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

2020 ◽  
Author(s):  
Janna K. Seifert ◽  
Martin Kraft ◽  
Martin Kühn ◽  
Laura J. Lukassen
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Clustering Algorithm ◽  
Free Field ◽  
Measurement Data ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Output Fluctuations

Abstract. The correlation of power output fluctuations of wind turbines in free field are investigated, taking into account the challenge of varying correlation states due to variable flow and wind turbine conditions within the wind farm. Based on eight months of 1 Hz SCADA data, measured at an offshore wind farm with 80 wind turbines, the influence of different parameters on the correlation of power output fluctuations is analysed. It is found that the correlation of power output fluctuations of wind turbines depends on the location of the wind turbines within the wind farm as well as the inflow conditions (free-stream or wake). Wind direction investigations show that the correlation is highest for streamwise aligned pairs and decreases towards spanwise pairs. Most importantly, the highly variable measurement data in a free-field wind farm has considerable influence on the identification of different correlation states. To account for that, the clustering algorithm k-means is used to group wind turbine pairs with similar correlations. The main outcome is that next to the location of a wind turbine pair in the wind farm the standard deviation in their power output and their power differences are suitable parameters to describe the correlation of power output fluctuations.

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.

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.

Site-Specific Design Optimization of 1.5–2.0 MW Wind Turbines

2001 ◽  
Vol 123 (4) ◽  
pp. 296-303 ◽  
Author(s):  
Peter Fuglsang ◽  
Kenneth Thomsen
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Load Prediction ◽  
Specific Design ◽  
Site Specific ◽  
Cost Of Energy ◽  
Fatigue Loads

A method is presented for site-specific design of wind turbines where cost of energy is minimized. A numerical optimization algorithm was used together with an aeroelastic load prediction code and a cost model. The wind climate was modeled in detail including simulated turbulence. Response time series were calculated for relevant load cases, and lifetime equivalent fatigue loads were derived. For the fatigue loads, an intelligent sensitivity analysis was used to reduce computational costs. Extreme loads were derived from statistical response calculations of the Davenport type. A comparison of a 1.5 MW stall regulated wind turbine in normal onshore flat terrain and in an offshore wind farm showed a potential increase in energy production of 28% for the offshore wind farm, but also significant increases in most fatigue loads and in cost of energy. Overall design variables were optimized for both sites. Compared to an onshore optimization, the offshore optimization increased swept area and rated power whereas hub height was reduced. Cost of energy from manufacture and installation for the offshore site was reduced by 10.6% to 4.6¢. This reduction makes offshore wind power competitive compared with today’s onshore wind turbines. The presented study was made for one wind turbine concept only, and many of the involved sub models were based on simplified assumptions. Thus there is a need for further studies of these models.

Framework for Risk-Based Inspection of an Offshore Wind Farm

2013 ◽  
Author(s):  
Z. Guédé ◽  
B. Bigourdan ◽  
A. Rouhan ◽  
J. Goyet ◽  
P. Renard
Keyword(s):  
Fatigue Crack ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Computational Effort ◽  
Offshore Wind ◽  
Structural Detail ◽  
Inspection Planning ◽  
Offshore Wind Farm ◽  
Set Up

In the present paper, a framework for Risk-Based Inspection planning is set up for a park of wind turbines taking into account the fact that only a sample of wind turbines is inspected at the scheduled inspection dates. A risk-based strategy is proposed to select the wind turbines to inspect, which significantly reduces the computational effort required by a crude application of Risk-Based Inspection analysis. The proposed framework is illustrated on a simple example of a wind farm where the failure of a wind turbine is driven by the fatigue crack of one of its critical structural detail.

Effects of Wind Farm Down-Regulation in the Offshore Wind Farm Alpha Ventus

2019 ◽  
Author(s):  
Matthias Kretschmer ◽  
Vasilis Pettas ◽  
Po Wen Cheng
Keyword(s):  
Wind Turbine ◽  
Wind Direction ◽  
Energy Production ◽  
Wind Farm ◽  
Measurement Data ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Down Regulation ◽  
Maximum Reduction ◽  
High Reduction

Abstract In recent years wind turbine down-regulation has been used or investigated for a variety of applications such as wind farm power optimisation, energy production curtailment and lifetime management. This study presents results from measurement data of tower loads and power obtained from two turbines located in the German offshore wind farm alpha ventus. The free streaming turbine, located closely to a fully equipped meteorological mast, was down-regulated to 50% for a period of 8 months, while the downwind turbine was operating normally. The results are compared to periods where both turbines were operated in normal conditions. Changes in loads and power are analysed according to incoming wind direction and magnitude. Results show a high reduction in the loads of the down regulated turbine, up to a level of 40%. For the turbine in wake the effects in loads are more prominent, showing a maximum reduction of 30%, compared to the effects in power and are seen in a wider sector of about 20° for loads and 10° for power.

scholarly journals Wind Turbine Clustering Algorithm of Large Offshore Wind Farms considering Wake Effects

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Binbin Zhang ◽  
Jun Liu
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Clustering Algorithm ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Wake Effect ◽  
Offshore Wind Farms ◽  
Power Simulation ◽  
Value Decomposition

This paper proposed the SVD (singular value decomposition) clustering algorithm to cluster wind turbines into some group for a large offshore wind farm, in order to reduce the high-dimensional problem in wind farm power control and numerical simulation. Firstly, wind farm wake relationship matrixes are established considering the wake effect in an offshore wind farm, and the SVD of wake relationship matrixes is used to cluster wind turbines into some groups by the fuzzy clustering algorithm. At last, the Horns Rev offshore wind farm is analyzed to test the clustering algorithm, and the clustering result and the power simulation show the effectiveness and feasibility of the proposed clustering strategy.

scholarly journals Optimal Temperature-Based Condition Monitoring System for Wind Turbines

2021 ◽  
Vol 6 (4) ◽  
pp. 50
Author(s):  
Payam Teimourzadeh Baboli ◽  
Davood Babazadeh ◽  
Amin Raeiszadeh ◽  
Susanne Horodyvskyy ◽  
Isabel Koprek
Keyword(s):  
Monitoring System ◽  
Wind Turbines ◽  
Wind Farm ◽  
Measurement Data ◽  
Real Data ◽  
Offshore Wind ◽  
Risk Indicator ◽  
Real World Data ◽  
The Real ◽  
Data Driven Modeling

With the increasing demand for the efficiency of wind energy projects due to challenging market conditions, the challenges related to maintenance planning are increasing. In this paper, a condition-based monitoring system for wind turbines (WTs) based on data-driven modeling is proposed. First, the normal condition of the WTs key components is estimated using a tailor-made artificial neural network. Then, the deviation of the real-time measurement data from the estimated values is calculated, indicating abnormal conditions. One of the main contributions of the paper is to propose an optimization problem for calculating the safe band, to maximize the accuracy of abnormal condition identification. During abnormal conditions or hazardous conditions of the WTs, an alarm is triggered and a proposed risk indicator is updated. The effectiveness of the model is demonstrated using real data from an offshore wind farm in Germany. By experimenting with the proposed model on the real-world data, it is shown that the proposed risk indicator is fully consistent with upcoming wind turbine failures.

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