A new control methodology of wind farm using short-term ahead wind speed prediction for load frequency control of power system

2008 ◽  
Author(s):  
Tomonobu Senjyu ◽  
Motoki Tokudome ◽  
Akie Uehara ◽  
Toshiaki Kaneko ◽  
Atsushi Yona ◽  
...  
Keyword(s):  
Wind Speed ◽  
Power System ◽  
Wind Farm ◽  
Frequency Control ◽  
Load Frequency Control ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Load Frequency ◽  
Speed Prediction ◽  
Control Methodology

scholarly journals Multi-time scale wind speed prediction based on WT-bi-LSTM

2020 ◽  
Vol 309 ◽  
pp. 05011
Author(s):  
Jinyong Xiang ◽  
Zhifeng Qiu ◽  
Qihan Hao ◽  
Huhui Cao
Keyword(s):  
Wind Speed ◽  
Wavelet Decomposition ◽  
Wind Farm ◽  
Short Term Memory ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Proposed Model ◽  
Speed Prediction ◽  
Long Short Term Memory ◽  
Auto Regression

The accurate and reliable wind speed prediction can benefit the wind power forecasting and its consumption. As a continuous signal with the high autocorrelation, wind speed is closely related to the past and future moments. Therefore, to fully use the information of two direction, an auto-regression model based on the bi-directional long short term memory neural network model with wavelet decomposition (WT-bi-LSTM) is built to predict the wind speed at multi-time scales. The proposed model are validated by using the actual wind speed series from a wind farm in China. The validation results demonstrated that, compared with other four traditional models, the proposed strategy can effectively improve the accuracy of wind speed prediction.

scholarly journals Sample Selection Based on Active Learning for Short-Term Wind Speed Prediction

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 337 ◽  
Author(s):  
Jian Yang ◽  
Xin Zhao ◽  
Haikun Wei ◽  
Kanjian Zhang
Keyword(s):  
Wind Speed ◽  
Active Learning ◽  
Wind Farm ◽  
Sample Selection ◽  
Support Vector ◽  
Short Term ◽  
Learning Methods ◽  
Wind Speed Prediction ◽  
Training Samples ◽  
Speed Prediction

Wind speed prediction is the key to wind power prediction, which is very important to guarantee the security and stability of the power system. Due to dramatic changes in wind speed, it needs high-frequency sampling to describe the wind. A large number of samples are generated and affect modeling time and accuracy. Therefore, two novel active learning methods with sample selection are proposed for short-term wind speed prediction. The main objective of active learning is to minimize the number of training samples and ensure the prediction accuracy. In order to verify the validity of the proposed methods, the results of support vector regression (SVR) and artificial neural network (ANN) models with different training sets are compared. The experimental data are from a wind farm in Jiangsu Province. The simulation results show that the two novel active learning methods can effectively select typical samples. While reducing the number of training samples, the prediction performance remains almost the same or slightly improved.

Short-Term Wind Speed Prediction on Base of Improved Least Squares Support Vector Machine

2014 ◽  
Vol 599-601 ◽  
pp. 1972-1975
Author(s):  
Zheng Zhao ◽  
Long Xin Zhang ◽  
Hai Tao Liu ◽  
Zi Rui Liu
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Wind Farm ◽  
Support Vector ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Neural Network Prediction ◽  
Speed Prediction

Accurate wind speed prediction is of significance to improve the ability to coordinate operation of a wind farm with a power system and ensure the safety of power grid operation. According to the randomness and volatility of wind speed, it is put forward that a WD_GA_LS_SVM short-term wind speed combination prediction model on basis of Wavelet decomposition (WD), Genetic alogorithms (GA) optimization and Least squares support vector machine (LS_SVM). Short-term wind speed prediction is carried out and compared with the neural network prediction model with use of the measured data of a wind farm. The results of error analysis indicate the combination prediction model selected is of higher prediction accuracy.

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