Model Predictive and Adaptive Control of Wind Farm Active Power Output

2016 ◽  
pp. 53-89
Author(s):  
John N. Jiang ◽  
Choon Yik Tang ◽  
Rama G. Ramakumar
Keyword(s):  
Adaptive Control ◽  
Power Output ◽  
Wind Farm ◽  
Active Power

Probabilistic Assessment of Wind Farm Active Power Based on Monte-Carlo Simulation

2013 ◽  
Vol 291-294 ◽  
pp. 536-540 ◽  
Author(s):  
Xin Wei Wang ◽  
Jian Hua Zhang ◽  
Cheng Jiang ◽  
Lei Yu
Keyword(s):  
Monte Carlo ◽  
Power Output ◽  
Wind Farm ◽  
Active Power ◽  
Assessment Methods ◽  
Probabilistic Methods ◽  
Model Parameters ◽  
Probability Assessment ◽  
Reliability Models ◽  
Reliability Parameters

The conventional deterministic methods have been unable to accurately assess the active power output of the wind farm being the random and intermittent of wind power, and the probabilistic methods commonly used to solve this problem. In this paper the multi-state fault model is built considering run, outage and derating state of wind turbine, and then the reliability model of the wind farm is established considering the randomness of the wind speed, the wind farm wake effects and turbine failure. The active wind farm output probability assessment methods and processes based on the Monte Carlo method. The related programs are written in MATLAB, and the probability assessment for active power output of a wind farm in carried out, the effectiveness and adaptability of built reliability models and assessment methods are illustrated by analysis of the effects of reliability parameters and model parameters on assessment results.

scholarly journals Optimization Control Strategy for Large Doubly-Fed Induction Generator Wind Farm Based on Grouped Wind Turbine

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4848
Author(s):  
Shijia Zhou ◽  
Fei Rong ◽  
Xiaojie Ning
Keyword(s):  
Control Strategy ◽  
Power Output ◽  
Wind Farm ◽  
Reactive Power ◽  
Optimization Problems ◽  
Active Power ◽  
Induction Generator ◽  
Doubly Fed Induction Generator ◽  
Optimization Control ◽  
Doubly Fed

This paper proposes a grouped, reactive power optimization control strategy to maximize the active power output of a doubly-fed induction generator (DFIG) based on a large wind farm (WF). Optimization problems are formulated based on established grouped loss models and the reactive power limits of the wind turbines (WTs). The WTs in the WF are grouped to relieve computational burden. The particle swarm optimization (PSO) algorithm is applied to optimize the distribution of reactive power among groups, and a proportional control strategy is used to distribute the reactive power requirements in each group. Furthermore, the proposed control strategy optimizes the reactive power distribution between the stator and the grid side converter (GSC) in each WT. The proposed control strategy greatly reduces the number of variables for optimization, and increases the calculation speed of the algorithm. Thus, the control strategy can not only increase the active power output of the WF but also enable the WF to track the reactive power dispatching instruction of the power grid. A simulation of the DFIG WF is given to verify the effectiveness of the proposed control strategy at different wind speeds and reactive power references.

Study on the Active Power Output Control of Wind Energy for Power Balance

2012 ◽  
Vol 608-609 ◽  
pp. 796-802
Author(s):  
Ming Song ◽  
Yuanyuan Su
Keyword(s):  
Power System ◽  
Power Control ◽  
Power Output ◽  
Wind Farm ◽  
Active Power ◽  
Control Mode ◽  
National Standard ◽  
Power Balance ◽  
Output Control ◽  
Active Power Control

This paper carried out a study on the contribution of wind farm to power balance of power system by wind power output control. The study mainly focuses on active power control capability of double feed induction generator (DFIG) based wind turbine, active power control mode of wind farm and capability of wind farm to participating in peak-load regulation. According to the National standard of China GB/T 19963-2011, a study case is given to simulate the impact of wind farm grid integration based on a real power system in order to validate the active power mode and control process presented in the paper.

scholarly journals GMM-HMM-based Medium- and Long-term Multi-Wind Farm Correlated Power Output Time Series Generation Method

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yufei Li ◽  
Bo Hu ◽  
Tao Niu ◽  
Shengpu Gao ◽  
Jiahao Yan ◽  
...  
Keyword(s):  
Time Series ◽  
Power Output ◽  
Wind Farm ◽  
Output Time

scholarly journals Spatiotemporal Correlations in the Power Output of Wind Farms: On the Impact of Atmospheric Stability

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1486 ◽  
Author(s):  
Nicolas Tobin ◽  
Adam Lavely ◽  
Sven Schmitz ◽  
Leonardo P. Chamorro
Keyword(s):  
Power Output ◽  
Wind Farm ◽  
Statistical Significance ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Temporal Correlations ◽  
Theoretical Predictions ◽  
Power Variance ◽  
Ambient Turbulence ◽  
The Impact

The dependence of temporal correlations in the power output of wind-turbine pairs on atmospheric stability is explored using theoretical arguments and wind-farm large-eddy simulations. For this purpose, a range of five distinct stability regimes, ranging from weakly stable to moderately convective, were investigated with the same aligned wind-farm layout used among simulations. The coherence spectrum between turbine pairs in each simulation was compared to theoretical predictions. We found with high statistical significance (p < 0.01) that higher levels of atmospheric instability lead to higher coherence between turbines, with wake motions reducing correlations up to 40%. This is attributed to higher dominance of atmospheric motions over wakes in strongly unstable flows. Good agreement resulted with the use of an empirical model for wake-added turbulence to predict the variation of turbine power coherence with ambient turbulence intensity (R 2 = 0.82), though other empirical relations may be applicable. It was shown that improperly accounting for turbine–turbine correlations can substantially impact power variance estimates on the order of a factor of 4.

Model-Free Adaptive Control of STATCOM for SSO Mitigation in DFIG-based Wind Farm

2021 ◽  
pp. 1-1
Author(s):  
Xi Wu ◽  
Mengting Wang ◽  
Mohammad Shahidehpour ◽  
Shuang Feng ◽  
Xi Chen
Keyword(s):  
Adaptive Control ◽  
Wind Farm ◽  
Model Free
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