Robust Decentralized Turbine/Governor Control Using Linear Matrix Inequalities

Siljak,; Stipanovic,; Zecevic,

doi:10.1109/mper.2002.4312399

Cited by 21 publications

(30 citation statements)

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Section: Introductionmentioning

confidence: 99%

“…The presented research here is motivated by the work of Siljak et al [14] on robust decentralized control of power systems. By proving that the nonlinearity in the considered model is quadratically bounded, decentralized control framework considering nonlinear model of power systems are developed in [14]. The ideas from [14] are then extended by Lian et al [15] with applications to enhancement of damping ratios of the inter-area oscillation through decentralized robust control [15].…”

Section: Introductionmentioning

confidence: 99%

“…By proving that the nonlinearity in the considered model is quadratically bounded, decentralized control framework considering nonlinear model of power systems are developed in [14]. The ideas from [14] are then extended by Lian et al [15] with applications to enhancement of damping ratios of the inter-area oscillation through decentralized robust control [15]. As for DSE methods, our recent work [3] assumes that a higher-order, multi-machine power system model is one-sided Lipschitz and quadratically inner-bounded.…”

Section: Introductionmentioning

confidence: 99%

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Characterizing the Nonlinearity of Power System Generator Models

Nugroho

Taha

2019

2019 American Control Conference (ACC)

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Power system dynamics are naturally nonlinear. The nonlinearity stems from power flows, generator dynamics, and electromagnetic transients. Characterizing the nonlinearity of the dynamical power system model is useful for designing superior estimation and control methods, providing better situational awareness and system stability. In this paper, we consider the synchronous generator model with a phasor measurement unit (PMU) that is installed at the terminal bus of the generator. The corresponding nonlinear process-measurement model is shown to be locally Lipschitz, i.e., the dynamics are limited in how fast they can evolve in an arbitrary compact region of the statespace. We then investigate different methods to compute Lipschitz constants for this model, which is vital for performing dynamic state estimation (DSE) or state-feedback control using Lyapunov theory. In particular, we compare a derived analytical bound with numerical methods based on low discrepancy sampling algorithms. Applications of the computed bounds to dynamic state estimation are showcased. The paper is concluded with numerical tests.Index Terms-Synchronous generator, dynamic state estimation, phasor measurement units, Lipschitz nonlinearity, Lipschitz-based observer, low discrepancy sequence.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characterizing the Nonlinearity of Power System Generator Models

Nugroho

Taha

2019

2019 American Control Conference (ACC)

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“…In recent years, the transient stability problem of power systems has attracted much attention [1][2][3][4][5][6]. Generally, power system stability control methods are divided into two categories: active power control [7,8] and reactive power control [1,9]. Active power control is used to balance the power across the transmission lines and suppress power oscillation.…”

Section: Introductionmentioning

confidence: 99%

Multi‐Agent Method Based H∞ Supplementary Turbine/Governor Control to Enhance Power System Stability

Meng

Xiao

Zhang

et al. 2019

IEEJ Transactions Elec Engng

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The stability of a power system has always been of concern. Now, wide-area measurement systems are used widely. In this paper, we focus on supplementary turbine/governor control to enhance the stability of a power system with wide-area measurement information. An H∞ supplementary turbine/governor controller based on the multi-agent method is introduced to enhance power system stability. First, the synchronous generator is modeled as a second-order agent. The information of communication topology and power network topology are used to build the system model and the controller design. Then, a linear output feedback controller is adopted. With a suitable Lyapunov function and by solving the linear matrix inequality, the parameters of the controller are tuned by the H∞ synthesis method. Finally, numerical simulations are presented on a Western Systems Coordinating Council three-generator nine-bus power system, validating the proposed design.

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“…For instance, based on linear matrix inequalities (LMI), Siljak and Stipanovic [14,15] have discussed robust stabilization for interconnected systems in continuous-time and discrete-time aspects. Besides this, they and their coworker [16] further applied the technique of robust decentralized stabilization for interconnected systems to turbine/governor control. However, a literature search indicates that the effect of modeling errors for nonlinear multiple time-delay interconnected systems with disturbances has not been discussed yet.…”

Section: Introductionmentioning

confidence: 99%

DECENTRALIZED STABILIZATION OF H FUZZY CONTROL FOR NONLINEAR MULTIPLE TIME‐DELAY INTERCONNECTED SYSTEMS

Hsiao

Lin

et al. 2007

Asian Journal of Control

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A robustness design of fuzzy control is proposed in this paper to overcome the effect of modeling errors between nonlinear multiple time‐delay systems and fuzzy models. In terms of Lyapunov's direct method, a stability criterion is derived to guarantee the UUB (uniformly ultimately bounded) stability of nonlinear multiple time‐delay interconnected systems with disturbances. Based on this criterion and the decentralized control scheme, a set of fuzzy controllers is then synthesized via the technique of parallel distributed compensation (PDC) to stabilize the nonlinear multiple time‐delay interconnected systems and the Hcontrol performance is achieved in the mean time.

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Robust Decentralized Turbine/Governor Control Using Linear Matrix Inequalities

Cited by 21 publications

References 0 publications

Characterizing the Nonlinearity of Power System Generator Models

Characterizing the Nonlinearity of Power System Generator Models

Multi‐Agent Method Based H∞ Supplementary Turbine/Governor Control to Enhance Power System Stability

DECENTRALIZED STABILIZATION OF H FUZZY CONTROL FOR NONLINEAR MULTIPLE TIME‐DELAY INTERCONNECTED SYSTEMS

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