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dc.contributor.advisorXie, Le
dc.creatorWu, Meng
dc.date.accessioned2019-01-16T20:32:07Z
dc.date.available2019-12-01T06:35:27Z
dc.date.created2017-12
dc.date.issued2017-12-05
dc.date.submittedDecember 2017
dc.identifier.urihttp://hdl.handle.net/1969.1/173140
dc.description.abstractThis dissertation is motivated by the lack of combined physics-based and data-driven framework for solving power system challenges that are introduced by the integration of new devices and new system components. As increasing number of stochastic generation, responsive loads, and dynamic measurements are involved in the planning and operations of modern power systems, utilities and system operators are in great need of new analysis framework that could combine physical models and measuring data together for solving challenging planning and operational problems. In view of the above challenges, the high-level objective of this dissertation is to develop a framework for integrating measurement data into large physical systems modeled by dynamical equations. To this end, the dissertation first identifies four critical tasks for the planning and operations of the modern power systems: the data collection and pre-processing, the system situational awareness, the decision making process, as well as the post-event analysis. The dissertation then takes one concrete application in each of these critical tasks as the example, and proposes the physics-based/data-driven approach for solving the challenging problems faced by this specific application. To this end, this dissertation focuses on solving the following specific problems using physics-based/data-driven approaches. First, for the data collection and pre-processing platform, a purely data-driven approach is proposed to detect bad metering data in the phasor measurement unit (PMU) monitoring systems, and ensure the overall PMU data quality. Second, for the situational awareness platform, a physics-based voltage stability assessment method is presented to improve the situational awareness of system voltage instabilities. Third, for the decision making platform, a combined physics-based and data-driven framework is proposed to support the decision making process of PMU-based power plant model validation. Forth, for the post-event analysis platform, a physics-based post-event analysis is presented to identify the root causes of the sub-synchronous oscillations induced by the wind farm integration. The above problems and proposed solutions are discussed in detail in Section 2 through Section 5. The results of this work can be integrated to address practical problems in modern power system planning and operations.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectphasor measurement uniten
dc.subjectbad data detectionen
dc.subjectwind farmen
dc.subjectsub-synchronous oscillationen
dc.subjectdoubly-fed induction generatoren
dc.subjectmodel initializationen
dc.subjectpower plant model validationen
dc.subjectmodel diagnosisen
dc.subjectpower system voltage stability.en
dc.titlePhysics-Based and Data-Driven Analytics for Enhanced Planning and Operations in Power Systems with Deep Renewable Penetrationen
dc.typeThesisen
thesis.degree.departmentElectrical and Computer Engineeringen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorTexas A & M Universityen
thesis.degree.nameDoctor of Philosophyen
thesis.degree.levelDoctoralen
dc.contributor.committeeMemberKezunovic, Mladen
dc.contributor.committeeMemberKumar, P. R.
dc.contributor.committeeMemberHuang, Jianhua
dc.type.materialtexten
dc.date.updated2019-01-16T20:32:07Z
local.embargo.terms2019-12-01
local.etdauthor.orcid0000-0003-3300-5484


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