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Power system data classification and prediction by functional data analysis

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dc.contributor.authorSun, Hongfei, author
dc.contributor.authorYang, Liuqing, advisor
dc.contributor.authorLuo, Jie, advisor
dc.contributor.authorZhang, Hongming, committee member
dc.contributor.authorDuan, Dongliang, committee member
dc.contributor.authorWang, Haonan, committee member
dc.date.accessioned2022-01-07T11:31:14Z
dc.date.available2023-01-06T11:31:14Z
dc.date.issued2021
dc.description.abstractThe last couple of decades have witnessed the development of our electric power grid. The growing population size and increasing consumerism have increased the load demand and brought more pressure on the grid. Meanwhile, new elements are being introduced to the power grid, such as various forms of renewable energy resources, electric vehicles, and so on, which need to be monitored constantly and managed adequately. In addition, the allocation of the various resources in the power systems is now conducted in a much more dynamic manner than ever. All these new dimensions have driven the development of the traditional grid into the smart grid and call for new methodologies in system design, operation, and control. This dissertation focuses on modeling power systems with data-driven approaches, with applications in power system cyber-attack detection and recovery, and large-scale, long-term load characterization. Firstly, the modeling of the spatial-temporal relationship among the quantities across the entire power systems is provided with applications to cyber-attack detection and data recovery. Then, the non-conforming load classification approaches based on Functional Principle Component Analysis (FPCA) will be introduced. This work is the first effort towards such loads due to the recently growing penetration of Distributed Energy Resources (DER) users. Lastly, we will introduce the regional high-resolution medium-term load forecasting approach. In order to satisfy the new purpose of load forecasting, serving for real-time applications, our approach can provide higher resolution than existing long-term load forecasting and longer leading time than the existing short-term load forecasting time-series load curve. Based on the presented case studies and simulation results, we provided the corresponding suggestions to the present industrial power system.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierSun_colostate_0053A_16967.pdf
dc.identifier.urihttps://hdl.handle.net/10217/234318
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectDER penetration
dc.subjectload prediction
dc.subjectcybersecurity
dc.subjectpower system modeling
dc.subjectload characterization
dc.titlePower system data classification and prediction by functional data analysis
dc.typeText
dcterms.embargo.expires2023-01-06
dcterms.embargo.terms2023-01-06
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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