Enabling grid stability: harnessing μPMU data for data-driven analysis of grid frequency events
The surge in Distributed Energy Resources connected to the power grid has significantly heightened challenges for grid operators. While the integration of Micro-synchrophasor Unit (μPMU) into the power grid infrastructure has facilitated the acquisition of high-resolution data, analyzing such vast d...
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://repository.londonmet.ac.uk/9639/1/paper_045.pdf |
| _version_ | 1824446483252903936 |
|---|---|
| author | Singh, Uttam Dey, Maitreyee Patel, Preeti |
| author_facet | Singh, Uttam Dey, Maitreyee Patel, Preeti |
| author_sort | Singh, Uttam |
| collection | LMU |
| description | The surge in Distributed Energy Resources connected to the power grid has significantly heightened challenges for grid operators. While the integration of Micro-synchrophasor Unit (μPMU) into the power grid infrastructure has facilitated the acquisition of high-resolution data, analyzing such vast datasets presents its own set of challenges. This paper offers insights into fifty days of μPMU data through data analysis techniques focusing on frequency events within the operational and hazardous ranges. Drawing from fifty days of real-world data collected from grid-connected solar farm in England, this paper examines and analyzes multiple frequency events while enhancing comprehension of their diverse patterns. Additionally, this study has explored the Isolation Forest Model for anomalous event detection, with results validated against power quality data. |
| first_indexed | 2025-02-19T01:15:52Z |
| format | Conference or Workshop Item |
| id | oai:repository.londonmet.ac.uk:9639 |
| institution | London Metropolitan University |
| language | English |
| last_indexed | 2025-02-19T01:15:52Z |
| publishDate | 2024 |
| record_format | eprints |
| spelling | oai:repository.londonmet.ac.uk:96392025-01-08T09:31:39Z https://repository.londonmet.ac.uk/9639/ Enabling grid stability: harnessing μPMU data for data-driven analysis of grid frequency events Singh, Uttam Dey, Maitreyee Patel, Preeti 000 Computer science, information & general works 620 Engineering & allied operations The surge in Distributed Energy Resources connected to the power grid has significantly heightened challenges for grid operators. While the integration of Micro-synchrophasor Unit (μPMU) into the power grid infrastructure has facilitated the acquisition of high-resolution data, analyzing such vast datasets presents its own set of challenges. This paper offers insights into fifty days of μPMU data through data analysis techniques focusing on frequency events within the operational and hazardous ranges. Drawing from fifty days of real-world data collected from grid-connected solar farm in England, this paper examines and analyzes multiple frequency events while enhancing comprehension of their diverse patterns. Additionally, this study has explored the Isolation Forest Model for anomalous event detection, with results validated against power quality data. 2024-06-06 Conference or Workshop Item PeerReviewed text en https://repository.londonmet.ac.uk/9639/1/paper_045.pdf Singh, Uttam, Dey, Maitreyee and Patel, Preeti (2024) Enabling grid stability: harnessing μPMU data for data-driven analysis of grid frequency events. In: 12th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA-2024), 6-7 June 2024, London Metropolitan University, London (UK) / Online. (In Press) |
| spellingShingle | 000 Computer science, information & general works 620 Engineering & allied operations Singh, Uttam Dey, Maitreyee Patel, Preeti Enabling grid stability: harnessing μPMU data for data-driven analysis of grid frequency events |
| title | Enabling grid stability: harnessing μPMU data for data-driven analysis of grid frequency events |
| title_full | Enabling grid stability: harnessing μPMU data for data-driven analysis of grid frequency events |
| title_fullStr | Enabling grid stability: harnessing μPMU data for data-driven analysis of grid frequency events |
| title_full_unstemmed | Enabling grid stability: harnessing μPMU data for data-driven analysis of grid frequency events |
| title_short | Enabling grid stability: harnessing μPMU data for data-driven analysis of grid frequency events |
| title_sort | enabling grid stability harnessing μpmu data for data driven analysis of grid frequency events |
| topic | 000 Computer science, information & general works 620 Engineering & allied operations |
| url | https://repository.londonmet.ac.uk/9639/1/paper_045.pdf |
| work_keys_str_mv | AT singhuttam enablinggridstabilityharnessingmpmudatafordatadrivenanalysisofgridfrequencyevents AT deymaitreyee enablinggridstabilityharnessingmpmudatafordatadrivenanalysisofgridfrequencyevents AT patelpreeti enablinggridstabilityharnessingmpmudatafordatadrivenanalysisofgridfrequencyevents |