A Modified RLS Algorithm for Online Estimation of Low-Frequency Oscillations in Power Systems
Saved in:
| Title: | A Modified RLS Algorithm for Online Estimation of Low-Frequency Oscillations in Power Systems |
|---|---|
| Authors: | Beza, Mebtu Bihonegn, 1981, Bongiorno, Massimo, 1976 |
| Source: | IEEE Transactions on Power Systems. 31(3):1703-1714 |
| Subject Terms: | Estimation technique, low-frequency oscillation, low-pass filter (LPF), variable forgetting factor, recursive least square (RLS), power oscillation damping (POD) |
| Description: | A number of methods have been proposed and implemented to improve system damping against low-frequency electromechanical oscillations in the power system. Among these, flexible ac transmission systems (FACTS) can be used to provide power oscillation damping (POD) function to the power system. The design of the POD algorithms in these devices requires estimation of the power oscillation frequency components and this is mostly achieved through the use of various filter combinations. However, these filter-based solutions are characterized by low bandwidth to extract the required signal components accurately, and this limits the dynamic performance of the FACTS controllers. Moreover, the filters are designed for specific frequencies, and a change in the system would reduce the performance of the methods. Thus, there is a need for a better estimation algorithm with fast and selective estimation of the required signal that is robust against system parameter uncertainties. In this paper, this is achieved by the use of a recursive least square algorithm that uses variable forgetting factor and a frequency adaptation mechanism. The investigated method has fast estimation in transient conditions without compromising its selectivity in steady state. The effectiveness of the proposed method is proven through simulation as well as experimental verification. |
| Access URL: | https://research.chalmers.se/publication/218554 |
| Database: | SwePub |
Full Text Finder 
Nájsť tento článok vo Web of Science Be the first to leave a comment!