Electromechanical Mode Online Estimation Using Regularized Robust RLS Methods

By Ning Zhou, D. Trudnowski, John W. Pierre, W. Mittelstadt. IEEE Transactions on Power Systems, 2008

243 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computational Mechanics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/7c6f0d2fa30755794222e2cd7a14cf72046f0bc4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Jimmy Chih-Hsien Peng
2. Nirmal-Kumar C. Nair
3. Luke Dosiek
4. John W. Pierre

In Computational Mechanics

1. Jimmy Chih-Hsien Peng
2. Nirmal-Kumar C. Nair
3. Luke Dosiek
4. John W. Pierre

In Electrical and Electronic Engineering

1. Jimmy Chih-Hsien Peng
2. Nirmal-Kumar C. Nair
3. Luke Dosiek
4. John W. Pierre
5. Subaran Singh
6. Praveen Tripathy
7. Suresh Chandra Srivastava

Citations in the corpus

• A Modified TLS-ESPRIT-Based Method for Low-Frequency Mode Identification in Power Systems Utilizing Synchrophasor Measurements (2011, methodology): “Reference [17] has used the regularized robust recursive least square (RLS) method.”
• Enhancing Kalman Filter for Tracking Ringdown Electromechanical Oscillations (2012, methodology): “According to [6], Prony analysis is regarded as a block-processing technique while Kalman filter is a recursive tracking method.”
• Estimating electromechanical modes and mode shapes using the multichannel ARMAX model (2013, methodology): “If the system is well-studied, one may conduct several Monte Carlo trials on a highly detailed system model to determine which model orders provide estimates with the smallest error as in [19].”