Load Estimation for Distribution Feeder Monitoring & Management

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dc.contributor.advisor Mesut E Baran, Committee Chair en_US
dc.contributor.advisor Kimberly Weems, Committee Member en_US
dc.contributor.advisor Winser E Alexander, Committee Member en_US
dc.contributor.author Singh, Urvir en_US
dc.date.accessioned 2010-08-19T18:19:14Z
dc.date.available 2010-08-19T18:19:14Z
dc.date.issued 2010-04-30 en_US
dc.identifier.other etd-04012010-150543 en_US
dc.identifier.uri http://www.lib.ncsu.edu/resolver/1840.16/6306
dc.description.abstract Load Estimation is an indispensable tool for distribution system studies, since knowledge of load profiles along the feeder has direct influence on system planning and operation activities. The main difficulties in the load modeling result from the random behavior of loads, diverse load shapes at customer sites, limitation and uncertainty in the information on loads. This thesis explores a new technique of load modeling and estimation on distribution systems. With the AMI technology on the distribution systems, real-time data about customer loads would be available at the control center, hence an estimate of loads on the distribution feeder can be made. With this estimation and the temperature forecast, a load model predicting the real-time load variations, can be made. This thesis elaborates the statistical approach used to build such a harmonics-based model with auto-correlated errors (a time series model). A time series approach to model and predict the random behavior of distribution feeder loads is explained, by harmonically decomposing the seasonal and daily variation of load consumption. With the historical power data of residential and commercial class available, statistical tools are used to perform load estimation on distribution feeder using SAS (Statistical Analysis System). Various load data sets can be grouped or clustered together, using available ‘clustering analysis’ techniques. The data of a meter whose readings are not available at any time instant can be estimated using the proposed time series method and other available meter readings from its respective cluster. en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dis sertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.subject load modeling en_US
dc.subject load estimation en_US
dc.subject distribution feeder en_US
dc.subject distribution feeder automation en_US
dc.title Load Estimation for Distribution Feeder Monitoring & Management en_US
dc.degree.name MS en_US
dc.degree.level thesis en_US
dc.degree.discipline Electrical Engineering en_US

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