Improving electrical power grid resiliency and optimizing post-storm recovery using LiDAR and machine learning

dc.contributor.advisorBajaj, Chandrajit
dc.creatorDavis, Michael Andrew, II
dc.date.accessioned2022-08-31T00:27:10Z
dc.date.available2022-08-31T00:27:10Z
dc.date.created2019-12
dc.date.issued2020-02-03
dc.date.submittedDecember 2019
dc.date.updated2022-08-31T00:27:11Z
dc.description.abstractWhile many external factors influence resiliency, weather remains the single greatest threat to the electric power grid, and the impacts caused by significant storms can be long-lasting and widespread. When damage occurs, it is very costly to identify due to the vast size of electrical transmission and distribution circuits, which can span hundreds of miles. Pinpointing a failure in a circuit requires the expensive process of dispatching human teams to “walk the line” and physically inspect the circuit to identify damage. It is proposed that this problem can be optimized through automation, by leveraging flight vehicles, light detection and ranging (LiDAR) technology, and machine learning. The goals for this project are: 1) Investigate the feasibility of, and problems associated with, developing a system to remotely inspect electrical power transmission and distribution infrastructure with lidar. 2) Investigate the feasibility of developing an automated system to classify and detect damage to terrestrial transmission and distribution assets with lidar and artificial intelligence. 3) Develop a proof of concept of such a system, including a simulation of real-time lidar data collection and damage assessment
dc.description.departmentElectrical and Computer Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/115472
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/42371
dc.language.isoen
dc.subjectElectrical
dc.subjectPower
dc.subjectGrid
dc.subjectResiliency
dc.subjectLidar
dc.subjectMachine
dc.subjectLearning
dc.subjectDamage
dc.subjectAssessment
dc.subjectTransmission
dc.subjectDistribution
dc.subjectDetection
dc.titleImproving electrical power grid resiliency and optimizing post-storm recovery using LiDAR and machine learning
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Engineering

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