Estimation of Energy Output from Wind Energy Conversion Systems
Date Issued
2009
Date
2009
Author(s)
Tu, Yi-Long
Abstract
The development of wind energy has rapidly and steadily progressed then other renewable energy for the last decade, which is driven by global warming and weather change. An accurate estimation of wind energy output (capacity factor) of a wind energy conversion system (WECS) of a site can help producer to reduce the risk of loss from investment of wind energy. Traditional approach is using the manufacturer’s nominal performance curve to estimate energy output. The Weibull method and chronological (time-series) method are usually used to collocate nominal performance curve to estimate wind energy output. Due to the lack of academic work that compares the differences of the measured and calculated capacity factors of WECS, the wind speed and wind power output data from 2002 to 2006 of the wind power stations, located in Mailiao and Jhongtun, Taiwan, are used to further explore the advantages and drawbacks of using these two methods for estimating capacity factors of WECS in chapter 3. It is shown that thecapacity factors calculated from the time-series approach have better agreement with the actual capacity factors than the Weibull approach.he traditional approach is simple and direct for estimating energy output, but it would cause significant estimation errors. Taking the Vestas V47-660kW as example, the performance curve is made under the standard condition of 1.225 kg/m3 air density, 15℃ temperature and 10% turbulence intensity. The estimation of energy output would result in significant errors, if the wind environment is different from the condition of the nominal performance curve. In addition, because of the climatic features of Asia monsoon, there are different prominent wind periods during a year in Taiwan. For improving the drawbacks of the traditional approach, artificial neuron network (ANN) was chosen to estimate wind energy output. The accuracy of estimating wind energy output by different dataase lengths and different types of database for ANN was studied in chapter 4. The results show that the type of strong and weak wind periods database have the best agreement with the actual capacity factors of all the database types with the same dataase lengths. he advantages and flaws of using different input variable for ANN, such as current and previous observations of wind speed, power output, pitch angle or yaw angle for the previous season and a period of ten days were investigated in chapter 5. It is shown that the ANN model with current observation of wind speed and previous observation of power output yields the best performance.
Subjects
Renewable energy
Wind power
Weibull distribution
Time series
Nominal performance curve
Artificial Neuron Nnetwork
SDGs
Type
thesis
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