McClellan, StanValles, DamianKoutitas, George2021-04-142021-04-142019-07-19McClellan, S., Valles, D., & Koutitas, G. (2019). Dynamic voltage optimization based on in-band sensors and machine learning. Applied Sciences, 9(14): 2902.https://hdl.handle.net/10877/13379A 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.Text25 pages1 file (.pdf)envolt/var optimizationdynamic voltage optimizationdemand responseconservation voltage reductionconservation voltage regulationpeak shavingsmart gridmachine learningIngram School of EngineeringArticle© 2019 The Authors.https://doi.org/10.3390/app9142902This work is licensed under a Creative Commons Attribution 4.0 International License.