Disturbance monitoring in distributed power systems
Glickman, Mark (2007) Disturbance monitoring in distributed power systems. PhD thesis, Queensland University of Technology.

Mark Glickman Thesis (PDF 1MB) 
Abstract
Power system generators are interconnected in a distributed network to allow sharing of power. If one of the generators cannot meet the power demand, spare power is diverted from neighbouring generators. However, this approach also allows for propagation of electric disturbances. An oscillation arising from a disturbance at a given generator site will affect the normal operation of neighbouring generators and might cause them to fail. Hours of production time will be lost in the time it takes to restart the power plant. If the disturbance is detected early, appropriate control measures can be applied to ensure system stability. The aim of this study is to improve existing algorithms that estimate the oscillation parameters from acquired generator data to detect potentially dangerous power system disturbances.
When disturbances occur in power systems (due to load changes or faults), damped oscillations (or "modes") are created. Modes which are heavily damped die out quickly and pose no threat to system stability. Lightly damped modes, by contrast, die out slowly and are more problematic. Of more concern still are "negatively damped" modes which grow exponentially with time and can ultimately cause the power system to fail. Widespread blackouts are then possible. To avert power system failures it is necessary to monitor the damping of the oscillating modes. This thesis proposes a number of damping estimation algorithms for this task. If the damping is found to be very small or even negative, then additional damping needs to be introduced via appropriate control strategies.
This thesis presents a number of new algorithms for estimating the damping of modal oscillations in power systems. The first of these algorithms uses multiple orthogonal sliding windows along with leastsquares techniques to estimate the modal damping. This algorithm produces results which are superior to those of earlier sliding window algorithms (that use only one pair of sliding windows to estimate the damping). The second algorithm uses a different modification of the standard sliding window damping estimation algorithm  the algorithm exploits the fact that the Signal to Noise Ratio (SNR) within the Fourier transform of practical power system signals is typically constant across a wide frequency range. Accordingly, damping estimates are obtained at a range of frequencies and then averaged. The third algorithm applied to power system analysis is based on optimal estimation theory. It is computationally efficient and gives optimal accuracy, at least for modes which are well separated in frequency.
Impact and interest:
Citation counts are sourced monthly from Scopus and Web of Science® citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.
Fulltext downloads:
Fulltext downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
ID Code:  16497 

Item Type:  QUT Thesis (PhD) 
Supervisor:  O'Shea, Peter, Ledwich, Gerard, & Palmer, Edward 
Keywords:  distributed power system, power system disturbance, power system oscillation, white noise, coloured noise, damping factor, meansquare error, CramerRao bound, orthogonal windows, leastsquares techniques, spectral averaging, optimal estimation theory, Prony analysis 
Divisions:  Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering Past > Schools > School of Engineering Systems 
Department:  Faculty of Built Environment and Engineering 
Institution:  Queensland University of Technology 
Copyright Owner:  Copyright Mark Glickman 
Deposited On:  03 Dec 2008 04:04 
Last Modified:  28 Oct 2011 19:48 
Export: EndNote  Dublin Core  BibTeX
Repository Staff Only: item control page