| Title: | Quantifying power system frequency quality and extracting typical patterns within short time scales below one hour |
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| Authors: | ID Mohammadi, Younes (Author)
ID Polajžer, Boštjan (Author)
ID Chouhy Leborgne, Roberto (Author)
ID Khodadad, Davood (Author) |
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| Files: |  1-s2.0-S2352467724000882-main.pdf (12,67 MB)
MD5: 11CAE3F1A106F4A25F4F67B39724E5AF
 https://www.sciencedirect.com/science/article/pii/S2352467724000882?via%3Dihub
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | This paper addresses the lack of consideration of short time scales, below one hour, such as sub-15-min and sub1-hr, in grid codes for frequency quality analysis. These time scales are becoming increasingly important due to
the flexible market-based operation of power systems as well as the rising penetration of renewable energy
sources and battery energy storage systems. For this, firstly, a set of frequency-quality indices is considered,
complementing established statistical indices commonly used in power-quality standards. These indices provide
valuable insights for quantifying variations, events, fluctuations, and outliers specific to the discussed time
scales. Among all the implemented indices, the proposed indices are based on over/under frequency events (6
indices), fast frequency rise/drop events (6 indices), and summation of positive and negative peaks (1 index), of
which the 5 with the lowest thresholds are identified as the most dominant. Secondly, k-means and k-medoids
clustering methods in a learning scheme are employed to identify typical patterns within the discussed time
windows, in which the number of clusters is determined based on prior knowledge linked to reality. In order to
clarify the frequency variations and patterns, three frequency case studies are analyzed: case 1 (sub-15-min scale,
10-s values, 6 months), case 2 (sub-1-hr scale, 10-s values, 6 months), and case 3 (sub-1-hr, 3-min values, the
year 2021). Results obtained from the indices and learning methods demonstrate a full picture of the information
within the windows. The maximum value of the highest frequency value minus the lowest one over the windows
is about 0.35 Hz for cases 1 and 2 and 0.25 Hz for case 3. Over-frequency values (with a typical 0.1% threshold)
slightly dominates under-frequency values in cases 1 and 2, while the opposite is observed in case 3. Medium
fluctuations occur in 35% of windows for cases 1 and 2 and 41% for case 3. Outlier values are detected using the
quartile method in 70% of windows for case 2, surpassing the other two cases. About six or seven typical patterns
are also extracted using the presented learning scheme, revealing the frequency trends within the short time
windows. The proposed approaches offer a simpler alternative than tracking frequency single values and also
capture more comprehensive information than existing approaches that analyze the aggregated frequency values
at the end of the specific time windows without considering the frequency trends. In this way, the network
operators have the possibility to monitor the frequency quality and trends within short time scales using the most
dominant indices and typical patterns. |
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| Keywords: | quantifying power system frequency quality, statistical indices, pattern extracting, machine learning, short time scales, renewable energy sources |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 09.06.2023 |
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| Article acceptance date: | 21.03.2024 |
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| Publication date: | 26.03.2024 |
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| Publisher: | Elsevier |
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| Year of publishing: | 2024 |
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| Number of pages: | 24 str. |
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| Numbering: | Vol. 38, [article no.] 101359 |
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| PID: | 20.500.12556/DKUM-90150  |
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| UDC: | 621.31 |
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| ISSN on article: | 2352-4677 |
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| COBISS.SI-ID: | 191174915 |
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| DOI: | 10.1016/j.segan.2024.101359 |
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| Copyright: | © 2024 The Author(s) |
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| Publication date in DKUM: | 23.08.2024 |
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| Views: | 180 |
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| Downloads: | 52 |
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| Metadata: |    |
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| Categories: | Misc.
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