Decentralized Adaptive Digital Control for Multi-machine Excitation Systems with Static Var Compensator

In this paper, a decentralized discrete adaptive quantized control (DDQC) method has been proposed for multi-machine excitation system (MES) with static var compensator (SVC). The mathematical model of the power system is studied in discrete-time form, the digital control scheme is realized by using the improved hysteresis quantizer, which can discretize the control signals in amplitude and time scale. Then, by using the discrete first-order low-pass filter (FLF), the complexity of discrete backstepping control method is reduced. Finally, the stability analysis proves that the trace error converges to the compact set and the simulation results illustrate the effectiveness of the control method.

[1]  Xiuyu Zhang,et al.  Adaptive fuzzy dynamic surface sliding mode control of large-scale power systems with prescribe output tracking performance. , 2020, ISA transactions.

[2]  Tianping Zhang,et al.  Decentralized adaptive fuzzy output feedback control of stochastic nonlinear large-scale systems with dynamic uncertainties , 2015, Inf. Sci..

[3]  Yao Zhang,et al.  A robust decentralized load frequency controller for interconnected power systems. , 2012, ISA transactions.

[4]  Yan Lin,et al.  Decentralized adaptive tracking control for a class of interconnected nonlinear systems with input quantization , 2017, Autom..

[5]  Zhang Xiu-yu,et al.  Decentralized adaptive neural network dynamic surface control design for multi-machine excitation systems , 2016, 2016 3rd International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS).

[6]  Youyi Wang,et al.  Robust decentralized nonlinear controller design for multimachine power systems , 1997, Autom..

[7]  P. Kokotovic,et al.  Adaptive control of a class of nonlinear discrete-time systems , 1995 .

[8]  Shuzhi Sam Ge,et al.  Adaptive NN control for a class of strict-feedback discrete-time nonlinear systems , 2003, Autom..

[9]  Bin Li,et al.  Adaptive Implicit Inverse Control for a Class of Discrete-Time Hysteretic Nonlinear Systems and Its Application , 2020, IEEE/ASME Transactions on Mechatronics.

[10]  Guang-Hong Yang,et al.  Adaptive Backstepping Stabilization of Nonlinear Uncertain Systems With Quantized Input Signal , 2014, IEEE Transactions on Automatic Control.

[11]  Sarah K. Spurgeon,et al.  Decentralised sliding-mode control for multimachine power systems using only output information , 2003 .

[12]  Wen-Jeng Liu,et al.  Decentralized Observer Design for a Class of Nonlinear Uncertain Large Scale Systems with Lumped Perturbations , 2016 .

[13]  Zhong-Ping Jiang,et al.  Decentralized and adaptive nonlinear tracking of large-scale systems via output feedback , 2000, IEEE Trans. Autom. Control..

[14]  Yi Guo,et al.  Nonlinear decentralized control of large-scale power systems , 2000, Autom..

[15]  Yue Wang,et al.  Compound Adaptive Fuzzy Quantized Control for Quadrotor and Its Experimental Verification , 2020, IEEE Transactions on Cybernetics.

[16]  Luiz Cera Zanetta,et al.  Robust control of electrical power systems using PSSs and Bilinear Matrix Inequalities , 2014 .

[17]  Bin Li,et al.  Decentralized Adaptive Quantized Excitation Control for Multi-Machine Power Systems by Considering the Line-Transmission Delays , 2018, IEEE Access.

[18]  Daizhan Cheng,et al.  Adaptive L2 disturbance attenuation control of multi-machine power systems with SMES units , 2006, Autom..

[19]  Petros A. Ioannou Decentralized adaptive control of interconnected systems , 1986 .

[20]  Shaocheng Tong,et al.  Adaptive NN Controller Design for a Class of Nonlinear MIMO Discrete-Time Systems , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[21]  Bing Chen,et al.  Approximation-Based Discrete-Time Adaptive Position Tracking Control for Interior Permanent Magnet Synchronous Motors , 2015, IEEE Transactions on Cybernetics.