Automatic Generation Control (AGC) of Wind Power System: An Least Squares-Support Vector Machine (LS-SVM) Radial Basis Function (RBF) Kernel Approach

2018 ◽  
Vol 46 (14-15) ◽  
pp. 1621-1633 ◽  
Author(s):  
Gulshan Sharma ◽  
Ibraheem Nasiruddin ◽  
K. R. Niazi ◽  
R. C. Bansal
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Basis Function ◽  
Automatic Generation ◽  
Automatic Generation Control ◽  
Support Vector ◽  
Rbf Kernel ◽  
Wind Power System ◽  
Kernel Approach ◽  
Generation Control

scholarly journals Wind power integration into the automatic generation control of power systems with large-scale wind power

2014 ◽  
Vol 2014 (10) ◽  
pp. 538-545 ◽  
Author(s):  
Abdul Basit ◽  
Anca Daniela Hansen ◽  
Mufit Altin ◽  
Poul Sørensen ◽  
Mette Gamst
Keyword(s):  
Power Systems ◽  
Wind Power ◽  
Large Scale ◽  
Automatic Generation ◽  
Automatic Generation Control ◽  
Wind Power Integration ◽  
Generation Control

Interpreting Brain Waves

2017 ◽  
pp. 695-714
Author(s):  
Noran Magdy El-Kafrawy ◽  
Doaa Hegazy ◽  
Mohamed F. Tolba
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Basis Function ◽  
Brain Computer Interface ◽  
Support Vector ◽  
Left Hand ◽  
Brain Waves ◽  
Mode Decomposition ◽  
Rbf Kernel ◽  
The Brain

BCI (Brain-Computer Interface) gives you the power to manipulate things around you just by thinking of what you want to do. It allows your thoughts to be interpreted by the computer and hence act upon it. This could be utilized in helping disabled people, remote controlling of robots or even getting personalized systems depending upon your mood. The most important part of any BCI application is interpreting the brain signalsasthere are many mental tasks to be considered. In this chapter, the authors focus on interpreting motor imagery tasks and more specifically, imagining left hand, right hand, foot and tongue. Interpreting the signal consists of two main steps: feature extraction and classification. For the feature extraction,Empirical Mode Decomposition (EMD) was used and for the classification,the Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel was used. The authors evaluated this system using the BCI competition IV dataset and reached a very promising accuracy.

A Unified Frequency-Domain Model for Automatic Generation Control Assessment Under Wind Power Uncertainty

2019 ◽  
Vol 10 (3) ◽  
pp. 2936-2947 ◽  
Author(s):  
Xiaoshuang Chen ◽  
Jin Lin ◽  
Can Wan ◽  
Yonghua Song ◽  
Haocheng Luo
Keyword(s):  
Frequency Domain ◽  
Wind Power ◽  
Automatic Generation ◽  
Domain Model ◽  
Automatic Generation Control ◽  
Generation Control

scholarly journals Bi-Level Model Predictive Control for Optimal Coordination of Multi-Area Automatic Generation Control Units under Wind Power Integration

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 669
Author(s):  
Xia ◽  
Liu
Keyword(s):  
Model Predictive Control ◽  
Wind Power ◽  
Predictive Control ◽  
Control Strategy ◽  
Automatic Generation ◽  
Frequency Stability ◽  
Automatic Generation Control ◽  
Level Model ◽  
Generation Control ◽  
Tie Line

With the high degree of wind power penetration integrated into multi-area AC/DC interconnected power grids, the frequency regulation capacity of automatic generation control (AGC) units is not sufficient in the wind power-penetrated area, making it difficult to effectively suppress the frequency stability caused by the fluctuation of wind power. Therefore, a coordinated control strategy for AGC units across areas based on bi-level model predictive control is proposed in this paper to achieve resource sharing. The control scheme uses economic model predictive control to realize steady power optimal allocation of the AGC units across areas in the upper layer and distributed model predictive control to realize dynamic frequency optimization control of the multi-area AGC units in the lower layer. Taking a three-area AC/DC interconnected power grid with a wind farm as an example, the simulation results show that, compared with model predictive control using tie-line frequency bias control (TBC) mode, the proposed control strategy can not only effectively maintain tie-line safety and frequency stability, but can also reduce the regulation cost of multi-area AGC units.

scholarly journals Research on Automatic Generation Control with Wind Power Participation Based on Predictive Optimal 2-Degree-of-Freedom PID Strategy for Multi-area Interconnected Power System

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3325 ◽  
Author(s):  
Xilin Zhao ◽  
Zhenyu Lin ◽  
Bo Fu ◽  
Li He ◽  
Na Fang
Keyword(s):  
Wind Speed ◽  
Power System ◽  
Wind Power ◽  
Pid Controller ◽  
Wind Farm ◽  
Automatic Generation ◽  
Degree Of Freedom ◽  
Automatic Generation Control ◽  
Interconnected Power System ◽  
Generation Control

High penetration of wind power in the modern power system renders traditional automatic generation control (AGC) methods more challenging, due to the uncertainty of the external environment, less reserve power, and small inertia constant of the power system. An improved AGC method named predictive optimal 2-degree-of-freedom proportion integral differential (PO-2-DOF-PID) is proposed in this paper, which wind farm will participate in the load frequency control process. Firstly, the mathematical model of the AGC system of multi-area power grid with penetration of wind power is built. Then, predictive optimal 2-degree-of-freedom PID controller is presented to improve the system robustness considering system uncertainties. The objective function is designed based on the wind speed and whether wind farm takes part in AGC or not. The controller solves the optimization problem through the predictive theory while taking into account given constraints. In order to obtain the predictive sequence of output of the whole system, the characteristic of the 2-DOF-PID controller is integrated in the system model. A three interconnected power system is introduced as an example to test the feasibility and effectiveness of the proposed method. When considering the penetration of wind power, two cases of high wind speed and low wind speed are analyzed. The simulation results indicate that the proposed method can effectively deal with the negative influence caused by wind power when wind power participates in AGC.

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