Situational Awareness in Distribution Grid Using Micro-PMU Data: A Machine Learning Approach

Shahsavari, Alireza; Farajollahi, Mohammad; Stewart, Emma M.; Cortez, Ed; Mohsenian-Rad, Hamed

doi:10.1109/tsg.2019.2898676

Title: Situational Awareness in Distribution Grid Using Micro-PMU Data: A Machine Learning Approach

Journal Article · Tue Feb 12 00:00:00 EST 2019 · IEEE Transactions on Smart Grid

DOI:https://doi.org/10.1109/tsg.2019.2898676· OSTI ID:1811286

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Univ. of California, Riverside, CA (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Riverside Public Utilities, CA (United States)

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.

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Research Organization:: Univ. of California, Riverside, CA (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE); National Aeronautics and Space Administration (NASA)

Grant/Contract Number:: EE0008001; NNX15AP99A

OSTI ID:: 1811286

Journal Information:: IEEE Transactions on Smart Grid, Vol. 10, Issue 6; ISSN 1949-3053

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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Event Detection in Micro-PMU Data: A Generative Adversarial Network Scoring Method Aligholian, Armin; Shahsavari, Alireza; Cortez, Ed arXiv https://doi.org/10.48550/arxiv.1912.05103	text	January 2019

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