A robust adaptive LQG/LTR TCSC controller applied to damp power system oscillations

Ferreira, André M.D.; Barreiros, José Augusto Lima; Barra, Walter; Brito-de-Souza, Jorge Roberto

doi:10.1016/j.epsr.2006.08.012

Cited by 31 publications

(18 citation statements)

References 13 publications

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“…Electromechanical oscillations are undesirable phenomena in electrical power systems because they can severely limit the power transfer between interconnected generating areas, due to reduced stability margins, as well as may decrease lifetime expectancy of system machines [1,2]. If these electromechanical oscillations are not satisfactorily damped, they may, under some operating conditions, even increase (in amplitude), causing the tripping of one or more interconnected generating units.…”

Section: Introductionmentioning

confidence: 98%

Development and field tests of a damping controller to mitigate electromechanical oscillations on large diesel generating units

Nogueira

Barreiros

Barra

et al. 2011

Electric Power Systems Research

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Section: Introductionmentioning

confidence: 98%

Development and field tests of a damping controller to mitigate electromechanical oscillations on large diesel generating units

Nogueira

Barreiros

Barra

et al. 2011

Electric Power Systems Research

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“…In dynamic application of the TCSC, various control techniques have been proposed for damping power oscillations to improve the system dynamic response [10][11][12][13][14][15][16][17][18][19]. Some evolutionary algorithms (EA) have been used optimal tuning of the TCSC based damping controller in order to enhance the damping of the power system low frequency oscillations in Ref.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome this issue, many robust control theories have been proposed to improve the robustness of the TCSC based damping controller under variations of operation conditions and model uncertainties in Ref. [12][13][14][15][16]. However, most of them have high-order controller as high as the open-loop plant which are not preferred in the industry practice.…”

Section: Introductionmentioning

confidence: 99%

TCSC Nonlinear Adaptive Damping Controller Design Based on RBF Neural Network to Enhance Power System Stability

Yao¹,

Zhao²,

Liu³

et al. 2013

Journal of Electrical Engineering and Technology

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-In this paper, a nonlinear adaptive damping controller based on radial basis function neural network (RBFNN), which can infinitely approximate to nonlinear system, is proposed for thyristor controlled series capacitor (TCSC). The proposed TCSC adaptive damping controller can not only have the characteristics of the conventional PID, but adjust the parameters of PID controller online using identified Jacobian information from RBFNN. Hence, it has strong adaptability to the variation of the system operating condition. The effectiveness of the proposed controller is tested on a two-machine five-bus power system and a four-machine two-area power system under different operating conditions in comparison with the lead-lag damping controller tuned by evolutionary algorithm (EA). Simulation results show that the proposed damping controller achieves good robust performance for damping the low frequency oscillations under different operating conditions and is superior to the lead-lag damping controller tuned by EA.

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“…Over the last four decades, various control approaches such as classical control 3–5, optimal control 8–10, digital control 4 etc., have been suggested to design PSSs for damping out oscillations in power systems. Most of these techniques suffer from performance degeneracy when the operating point or parameters of the system are changed 4–6, 10–13. In other words, a PSS has to perform well under these operating points and parameter variations.…”

Section: Introductionmentioning

confidence: 99%

Observer‐based model following discrete sliding mode PSS for single machine system

Yazıcı

Özdemir

2011

Int Trans Elec Energy Syst

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Power system stabilisers (PSSs) are added to excitation systems of a synchronous generator as a supplementary controller to improve the dynamic stability of a power system by damping the electromechanical oscillations occured in the system. Over the last four decades various approaches, such as phase lead-lag, Proportional-Integral-Derivative (PID), linear quadratic regulator, H 1, sliding mode controller (SMC) and etc., have been reported in the literature for the PSS design. The SMC has some advantages, such as parameter insensitivity and realisation simplicity, over the other approaches. To the best of our knowledge, observer-based model following discrete SMC (OMFDSMC) method has not been used in the PSS design. Therefore, this paper is the first study that used OMFDSMC method in the PSS design in the literature. Simulation results show the effectiveness and robustness of the proposed OMFDSMC-based PSS under normal load operations, operating point and parameters variation conditions. S 1 and S 2 in (13) should be chosen in such a way that (15) is stable. Note from (15) that, the problem of finding the design matrix S 12 is a classical state feedback problem. Therefore, again, one can design S 12 Figure 5. Dynamic responses of Dw for 20% change in K A and T d0 following a 5% change in DT m (a) CPSS, (b) LQR, (c) OMFDSMC, -nominal parameters, -20% changed parameters.30. Hui S, Zak SH. On discrete-time variable structure sliding mode control. Systems and Control Letters 1999; 38:283-288. 31. Ogata K. Discrete-Time Control Systems, Prentice Hall: New Jersey, USA, 1995; 377-633. 32. IEEE Std. 421.5 Recommended practice for excitation system models for power system stability studies, 2005. 33. Lee SS, Park JK. Design of reduced-order observer-based variable structure power system stabiliser for unmeasurable state variables.

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A robust adaptive LQG/LTR TCSC controller applied to damp power system oscillations

Cited by 31 publications

References 13 publications

Development and field tests of a damping controller to mitigate electromechanical oscillations on large diesel generating units

Development and field tests of a damping controller to mitigate electromechanical oscillations on large diesel generating units

TCSC Nonlinear Adaptive Damping Controller Design Based on RBF Neural Network to Enhance Power System Stability

Observer‐based model following discrete sliding mode PSS for single machine system

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