Direct Coherency Identification of Synchronous Generators in Taiwan Power System Based on Fuzzy c-Means Clustering

Wang, Shu-Chen; Huang, Pei-Hwa; Wu, Chi‐Jui; Chuang, Yung-Sung

doi:10.1093/ietfec/e90-a.10.2223

Cited by 6 publications

(12 citation statements)

References 17 publications

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“…Fig. 8 shows that, generators 10, 11, 12, and 14 have similar rotor angle responses so they are in the same coherent group, i.e., cluster 6 (10,11,12,14) in the system following the disturbance in the line 70-46.…”

Section: A Cluster Formation Example On the Test Systemmentioning

confidence: 97%

“…In this paper, a time domain coherency measure, C mn , which is derived from swing curves, is used to evaluate the coherency behavior of system generators [12].Considering a system of N generators, the coherency measure between generator m and generator n is given by Equation (1).…”

Section: A Coherency Measurementioning

confidence: 99%

“…Hence, according to the validity measure, the identified optimal number of clusters is equal to seven, which gives the minimum XB index value of (0.0486). From Table 1, the clustering result reveals that generator (6,7,8) and (10,11,12,14) can be combined to become an equivalent one. Generators (3,5) and (9,13) are another cluster and generator 2, 4 and 15 are alone in the other clusters.…”

Section: A Cluster Formation Example On the Test Systemmentioning

confidence: 99%

“…Table IV shows the coherent group of generators for the obtained optimal number of clusters. The clustering result reveals that generator (10,11,12,14,15) and (6,7,8) can be combined to become an equivalent one. Generators (3,5) and (9,13) are another cluster and generator 2 and 4 are alone in the other clusters.…”

Section: B Effect Of Fault Locationsmentioning

confidence: 99%

“…A technique based on wide-area signal measurements using hierarchical clustering algorithm is developed in [11]. An application of fuzzy c-means clustering to the direct identification of the coherent synchronous generators is shown in [12].…”

Section: Introductionmentioning

confidence: 99%

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Identification of coherent synchronous generators in a Multi-Machine Power System using Support Vector Clustering

Agrawal

Thukaram

2011

2011 International Conference on Power and Energy Systems

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This paper illustrates the application of a new technique, based on Support Vector Clustering (SVC) for the direct identification of coherent synchronous generators in a large interconnected Multi-Machine Power Systems. The clustering is based on coherency measures, obtained from the time domain responses of the generators following system disturbances. The proposed clustering algorithm could be integrated into a widearea measurement system that enables fast identification of coherent clusters of generators for the construction of dynamic equivalent models. An application of the proposed method is demonstrated on a practical 15 generators 72-bus system, an equivalent of Indian Southern grid in an attempt to show the effectiveness of this clustering approach. The effects of short circuit fault locations on coherency are also investigated.

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Section: A Cluster Formation Example On the Test Systemmentioning

confidence: 97%

Section: A Coherency Measurementioning

confidence: 99%