OEDI SI/Use Cases/Event Detection and Identification

Event Detection and Identification Summary

Timely anomaly detection and identification in measurement signals for distribution systems allows the design of preventive and corrective control actions to avoid damage or loss of equipment, as well as partial or total power outages. The majority of research in the area focuses on measurement deviation [1], frequency oscillations [2], network topology change [3], and high impedance faults [4]. The detection and identification of events normally rely on pre- and post-event measurements from power system monitoring units such as Supervisory Control and Data Acquisition (SCADA) and Phasor Measurement Units (PMUs). Numerous event detection and identification algorithms are available in the literature and can be broadly classified as non-training based and training-based. Non-training based methods are typically based on standard deviation, wavelet transform, principal component analysis, etc. While training-based techniques employ a classifier based on machine learning or deep learning methods, which are used for real-time event detection and identification.

Scenarios

Scenarios	Scenario Description	Raw Data	Organization	Date Created
Event Classification for Field Measured Data	CNN based event classification algorithm for field measured data		PNNL	2024/09/24
IBR System Stability Analysis	IBR System Stability Analysis		University of South Florida and ORNL	2024/04/18
CNN based protection zone identification	Identify and classify the faults into protection-zones	IEEE 123-bus Distribution Network Data	PNNL	2024/01/08
Machine Learning Based High Impedance Fault Identification	Machine Learning Based High Impedance Fault Identification		NREL	2024/05/24
Transient Fault Detection and Identification	Transient Fault Detection and Identification for distribution power grids.	IEEE 123-bus Distribution Network Data	NREL	2024/06/25

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Event Detection and Identification

K D. Granados-Lieberman, R. Romero-Troncoso, R. Osornio-Rios, A. Garcia-Perez, and E. Cabal-Yepez, “Techniques and methodologies for power quality analysis and disturbances classification in power systems: a review,” IET Generation, Transmission & Distribution, vol. 5, no. 4, pp. 519–529, 2011.

S. Zhang, X. Xie, and J. Wu, “Wams-based detection and early-warning of low-frequency oscillations in large-scale power systems,” Electric Power Systems Research, vol. 78, no. 5, pp. 897–906, 2008.

F. Chowdhury, J. P. Christensen, and J. L. Aravena, “Power system fault detection and state estimation using kalman filter with hypothesis testing,” IEEE transactions on Power Delivery, vol. 6, no. 3, pp. 1025–1030, 1991.

I. Baqui, I. Zamora, J. Mazón, and G. Buigues, “High impedance fault detection methodology using wavelet transform and artificial neural networks,” Electric Power Systems Research, vol. 81, no. 7, pp. 1325–1333, 2011.

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