Application of Regression Analysis in Novel Power System Stabilizer Design

Abstract

The paper presents the methodology for de-signing knowledge based power system stabiliz-ers (PSSs). The proposed methodology relies on extensive nonlinear digital or analog simulation to detect correlation patterns between input damping states and the PSS output signal. Use of linear and nonlinear least square regression analysis results in a linear and nonlinear state feedback structure that is easier to implement and effective in damping either local or inter-area electromechanical modes of oscillation. The PSS design technique is a two-step process: firstly, to identify the most effective damping signals for feedback via fast Fourier spectra analysis and SISO transfer function identifica-tion, secondly, to search for the most effective linear]nonlinear regression law that ensures the damping effectiveness. The regression law is based on an ideal PSS analog 'model ' with excellent damping performance with the objec-tive of emulating, or duplicating, such perfor-mance over a given time period (3-5s). PSS designs are compared using a specified state variation weighting function J with the objec-tives of minimizing the machine speed and ac-celeration power, as well as the active and reactive power deviations. This is done itera-tively for each design simulation run and the best regression law is adopted