scholarly journals Predicting Frequency, Time-To-Repair and Costs of Wind Turbine Failures

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1149
Author(s):  
Samet Ozturk ◽  
Vasilis Fthenakis
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
High Capacity ◽  
High Elevation ◽  
Machine Learning Techniques ◽  
Time To Failure ◽  
Direct Drive ◽  
Support Tool ◽  
Failure Frequency

Operation and maintenance (O&M) costs, and associated uncertainty, for wind turbines (WTs) is a significant burden for wind farm operators. Many wind turbine failures are unpredictable while causing loss of energy production, and may also cause loss of asset. This study utilized 753 O&M event data from 21 wind turbines operating in Germany, to improve the prediction of failure frequency and associated costs. We applied Bayesian updating to predict wind turbine failure frequency and time-to-repair (TTR), in conjunction to machine learning techniques for assessing costs associated with failures. We found that time-to-failure (TTF), time-to-repair and the cost of failures depend on operational and environmental conditions. High elevation (>100 m) of the wind turbine installation was found to increase both the probability of failures and probability of delayed repairs. Furthermore, it was determined that direct-drive turbines are more favorable at locations with high capacity factor (more than 40%) whereas geared-drive turbines show lower failure costs than direct-drive ones at temperate-coastal locations with medium capacity factors (between 20% and 40%). Based on these findings, we developed a decision support tool that can guide a site-specific selection of wind turbine types, while providing a thorough estimation of O&M budgets.

scholarly journals A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1845
Author(s):  
Annalisa Santolamazza ◽  
Daniele Dadi ◽  
Vito Introna
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Machine Learning Techniques ◽  
Real Time Control ◽  
Time Control ◽  
Artificial Neural ◽  
Using Data

Wind energy has shown significant growth in terms of installed power in the last decade. However, one of the most critical problems for a wind farm is represented by Operation and Maintenance (O&M) costs, which can represent 20–30% of the total costs related to power generation. Various monitoring methodologies targeted to the identification of faults, such as vibration analysis or analysis of oils, are often used. However, they have the main disadvantage of involving additional costs as they usually entail the installation of other sensors to provide real-time control of the system. In this paper, we propose a methodology based on machine learning techniques using data from SCADA systems (Supervisory Control and Data Acquisition). Since these systems are generally already implemented on most wind turbines, they provide a large amount of data without requiring extra sensors. In particular, we developed models using Artificial Neural Networks (ANN) to characterize the behavior of some of the main components of the wind turbine, such as gearbox and generator, and predict operating anomalies. The proposed method is tested on real wind turbines in Italy to verify its effectiveness and applicability, and it was demonstrated to be able to provide significant help for the maintenance of a wind farm.

Fatigue Life Analysis of Offshore Wind Turbine Support Structures in an Offshore Wind Farm

2018 ◽  
Author(s):  
Bryan Nelson ◽  
Yann Quéméner
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
The Body ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Analysis Tool ◽  
Support Structures ◽  
Offshore Wind Farm ◽  
Actuator Disk

This study evaluated, by time-domain simulations, the fatigue lives of several jacket support structures for 4 MW wind turbines distributed throughout an offshore wind farm off Taiwan’s west coast. An in-house RANS-based wind farm analysis tool, WiFa3D, has been developed to determine the effects of the wind turbine wake behaviour on the flow fields through wind farm clusters. To reduce computational cost, WiFa3D employs actuator disk models to simulate the body forces imposed on the flow field by the target wind turbines, where the actuator disk is defined by the swept region of the rotor in space, and a body force distribution representing the aerodynamic characteristics of the rotor is assigned within this virtual disk. Simulations were performed for a range of environmental conditions, which were then combined with preliminary site survey metocean data to produce a long-term statistical environment. The short-term environmental loads on the wind turbine rotors were calculated by an unsteady blade element momentum (BEM) model of the target 4 MW wind turbines. The fatigue assessment of the jacket support structure was then conducted by applying the Rainflow Counting scheme on the hot spot stresses variations, as read-out from Finite Element results, and by employing appropriate SN curves. The fatigue lives of several wind turbine support structures taken at various locations in the wind farm showed significant variations with the preliminary design condition that assumed a single wind turbine without wake disturbance from other units.

scholarly journals Correlations of power output fluctuations in an offshore wind farm using high-resolution SCADA data

2021 ◽  
Vol 6 (4) ◽  
pp. 997-1014
Author(s):  
Janna Kristina Seifert ◽  
Martin Kraft ◽  
Martin Kühn ◽  
Laura J. Lukassen
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Flow Conditions ◽  
Output Fluctuations ◽  
Power Difference ◽  
Time Correlations

