scholarly journals A New Perspective of Wind Power Grid Codes Under Unbalanced and Distorted Grid Conditions

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 15931-15944 ◽  
Author(s):  
Md Moinul Islam ◽  
Eklas Hossain ◽  
Sanjeevikumar Padmanaban ◽  
Charles W. Brice
Keyword(s):  
Wind Power ◽  
Power Grid ◽  
Distorted Grid ◽  
Grid Codes ◽  
New Perspective

Study on Dispersed Wind Power Connected to Power Grid

2014 ◽  
Vol 672-674 ◽  
pp. 262-268
Author(s):  
Wei Xu ◽  
Xiang Ning Xiao ◽  
Zhi Chao Zhou
Keyword(s):  
Wind Power ◽  
Reactive Power ◽  
Frequency Control ◽  
Power Grid ◽  
Active Power ◽  
Fault Ride Through ◽  
Power Frequency ◽  
Grid Codes ◽  
Definition Of ◽  
Technology Indicators

The necessity for grid codes of the dispersed wind power connected to power grid is described briefly and the definition of the dispersed wind power is discussed compared with the distributed wind power in China. Aimed at the dispersed wind power, the main technology indicators of wind power grid codes between Denmark (below 100kV), Germany (below 60kV) and China in aspects of access principle, connection mode, active power / frequency control, reactive power / voltage control, fault ride through and power quality are compared to provide reference for the modification and completion of the dispersed wind power grid code in China.

Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

2021 ◽  
pp. 0309524X2110568
Author(s):  
Lian Lian ◽  
Kan He
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Power Grid ◽  
Wavelet Denoising ◽  
Slime Mold ◽  
Support Vector ◽  
Power Prediction ◽  
Wind Power Prediction

The accuracy of wind power prediction directly affects the operation cost of power grid and is the result of power grid supply and demand balance. Therefore, how to improve the prediction accuracy of wind power is very important. In order to improve the prediction accuracy of wind power, a prediction model based on wavelet denoising and improved slime mold algorithm optimized support vector machine is proposed. The wavelet denoising algorithm is used to denoise the wind power data, and then the support vector machine is used as the prediction model. Because the prediction results of support vector machine are greatly affected by model parameters, an improved slime mold optimization algorithm with random inertia weight mechanism is used to determine the best penalty factor and kernel function parameters in support vector machine model. The effectiveness of the proposed prediction model is verified by using two groups actually collected wind power data. Seven prediction models are selected as the comparison model. Through the comparison between the predicted value and the actual value, the prediction error and its histogram distribution, the performance indicators, the Pearson’s correlation coefficient, the DM test, box-plot distribution, the results show that the proposed prediction model has high prediction accuracy.

Sign in / Sign up

Export Citation Format

Share Document