{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/83558"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/83558","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Designing Reactive Power Control Rules for Smart Inverters using Machine Learning","abstract":"Due to increasing penetration of solar power generation, distribution grids are facing a number of challenges. Frequent reverse active power flows can result in rapid fluctuations in voltage magnitudes. However, with the revised IEEE 1547 standard, smart inverters can actively control their reactive power injection to minimize voltage deviations and power losses in the grid. Reactive power control and globally optimal inverter coordination in real-time is computationally and communication-wise demanding, whereas the local Volt-VAR or Watt-VAR control rules are subpar for enhanced grid services. This thesis uses machine learning tools and poses reactive power control as a kernel-based regression task to learn policies and evaluate the reactive power injections in real-time. This novel approach performs inverter coordination through non-linear control policies centrally designed by the operator on a slower timescale using anticipated scenarios for load and generation. In real-time, the inverters feed locally and/or globally collected grid data to the customized control rules. The developed models are highly adjustable to the available computation and communication resources. The developed control scheme is tested on the IEEE 123-bus system and is seen to efficiently minimize losses and regulate voltage within the permissible limits.","abstract_html":"Due to increasing penetration of solar power generation, distribution grids are facing a number of challenges. Frequent reverse active power flows can result in rapid fluctuations in voltage magnitudes. However, with the revised IEEE 1547 standard, smart inverters can actively control their reactive power injection to minimize voltage deviations and power losses in the grid. Reactive power control and globally optimal inverter coordination in real-time is computationally and communication-wise demanding, whereas the local Volt-VAR or Watt-VAR control rules are subpar for enhanced grid services. This thesis uses machine learning tools and poses reactive power control as a kernel-based regression task to learn policies and evaluate the reactive power injections in real-time. This novel approach performs inverter coordination through non-linear control policies centrally designed by the operator on a slower timescale using anticipated scenarios for load and generation. In real-time, the inverters feed locally and/or globally collected grid data to the customized control rules. The developed models are highly adjustable to the available computation and communication resources. The developed control scheme is tested on the IEEE 123-bus system and is seen to efficiently minimize losses and regulate voltage within the permissible limits.","abstract_has_math":false,"creators":["Garg, Aditie"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Electrical Engineering","degree_department":"Electrical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Kekatos, Vasileios"],"committee_members":["De La Ree, Jaime","Centeno, Virgilio A."],"year":2018,"date_issued":"2018-06-14","date_published":"2018-06-14","updated_at":"2026-07-22T22:18:53Z","subjects":["smart inverters","support vector machines","kernel-based learning","voltage regulation","power loss minimization","linearized distribution flow model"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:15867"],"render_values":[{"text":"vt_gsexam:15867","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/83558","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Kekatos, Vasileios"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["De La Ree, Jaime","Centeno, Virgilio A."]},{"key":"dc:contributor.department","label":"Department","values":["Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["Garg, Aditie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-06-15T08:00:46Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-06-15T08:00:46Z"]},{"key":"dc:date.issued","label":"Date","values":["2018-06-14"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["smart inverters","support vector machines","kernel-based learning","voltage regulation","power loss minimization","linearized distribution flow model"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:15867"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/83558"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Due to increasing penetration of solar power generation, distribution grids are facing a number of challenges. Frequent reverse active power flows can result in rapid fluctuations in voltage magnitudes. However, with the revised IEEE 1547 standard, smart inverters can actively control their reactive power injection to minimize voltage deviations and power losses in the grid. Reactive power control and globally optimal inverter coordination in real-time is computationally and communication-wise demanding, whereas the local Volt-VAR or Watt-VAR control rules are subpar for enhanced grid services. This thesis uses machine learning tools and poses reactive power control as a kernel-based regression task to learn policies and evaluate the reactive power injections in real-time. This novel approach performs inverter coordination through non-linear control policies centrally designed by the operator on a slower timescale using anticipated scenarios for load and generation. In real-time, the inverters feed locally and/or globally collected grid data to the customized control rules. The developed models are highly adjustable to the available computation and communication resources. The developed control scheme is tested on the IEEE 123-bus system and is seen to efficiently minimize losses and regulate voltage within the permissible limits."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["The increasing integration of solar photovoltaic (PV) systems poses both opportunities and technical challenges for the electrical distribution grid. Although PV systems provide more power to the grid but, can also lead to problems in the operation of the grid like overvoltages and voltage fluctuations. These variations can lead to overheating and burning of electrical devices and equipment malfunction. Since the solar generation is highly dependent on weather and geographical location, they are uncertain in their output. The uncertainity in the solar irradiance can not be handled with the existing voltage control devices as they need to operate more frequently than usual which can cause recurring maintenance needs for these devices. Thus, to make solar PV more flexible and grid-friendly, smart inverters are being developed. Smart inverters have the capability of advanced sensing, communication, and controllability which can be utilized for voltage control. The research discusses how the inverters can be used to improve the grid profile by providing reactive power support to reduce the power losses and maintain voltages in their limits for a safer operation."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Designing Reactive Power Control Rules for Smart Inverters using Machine Learning"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Kekatos, Vasileios"],"dc:contributor.committeemember":["De La Ree, Jaime","Centeno, Virgilio A."],"dc:contributor.department":["Electrical Engineering"],"dc:creator":["Garg, Aditie"],"dc:date.accessioned":["2018-06-15T08:00:46Z"],"dc:date.available":["2018-06-15T08:00:46Z"],"dc:date.issued":["2018-06-14"],"dc:description.abstract":["Due to increasing penetration of solar power generation, distribution grids are facing a number of challenges. Frequent reverse active power flows can result in rapid fluctuations in voltage magnitudes. However, with the revised IEEE 1547 standard, smart inverters can actively control their reactive power injection to minimize voltage deviations and power losses in the grid. Reactive power control and globally optimal inverter coordination in real-time is computationally and communication-wise demanding, whereas the local Volt-VAR or Watt-VAR control rules are subpar for enhanced grid services. This thesis uses machine learning tools and poses reactive power control as a kernel-based regression task to learn policies and evaluate the reactive power injections in real-time. This novel approach performs inverter coordination through non-linear control policies centrally designed by the operator on a slower timescale using anticipated scenarios for load and generation. In real-time, the inverters feed locally and/or globally collected grid data to the customized control rules. The developed models are highly adjustable to the available computation and communication resources. The developed control scheme is tested on the IEEE 123-bus system and is seen to efficiently minimize losses and regulate voltage within the permissible limits."],"dc:description.abstractgeneral":["The increasing integration of solar photovoltaic (PV) systems poses both opportunities and technical challenges for the electrical distribution grid. Although PV systems provide more power to the grid but, can also lead to problems in the operation of the grid like overvoltages and voltage fluctuations. These variations can lead to overheating and burning of electrical devices and equipment malfunction. Since the solar generation is highly dependent on weather and geographical location, they are uncertain in their output. The uncertainity in the solar irradiance can not be handled with the existing voltage control devices as they need to operate more frequently than usual which can cause recurring maintenance needs for these devices. Thus, to make solar PV more flexible and grid-friendly, smart inverters are being developed. Smart inverters have the capability of advanced sensing, communication, and controllability which can be utilized for voltage control. The research discusses how the inverters can be used to improve the grid profile by providing reactive power support to reduce the power losses and maintain voltages in their limits for a safer operation."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:15867"],"dc:identifier.uri":["http://hdl.handle.net/10919/83558"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["smart inverters","support vector machines","kernel-based learning","voltage regulation","power loss minimization","linearized distribution flow model"],"dc:title":["Designing Reactive Power Control Rules for Smart Inverters using Machine Learning"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:18:53Z"}