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Unsupervised Event Detection, Clustering, and Use Case Exposition in
  Micro-PMU Measurements

Unsupervised Event Detection, Clustering, and Use Case Exposition in Micro-PMU Measurements

30 July 2020
Armin Aligholian
A. Shahsavari
E. Stewart
Ed Cortez
Hamed Mohsenian-Rad
ArXivPDFHTML

Papers citing "Unsupervised Event Detection, Clustering, and Use Case Exposition in Micro-PMU Measurements"

3 / 3 papers shown
Title
DynamoPMU: A Physics Informed Anomaly Detection and Prediction
  Methodology using non-linear dynamics from $μ$PMU Measurement Data
DynamoPMU: A Physics Informed Anomaly Detection and Prediction Methodology using non-linear dynamics from μμμPMU Measurement Data
Divyanshi Dwivedi
P. Yemula
M. Pal
13
0
0
31 Mar 2023
GraphPMU: Event Clustering via Graph Representation Learning Using
  Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU
  Measurements
GraphPMU: Event Clustering via Graph Representation Learning Using Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU Measurements
Armin Aligholian
Hamed Mohsenian-Rad
24
8
0
26 May 2022
Soft-DTW: a Differentiable Loss Function for Time-Series
Soft-DTW: a Differentiable Loss Function for Time-Series
Marco Cuturi
Mathieu Blondel
AI4TS
141
611
0
05 Mar 2017
1