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Volume 16
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Article

Realistic μPMU Data Generation for Different Real-Time Events in an Unbalanced Distribution Network

by
Abdul Haleem Medattil Ibrahim
1,2,*,
Madhu Sharma
1 and
Vetrivel Subramaniam Rajkumar
2
1
Department of Electrical and Electronics Engineering, University of Petroleum and Energy Studies, Dehradun 248007, India
2
Department of Electrical Sustainable Energy, Delft University of Technology, 2628 CD Delft, The Netherlands
*
Author to whom correspondence should be addressed.
Energies 2023, 16(9), 3842; https://doi.org/10.3390/en16093842
Submission received: 22 March 2023 / Revised: 25 April 2023 / Accepted: 26 April 2023 / Published: 29 April 2023
(This article belongs to the Special Issue Modeling and Analysis of Active Distribution Networks and Smart Grids)
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Abstract

Monitoring, protection, and control processes are becoming more complex as distributed energy resources (DERs) penetrate distribution networks (DNs). This is due to the inherent nature of power DNs and the bi-directional flow of current from various sources to the loads. To improve the system’s situational awareness, the grid dynamics of the entire DER integration processes must be carefully monitored using synchronized high-resolution real-time measurement data from physical devices installed in the DN. μPMUs have been introduced into the DN to help with this. In comparison to traditional measurement devices, μPMUs can measure voltage, current, and their phasors, in addition to frequency and rate of frequency change (ROCOF). In this study, an approach to generating realistic event data for a real utility DN utilizing strategically installed μPMUs is proposed. The method employs an IEEE 34 test feeder with 12 μPMUs installed in strategic locations to generate real-time events-based realistic μPMU data for various situational awareness applications in an unbalanced DN. The node voltages and line currents were used to analyze the various no-fault and fault events. The author generated the data as part of his PhD research project, utilizing his real-time utility grid operation experience to be used for various situational awareness and fault location studies in a real unbalanced DN. The DN was modeled in DIgSILENT PowerFactory (DP) software. The generated realistic μPMU data can be utilized for developing data-driven algorithms for different event-detection, classification and section-identification research works.
Keywords: μPMUs; real-time events; data generation; distribution network; fault events; situational awareness; modeling and simulation; load flow analysis; RMS simulation μPMUs; real-time events; data generation; distribution network; fault events; situational awareness; modeling and simulation; load flow analysis; RMS simulation

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MDPI and ACS Style

Medattil Ibrahim, A.H.; Sharma, M.; Subramaniam Rajkumar, V. Realistic μPMU Data Generation for Different Real-Time Events in an Unbalanced Distribution Network. Energies 2023, 16, 3842. https://doi.org/10.3390/en16093842

AMA Style

Medattil Ibrahim AH, Sharma M, Subramaniam Rajkumar V. Realistic μPMU Data Generation for Different Real-Time Events in an Unbalanced Distribution Network. Energies. 2023; 16(9):3842. https://doi.org/10.3390/en16093842

Chicago/Turabian Style

Medattil Ibrahim, Abdul Haleem, Madhu Sharma, and Vetrivel Subramaniam Rajkumar. 2023. "Realistic μPMU Data Generation for Different Real-Time Events in an Unbalanced Distribution Network" Energies 16, no. 9: 3842. https://doi.org/10.3390/en16093842

APA Style

Medattil Ibrahim, A. H., Sharma, M., & Subramaniam Rajkumar, V. (2023). Realistic μPMU Data Generation for Different Real-Time Events in an Unbalanced Distribution Network. Energies, 16(9), 3842. https://doi.org/10.3390/en16093842

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