Situational Awareness in Distribution Grid Using Micro-PMU Data: A Machine Learning Approach
Abstract
The recent development of distribution-level phasor measurement units, a.k.a. micro-PMUs, has been an important step towards achieving situational awareness in power distribution networks. The challenge however is to transform the large amount of data that is generated by micro-PMUs to actionable information and then match the information to use cases with practical value to system operators. This open problem is addressed in this paper. First, we introduce a novel data-driven event detection technique to pick out valuable portion of data from extremely large raw micro-PMU data. Subsequently, a datadriven event classifier is developed to effectively classify power quality events. Importantly, we use field expert knowledge and utility records to conduct an extensive data-driven event labeling. Moreover, certain aspects from event detection analysis are adopted as additional features to be fed into the classifier model. In this regard, a multi-class support vector machine (multi-SVM) classifier is trained and tested over 15 days of real-world data from two micro-PMUs on a distribution feeder in Riverside, CA. In total, we analyze 1.2 billion measurement points, and 10,700 events. Here, the effectiveness of the developed event classifier is compared with prevalent multi-class classification methods, including k-nearest neighbor method as well as decision-tree method. Importantly,more »
- Authors:
-
- Univ. of California, Riverside, CA (United States)
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Riverside Public Utilities, CA (United States)
- Publication Date:
- Research Org.:
- Univ. of California, Riverside, CA (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE); National Aeronautics and Space Administration (NASA)
- OSTI Identifier:
- 1811286
- Grant/Contract Number:
- EE0008001; NNX15AP99A
- Resource Type:
- Accepted Manuscript
- Journal Name:
- IEEE Transactions on Smart Grid
- Additional Journal Information:
- Journal Volume: 10; Journal Issue: 6; Journal ID: ISSN 1949-3053
- Publisher:
- IEEE
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 24 POWER TRANSMISSION AND DISTRIBUTION; Machine learning; distribution synchrophasors; situational awareness; event detection; event classification; big-data
Citation Formats
Shahsavari, Alireza, Farajollahi, Mohammad, Stewart, Emma M., Cortez, Ed, and Mohsenian-Rad, Hamed. Situational Awareness in Distribution Grid Using Micro-PMU Data: A Machine Learning Approach. United States: N. p., 2019.
Web. doi:10.1109/tsg.2019.2898676.
Shahsavari, Alireza, Farajollahi, Mohammad, Stewart, Emma M., Cortez, Ed, & Mohsenian-Rad, Hamed. Situational Awareness in Distribution Grid Using Micro-PMU Data: A Machine Learning Approach. United States. https://doi.org/10.1109/tsg.2019.2898676
Shahsavari, Alireza, Farajollahi, Mohammad, Stewart, Emma M., Cortez, Ed, and Mohsenian-Rad, Hamed. Tue .
"Situational Awareness in Distribution Grid Using Micro-PMU Data: A Machine Learning Approach". United States. https://doi.org/10.1109/tsg.2019.2898676. https://www.osti.gov/servlets/purl/1811286.
@article{osti_1811286,
title = {Situational Awareness in Distribution Grid Using Micro-PMU Data: A Machine Learning Approach},
author = {Shahsavari, Alireza and Farajollahi, Mohammad and Stewart, Emma M. and Cortez, Ed and Mohsenian-Rad, Hamed},
abstractNote = {The recent development of distribution-level phasor measurement units, a.k.a. micro-PMUs, has been an important step towards achieving situational awareness in power distribution networks. The challenge however is to transform the large amount of data that is generated by micro-PMUs to actionable information and then match the information to use cases with practical value to system operators. This open problem is addressed in this paper. First, we introduce a novel data-driven event detection technique to pick out valuable portion of data from extremely large raw micro-PMU data. Subsequently, a datadriven event classifier is developed to effectively classify power quality events. Importantly, we use field expert knowledge and utility records to conduct an extensive data-driven event labeling. Moreover, certain aspects from event detection analysis are adopted as additional features to be fed into the classifier model. In this regard, a multi-class support vector machine (multi-SVM) classifier is trained and tested over 15 days of real-world data from two micro-PMUs on a distribution feeder in Riverside, CA. In total, we analyze 1.2 billion measurement points, and 10,700 events. Here, the effectiveness of the developed event classifier is compared with prevalent multi-class classification methods, including k-nearest neighbor method as well as decision-tree method. Importantly, two real-world use-cases are presented for the proposed data analytics tools, including remote asset monitoring and distribution-level oscillation analysis.},
doi = {10.1109/tsg.2019.2898676},
journal = {IEEE Transactions on Smart Grid},
number = 6,
volume = 10,
place = {United States},
year = {Tue Feb 12 00:00:00 EST 2019},
month = {Tue Feb 12 00:00:00 EST 2019}
}
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Works referencing / citing this record:
Event Detection in Micro-PMU Data: A Generative Adversarial Network Scoring Method
text, January 2019
- Aligholian, Armin; Shahsavari, Alireza; Cortez, Ed
- arXiv