Decentralized robust adaptive neural dynamic surface control for multi‐machine excitation systems with static var compensator

Zhang, Xiuyu; Wang, Shuran; Zhu, Guoqiang; Ma, Jia; Li, Xiaoming; Chen, Xinkai

doi:10.1002/acs.2953

Cited by 19 publications

(9 citation statements)

References 50 publications

(130 reference statements)

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“…with gain c B > 0. It is noticed that in (19), the time delay inserted from the z 3 measurement and feedback denoted by τ(t) is taken into account, and the following delayed ODE is obtained:…”

Section: Control Design Based On Backstepping and Feedback Linearizat...mentioning

confidence: 99%

“…τ(t) ≤ η for some τ, τ, η > 0. The delayed term in (19) is the result of the fact that the variable z 3 (t) does not involve local SVC measurements, but uses distant, and thus delayed, measurements from the generator. Thus, at time t, the z 3 (t − τ(t)) is available for use in the SVC controller.…”

Section: Control Design Based On Backstepping and Feedback Linearizat...mentioning

confidence: 99%

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Coordinated Excitation and Static Var Compensator Control with Delayed Feedback Measurements in SGIB Power Systems

Psillakis

Alexandridis

2020

Energies

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In this paper, we present a nonlinear coordinated excitation and static var compensator (SVC) control for regulating the output voltage and improving the transient stability of a synchronous generator infinite bus (SGIB) power system. In the first stage, advanced nonlinear methods are applied to regulate the SVC susceptance in a manner that can potentially improve the overall transient performance and stability. However, as distant from the generator measurements are needed, time delays are expected in the control loop. This fact substantially complicates the whole design. Therefore, a novel design is proposed that uses backstepping methodologies and feedback linearization techniques suitably modified to take into account the delayed measurement feedback laws in order to implement both the excitation voltage and the SVC compensator input. A detailed and rigorous Lyapunov stability analysis reveals that if the time delays do not exceed some specific limits, then all closed-loop signals remain bounded and the frequency deviations are effectively regulated to approach zero. Applying this control scheme, output voltage changes occur after the large power angle deviations have been eliminated. The scheme is thus completed, in a second stage, by a soft-switching mechanism employed on a classical proportional integral (PI) PI voltage controller acting on the excitation loop when the frequency deviations tend to zero in order to smoothly recover the output voltage level at its nominal value. Detailed simulation studies verify the effectiveness of the proposed design approach.

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Section: Control Design Based On Backstepping and Feedback Linearizat...mentioning

confidence: 99%

Section: Control Design Based On Backstepping and Feedback Linearizat...mentioning

confidence: 99%

Coordinated Excitation and Static Var Compensator Control with Delayed Feedback Measurements in SGIB Power Systems

Psillakis

Alexandridis

2020

Energies

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“…and j = 1, ..., n. Then combine (14) and Lemma 1, the form of neural network approximation unknown function with delay can be expressed as:…”

Section: Neural Network With Gaussian Basis Functionmentioning

confidence: 99%

“…Compared with the backstepping method, the dynamic surface technology makes the final control law more simple and avoids the calculation of higher-order derivatives. In [14]- [16], dynamic surface technology was elaborated and applied to Multi-machine excitation power system combining with neural network or fuzzy logic system. By combining with the dynamic surface technique and the initialization technique, the L ∞ tracking performance of Multi-machine excitation power system can be achieved.…”

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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“…The structure of nonlinearity, appointed by equation (5), has been used in the control problem of interconnected systems to describe nonlinearities in several physical processes as multimachine power systems (Guo et al , 2000), (Zecevic and Siljak, 2008), (Zhang et al , 2019). It is worth pointing out that in this paper we do not set certain values for the interconnection bounds but we look for their maximization so that the control structure may be available for a larger nonlinear coverage.…”

Section: Problem Statementmentioning

confidence: 99%

Dynamic output feedback control for nonlinear large-scale interconnected systems

Touti

Tlili

Almutiry

2020

COMPEL

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Purpose This paper aims to focus on the design of a decentralized observation and control method for a class of large-scale systems characterized by nonlinear interconnected functions that are assumed to be uncertain but quadratically bounded. Design/methodology/approach Sufficient conditions, under which the designed control scheme can achieve the asymptotic stabilization of the augmented system, are developed within the Lyapunov theory in the framework of linear matrix inequalities (LMIs). Findings The derived LMIs are formulated under the form of an optimization problem whose resolution allows the concurrent computation of the decentralized control and observation gains and the maximization of the nonlinearity coverage tolerated by the system without becoming unstable. The reliable performances of the designed control scheme, compared to a distinguished decentralized guaranteed cost control strategy issued from the literature, are demonstrated by numerical simulations on an extensive application of a three-generator infinite bus power system. Originality/value The developed optimization problem subject to LMI constraints is efficiently solved by a one-step procedure to analyze the asymptotic stability and to synthesize all the control and observation parameters. Therefore, such a procedure enables to cope with the conservatism and suboptimal solutions procreated by optimization problems based on iterative algorithms with multi-step procedures usually used in the problem of dynamic output feedback decentralized control of nonlinear interconnected systems.

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Decentralized robust adaptive neural dynamic surface control for multi‐machine excitation systems with static var compensator

Cited by 19 publications

References 50 publications

Coordinated Excitation and Static Var Compensator Control with Delayed Feedback Measurements in SGIB Power Systems

Coordinated Excitation and Static Var Compensator Control with Delayed Feedback Measurements in SGIB Power Systems

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

Dynamic output feedback control for nonlinear large-scale interconnected systems

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