Abstract
With the increased penetration of renewable generation in the smart grid , it is crucial to have knowledge of rapid changes of system states. The information of real-time electro-mechanical dynamic states of generators are essential to ensuring reliability and detecting instability of the grid. The conventional SCADA based Dynamic State Estimation (DSE) was limited by the slow sampling rates (2-4 Hz). With the advent of PMU based synchro-phasor technology in tandem with Wide Area Monitoring System (WAMS), it has become possible to avail rapid real-time measurements at the network nodes. These measurements can be exploited for better estimates of system dynamic states. In this research, we have proposed a novel Artificial Intelligence (AI) based real-time neuro-adaptive algorithm for rotor angle and speed estimation of synchronous generators. Generator swing equations and power flow models are incorporated in the online learning. The algorithm learns and adapts in real-time to achieve accurate estimates. Simulation is carried out on 68 bus 16 generator NETS-NYPS model. The neuro-adaptive algorithm is compared with classical Kalman Filter based DSE. Applicability and accuracy of the proposed method is demonstrated under the influence of noise and faulty conditions.
Notes
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Graduation Date
2017
Semester
Fall
Advisor
Zhou, Qun
Degree
Master of Science in Electrical Engineering (M.S.E.E.)
College
College of Engineering and Computer Science
Department
Electrical Engineering and Computer Engineering
Degree Program
Electrical Engineering
Format
application/pdf
Identifier
CFE0006858
URL
http://purl.fcla.edu/fcla/etd/CFE0006858
Language
English
Release Date
December 2017
Length of Campus-only Access
None
Access Status
Masters Thesis (Open Access)
STARS Citation
Birari, Rahul, "Online Neuro-Adaptive Learning For Power System Dynamic State Estimation" (2017). Electronic Theses and Dissertations. 5686.
https://stars.library.ucf.edu/etd/5686