is to monitor the different parameters of the turbine using respective sensors. The acquired sensor data are uploaded to the cloud via WiFi module for online monitoring and further data analysis. IFTTT Server of Adafruit io cloud is used to send the warning notification of the critical sensor value to the concerned person. Also the sensor node life time is taken care by implementing a proposed compression algorithm in each node that reduces the amount of data transmitted and thereby the energy consumed during transmission.

APA, Harvard, Vancouver, ISO, and other styles

41

Pei, Shenglei, and Yifen Li. "Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique." Applied Sciences 9, no. 22 (November 16, 2019): 4930. http://dx.doi.org/10.3390/app9224930.

Full text

Abstract:

A power curve of a wind turbine describes the nonlinear relationship between wind speed and the corresponding power output. It shows the generation performance of a wind turbine. It plays vital roles in wind power forecasting, wind energy potential estimation, wind turbine selection, and wind turbine condition monitoring. In this paper, a hybrid power curve modeling technique is proposed. First, fuzzy c-means clustering is employed to detect and remove outliers from the original wind data. Then, different extreme learning machines are trained with the processed data. The corresponding wind power forecasts can also be obtained with the trained models. Finally, support vector regression is used to take advantage of different forecasts from different models. The results show that (1) five-parameter logistic function is superior to the others among the parametric models; (2) generally, nonparametric power curve models perform better than parametric models; (3) the proposed hybrid model can generate more accurate power output estimations than the other compared models, thus resulting in better wind turbine power curves. Overall, the proposed hybrid strategy can also be applied in power curve modeling, and is an effective tool to get better wind turbine power curves, even when the collected wind data is corrupted by outliers.

APA, Harvard, Vancouver, ISO, and other styles

42

Yang, Fei, Chang Hai Yu, and Jie Liang Dong. "1.25MW Wind Turbine Vibration Performance Study of the Internal Cabin." Applied Mechanics and Materials 229-231 (November 2012): 340–46. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.340.

Full text

Abstract:

The article introduces the vibrating test of gear box in the engineroom of 1.25MW wind turbine in wind farm in Inner Mongolia province, and obtains the detailed structure form and parameters of the wind turbine, and has real-time monitoring on four wind turbines of four typical areas in the wind farm, and gets time domain response of wind turbine. The on-line condition monitoring was applied on full power test platform that uses 1.25MW wind turbine, vibrating test was used for key parts in the engineroom of wind turbine, spindle, gearbox, generator ,generator underframe and so on, and the natural frequency of generator underframe and time and frequency domain response of gearbox, ring gear, generator and generator underframe are obtained. Through two tests, the vibration characteristic in the engineroom is obtained.

APA, Harvard, Vancouver, ISO, and other styles

43

Zhang, Le, and Qiang Yang. "Investigation of the Design and Fault Prediction Method for an Abrasive Particle Sensor Used in Wind Turbine Gearbox." Energies 13, no. 2 (January 11, 2020): 365. http://dx.doi.org/10.3390/en13020365.

Full text

Abstract:

The gearbox is a key sub-component of a wind power generation system with high failure rate leading to shutdowns. By monitoring the abrasive particles in the lubricating oil when the gearbox is running, any abnormal condition of the gearbox can be found in advance. This information may be used to improve the operational safety of the wind turbine and reduce losses because of shutdowns and maintenance. In this paper, a three-coil induction abrasive particle sensor is designed based on the application of high-power wind turbine gearbox. The performance of the sensor and the design method of the detection circuit are described in detail, and the sensor operation performance used in the 2 MW wind turbine is verified. The results show that the sensor has superior performance in identifying ferromagnetic abrasive particles above 200 μm and plays a good role in status monitoring and fault prediction for the gearbox.

APA, Harvard, Vancouver, ISO, and other styles

44

Li, Guo Jian, and Yan Jun Hu. "Analysis and Discussion of the Influence Factors of the Wind Power." Advanced Materials Research 383-390 (November 2011): 7595–99. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.7595.

