scholarly journals Health Status Assessment for Wind Turbine with Recurrent Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Zexian Sun ◽  
Hexu Sun
Keyword(s):  
Health Status ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Large Scale ◽  
Short Term Memory ◽  
Assessment Method ◽  
Sensory Data ◽  
Proposed Model ◽  
Status Assessment ◽  
Real World Datasets

In order to improve the safety, efficiency, and reliability in large scale wind turbines, a great deal of statistical and machine-learning models for wind turbine health monitoring system (WTHMS) are proposed based on SCADA variables. The data-driven WTHMS have been performed widely with the attentions on predicting the failures of the wind turbine or primary components. However, the health status of wind turbine often degrades gradually rather than suddenly. Thus, the SCADA variables change continuously to the occurrence of certain faults. Inspired by the ability of recurrent neural network (RNN) in redefining the raw sensory data, we introduce a hybrid methodology that combines the analysis of variance for each sequential SCADA variable with RNN to assess the health status of wind turbine. First, each original sequence is split by different variance ranges into several categories to improve the generalized ability of the RNN. Then, the long short-term memory (LSTM) is procured on the normal running sequence to learn the gradually changing situations. Finally, a weighted assessment method incorporating the health of primary components is applied to judge the health level of the wind turbine. Experiments on real-world datasets from two wind turbines demonstrate the effectiveness and generalization of the proposed model.

Health Assessment Methods for Wind Turbines Based on Power Prediction and Mahalanobis Distance

2018 ◽  
Vol 33 (02) ◽  
pp. 1951001 ◽  
Author(s):  
Jun Zhan ◽  
Ronglin Wang ◽  
Lingzhi Yi ◽  
Yaguo Wang ◽  
Zhengjuan Xie
Keyword(s):  
Health Status ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Mahalanobis Distance ◽  
Wind Farm ◽  
Health Status Assessment ◽  
Power Prediction ◽  
Residual Set ◽  
Scada System ◽  
Status Assessment

The output power of wind turbine has great relation with its health state, and the health status assessment for wind turbines influences operational maintenance and economic benefit of wind farm. Aiming at the current problem that the health status for the whole machine in wind farm is hard to get accurately, in this paper, we propose a health status assessment method in order to assess and predict the health status of the whole wind turbine, which is based on the power prediction and Mahalanobis distance (MD). Firstly, on the basis of Bates theory, the scientific analysis for historical data from SCADA system in wind farm explains the relation between wind power and running states of wind turbines. Secondly, the active power prediction model is utilized to obtain the power forecasting value under the health status of wind turbines. And the difference between the forecasting value and actual value constructs the standard residual set which is seen as the benchmark of health status assessment for wind turbines. In the process of assessment, the test set residual is gained by network model. The MD is calculated by the test residual set and normal residual set and then normalized as the health status assessment value of wind turbines. This method innovatively constructs evaluation index which can reflect the electricity generating performance of wind turbines rapidly and precisely. So it effectively avoids the defect that the existing methods are generally and easily influenced by subjective consciousness. Finally, SCADA system data in one wind farm of Fujian province has been used to verify this method. The results indicate that this new method can make effective assessment for the health status variation trend of wind turbines and provide new means for fault warning of wind turbines.

Simulation of Flow Field on a Large Scale Wind Turbine

2008 ◽  
Author(s):  
I. Janajreh ◽  
C. Ghenai
Keyword(s):  
Flow Field ◽  
Wind Energy ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Cross Sections ◽  
Large Scale ◽  
Wind Farms ◽  
Operating Characteristics ◽  
Cross Sectional ◽  
Capital Costs

Large scale wind turbines and wind farms continue to evolve mounting 94.1GW of the electrical grid capacity in 2007 and expected to reach 160.0GW in 2010 according to World Wind Energy Association. They commence to play a vital role in the quest for renewable and sustainable energy. They are impressive structures of human responsiveness to, and awareness of, the depleting fossil fuel resources. Early generation wind turbines (windmills) were used as kinetic energy transformers and today generate 1/5 of the Denmark’s electricity and planned to double the current German grid capacity by reaching 12.5% by year 2010. Wind energy is plentiful (72 TW is estimated to be commercially viable) and clean while their intensive capital costs and maintenance fees still bar their widespread deployment in the developing world. Additionally, there are technological challenges in the rotor operating characteristics, fatigue load, and noise in meeting reliability and safety standards. Newer inventions, e.g., downstream wind turbines and flapping rotor blades, are sought to absorb a larger portion of the cost attributable to unrestrained lower cost yaw mechanisms, reduction in the moving parts, and noise reduction thereby reducing maintenance. In this work, numerical analysis of the downstream wind turbine blade is conducted. In particular, the interaction between the tower and the rotor passage is investigated. Circular cross sectional tower and aerofoil shapes are considered in a staggered configuration and under cross-stream motion. The resulting blade static pressure and aerodynamic forces are investigated at different incident wind angles and wind speeds. Comparison of the flow field results against the conventional upstream wind turbine is also conducted. The wind flow is considered to be transient, incompressible, viscous Navier-Stokes and turbulent. The k-ε model is utilized as the turbulence closure. The passage of the rotor blade is governed by ALE and is represented numerically as a sliding mesh against the upstream fixed tower domain. Both the blade and tower cross sections are padded with a boundary layer mesh to accurately capture the viscous forces while several levels of refinement were implemented throughout the domain to assess and avoid the mesh dependence.

