Adaptive fuzzy dynamic surface sliding mode control of large-scale power systems with prescribe output tracking performance

Zhu, Guoqiang; Nie, Linlin; Lv, Zijian; Sun, Lingfang; Zhang, Xiuyu; Wang, Chenliang

doi:10.1016/j.isatra.2019.08.063

Cited by 35 publications

(16 citation statements)

References 43 publications

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“…N EW energy development, emergence of new models of electricity consumption, energy structural adjustment and the maturity of ultrahigh voltage transmission technology have put forward requirements on the development of the scale of the power grid system [1]- [4]. Then with the development of power grid system, the structure of power grid system has become highly nonlinearity, complexity and coupling which will introduce unstable factors into power system [5]- [7]. For the sake of the impact of the change of power grid system structure, flexible AC transmission equipment, such as static var compensator (SVC) , is introduced, which stabilize the excitation power system effectively [8].…”

Section: Introductionmentioning

confidence: 99%

K-Filter Observer Based Adaptive Quantized Decentralized Excitation Control for Multi-Machine Power Systems With the Line Transmission Delays

Wang

et al. 2021

IEEE Access

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For a class of Multi-machine excitation system with transmission delays, an output feedback adaptive quantized control protocol based on k-filter observer has been proposed. Not all the states in Multi-machine excitation power system need to be measured, a class of k-filter observers is constructed to estimate the unmeasured state in the system, and to compensate the estimated error of the neural network approximator, the nonlinearity introduced by the quantizer and external disturbances. The traditional assumption of the upper bound of time-delay function is no longer needed. Instead, time-delay function approximator is used to deal with the influence of transmission delay between excitation systems. This kind of time-delay function approximator is composed of neural network. And according to the finite coverage lemma, it can approximate the unknown function with time delay only by entering a limited number of previous states. Moreover, by the initialization technique, arbitrary small L ∞ tracking error is achieved. Finally, the control signal is quantized by the quantizer and then transmitted to the communication cable, so that the Multi-machine excitation system is realized by computer. A group of experimental results show the effectiveness of the proposed control protocol.

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Section: Introductionmentioning

confidence: 99%

K-Filter Observer Based Adaptive Quantized Decentralized Excitation Control for Multi-Machine Power Systems With the Line Transmission Delays

Wang

et al. 2021

IEEE Access

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“…In [11], stability sensitive parameters are brought into the multi-machine power system model and a robust adaptive backstepping excitation controller that can which overcome the over-parameterization problem of stability sensitive parameters was design. In [12], by introducing the sliding mode surface into the dynamic surface controller, the robustness and anti-interference ability of the system are improved. Fuzzy logic systems (FLSs) and neural networks (NNs) are usually used to approximate the system's uncertainties because of their good approximation capabil- VOLUME 4, 2016 ities [13]- [21].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Dynamic Surface Control for Multi-Machine Power System Based on Composite Learning Method and Disturbance Observer

et al. 2020

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A composite learning dynamic surface control is proposed for a class of multi-machine power systems with uncertainties and external disturbances by using fuzzy logic systems (FLSs) and disturbance observer (DOB). The main characteristics of the proposed strategy are as follows: (1) The approximation ability of FLSs for nonlinear model of multi-machine power systems is enhanced considerably by using the composite learning method and providing additional correction information for the FLSs. These findings differ considerably from previous designs that focus directly on the system's tracking performance. (2) The filtering errors caused by the utilizations of the first-order low-pass filters in dynamic surface control (DSC) are compensated effectively by designing the compensating signals in the control law design process. (3) The compound disturbances including the FLSs' approximation error and external disturbances are estimated and mitigated by constructing DOB. Finally, the proposed control algorithm is verified on the StarSim Hardware-in-loop experimental platform, and the experimental results validate the effectiveness of the proposed control strategy in suppressing disturbances and enhancing the robustness of the controller.

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“…Adaptive fuzzy systems are used to approximate the uncertainties of the system in some researches. [24][25][26] In addition, it is worth noting that the neural network has strong nonlinear function approximation ability and has been widely used in control system design. [27][28][29][30][31] Radial basis function (RBF) neural network was always used to approximate the nonlinear functions in robot system dynamics brought by the unknown environment.…”

Section: Introductionmentioning

confidence: 99%

“…As far as we know, however, uncertainties related to system status can usually be represented by more complex nonlinear functions. Adaptive fuzzy systems are used to approximate the uncertainties of the system in some researches 24‐26 . In addition, it is worth noting that the neural network has strong nonlinear function approximation ability and has been widely used in control system design 27‐31 .…”

Section: Introductionmentioning

confidence: 99%

Modeling and distributed adaptive fault‐tolerant vibration control for bridge beam with single‐parameter adaptive neural network

Gao

Yang

Liu

2020

Adaptive Control & Signal

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Modeling and vibration control of a bridge beam system are considered in this article. The beam bridge with both ends fixed can be regarded as an Euler-Bernoulli beam, which is a typical distributed parameter system. First, the partial differential equations (PDE) model of the bridge was established according to the Hamilton principle. Then, a reasonable distributed control law was designed on the PDE model to eliminate the elastic deformation and suppress the vibration of the bridge. At the same time, uncertainties related to system status were considered during the design of the closed-loop system. In addition, the possible actuator and sensor faults in the control system were analyzed. Single-parameter adaptive neural networks were used to estimate the effects of coupling terms for uncertainties and faults. The parameter estimation adaptive law was designed to replace the adjustment of neural network weights, which simplifies the algorithm and facilitates practical engineering applications. Finally, the feasibility of the control system was verified by simulation.

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Adaptive fuzzy dynamic surface sliding mode control of large-scale power systems with prescribe output tracking performance

Cited by 35 publications

References 43 publications

K-Filter Observer Based Adaptive Quantized Decentralized Excitation Control for Multi-Machine Power Systems With the Line Transmission Delays

K-Filter Observer Based Adaptive Quantized Decentralized Excitation Control for Multi-Machine Power Systems With the Line Transmission Delays

Adaptive Fuzzy Dynamic Surface Control for Multi-Machine Power System Based on Composite Learning Method and Disturbance Observer

Modeling and distributed adaptive fault‐tolerant vibration control for bridge beam with single‐parameter adaptive neural network

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