Power Output Fluctuation Analysis of Grid-Connected Wind Turbine-Generator System With Limiting Maximum Power Output

Author(s):  
Tetsuya Wakui ◽  
Ryohei Yokoyama

The reduction in the power output fluctuation of grid-connected wind turbine-generator systems is strongly required to further increase their total installed capacity in Japan. This study focuses on limiting the maximum electric power output by changing the set point of the power output control as a reduction technique. The influence of limiting the maximum electric power output of a 2 MW-system, which adopts the variable speed operation, on the power output fluctuation characteristic is analyzed through numerical simulation conducted by using an observed field wind data. The focus is on the power output fluctuation, which is important for management of commercial power systems including power plants, of the 2 MW-system with six cases of the maximum electric power output. The results show that limiting the maximum electric power output does not have an influence on the power output fluctuation characteristic and control performance during the pitch angle operation at high wind speeds. However, the year-round simulation reveals that limiting the maximum electric power output brings a tradeoff between the reduction in the power output fluctuation and the generating performance.

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Mingzhu Tang ◽  
Zijie Kuang ◽  
Qi Zhao ◽  
Huawei Wu ◽  
Xu Yang

In response to the unbalanced sample categories and complex sample distribution of the operating data of the pitch system of the wind turbine generator system, this paper proposes a method for fault detection of the pitch system of the wind turbine generator system based on the multiclass optimal margin distribution machine. In this method, the power output of the wind turbine generator system is used as the main status parameter, and the operating data history of the wind turbine generator system in the wind power supervisory control and data acquisition (SCADA) system is subject to correlation analysis with the Pearson correlation coefficient, to eliminate the features that have low correlation with the power output status parameter. Secondary analysis is performed to the remaining features, thus reducing the number and complexity of samples. Datasets are divided into the training set for training of the multiclass optimal margin distribution machine fault detection model and test set for testing. Experimental verification was carried out with the operating data of one wind farm in China. Experimental results show that, compared with other support vector machines, the proposed method has higher fault detection accuracy and precision and lower false-negative rate and false-positive rate.


Sign in / Sign up

Export Citation Format

Share Document