Long-term quantification and characterisation of wind farm noise amplitude modulation

Measurement ◽  
2021 ◽  
pp. 109678
Author(s):  
Phuc D. Nguyen ◽  
Kristy L. Hansen ◽  
Peter Catcheside ◽  
Colin Hansen ◽  
Branko Zajamsek
Keyword(s):  
Amplitude Modulation ◽  
Wind Farm ◽  
Noise Amplitude

Evaluation of wind farm noise amplitude modulation synthesis quality

2020 ◽  
Vol 166 ◽  
pp. 107349
Author(s):  
Duc Phuc Nguyen ◽  
Kristy Hansen ◽  
Branko Zajamsek ◽  
Peter Catcheside
Keyword(s):  
Amplitude Modulation ◽  
Wind Farm ◽  
Noise Amplitude

scholarly journals Analysis of long-term temperature monitoring of multiple wind turbines

2021 ◽  
pp. 002029402110130
Author(s):  
Xian Wang ◽  
Qian-cheng Zhao ◽  
Xue-bing Yang ◽  
Bing Zeng
Keyword(s):  
Wind Turbines ◽  
Temperature Rise ◽  
Performance Optimization ◽  
Wind Farm ◽  
Engineering Practice ◽  
Multiple Perspectives ◽  
Temperature State ◽  
Condition Monitoring Systems ◽  
Historical Temperature

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.

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

IEEE Access ◽  
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

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

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.

Long-Term Wind Power Prediction Based on Rough Set

2013 ◽  
Vol 329 ◽  
pp. 411-415 ◽  
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.

Detection and Evaluation of Amplitude Modulation From a Wind Energy Development, Ireland

2019 ◽  
Vol 105 (6) ◽  
pp. 1042-1052
Author(s):  
Simon Jennings ◽  
John Kennedy
Keyword(s):  
Wind Energy ◽  
Amplitude Modulation ◽  
Wind Farm ◽  
Quantitative Measure ◽  
Reference Method ◽  
Energy Development ◽  
Aerodynamic Noise ◽  
Subjective Report ◽  
Wind Energy Development ◽  
Subjective Reports

Amplitude modulation (AM) is a characteristic of wind turbine noise that has only been recognised as an issue in recent years. It is a characteristic related to aerodynamic noise and descriptions of it include "swishing", "whooshing" or less frequently a "thumping" sound. Due to increased awareness among exposed communities AM presents a potentially serious obstacle to future wind farm developments. This work reports on the application of a recently developed calculation method for AM in a practical setting. Correlations will be drawn with subjective reports of AM by nearby residents keeping a noise diary. The suitability of the method and its ability to quantitatively confirm subjective reports of AM will be assessed. A study is presented here whereby subjectively recorded occurrences of AM by residents living near a wind energy development in Ireland are correlated to calculated levels over a twenty day period. In order to detect and calculate AM a method published by the Amplitude Modulation Working Group of the Institute of Acoustics, referred to as the Reference Method, is applied. A subjective assessment of the sound recordings to confirm the presence of AM is discussed, including estimating the expected frequency range that AM occurs. The results of the Reference Method calculation are presented for periods with and without a subjective report of AM by the residents. Consideration is given to the criteria and thresholds for valid AM ratings within the Reference Method especially where intermittent periods of AM are identified. The Reference Method is shown to be highly suitable as a quantitative measure of AM which correlates well with subjective reports. Caution must be taken when using the method as valid periods of AM may be overlooked due to the rigorous detection thresholds set by the method.

Jamming Effectiveness of Noise Amplitude Modulation Signal to Pesudo-Random Code Fuze

2014 ◽  
Vol 875-877 ◽  
pp. 2184-2189
Author(s):  
Shuai Li ◽  
Xiao Peng Yan ◽  
Jian Tao Wang ◽  
Ping Li
Keyword(s):  
Amplitude Modulation ◽  
Operating Principle ◽  
Noise Power ◽  
Noise Amplitude ◽  
Random Code ◽  
Signal Jamming ◽  
Optimal Value ◽  
Modulation Signal

Pseudo-random code fuze has strong anti-interference ability. The paper studies about the jamming effectiveness of noise amplitude modulation operating on pseudo-random code fuze. Firstly the operating principle of pseudo-random code fuze is introduced. Then we analysis the characteristic of the noise amplitude modulation signal, following that is the study on the pseudo-random code fuze responses at all levels under noise amplitude modulation signal jamming. Studies have shown that the value of pseudo-random code fuze correlator output will decrease linearly as noise power increases when the power of noise amplitude modulation is low, but tends to be constant when the power of noise amplitude modulation arrives at a certain level. So under noise amplitude modulation jamming, higher jamming power doesnt show the best performance, but there is an optimal value of jamming power.

Long-Term Planning of Wind Farm Siting in the Electricity Grid

2011 ◽  
pp. 1804-1808 ◽  
Author(s):  
Jingjie Xiao ◽  
Bri-Mathias S. Hodge ◽  
Andrew L. Liu ◽  
Joseph F. Pekny ◽  
Gintaras V. Reklaitis
Keyword(s):  
Wind Farm ◽  
Electricity Grid
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