Abstract. Space–time correlations of power output fluctuations of wind turbine pairs provide information on the flow conditions within a wind farm and the interactions of wind turbines. Such information can play an essential role in controlling wind turbines and short-term load or power forecasting. However, the challenges of analysing correlations of power output fluctuations in a wind farm are the highly varying flow conditions. Here, we present an approach to investigate space–time correlations of power output fluctuations of streamwise-aligned wind turbine pairs based on high-resolution supervisory control and data acquisition (SCADA) data. The proposed approach overcomes the challenge of spatially variable and temporally variable flow conditions within the wind farm. We analyse the influences of the different statistics of the power output of wind turbines on the correlations of power output fluctuations based on 8 months of measurements from an offshore wind farm with 80 wind turbines. First, we assess the effect of the wind direction on the correlations of power output fluctuations of wind turbine pairs. We show that the correlations are highest for the streamwise-aligned wind turbine pairs and decrease when the mean wind direction changes its angle to be more perpendicular to the pair. Further, we show that the correlations for streamwise-aligned wind turbine pairs depend on the location of the wind turbines within the wind farm and on their inflow conditions (free stream or wake). Our primary result is that the standard deviations of the power output fluctuations and the normalised power difference of the wind turbines in a pair can characterise the correlations of power output fluctuations of streamwise-aligned wind turbine pairs. Further, we show that clustering can be used to identify different correlation curves. For this, we employ the data-driven k-means clustering algorithm to cluster the standard deviations of the power output fluctuations of the wind turbines and the normalised power difference of the wind turbines in a pair. Thereby, wind turbine pairs with similar power output fluctuation correlations are clustered independently from their location. With this, we account for the highly variable flow conditions inside a wind farm, which unpredictably influence the correlations.

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.

scholarly journals Floating Performance of a Composite Bucket Foundation with an Offshore Wind Tower during Transportation

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 882 ◽  
Author(s):  
Hongyan Ding ◽  
Zuntao Feng ◽  
Puyang Zhang ◽  
Conghuan Le ◽  
Yaohua Guo
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Interaction Force ◽  
Offshore Wind ◽  
Construction Technique ◽  
Bucket Foundation ◽  
Offshore Wind Turbines ◽  
One Step

The composite bucket foundation (CBF) for offshore wind turbines is the basis for a one-step integrated transportation and installation technique, which can be adapted to the construction and development needs of offshore wind farms due to its special structural form. To transport and install bucket foundations together with the upper portion of offshore wind turbines, a non-self-propelled integrated transportation and installation vessel was designed. In this paper, as the first stage of applying the proposed one-step integrated construction technique, the floating behavior during the transportation of CBF with a wind turbine tower for the Xiangshui wind farm in the Jiangsu province was monitored. The influences of speed, wave height, and wind on the floating behavior of the structure were studied. The results show that the roll and pitch angles remain close to level during the process of lifting and towing the wind turbine structure. In addition, the safety of the aircushion structure of the CBF was verified by analyzing the measurement results for the interaction force and the depth of the liquid within the bucket. The results of the three-DOF (degree of freedom) acceleration monitoring on the top of the test tower indicate that the wind turbine could meet the specified acceleration value limits during towing.

Floating Offshore Wind Turbines: Responses in a Seastate Pareto Optimal Designs and Economic Assessment

2008 ◽  
Author(s):  
Paul Sclavounos ◽  
Christopher Tracy ◽  
Sungho Lee
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Economic Assessment ◽  
Offshore Wind ◽  
Pareto Optimal ◽  
Mooring System ◽  
Floating Structure ◽  
Water Depths

Wind is the fastest growing renewable energy source, increasing at an annual rate of 25% with a worldwide installed capacity of 74 GW in 2007. The vast majority of wind power is generated from onshore wind farms. Their growth is however limited by the lack of inexpensive land near major population centers and the visual pollution caused by large wind turbines. Wind energy generated from offshore wind farms is the next frontier. Large sea areas with stronger and steadier winds are available for wind farm development and 5MW wind turbine towers located 20 miles from the coastline are invisible. Current offshore wind turbines are supported by monopoles driven into the seafloor at coastal sites a few miles from shore and in water depths of 10–15m. The primary impediment to their growth is visual pollution and the prohibitive cost of seafloor mounted monopoles in larger water depths. This paper presents a fully coupled dynamic analysis of floating wind turbines that enables a parametric design study of floating wind turbine concepts and mooring systems. Pareto optimal designs are presented that possess a favorable combination of nacelle acceleration, mooring system tension and displacement of the floating structure supporting a five megawatt wind turbine. All concepts are selected so that they float stably while in tow to the offshore wind farm site and prior to their connection to the mooring system. A fully coupled dynamic analysis is carried out of the wind turbine, floater and mooring system in wind and a sea state based on standard computer programs used by the offshore and wind industries. The results of the parametric study are designs that show Pareto fronts for mean square acceleration of the turbine versus key cost drivers for the offshore structure that include the weight of the floating structure and the static plus dynamic mooring line tension. Pareto optimal structures are generally either a narrow deep drafted spar, or a shallow drafted barge ballasted with concrete. The mooring systems include both tension leg and catenary mooring systems. In some of the designs, the RMS acceleration of the wind turbine nacelle can be as low as 0.03 g in a sea state with a significant wave height of ten meters and water depths of up to 200 meters. These structures meet design requirements while possessing a favorable combination of nacelle accleration, total mooring system tension and weight of the floating structure. Their economic assessment is also discussed drawing upon a recent financial analysis of a proposed offshore wind farm.

