Adaptive Discrete-Time Quantized Control for Multi-machine Power System with SVC

In this paper, a discrete-time adaptive dynamic surface quantization control method has been proposed for a class of multi-machine power systems with Static Var Compensator (SVC). The strategy combines RBF neural network and discrete low-pass filter to solve the differential explosion problem in bac...

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Bibliographic Details
Published in2024 5th International Conference on Computer Engineering and Application (ICCEA) pp. 1757 - 1761
Main Authors Li, Yan, Yuan, Xuezhu, Tian, Bing, Yuan, Rong, Xu, Nan, Zhu, Guoqiang
Format Conference Proceeding
LanguageEnglish
Published IEEE 12.04.2024
Subjects
Adaptive dynamic surface
Adaptive systems
Discrete time
Hysteresis quantizer
Low-pass filters
Multi-machine power system
Power system dynamics
Process control
Quantization (signal)
RBF neural network
Simulation
Static VAr compensators
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Summary:In this paper, a discrete-time adaptive dynamic surface quantization control method has been proposed for a class of multi-machine power systems with Static Var Compensator (SVC). The strategy combines RBF neural network and discrete low-pass filter to solve the differential explosion problem in backstepping and simplify the control law. Chatter is also reduced by a modified hysteresis quantizer, and digital control is achieved. Ultimate uniformly bounded properties of closed-loop systems are proved by designing Lyapunov functions and the simulation results confirm the effectiveness of the method.
ISSN:2159-1288
DOI:10.1109/ICCEA62105.2024.10603847