Flexible fault ride through strategy for wind farm clusters in power systems with high wind power penetration

2015 ◽  
Vol 93 ◽  
pp. 239-248 ◽  
Author(s):  
Songyan Wang ◽  
Ning Chen ◽  
Daren Yu ◽  
Aoife Foley ◽  
Lingzhi Zhu ◽  
...  
Keyword(s):  
Power Systems ◽  
Wind Power ◽  
Wind Farm ◽  
High Wind ◽  
Fault Ride Through

Robust Dispatch of High Wind Power-Penetrated Power Systems Against Transient Instability

2018 ◽  
Vol 33 (1) ◽  
pp. 174-186 ◽  
Author(s):  
Yan Xu ◽  
Minghui Yin ◽  
Zhao Yang Dong ◽  
Rui Zhang ◽  
David J. Hill ◽  
...  
Keyword(s):  
Power Systems ◽  
Wind Power ◽  
High Wind ◽  
Transient Instability

Preliminary Study on the Influence of Wind Power on Low-Frequency Oscillation under Outward Transmitting Thermal Generated Power Bundled with Wind Power Typical Scene

2013 ◽  
Vol 391 ◽  
pp. 271-276
Author(s):  
Peng Li ◽  
Ning Bo Wang ◽  
De Zhi Chen ◽  
Xiao Rong Zhu ◽  
Yun Ting Song
Keyword(s):  
Power Systems ◽  
Power System ◽  
Wind Power ◽  
Wind Farm ◽  
Reactive Power ◽  
Simulation Analysis ◽  
Low Frequency ◽  
Simulation Software ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation

Increasing penetration level of wind power integration has a significant impact on low-frequency oscillations of power systems. Based on PSD-BPA simulation software, time domain simulation analysis and eigenvalue analysis are employed to investigate its effect on power system low-frequency oscillation characteristic in an outward transmitting thermal generated power bundled with wind power illustrative power system. System damping enhances markedly and the risk of low-frequency oscillation reduce when the generation of wind farm increase. In addition, dynamic reactive power compensations apply to wind farm, and the simulation result indicates that it can improve dynamic stability and enhance the system damping.

Forecast of Short-Term Wind Power Based on GA Optimized Elman Neural Network

2014 ◽  
Vol 536-537 ◽  
pp. 470-475
Author(s):  
Ye Chen
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Wind Power ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Prediction ◽  
Elman Neural Network ◽  
Wind Power Prediction ◽  
Forecasting Method ◽  
Safety And Stability

Due to the features of being fluctuant, intermittent, and stochastic of wind power, interconnection of large capacity wind farms with the power grid will bring about impact on the safety and stability of power systems. Based on the real-time wind power data, wind power prediction model using Elman neural network is proposed. At the same time in order to overcome the disadvantages of the Elman neural network for easily fall into local minimum and slow convergence speed, this paper put forward using the GA algorithm to optimize the weight and threshold of Elman neural network. Through the analysis of the measured data of one wind farm, shows that the forecasting method can improve the accuracy of the wind power prediction, so it has great practical value.

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