scholarly journals LOADS ON WIND TURBINES ACCESS PLATFORMS WITH GRATINGS

2011 ◽  
Vol 1 (32) ◽  
pp. 65
Author(s):  
Thomas Lykke Andersen ◽  
Peter Frigaard ◽  
Michael R Rasmussen ◽  
Luca Martinelli
Keyword(s):  
Drag Coefficient ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Large Scale ◽  
Peak Load ◽  
Multiplication Factor ◽  
Preliminary Results ◽  
Different Types ◽  
The Many ◽  
Large Scale Tests

The present paper deals with loads on wind turbine access platforms. The many planned new wind turbine parks together with the observed damages on platforms in several existing parks make the topic very important. The paper gives an overview of recently developed design formulae for different types of entrance platforms. Moreover, the paper present new results on loads on grates based on both drag coefficient measurements and preliminary results on slamming from large scale tests. As expected both investigations show that platforms with grates give a significant reduction in the loads compared to closed plate platforms. The grate multiplication factor, defined as the peak load on the grate platform relative to the peak load on a closed plate platform was found approximately equal to the solidity of the grate.

scholarly journals Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3396 ◽  
Author(s):  
Mingzhu Tang ◽  
Wei Chen ◽  
Qi Zhao ◽  
Huawei Wu ◽  
Wen Long ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Support Vector Regression ◽  
Confidence Intervals ◽  
Wind Turbines ◽  
Large Scale ◽  
Support Vector ◽  
Effective Response ◽  
Support Vector Regression Model ◽  
Doubly Fed

Fault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary equipment, this research presents a novel fault diagnosis and forecasting approach underpinned by a support vector regression model using data obtained by the supervisory control and data acquisition system (SCADA) of wind turbines (WT). To operate, the extraction of fault diagnosis features is conducted by measuring SCADA parameters. After that, confidence intervals are set up to guide the fault diagnosis implemented by the support vector regression (SVR) model. With the employment of confidence intervals as the performance indicators, an SVR-based fault detecting approach is then developed. Based on the WT SCADA data and the SVR model, a fault diagnosis strategy for large-scale doubly-fed wind turbine systems is investigated. A case study including a one-year monitoring SCADA data collected from a wind farm in Southern China is employed to validate the proposed methodology and demonstrate how it works. Results indicate that the proposed strategy can support the troubleshooting of wind turbine systems with high precision and effective response.

Wake Analysis of a Finite Width Gurney Flap

2015 ◽  
Author(s):  
D. Holst ◽  
A. B. Bach ◽  
C. N. Nayeri ◽  
C. O. Paschereit ◽  
G. Pechlivanoglou
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Large Scale ◽  
Reynolds Numbers ◽  
Tip Vortex ◽  
Finite Width ◽  
Low Reynolds Numbers ◽  
Gurney Flaps ◽  
Gurney Flap ◽  
Pitch System

The results of stereo Particle-Image-Velocimetry measurements are presented in this paper to gain further insight into the wake of a finite width Gurney flap. It is attached to an FX 63-137 airfoil which is known for a very good performance at low Reynolds numbers and is therefore used for small wind turbines and is most appropriate for tests in the low speed wind tunnel presented in this study. The Gurney flaps are a promising concept for load control on wind turbines but can have adverse side effects, e.g. shedding of additional vortices. The investigation focuses on frequencies and velocity distributions in the wake as well as on the structure of the induced tip vortices. Phase averaged velocity fields are derived of a Proper-Orthogonal-Decomposition based on the stereo PIV measurements. Additional hot-wire measurements were conducted to analyze the fluctuations downstream of the finite width Gurney flaps. Experiments indicate a general tip vortex structure that is independent from flap length but altered by the periodic shedding downstream of the flap. The influence of Gurney flaps on a small wind turbine is investigated by simulating a small 40 kW turbine in Q-Blade. They can serve as power control without the need of an active pitch system and the starting performance is additionally improved. The application of Gurney flaps imply tonal frequencies in the wake of the blade. Simulation results are used to estimate the resulting frequencies. However, the solution of Gurney flaps is a good candidate for large scale wind turbine implementation as well. A FAST simulation of the NREL 5MW turbine is used to generate realistic time series of the lift. The estimations of control capabilities predict a reduction in the standard deviation of the lift of up to 65%. Therefore finite width Gurney flaps are promising to extend the lifetime of future wind turbines.

