Wind Farm - Long term noise and vibration measurements

The historical temperature data logged in the supervisory control and data acquisition (SCADA) system contains a wealth of information that can assist with the performance optimization of wind turbines (WTs). However, mining and using these long-term data is difficult and time-consuming due to their complexity, volume, etc. In this study, we tracked and analyzed the 5-year trends of major SCADA temperature rise variables in relation to the active power of four WTs in a real wind farm. To uncover useful information, an extended version of the bins method, which calculates the standard deviation (SD) as well as the average, is proposed and adopted. The implications of the analysis for engineering practice are discussed from multiple perspectives. The research results demonstrate a change in the patterns of the main temperature rise variables in a real wind farm, completeness of the monitoring of the WT internal temperature state, influence of wind turbine aging on temperature signals, a correlation between different measurement points, and a correlation between signals from different years. The knowledge gained from this research provides a reference for the development of more practical and comprehensive condition monitoring systems and methods, as well as better operation maintenance strategies.

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GMM-HMM-based Medium- and Long-term Multi-Wind Farm Correlated Power Output Time Series Generation Method

IEEE Access ◽

10.1109/access.2021.3091460 ◽

2021 ◽

pp. 1-1

Author(s):

Yufei Li ◽

Bo Hu ◽

Tao Niu ◽

Shengpu Gao ◽

Jiahao Yan ◽

...

Keyword(s):

Time Series ◽

Power Output ◽

Wind Farm ◽

Output Time

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Inter-annual variability of wind climates and wind turbine annual energy production

10.5194/wes-2018-48 ◽

2018 ◽

Author(s):

Sara C. Pryor ◽

Tristan J. Shepherd ◽

Rebecca J. Barthelmie

Keyword(s):

Standard Deviation ◽

Wind Energy ◽

Wind Turbine ◽

Energy Production ◽

Wind Farm ◽

Wind Farms ◽

Wind Speeds ◽

Annual Variability ◽

Eastern Usa

Abstract. Inter-annual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in pre-construction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derives in part from variability in wind climates. However, the magnitude of IAV in wind speeds at/close to wind turbine hub-heights is poorly constrained and maybe overestimated by the 6 % standard deviation of annual mean wind speeds that is widely applied within the wind energy industry. Thus there is a need for improved understanding of the long-term wind resource and the inter-annual variability therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub-heights over the eastern USA indicate median gross capacity factors (computed using 10-minute wind speeds close to wind turbine hub-heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at/near to typical wind turbine hub-heights in these simulations is lower than is implied by assuming a standard deviation of 6 %. Indeed, rather than in 9 in 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP, results presented herein indicate that over 90 % of the area in the eastern USA that currently has operating wind turbines simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to pre-construction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

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Long-Term Wind Power Prediction Based on Rough Set

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.329.411 ◽

2013 ◽

Vol 329 ◽

pp. 411-415 ◽

Cited By ~ 1

Author(s):

Shuang Gao ◽

Lei Dong ◽

Xiao Zhong Liao ◽

Yang Gao

Keyword(s):

Neural Network ◽

Wind Power ◽

Rough Set ◽

Wind Farm ◽

Rough Set Theory ◽

Prediction Method ◽

Key Factors ◽

Power Prediction ◽

Wind Power Prediction

In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.

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