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.
Bibliographical noteFunding Information:
Paper MSDAD-08-16, presented at the 2007 Industry Applications Society Annual Meeting, New Orleans, LA, September 23–27, and approved for publication in the IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS by the Industrial Automation and Control Committee of the IEEE Industry Applications Society. Manuscript submitted for review October 30, 2007 and released for publication April 23, 2008. Current version published January 21, 2009. This work was supported by the Seoul Research and Business Development (R&BD) program under Grant 10988.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Industrial and Manufacturing Engineering
- Electrical and Electronic Engineering