Short-term power fluctuations of large wind power plants

2002 ◽  
Author(s):  
Y.-H. Wan ◽  
D. Bucane
Keyword(s):  
Wind Power ◽  
Power Plants ◽  
Short Term ◽  
Wind Power Plants ◽  
Power Fluctuations ◽  
Large Wind

Short-Term Power Fluctuations of Large Wind Power Plants*

2002 ◽  
Vol 124 (4) ◽  
pp. 427-431 ◽  
Author(s):  
Yih-huei Wan ◽  
Demy Bucaneg,
Keyword(s):  
Wind Speed ◽  
Power Systems ◽  
Wind Power ◽  
Power Plants ◽  
Power Level ◽  
Short Term ◽  
Wind Power Plants ◽  
Weather Events ◽  
Power Fluctuations ◽  
Large Wind

To evaluate short-term wind power fluctuations and their impact on electric power systems, the National Renewable Energy Laboratory, in cooperation with Enron Wind, has started a project to record output power from several large commercial wind power plants at the 1-Hertz rate. This paper presents statistical properties of the data collected so far and discusses the results of data analysis. From the available data, we can already conclude that despite the stochastic nature of wind power fluctuations, the magnitudes and rates of wind power changes caused by wind speed variations are seldom extreme, nor are they totally random. Their values are bounded in narrow ranges. Power output data also show significant spatial variations within a large wind power plant. The data also offer encouraging evidence that accurate wind power forecasting is feasible. To the utility system, large wind power plants are not really random burdens. The narrow range of power level step changes provides a lot of information with which system operators can make short-term predictions of wind power. Large swings of wind power do occur, but those infrequent large changes (caused by wind speed changes) are always related to well-defined weather events, most of which can be accurately predicted in advance.

Short-Term Power Fluctuations of Large Wind Power Plants

2002 ◽  
Author(s):  
Yih-Huei Wan ◽  
Demy Bucaneg
Keyword(s):  
Wind Power ◽  
Power Output ◽  
Power Plants ◽  
Spatial Diversity ◽  
Ancillary Services ◽  
Short Term ◽  
Output Data ◽  
Wind Power Plants ◽  
Power Fluctuations ◽  
Large Wind

With electric utilities and other power providers showing increased interest in wind power and with growing penetration of wind capacity into the market, questions about how wind power fluctuations affect power system operations and about wind power’s ancillary services requirements are receiving lots of attention. To evaluate short-term wind power fluctuations and the range of ancillary service of wind power plants, the National Renewable Energy Laboratory (NREL), in cooperation with Enron Wind, has started a project to record output power from several large commercial wind power plants at the 1-Hertz rate. The project’s purpose is to acquire actual, long-term wind power output data for analyzing wind power fluctuations, frequency distribution of the changes, the effects of spatial diversity, and wind power ancillary services. This paper presents statistical properties of the data collected so far and discusses the results of data analysis. Although the efforts to monitor wind power plants are ongoing, we can already conclude from the available data that despite the stochastic nature of wind power fluctuations, the magnitudes and rates of wind power changes caused by wind speed variations are seldom extreme, nor are they totally random. Their values are bounded in narrow ranges. Power output data also show significant spatial variations within a large wind power plant.

Analysis of the Use of Remote Sensing Measurements for Developing Wind Power Projects

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Gustavo S. Böhme ◽  
Eliane A. Fadigas ◽  
Julio R. Martinez ◽  
Carlos E. M. Tassinari
Keyword(s):  
Remote Sensing ◽  
Wind Power ◽  
Power Plants ◽  
Measurement Data ◽  
Flow Modeling ◽  
Short Term ◽  
Wind Power Plants ◽  
Reduced Data ◽  
Average Uncertainty

Micrositing wind flow modeling presents one of the most relevant uncertainties in the project of wind power plants. Studies in the area indicate that the average uncertainty related to this item varies between 2.4% and 8% of the annual energy production (AEP). The most efficient form to mitigate this uncertainty is to obtain additional measurements from the site. This can be achieved by installing met masts and by applying short-term remote sensing campaigns (LIDAR and SODAR). Ideally, measurement campaigns should have at least one complete year of data to capture seasonal changes in the local wind behavior and to increase the long-term representation of the sample. However, remote sensing is frequently performed in reduced periods of measurement, coming down to months or even weeks of campaign. The main contribution of this paper is to analyze whether short-term remote sensing measurements contribute to the development of wind power projects, given the associated uncertainties due to low representativeness of the reduced data sample. This study was performed using over 60 years of wind measurement data. Its main findings indicate that the contribution of short-term remote sensing campaigns vary depending on the complexity of the local terrain, and the respective uncertainty related to horizontal and vertical extrapolation of micrositing models. The results showed that in only 30% of the cases, a 3 month measurement campaign reduced the projects overall uncertainty. This number increases to 50% for a 6 month campaign and 90% for a 10 month campaign.

Short-Term Wind Power Forecasting in Wind Power Plants of North China Based on Support Vector Regression Analysis

2014 ◽  
Vol 705 ◽  
pp. 284-288
Author(s):  
Hai Jian Shao ◽  
Hai Kun Wei
Keyword(s):  
Support Vector Regression ◽  
Wind Power ◽  
Power Plants ◽  
Heuristic Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Wind Power Plants ◽  
Power Forecasting

This paper investigates the short-term wind power forecasting and demonstrates accurate modeling, which utilizes two representative heuristic algorithms (i.e. wavelet neural network (WNN) and Multilayer Perceptron (MLP)), and statistical machine learning techniques (i.e. Support Vector Regression (SVR)). The proposed method generates the performances of different approaches for random time series, characterized with high accuracy and high generalization capability. The employed data is obtained through Sampling equipment in Real Wind Power Plants (Power generation equipment is Dongfang Steam Turbine Co., Ltd. weak wind turbine type--FD77 with German REpower company technology). The main innovation of this paper comes from: (a) problem may encounter in the real application is in consideration such as corrupt, missing value and noisy data. (b) Data lag estimation are provided to investigate the data distribution and obtain the best input variables, respectively. (c) Comparison between MLP neural networks, WNN and SVR with optimized kernel parameters based on Grid-search method are provided to demonstrate the best forecasting approaches. The purpose of this paper is to provide a method with reference value for short-term wind power forecasting.

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