A Data Analytics Framework for Regional Voltage Control
| dc.contributor.author | Yang, Duotong | en |
| dc.contributor.committeechair | Centeno, Virgilio A. | en |
| dc.contributor.committeemember | Thorp, James S. | en |
| dc.contributor.committeemember | De La Ree, Jaime | en |
| dc.contributor.committeemember | Southward, Steve C. | en |
| dc.contributor.committeemember | Tokekar, Pratap | en |
| dc.contributor.department | Electrical and Computer Engineering | en |
| dc.date.accessioned | 2017-08-17T08:00:33Z | en |
| dc.date.available | 2017-08-17T08:00:33Z | en |
| dc.date.issued | 2017-08-16 | en |
| dc.description.abstract | Modern power grids are some of the largest and most complex engineered systems. Due to economic competition and deregulation, the power systems are operated closer their security limit. When the system is operating under a heavy loading condition, the unstable voltage condition may cause a cascading outage. The voltage fluctuations are presently being further aggravated by the increasing integration of utility-scale renewable energy sources. In this regards, a fast response and reliable voltage control approach is indispensable. The continuing success of synchrophasor has ushered in new subdomains of power system applications for real-time situational awareness, online decision support, and offline system diagnostics. The primary objective of this dissertation is to develop a data analytic based framework for regional voltage control utilizing high-speed data streams delivered from synchronized phasor measurement units. The dissertation focuses on the following three studies: The first one is centered on the development of decision-tree based voltage security assessment and control. The second one proposes an adaptive decision tree scheme using online ensemble learning to update decision model in real time. A system network partition approach is introduced in the last study. The aim of this approach is to reduce the size of training sample database and the number of control candidates for each regional voltage controller. The methodologies proposed in this dissertation are evaluated based on an open source software framework. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:12343 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/78712 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Regional Voltage Control | en |
| dc.subject | Data Analytics | en |
| dc.subject | Decision Tree | en |
| dc.subject | Online Boosting. Wide Area Measurements | en |
| dc.subject | Data Mining | en |
| dc.subject | Network Partition | en |
| dc.subject | Machine learning | en |
| dc.title | A Data Analytics Framework for Regional Voltage Control | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Electrical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |