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Article

A Novel Framework for Synchrophasor Based Online Recognition and Efficient Post-Mortem Analysis of Disturbances in Power Systems

1
Department of Energy, Fraunhofer IOSB, IOSB-AST Ilmenau, Fraunhofer Institute for Optronics, System Technologies and Image Exploitation, Am Vogelherd 90, 98693 Ilmenau, Germany
2
Institute for Electrical Energy and Control Technology, Technische Universität Ilmenau, Gustav-Kirchhoff-Strasse 1, 98693 Ilmenau, Germany
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(15), 5209; https://doi.org/10.3390/app10155209
Received: 30 April 2020 / Revised: 20 July 2020 / Accepted: 23 July 2020 / Published: 28 July 2020
(This article belongs to the Special Issue Phasor Measurement Units: Algorithms, Challenges and Perspectives)
Synchrophasor based applications become more and more popular in today’s control centers to monitor and control transient system events. This can ensure secure system operation when dealing with bidirectional power flows, diminishing reserves and an increased number of active grid components. Today’s synchrophasor applications provide a lot of additional information about the dynamic system behavior but without significant improvement of the system operation due to the lack of interpretable and condensed results as well as missing integration into existing decision-making processes. This study presents a holistic framework for novel machine learning based applications analyzing both historical as well as online synchrophasor data streams. Different methods from dimension reduction, anomaly detection as well as time series classification are used to automatically detect disturbances combined with a web-based online visualization tool. This enables automated decision-making processes in control centers to mitigate critical system states and to ensure secure system operations (e.g., by activating curate actions). Measurement and simulation-based results are presented to evaluate the proposed synchrophasor application modules for different use cases at the transmission and distribution level. View Full-Text
Keywords: disturbance detection; data compression; post-mortem analysis disturbance detection; data compression; post-mortem analysis
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MDPI and ACS Style

Kummerow, A.; Monsalve, C.; Brosinsky, C.; Nicolai, S.; Westermann, D. A Novel Framework for Synchrophasor Based Online Recognition and Efficient Post-Mortem Analysis of Disturbances in Power Systems. Appl. Sci. 2020, 10, 5209. https://doi.org/10.3390/app10155209

AMA Style

Kummerow A, Monsalve C, Brosinsky C, Nicolai S, Westermann D. A Novel Framework for Synchrophasor Based Online Recognition and Efficient Post-Mortem Analysis of Disturbances in Power Systems. Applied Sciences. 2020; 10(15):5209. https://doi.org/10.3390/app10155209

Chicago/Turabian Style

Kummerow, Andre, Cristian Monsalve, Christoph Brosinsky, Steffen Nicolai, and Dirk Westermann. 2020. "A Novel Framework for Synchrophasor Based Online Recognition and Efficient Post-Mortem Analysis of Disturbances in Power Systems" Applied Sciences 10, no. 15: 5209. https://doi.org/10.3390/app10155209

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