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Title: Machine Learning Using a Simple Feature for Detecting Multiple Types of Events From PMU Data

Conference · · 2022 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)
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  1. Texas A & M Univ., College Station, TX (United States)
  2. Temple Univ., Philadelphia, PA (United States)
  3. Quanta Technology, Raleigh, NC (United States)
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This paper describes simple and efficient machine learning (ML) methods for efficiently detecting multiple types of power system events captured by PMUs scarcely placed in a large power grid. It uses a single feature from each PMU based on a rectangle area enclosing the event in a given data window. This single feature is sufficient to enable commonly used ML models to detect different types of events quickly and accurately. The feature is used by five ML models on four different data-window sizes. The results indicated a tradeoff between the execution speed and detection accuracy in variety of data-window size choices. Here, the proposed method is insensitive to most data quality issues typical for data from field PMUs, and thus it does not require major data cleansing efforts prior to feature extraction.

Research Organization:
Texas A & M Univ., College Station, TX (United States). Texas A & M Engineering Experiment Station
Sponsoring Organization:
USDOE Office of Electricity (OE)
DOE Contract Number:
OE0000913
OSTI ID:
1891314
Journal Information:
2022 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA), Conference: 2022 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA), Split (Croatia), 24-26 May 2022
Country of Publication:
United States
Language:
English

References (16)

Parallel Detrended Fluctuation Analysis for Fast Event Detection on Massive PMU Data journal January 2015
Transfer Learning for Event Detection From PMU Measurements With Scarce Labels journal January 2021
Data quality issues for synchrophasor applications Part I: a review journal July 2016
Hierarchical Convolutional Neural Networks for Event Classification on PMU Measurements journal January 2021
Dimensionality Reduction of Synchrophasor Data for Early Event Detection: Linearized Analysis journal November 2014
Real-Time Multiple Event Detection and Classification Using Moving Window PCA journal September 2016
Power Grid Online Surveillance Through PMU-Embedded Convolutional Neural Networks journal March 2020
Real-Time Identification of Dynamic Events in Power Systems Using PMU Data, and Potential Applications—Models, Promises, and Challenges journal February 2017
Supervisory Protection and Automated Event Diagnosis Using PMU Data journal August 2016
Wavelet-Based Event Detection Method Using PMU Data journal May 2017
Comprehensive Clustering of Disturbance Events Recorded by Phasor Measurement Units journal June 2014
Power System Event Identification Based on Deep Neural Network With Information Loading journal November 2021
Situational Awareness in Distribution Grid Using Micro-PMU Data: A Machine Learning Approach journal November 2019
A Novel Event Detection Method Using PMU Data With High Precision journal January 2019
A PMU-Based Data-Driven Approach for Classifying Power System Events Considering Cyberattacks journal September 2020
Real-Time Multiple Event Detection and Classification in Power System Using Signal Energy Transformations journal March 2019

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Related Subjects

Big data
Event detection
Machine learning
Phasor measurement units Power system faults
Time series analysis