Open AccessSupport vector clustering‐based direct coherency identification of generators in a multi‐machine power system
Author(s) -
Agrawal Rimjhim,
Thukaram Dhadbanjan
Publication year - 2013
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2012.0681
Subject(s) - cluster analysis , generator (circuit theory) , computer science , identification (biology) , electric power system , measure (data warehouse) , data mining , set (abstract data type) , permanent magnet synchronous generator , fault (geology) , power (physics) , algorithm , artificial intelligence , physics , botany , quantum mechanics , seismology , biology , programming language , geology
This study investigates the application of support vector clustering (SVC) for the direct identification of coherent synchronous generators in large interconnected multi‐machine power systems. The clustering is based on coherency measure, which indicates the degree of coherency between any pair of generators. The proposed SVC algorithm processes the coherency measure matrix that is formulated using the generator rotor measurements to cluster the coherent generators. The proposed approach is demonstrated on IEEE 10 generator 39‐bus system and an equivalent 35 generators, 246‐bus system of practical Indian southern grid. The effect of number of data samples and fault locations are also examined for determining the accuracy of the proposed approach. An extended comparison with other clustering techniques is also included, to show the effectiveness of the proposed approach in grouping the data into coherent groups of generators. This effectiveness of the coherent clusters obtained with the proposed approach is compared in terms of a set of clustering validity indicators and in terms of statistical assessment that is based on the coherency degree of a generator pair.