Full text

Abstract:

Wind as a renewable energy, is typical of clean energy, and wind power generation has good social and environmental benefits, which has developed rapidly in worldwide. In this paper, the problems of China's wind power industry and the world wind power industry experience are discussed. The distribution of resources for wind energy, wind energy resource assessment, monitoring and forecasting system, wind industry, policy influencing factors are detailed analysis, and based on China conditions for its development were discussed.

APA, Harvard, Vancouver, ISO, and other styles

45

Jing, Bo, Zheng Qian, Hamidreza Zareipour, Yan Pei, and Anqi Wang. "Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression." Applied Sciences 11, no. 7 (March 29, 2021): 3048. http://dx.doi.org/10.3390/app11073048.

Full text

Abstract:

The wind turbine power curve (WTPC) is of great significance for wind power forecasting, condition monitoring, and energy assessment. This paper proposes a novel WTPC modelling method with logistic functions based on quantile regression (QRLF). Firstly, we combine the asymmetric absolute value function from the quantile regression (QR) cost function with logistic functions (LF), so that the proposed method can describe the uncertainty of wind power by the fitting curves of different quantiles without considering the prior distribution of wind power. Among them, three optimization algorithms are selected to make comparative studies. Secondly, an adaptive outlier filtering method is developed based on QRLF, which can eliminate the outliers by the symmetrical relationship of power distribution. Lastly, supervisory control and data acquisition (SCADA) data collected from wind turbines in three wind farms are used to evaluate the performance of the proposed method. Five evaluation metrics are applied for the comparative analysis. Compared with typical WTPC models, QRLF has better fitting performance in both deterministic and probabilistic power curve modeling.

APA, Harvard, Vancouver, ISO, and other styles

46

Zhang, Tao, Xi Tian Wang, and Yang Zhang. "Study of Sub-Synchronous Interaction in Doubly-Fed Wind Power Systems." Applied Mechanics and Materials 722 (December 2014): 276–80. http://dx.doi.org/10.4028/www.scientific.net/amm.722.276.

Full text

Abstract:

When doubly fed wind power generation system transmits power to power grid through the series compensation, once the external disturbances is close to the natural sonant frequency, there is a trouble of synchronous oscillation between the rotor side and the series capacitor. The paper analyzes the condition of resonance and its mechanism. According the actual argument provide by north china wind power plant, we use PSCAD to build a 1.25MW doubly fed wind power generation model to analyze the synchronous oscillation. We adopt time-domain simulation method to analyze that as the change of series compensation degree the system subsynchronous resonance frequency changes as well as the influence to doubly fed fan current and power. The simulation result is basically the same as the fault wave record monitoring data result of actual site when wind power plant take off the network .Consequently to verify the validity of the simulation platform. To verify the improvement of the converter control strategy to build simulation platform.

APA, Harvard, Vancouver, ISO, and other styles

47

Astolfi, Davide, Francesco Castellani, Andrea Lombardi, and Ludovico Terzi. "Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring." Energies 14, no. 4 (February 19, 2021): 1105. http://dx.doi.org/10.3390/en14041105.

Full text

Abstract:

