Scholarly
    Communications Project


Document Type:Master's Thesis
Name:Pierre Cugnet
Email address:pcugnet@vt.edu
URN:
Title:Confidence Interval Estimation for Distribution Systems Power Consumption by using the Bootstrap Method
Degree:Master of Science
Department:Electrical Engineering
Committee Chair: Lamine.M.Mili
Chair's email:lmili@vt.edu
Committee\ Members:
Keywords:Distribution Systems, Power Consumption Estimation, Confidence Intervals,Bootstrap Method
Date of defense:July 15, 1997
Availability:Release the entire work for Virginia Tech access only.
After one year release worldwide only with written permission of the student and the advisory committee chair.

Abstract:

The objective of this thesis is to estimate, for a distribution network, confidence intervals containing the values of nodal hourly power consumption and nodal maximum power consumption per customer where they are not measured. The values of nodal hourly power consumption are needed in operational as well as in planning stages to carry out load flow studies. As for the values of nodal maximum power consumption per customer, they are used to solve planning problems such as transformer sizing. Confidence interval estimation was preferred to point estimation because it takes into consideration the large variability of the consumption values. A computationally intensive statistical technique, namely the bootstrap method, is utilized to estimate these intervals. It allows us to replace idealized model assumptions for the load distributions by model free analyses.

Two studies have been executed. The first one is based on the original nonparametric bootstrap method to calculate a 95% confidence interval for nodal hourly power consumption. This estimation is carried out for a given node and a given hour of the year. The second one makes use of the parametric bootstrap method in order to infer a 95% confidence interval for nodal maximum power consumption per customer. This estimation is realized for a given node and a given month. Simulation results carried out on a real data set are presented and discussed.


List of Attached Files

Ch1.pdf Ch2.pdf Ch3.pdf
Ch4.pdf Ch5.pdf Ch6.pdf
Ch7.pdf Etd.pdf Lastfile.pdf

At the author's request, all materials (PDF files, images, etc.) associated with this ETD are accessible from the Virginia Tech network only.


The author grants to Virginia Tech or its agents the right to archive and display their thesis or dissertation in whole or in part in the University Libraries in all forms of media, now or hereafter known. The author retains all proprietary rights, such as patent rights. The author also retains the right to use in future works (such as articles or books) all or part of this thesis or dissertation.