Adaptive Optimal Control for a One-Machine Infinite-Bus Power System

2008 ◽  
Author(s):  
Amir Mohammad Sayidi ◽  
M. Ali Nekoui ◽  
N. Seifi Boghrabidi
Keyword(s):  
Optimal Control ◽  
Power System ◽  
Adaptive Optimal Control ◽  
One Machine

AGC using adaptive optimal control approach in restructured power system

2018 ◽  
Vol 35 (5) ◽  
pp. 4953-4962 ◽  
Author(s):  
Akhilesh Singh ◽  
Nagendra Kumar ◽  
Bhagawati Prasad Joshi ◽  
Kunwar Singh Vaisla
Keyword(s):  
Optimal Control ◽  
Power System ◽  
Adaptive Optimal Control ◽  
Control Approach ◽  
Restructured Power System ◽  
Optimal Control Approach

scholarly journals Linearization threshold condition and stability analysis of a stochastic dynamic model of one-machine infinite-bus (OMIB) power systems

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Lijuan Li ◽  
Yongdong Chen ◽  
Bin Zhou ◽  
Hongliang Liu ◽  
Yongfei Liu
Keyword(s):  
Power Systems ◽  
Power System ◽  
Dynamic Model ◽  
Threshold Model ◽  
Security Evaluation ◽  
Stochastic Dynamic ◽  
Threshold Condition ◽  
Stochastic Excitation ◽  
Predictor Corrector ◽  
One Machine

AbstractWith the increase in the proportion of multiple renewable energy sources, power electronics equipment and new loads, power systems are gradually evolving towards the integration of multi-energy, multi-network and multi-subject affected by more stochastic excitation with greater intensity. There is a problem of establishing an effective stochastic dynamic model and algorithm under different stochastic excitation intensities. A Milstein-Euler predictor-corrector method for a nonlinear and linearized stochastic dynamic model of a power system is constructed to numerically discretize the models. The optimal threshold model of stochastic excitation intensity for linearizing the nonlinear stochastic dynamic model is proposed to obtain the corresponding linearization threshold condition. The simulation results of one-machine infinite-bus (OMIB) systems show the correctness and rationality of the predictor-corrector method and the linearization threshold condition for the power system stochastic dynamic model. This study provides a reference for stochastic modelling and efficient simulation of power systems with multiple stochastic excitations and has important application value for stability judgment and security evaluation.

Adaptive optimal control of unknown nonlinear systems with different time scales

2017 ◽  
Vol 238 ◽  
pp. 179-190 ◽  
Author(s):  
Zhi-Jun Fu ◽  
Wen-Fang Xie ◽  
Subhash Rakheja ◽  
Dong-Dong Zheng
Keyword(s):  
Optimal Control ◽  
Nonlinear Systems ◽  
Time Scales ◽  
Adaptive Optimal Control ◽  
Different Time Scales
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