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Article

Machine Committee Framework for Power Grid Disturbances Analysis Using Synchrophasors Data

1
College of Engineering, University of Tennessee, Knoxville, TN 37996, USA
2
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
*
Author to whom correspondence should be addressed.
Received: 19 November 2020 / Revised: 18 December 2020 / Accepted: 20 December 2020 / Published: 22 December 2020
(This article belongs to the Special Issue Applied Artificial Intelligence in Energy Systems)
Events detection is a key challenge in power grid frequency disturbances analysis. Accurate detection of events is crucial for situational awareness of the power system. In this paper, we study the problem of events detection in power grid frequency disturbance analysis using synchrophasors data streams. Current events detection approaches for power grid rely on individual detection algorithm. This study integrates some of the existing detection algorithms using the concept of machine committee to develop improved detection approaches for grid disturbance analysis. Specifically, we propose two algorithms—an Event Detection Machine Committee (EDMC) algorithm and a Change-Point Detection Machine Committee (CPDMC) algorithm. Both algorithms use parallel architecture to fuse detection knowledge of its individual methods to arrive at an overall output. The EDMC algorithm combines five individual event detection methods, while the CPDMC algorithm combines two change-point detection methods. Each method performs the detection task separately. The overall output of each algorithm is then computed using a voting strategy. The proposed algorithms are evaluated using three case studies of actual power grid disturbances. Compared with the individual results of the various detection methods, we found that the EDMC algorithm is a better fit for analyzing synchrophasors data; it improves the detection accuracy; and it is suitable for practical scenarios. View Full-Text
Keywords: frequency disturbance events; situational awareness; phasor measurement units; event detection; anomaly detection; machine committee; smart grid; artificial intelligence frequency disturbance events; situational awareness; phasor measurement units; event detection; anomaly detection; machine committee; smart grid; artificial intelligence
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MDPI and ACS Style

Niu, H.; Omitaomu, O.A.; Cao, Q.C. Machine Committee Framework for Power Grid Disturbances Analysis Using Synchrophasors Data. Smart Cities 2021, 4, 1-16. https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities4010001

AMA Style

Niu H, Omitaomu OA, Cao QC. Machine Committee Framework for Power Grid Disturbances Analysis Using Synchrophasors Data. Smart Cities. 2021; 4(1):1-16. https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities4010001

Chicago/Turabian Style

Niu, Haoran, Olufemi A. Omitaomu, and Qing C. Cao 2021. "Machine Committee Framework for Power Grid Disturbances Analysis Using Synchrophasors Data" Smart Cities 4, no. 1: 1-16. https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities4010001

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