Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
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 a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-07-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/9/14/2902 |
| _version_ | 1817994744628772864 |
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| author | Stan McClellan Damian Valles George Koutitas |
| author_facet | Stan McClellan Damian Valles George Koutitas |
| author_sort | Stan McClellan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T01:56:19Z |
| format | Article |
| id | doaj.art-6ea5e5a9661045929f31e434f48b3bd5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-14T01:56:19Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6ea5e5a9661045929f31e434f48b3bd52022-12-22T02:19:00ZengMDPI AGApplied Sciences2076-34172019-07-01914290210.3390/app9142902app9142902Dynamic Voltage Optimization Based on In-Band Sensors and Machine LearningStan McClellan0Damian Valles1George Koutitas2Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USAIngram School of Engineering, Texas State University, San Marcos, TX 78666, USAIngram School of Engineering, Texas State University, San Marcos, TX 78666, USAA 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.https://www.mdpi.com/2076-3417/9/14/2902volt/var optimizationdynamic voltage optimizationdemand responseconservation voltage reductionconservation voltage regulationpeak shavingsmart gridmachine learning |
| spellingShingle | Stan McClellan Damian Valles George Koutitas Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning Applied Sciences volt/var optimization dynamic voltage optimization demand response conservation voltage reduction conservation voltage regulation peak shaving smart grid machine learning |
| title | Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning |
| title_full | Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning |
| title_fullStr | Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning |
| title_full_unstemmed | Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning |
| title_short | Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning |
| title_sort | dynamic voltage optimization based on in band sensors and machine learning |
| topic | volt/var optimization dynamic voltage optimization demand response conservation voltage reduction conservation voltage regulation peak shaving smart grid machine learning |
| url | https://www.mdpi.com/2076-3417/9/14/2902 |
| work_keys_str_mv | AT stanmcclellan dynamicvoltageoptimizationbasedoninbandsensorsandmachinelearning AT damianvalles dynamicvoltageoptimizationbasedoninbandsensorsandmachinelearning AT georgekoutitas dynamicvoltageoptimizationbasedoninbandsensorsandmachinelearning |