Abstract
Identification of coherent generators (CGs) is necessary for the area-based monitoring and protection system of a wide area power system. Synchrophasor has enabled smarter monitoring and control measures to be devised; hence, measurement-based methodologies can be implemented in online applications to identify the CGs. This paper presents a new framework for coherency identification that is based on the dynamic coupling of generators. A distance matrix that contains the dissimilarity indices between any pair of generators is constructed from the pairwise dynamic coupling of generators after the post-disturbance data are obtained by phasor measurement units (PMUs). The dataset is embedded in Euclidean space to produce a new dataset with a metric distance between the points, and then the support vector clustering (SVC) technique is applied to the embedded dataset to identify the final clusters of generators. Unlike other clustering methods that need a priori knowledge about the number of clusters or the parameters of clustering, this information is set in an automatic search procedure that results in the optimal number of clusters. The algorithm is verified by time-domain simulations of defined scenarios in 39 bus and 118 bus test systems. Finally, the clustering result of 39 bus systems is validated by cluster validity measures, and a comparative study investigates the efficacy of the proposed algorithm to cluster the generators with an optimal number of clusters and also its computational efficiency compared with other clustering methods.
Highlights
Despite all the efforts have been devoted to group the generators in certain coherent groups according to their similar oscillatory behavior after a disturbance, the traditional offline coherency studies are not able to fully demonstrate the dynamic behavior of power systems [1]
This study proposes the dynamic coupling between the generators as the coherency measure along with applying support vector clustering (SVC) technique on embedded data points to determine the coherent generators (CGs)
ON SUPPORT VECTOR CLUSTERING SVC technique was inspired by the concept of Support Vector Machine (SVM) that is generally used for classification of data points [44]
Summary
Despite all the efforts have been devoted to group the generators in certain coherent groups according to their similar oscillatory behavior after a disturbance, the traditional offline coherency studies are not able to fully demonstrate the dynamic behavior of power systems [1]. This study proposes the dynamic coupling between the generators as the coherency measure along with applying SVC technique on embedded data points to determine the CGs. All referred clustering-based methods are dependent on a priori knowledge about the number of clusters [39] or have some shortcomings such as recursive separation as seen in [11] and [40]. All referred clustering-based methods are dependent on a priori knowledge about the number of clusters [39] or have some shortcomings such as recursive separation as seen in [11] and [40] To overcome these limitations, an online coherency identification method is introduced in this study that results in the formation of CGs with more reliable coherency measure and is independent of a predefined number of clusters and can automatically set the parameters of the clustering procedure such that the optimal clustering structure is obtained.
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