Voltage Regulation of Smart Grids using Machine Learning Tools

dc.contributor.authorJalali, Manaen
dc.contributor.committeechairKekatos, Vasileiosen
dc.contributor.committeememberDe La Ree, Jaimeen
dc.contributor.committeememberCenteno, Virgilio A.en
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2019-12-10T17:16:18Zen
dc.date.available2019-12-10T17:16:18Zen
dc.date.issued2019-09-23en
dc.description.abstractSmart inverters have been considered the primary fast solution for voltage regulation in power distribution systems. Optimizing the coordination between inverters can be computationally challenging. Reactive power control using fixed local rules have been shown to be subpar. Here, nonlinear inverter control rules are proposed by leveraging machine learning tools. The designed control rules can be expressed by a set of coefficients. These control rules can be nonlinear functions of both remote and local inputs. The proposed control rules are designed to jointly minimize the voltage deviation across buses. By using the support vector machines, control rules with sparse representations are obtained which decrease the communication between the operator and the inverters. The designed control rules are tested under different grid conditions and compared with other reactive power control schemes. The results show promising performance.en
dc.description.abstractgeneralWith advent of renewable energies into the power systems, innovative and automatic monitoring and control techniques are required. More specifically, voltage regulation for distribution grids with solar generation is a can be a challenging task. Moreover, due to frequency and intensity of the voltage changes, traditional utility-owned voltage regulation equipment are not useful in long term. On the other hand, smart inverters installed with solar panels can be used for regulating the voltage. Smart inverters can be programmed to inject or absorb reactive power which directly influences the voltage. Utility can monitor, control and sync the inverters across the grid to maintain the voltage within the desired limits. Machine learning and optimization techniques can be applied for automation of voltage regulation in smart grids using the smart inverters installed with solar panels. In this work, voltage regulation is addressed by reactive power control.en
dc.format.mediumETDen
dc.identifier.urihttp://hdl.handle.net/10919/95962en
dc.language.isoen_USen
dc.publisherVirginia Techen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectmachine Learningen
dc.subjectOptimizationen
dc.subjectVoltage regulationen
dc.subjectDistribution Systemsen
dc.titleVoltage Regulation of Smart Grids using Machine Learning Toolsen
dc.typeThesisen
thesis.degree.disciplineElectrical engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameM.S.en

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Jalali_M_T_2019.pdf
Size:
762.87 KB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
Name:
license.txt
Size:
1.5 KB
Format:
Item-specific license agreed upon to submission
Description:

Collections