scholarly journals Impact of Economic Indicators on the Integrated Design of Wind Turbine Systems

2018 ◽  
Vol 8 (9) ◽  
pp. 1668 ◽  
Author(s):  
Jianghai Wu ◽  
Tongguang Wang ◽  
Long Wang ◽  
Ning Zhao
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Production ◽  
Large Scale ◽  
Wind Farm ◽  
Net Present Value ◽  
Integrated Design ◽  
Aerodynamic Performance ◽  
System Level ◽  
Blade Length

This article presents a framework to integrate and optimize the design of large-scale wind turbines. Annual energy production, load analysis, the structural design of components and the wind farm operation model are coupled to perform a system-level nonlinear optimization. As well as the commonly used design objective levelized cost of energy (LCoE), key metrics of engineering economics such as net present value (NPV), internal rate of return (IRR) and the discounted payback time (DPT) are calculated and used as design objectives, respectively. The results show that IRR and DPT have the same effect as LCoE since they all lead to minimization of the ratio of the capital expenditure to the energy production. Meanwhile, the optimization for NPV tends to maximize the margin between incomes and costs. These two types of economic metrics provide the minimal blade length and maximal blade length of an optimal blade for a target wind turbine at a given wind farm. The turbine properties with respect to the blade length and tower height are also examined. The blade obtained with economic optimization objectives has a much larger relative thickness and smaller chord distributions than that obtained for high aerodynamic performance design. Furthermore, the use of cost control objectives in optimization is crucial in improving the economic efficiency of wind turbines and sacrificing some aerodynamic performance can bring significant reductions in design loads and turbine costs.

scholarly journals Dynamic Modeling of Wind Turbines Based on Estimated Wind Speed under Turbulent Conditions

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1907 ◽  
Author(s):  
Ahmed G. Abo-Khalil ◽  
Saeed Alyami ◽  
Khairy Sayed ◽  
Ayman Alhejji
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Shear ◽  
Large Scale ◽  
Estimation Error ◽  
Turbine Rotor ◽  
Average Wind Speed ◽  
Rotating Speed ◽  
Tower Shadow

Large-scale wind turbines with a large blade radius rotates under fluctuating conditions depending on the blade position. The wind speed is maximum in the highest point when the blade in the upward position and minimum in the lowest point when the blade in the downward position. The spatial distribution of wind speed, which is known as the wind shear, leads to periodic fluctuations in the turbine rotor, which causes fluctuations in the generator output voltage and power. In addition, the turbine torque is affected by other factors such as tower shadow and turbine inertia. The space between the blade and tower, the tower diameter, and the blade diameter are very critical design factors that should be considered to reduce the output power fluctuations of a wind turbine generator. To model realistic characteristics while considering the critical factors of a wind turbine system, a wind turbine model is implemented using a squirrel-cage induction motor. Since the wind speed is the most important factor in modeling the aerodynamics of wind turbine, an accurate measurement or estimation is essential to have a valid model. This paper estimates the average wind speed, instead of measuring, from the generator power and rotating speed and models the turbine’s aerodynamics, including tower shadow and wind shear components, without having to measure the wind speed at any height. The proposed algorithm overcomes the errors of measuring wind speed in single or multiple locations by estimating the wind speed with estimation error less than 2%.

Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method

2020 ◽  
Author(s):  
Hao Lv ◽  
Fu-Ying Dao ◽  
Zheng-Xing Guan ◽  
Hui Yang ◽  
Yan-Wen Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Short Term Memory ◽  
Information Gain ◽  
Independent Set ◽  
Cost Effective ◽  
Cellular Regulation ◽  
Proposed Model ◽  
Experimental Approaches ◽  
Memory Network

Abstract As a newly discovered protein posttranslational modification, histone lysine crotonylation (Kcr) involved in cellular regulation and human diseases. Various proteomics technologies have been developed to detect Kcr sites. However, experimental approaches for identifying Kcr sites are often time-consuming and labor-intensive, which is difficult to widely popularize in large-scale species. Computational approaches are cost-effective and can be used in a high-throughput manner to generate relatively precise identification. In this study, we develop a deep learning-based method termed as Deep-Kcr for Kcr sites prediction by combining sequence-based features, physicochemical property-based features and numerical space-derived information with information gain feature selection. We investigate the performances of convolutional neural network (CNN) and five commonly used classifiers (long short-term memory network, random forest, LogitBoost, naive Bayes and logistic regression) using 10-fold cross-validation and independent set test. Results show that CNN could always display the best performance with high computational efficiency on large dataset. We also compare the Deep-Kcr with other existing tools to demonstrate the excellent predictive power and robustness of our method. Based on the proposed model, a webserver called Deep-Kcr was established and is freely accessible at http://lin-group.cn/server/Deep-Kcr.

scholarly journals Unsteady aerodynamic analysis for offshore floating wind turbines under different wind conditions

2015 ◽  
Vol 373 (2035) ◽  
pp. 20140080 ◽  
Author(s):  
B. F. Xu ◽  
T. G. Wang ◽  
Y. Yuan ◽  
J. F. Cao
Keyword(s):  
Experimental Data ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Large Scale ◽  
Aerodynamic Performance ◽  
Small Scale ◽  
Floating Platform ◽  
Unsteady Aerodynamic ◽  
Blade Root ◽  
Platform Motions

A free-vortex wake (FVW) model is developed in this paper to analyse the unsteady aerodynamic performance of offshore floating wind turbines. A time-marching algorithm of third-order accuracy is applied in the FVW model. Owing to the complex floating platform motions, the blade inflow conditions and the positions of initial points of vortex filaments, which are different from the fixed wind turbine, are modified in the implemented model. A three-dimensional rotational effect model and a dynamic stall model are coupled into the FVW model to improve the aerodynamic performance prediction in the unsteady conditions. The effects of floating platform motions in the simulation model are validated by comparison between calculation and experiment for a small-scale rigid test wind turbine coupled with a floating tension leg platform (TLP). The dynamic inflow effect carried by the FVW method itself is confirmed and the results agree well with the experimental data of a pitching transient on another test turbine. Also, the flapping moment at the blade root in yaw on the same test turbine is calculated and compares well with the experimental data. Then, the aerodynamic performance is simulated in a yawed condition of steady wind and in an unyawed condition of turbulent wind, respectively, for a large-scale wind turbine coupled with the floating TLP motions, demonstrating obvious differences in rotor performance and blade loading from the fixed wind turbine. The non-dimensional magnitudes of loading changes due to the floating platform motions decrease from the blade root to the blade tip.

Aerodynamic Measurements on a Vertical Axis Wind Turbine in a Large Scale Wind Tunnel

2010 ◽  
Author(s):  
L. Battisti ◽  
L. Zanne ◽  
S. Dell’Anna ◽  
V. Dossena ◽  
B. Paradiso ◽  
...  
Keyword(s):  
Wind Tunnel ◽  
Flow Field ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Large Scale ◽  
Fluid Dynamic ◽  
Vertical Axis ◽  
Vertical Axis Wind Turbine ◽  
Aerodynamic Optimization ◽  
Vertical Axis Wind Turbines

This paper presents the first results of a wide experimental investigation on the aerodynamics of a vertical axis wind turbine. Vertical axis wind turbines have recently received particular attention, as interesting alternative for small and micro generation applications. However, the complex fluid dynamic mechanisms occurring in these machines make the aerodynamic optimization of the rotors still an open issue and detailed experimental analyses are now highly recommended to convert improved flow field comprehensions into novel design techniques. The experiments were performed in the large-scale wind tunnel of the Politecnico di Milano (Italy), where real-scale wind turbines for micro generation can be tested in full similarity conditions. Open and closed wind tunnel configurations are considered in such a way to quantify the influence of model blockage for several operational conditions. Integral torque and thrust measurements, as well as detailed aerodynamic measurements were applied to characterize the 3D flow field downstream of the turbine. The local unsteady flow field and the streamwise turbulent component, both resolved in phase with the rotor position, were derived by hot wire measurements. The paper critically analyses the models and the correlations usually applied to correct the wind tunnel blockage effects. Results evidence that the presently available theoretical correction models does not provide accurate estimates of the blockage effect in the case of vertical axis wind turbines. The tip aerodynamic phenomena, in particular, seem to play a key role for the prediction of the turbine performance; large-scale unsteadiness is observed in that region and a simple flow model is used to explain the different flow features with respect to horizontal axis wind turbines.

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