Dynamic voltage optimization based on in-band sensors and machine learning

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dc.contributor.authorMcClellan, Stan
dc.contributor.authorValles, Damian
dc.contributor.authorKoutitas, George
dc.date.accessioned2021-04-14T17:54:07Z
dc.date.available2021-04-14T17:54:07Z
dc.date.issued2019-07-19
dc.description.abstractA feedback-based architecture is presented for the distribution grid which enables the use of Machine Learning (ML) techniques for various applications, including Dynamic Voltage Optimization (DVO) and Demand Response (DR). In this architecture, sensor devices are resident on the distribution grid and therefore have a unique awareness of multiple system parameters. This enables the use of ongoing ML techniques for implementation of critical applications in the Smart Grid. Monitoring devices are placed at the endpoints and monitoring/control devices are placed along the power line on various types of grid-resident systems. Because the devices are grid-resident and interact directly with other devices on the same physical link, applications such as ML-assisted DVO can be targeted with very high confidence.
dc.description.departmentEngineering
dc.formatText
dc.format.extent25 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationMcClellan, S., Valles, D., & Koutitas, G. (2019). Dynamic voltage optimization based on in-band sensors and machine learning. Applied Sciences, 9(14): 2902.
dc.identifier.doihttps://doi.org/10.3390/app9142902
dc.identifier.urihttps://hdl.handle.net/10877/13379
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute
dc.rights.holder© 2019 The Authors.
dc.rights.licenseThis work is licensed under a Creative Commons Attribution 4.0 International License.
dc.sourceApplied Sciences, 2019, Vol. 9, No. 14, Article 2902.
dc.subjectvolt/var optimization
dc.subjectdynamic voltage optimization
dc.subjectdemand response
dc.subjectconservation voltage reduction
dc.subjectconservation voltage regulation
dc.subjectpeak shaving
dc.subjectsmart grid
dc.subjectmachine learning
dc.subjectIngram School of Engineering
dc.titleDynamic voltage optimization based on in-band sensors and machine learning
dc.typeArticle

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