Due to the stochastic nature of the source, wind turbines operate under non-stationary conditions and the extracted power depends non-trivially on ambient conditions and working parameters. It is therefore difficult to establish a normal behavior model for monitoring the performance of a wind turbine and the most employed approach is to be driven by data. The power curve of a wind turbine is the relation between the wind intensity and the extracted power and is widely employed for monitoring wind turbine performance. On the grounds of the above considerations, a recent trend regarding wind turbine power curve analysis consists of the incorporation of the main working parameters (as, for example, the rotor speed or the blade pitch) as input variables of a multivariate regression whose target is the power. In this study, a method for multivariate wind turbine power curve analysis is proposed: it is based on sequential features selection, which employs Support Vector Regression with Gaussian Kernel. One of the most innovative aspects of this study is that the set of possible covariates includes also minimum, maximum and standard deviation of the most important environmental and operational variables. Three test cases of practical interest are contemplated: a Senvion MM92, a Vestas V90 and a Vestas V117 wind turbines owned by the ENGIE Italia company. It is shown that the selection of the covariates depends remarkably on the wind turbine model and this aspect should therefore be taken in consideration in order to customize the data-driven monitoring of the power curve. The obtained error metrics are competitive and in general lower with respect to the state of the art in the literature. Furthermore, minimum, maximum and standard deviation of the main environmental and operation variables are abundantly selected by the feature selection algorithm: this result indicates that the richness of the measurement channels contained in wind turbine Supervisory Control And Data Acquisition (SCADA) data sets should be exploited for monitoring the performance as reliably as possible.

APA, Harvard, Vancouver, ISO, and other styles

48

Chaudhari, Ramesh, Bharat Chaudhari, Pratiksinh Dilipsinh, and Vijya Leela bhai. "To Study the Temporal Variation of Capacity Utilization Factor (CUF) of PV Based Solar Power Plant with Respect to Climatic Condition." Current World Environment 11, no. 2 (August 25, 2016): 654–61. http://dx.doi.org/10.12944/cwe.11.2.38.

Full text

Abstract:

Performance, quality and reliability of technology are becoming more and more important for the emerging photovoltaic markets worldwide. In this experiment the monitoring of climatic factors like, solar radiation, Ambient Temperature, Module Temperature, Relative Humidity and Wind Speed was carried out on daily basis for six months, between 7:00 A.M to 6:00 P.M. Data was measured with SCADA system. This analysis was carried out by monitoring the fluctuation in power output of the system with climatic factors. From the results, there is direct proportionality between the power output of the system and the climatic factors. The correlation between ambient air temperature, PV module temperature and CUF is strongly positive. The other climatic factor like wind speed is does not have much significant effect on CUF. The Relative humidity is negatively correlated with CUF. The correlation between solar radiation and the CUF is strongly positive.

APA, Harvard, Vancouver, ISO, and other styles

49

Sheng, Xiaoling, Shuting Wan, Kanru Cheng, and Xuan Wang. "Research on the Fault Characteristic of Wind Turbine Generator System Considering the Spatiotemporal Distribution of the Actual Wind Speed." Energies 13, no. 2 (January 10, 2020): 356. http://dx.doi.org/10.3390/en13020356.

Full text

Abstract:

A reliable fault monitoring system is one of the conditions that must be considered in the design of large wind farms today. The most important factor for the fault monitoring should be the accurate diagnosis criteria with sensitive fault characteristics. Most of the current fault diagnosis criteria are obtained based on the average wind speed at the center of the hub which is not in accord with the actual wind condition in nature. So, this paper utilizes an equivalent wind speed (EWS), which can describe the actual wind speed spatiotemporal distribution on the rotor disk area considering the effects of wind shear and tower shadow, to analyze the common mechanical and electrical faults again. Firstly, the EWS model applicable to the 3-blade wind turbines is introduced; then the new fault characteristics of the wind turbine rotor aerodynamic imbalance and the stator winding asymmetry are theoretically analyzed based on the EWS model; finally, the simulation platform is built in Matlab/Simulink for comparison and the simulation result is well consistent with the theory analysis. The aim of this research is to find more accurate fault characteristics and help promoting the healthy development of wind power industry.

APA, Harvard, Vancouver, ISO, and other styles

50

Hu, Yang, Yunhua Xi, Chenyang Pan, Gengda Li, and Baowei Chen. "Daily condition monitoring of grid-connected wind turbine via high-fidelity power curve and its comprehensive rating." Renewable Energy 146 (February 2020): 2095–111. http://dx.doi.org/10.1016/j.renene.2019.08.043.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Wind power condition monitoring'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles