Optimal Tuning for Linear and Nonlinear Parameters of Power System Stabilizers in Hybrid System Modeling

被引:17
作者
Baek, Seung-Mook [1 ]
Park, Jung-Wook [1 ]
Hiskens, Ian A. [2 ]
机构
[1] Yonsei Univ, Sch Elect & Elect Engn, Seoul 120749, South Korea
[2] Univ Wisconsin, Madison, WI 53706 USA
关键词
Eigenvalue analysis; feedforward neural network (FFNN); Hessian matrix estimation; hybrid system; nonlinearities; parameter optimization; power system stabilizer (PSS); trajectory sensitivities;
D O I
10.1109/TIA.2008.2009478
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper focuses on the systematic optimal tuning of the power system stabilizer (PSS), which can improve the system damping performance immediately following a large disturbance. As the PSS consists of both linear parameters (such as the gain and time constant) and nonsmooth nonlinear parameters (such as saturation limits of the PSS), two methods are applied for the optimal tuning of all parameters. One is to use the optimization technique based on the Hessian matrix estimated by the feedforward neural network, which identifies the first-order derivatives obtained by the trajectory sensitivities, for the nonlinear parameters. Moreover, the other is to use the eigenvalue analysis for the linear parameters. The performances of parameters optimized by the proposed method are evaluated by the case studies based on time-domain simulation and real-time hardware implementation.
引用
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页码:87 / 97
页数:11
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