June 30, 2006
Conference Paper

A Bootstrap Method for Statistical Power System Mode Estimation and Probing Signal Selection

Abstract

Near real-time measurement-based electromechanical-mode estimation offers considerable potential for many future power-system operation and control strategies. Recent research has investigated the use of low-level Pseudo Random Noise (PRN) probing signals injected into power systems to estimate the low-frequency electromechanical modes. Because of the random na-ture of a power system, estimating the modes from a single prob-ing experiment is very difficult. Ideally, one would use a Monte-Carlo approach with multiple independent probing experiments resulting in a mode estimation distribution. Then, one could state that the mode is within a region of the complex plane. Unfortu-nately, conducting multiple probing experiments is prohibitive for most power-system applications. This paper presents a method-ology for estimating the mode distribution based on one probing test using a Bootstrap algorithm. The proposed method is applied to both simulation data and actual-system measurement data to illustrate its performance and application. It is demonstrated that the method can provide valuable information to PRN tests and guide future PRN probing signal design and selection.?

Revised: April 27, 2011 | Published: June 30, 2006

Citation

Zhou N., J.W. Pierre, and D. Trudnowski. 2006. A Bootstrap Method for Statistical Power System Mode Estimation and Probing Signal Selection. In 2006 IEEE Power Engineering Society General Meeting, 18-22 June 2006, 7 pages. Piscataway, New Jersey:Institute of Electrical and Electronics Engineers. PNNL-SA-48850.