Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
Source abstract
Abstract
A 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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Grid Energy Management · Smart Grid Security and Resilience · Power Line Communications and Noise
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA
High-Performance Engineering (HiPE) Research Group, Texas State University, San Marcos, TX 78666, USA
X-Reality Research Lab, Texas State University, San Marcos, TX 78666, USA
References (51)
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