Dissertation/Thesis Abstract

Data-driven Analysis of Power Distribution Synchrophasors with Applications to Situational Awareness, Load Modeling, and Reliability
by Shahsavari, Alireza, Ph.D., University of California, Riverside, 2019, 199; 22589363
Abstract (Summary)

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

thesis. First, we introduce novel data-driven event detection techniques to pick out valuable portion of data from extremely large raw micro-PMU data. Subsequently, data-driven event classifier are 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. 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, five real-world use-cases are presented for the proposed data analytics tools, including:

Transient Load Modeling for Application in Frequency Regulation Market;

Static Load Modeling;

Remote Asset Monitoring;

Protection System Diagnosis;

Lightning Initiated Contingency Analysis.

Indexing (document details)
Advisor: Mohsenian Rad, Hamed
Commitee: Abu-Ghazaleh, Nael, Karydis, Konstantinos
School: University of California, Riverside
Department: Electrical Engineering
School Location: United States -- California
Source: DAI-B 81/4(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Engineering, Energy, Computer Engineering
Keywords: Load Modeling, Machine Learning, Micro PMU, Power Distribution System, Situational Awareness, Synchrophasors
Publication Number: 22589363
ISBN: 9781392772065
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