*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Title: Improving Distribution System Model Accuracy by Leveraging Ubiquitous Sensors
Committee:
Dr. Santiago Grijalva, ECE, Chair , Advisor
Dr. Ronald Harley, ECE
Dr. Maryam Saeedifard, ECE
Dr. Sakis Meliopoulos, ECE
Dr. Shabbir Ahmed, ISyE
Abstract:
This dissertation addresses the need for utilities to improve the analytical and operational distribution system modeling accuracy as well as to manage the Big Data from modern distribution system measurement sources for future advanced distribution automation schemes with ubiquitous distributed energy resources. In particular, this dissertation presents accurate, flexible, and computationally efficient parameter and topology estimation methods to calibrate existing utility distribution system secondary circuit low-voltage models. Several methods are presented to handle unknown and known secondary circuit topologies and different available measurement datasets. The presented methods leverage the Big Data generated by the modern distribution system measurements from AMI and PV micro inverters. This dissertation also presents data validation and imputation methods to manage the granularity, accuracy, and reliability issues related to the modern distribution system measurements. The developed data validation methods were effectively used to detect numerous issues in Georgia Tech AMI. Compared to conventional approaches, the developed computationally efficient data imputation method has a superior average accuracy in imputing Georgia Tech AMI measurements. The method creates a series of imputed samples that have a continuous profile with respect to the adjacent available measurements, which is a highly desirable feature for time-series analyses.
