Seeking patterns in rms voltage variations at the sub-10-minute scale from multiple locations via unsupervised learning and patterns’ post-processing
2022 (English)In: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 143, article id 108516Article in journal (Refereed) Published
Abstract [en]
This paper addresses the issue of seeking sub-10-min patterns in fast rms voltage variations from time-limited measurement data at multiple locations worldwide. This is a rarely considered time scale in studies that could be important for the incorrect operation of end-user equipment. Moreover, measurements from multiple locations could be significant from the view of seeking pattern methods. To learn more about this time scale, we propose an unsupervised learning method that employs a Kernel Principal Component Analysis (KPCA) with a Cosine kernel to extract principal features from 10-min time series of voltage variations with a 1-s resolution followed by a k-means clustering to group the features. The scheme is applied to measurements from 57 low-voltage locations in 19 countries from 2009 to 2018. Fifteen initial clusters/patterns are then extracted and converted to ten new (general) patterns using a clusters’ merging strategy with highly similar patterns employed in a new post-processing approach useful for multiple locations. Utilizing data from multiple locations in multiple countries ensures a level of generality of the patterns. It also allows comparing the locations. Next to the ten general patterns, some typical patterns are extracted separately for every location. A statistical indices analysis confirms that a complete picture of sub-10-min oscillations needs both statistical indices (quantifying level and variations) and the proposed framework (quantifying patterns). The extracted patterns could be used as a reference for testing/putting requirements on the grid-connected equipment and quantifying the grid’s hosting capacity for different types of new distributed generations connected to the grid. The framework is scalable and computationally cheap, making it appropriate for seeking typical patterns in the big data domain. Applying the framework to the much less understood phenomenon will result in providing general knowledge in the field of power quality.
Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 143, article id 108516
Keywords [en]
Power quality, Fast voltage variations, Seeking patterns, Unsupervised learning, Kernel PCA (KPCA), Time series clustering, Post-processing
National Category
Computer Sciences Fluid Mechanics and Acoustics
Research subject
Electric Power Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-92413DOI: 10.1016/j.ijepes.2022.108516ISI: 000862963800005Scopus ID: 2-s2.0-85135358765OAI: oai:DiVA.org:ltu-92413DiVA, id: diva2:1686311
Funder
Swedish Energy Agency
Note
Validerad;2023;Nivå 2;2023-06-12 (marisr);
For correction, see: Younes Mohammadi, Seyed Mahdi Miraftabzadeh, Math H.J. Bollen, Michela Longo: Corrigendum to “Seeking patterns in rms voltage variations at the sub-10-minute scale from multiple locations via unsupervised learning and patterns’ post-processing” [Int. J. Electr. Power Energy Syst. 143 (2022) 108516]. International Journal of Electrical Power & Energy Systems, Available online 29 September 2022, Pages 108582. https://doi.org/10.1016/j.ijepes.2022.108582
2022-08-092022-08-092023-06-12Bibliographically approved