Coherency identification in power systems through principal component analysis
By K. Anaparthi, B. Chaudhuri, N. Thornhill, Bikash Chandra Pal. IEEE Transactions on Power Systems, 2005
188 citations recorded by the source corpus.
In this letter, a new technique to identify coherent generators in large interconnected power system using measurements of generator speed and bus angle data has been presented. This is based on the application of principal component analysis (PCA) to measurements obtained from simulation studies that represent examples of interarea events. The results of application of PCA separately to data sets of generator speeds and bus angles, respectively, are presented. The approach of PCA was able to highlight clusters of generators showing common features when compared with the conventional modal analysis technique.
Subfields: Control and Systems Engineering, Electrical and Electronic Engineering, Electronic, Optical and Magnetic Materials
View Paper: https://www.semanticscholar.org/paper/1f92a6b43c0d8942e4e68de9ae47f2e0ce2c7810
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
In Electrical and Electronic Engineering
In Electronic, Optical and Magnetic Materials
Citations in the corpus
- Generator Coherency Using the Hilbert–Huang Transform (2008, methodology): “Empirical evaluation of generator coherency from power system measurements has been implemented using fast Fourier transform (FFT)-based spectral techniques [15], [16] and principal components analysis technique [ 17 ].”
- Handbook of Big Data (2016, methodology): “[1] and Moore [12] is chosen to identify coherency groups, and the MATLAB clustering toolbox is used to aid the analysis.”