Commonly Used Wind Generator Systems: A Comparison Note

2017 ◽  
Vol 7 (2) ◽  
pp. 299
Author(s):  
Prashanth N.A ◽  
P Sujatha
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
High Efficiency ◽  
Low Cost ◽  
Maintenance Cost ◽  
Induction Generator ◽  
Doubly Fed Induction Generator ◽  
Direct Drive ◽  
Gear Box ◽  
Doubly Fed

<p>Amongst all renewable energy generation sources, wind power exhibits fastest growth rate. The increasing number of wind farm installations worldwide demand low maintenance, cost and failure rates with high efficiency. Determining the optimal drive train configuration amongst various configurations available for wind turbines is a challenge. In this paper commonly used, doubly fed induction generator with single stage gear box (GDFIG), doubly fed induction generator with multi stage gear box (DFIG) and the direct-drive permanent-magnet generator (DDPMG) are compared. Modelling of wind turbine with efficiency computations is presented. Considering common wind turbine parameters, performance of GDFIG, DFIG and DDPMG is compared through an experimental study. Considering a reference 5 MW variable speed wind turbine, efficiency of DDPMG is 96% when compared to 93.58%, 93.12% for DFIG and GDFIG. The experimental results presented prove that the DDPMG is a preferable solution considering low cost and high efficiency.</p>

Progressive Drift of Moored Floating Wind Turbines in a Wind Farm: Improved Mooring Capacity Model

2011 ◽  
Author(s):  
Hideyuki Suzuki ◽  
Yu Kitahara ◽  
Yukinari Fukumoto
Keyword(s):  
Wind Turbine ◽  
Safety Factor ◽  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Floating Platform ◽  
Main Research ◽  
Electric Power Supply ◽  
Analysis And Design ◽  
Wide Range

A wide range of platform concepts have been investigated for a floating wind turbine. So far analysis and design of motion characteristics of the platform is main research concern. One key research area less focused is floating platform related risk. If the wind energy would be one of the major sources of electric power supply, wind farms which are comprised of large number of floating wind turbines must be deployed in the ocean. Wind turbines are relatively closely arranged in a wind farm. In such an arrangement, a wind turbine accidentally started drifting will have some possibility to collide with floater and moorings of neighboring moored floating wind turbines, and might initiate another drift which might cause progressive drifting of wind turbines. In the previous report, a scenario of progressive drifting of wind turbines was investigated and associated risk was formulated. Quantitative risk of several arrangements of wind farm was estimated. Effects of arrangement of wind turbines in a wind farm and safety factor used in the design of moorings is discussed. Probability of initial drift was evaluated analyzing past records of accidents and design of mooring. In this research, strength of mooring system was modeled more precisely and probabilistic model was developed considering aged deterioration. Risk of progressive drifting was evaluated and safety factor required to realize a acceptable risk of a wind farm was discussed.

Dynamic Simulation of a Wind Farm With Variable Speed Wind Turbines

2003 ◽  
Author(s):  
E. Muljadi ◽  
C. P. Butterfield
Keyword(s):  
Wind Turbine ◽  
Wind Power ◽  
Wind Turbines ◽  
Wind Farm ◽  
Reactive Power ◽  
Power Network ◽  
Variable Speed ◽  
Power Capacity ◽  
Advantages And Disadvantages ◽  
The Impact

Wind power generation has increased very rapidly in the past few years. The total U.S. wind power capacity by the end of 2001 was 4,260 megawatts. As wind power capacity increases, it becomes increasingly important to study the impact of wind farm output on the surrounding power networks. In this paper, we attempt to simulate a wind farm by including the properties of the wind turbine, the wind speed time series, the characteristics of surrounding power network, and reactive power compensation. Mechanical stress and fatigue load of the wind turbine components are beyond the scope this paper. The paper emphasizes the impact of the wind farms on the electrical side of the power network. A typical wind farm with variable speed wind turbines connected to an existing power grid is investigated. Different control strategies for feeding wind energy into the power network are investigated, and the advantages and disadvantages are presented.

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