In measurement-based approaches, generators coherency can be determined on the basis of excited modes, which are obtained in the form of a vector of complex values. In order to find coherent generators in frequency domain, which is suitable for applications such as wide-area control of power systems, these complex vectors should be clustered on the basis of their similarities. To do that, in this paper a new density-based unsupervised learning approach is proposed for clustering complex vectors, which is crucial for finding coherent generators in online applications. The proposed coherency evaluation approach is very simple and more practical according to the reality of power systems since (i) eliminating the complexities in previous studies, it evaluates the coherency from the excited modes point of view using the complex correlation of frequency spectrums, (ii) it uses a new density-based learning approach with only one parameter setting making it more suitable for clustering generators. These features have been demonstrated on the well-known 16-machine, 68-bus system.
Rezaeian Koochi, M. H., & Hemmatpour, M. H. (2024). Density-based Unsupervised Learning Approach for Generators Coherency Evaluation in Complex Domain. Scientia Iranica, (), -. doi: 10.24200/sci.2024.62573.7915
MLA
Mohammad Hossein Rezaeian Koochi; Mohammad Hasan Hemmatpour. "Density-based Unsupervised Learning Approach for Generators Coherency Evaluation in Complex Domain". Scientia Iranica, , , 2024, -. doi: 10.24200/sci.2024.62573.7915
HARVARD
Rezaeian Koochi, M. H., Hemmatpour, M. H. (2024). 'Density-based Unsupervised Learning Approach for Generators Coherency Evaluation in Complex Domain', Scientia Iranica, (), pp. -. doi: 10.24200/sci.2024.62573.7915
VANCOUVER
Rezaeian Koochi, M. H., Hemmatpour, M. H. Density-based Unsupervised Learning Approach for Generators Coherency Evaluation in Complex Domain. Scientia Iranica, 2024; (): -. doi: 10.24200/sci.2024.62573.7915
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