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Decomposition Techniques for Power System Analysis Open Access


Other title
Demand response
Load decomposition
Smart grid
Type of item
Degree grantor
University of Alberta
Author or creator
Supervisor and department
Xu, Wilsun (Electrical and Computer Engineering)
Examining committee member and department
Musilek Petr(Electrical and Computer Engineering)
Dick Scott(Electrical and Computer Engineering)
Xu, Wilsun (Electrical and Computer Engineering)
Ardakani Masoud (Electrical and Computer Engineering)
Dinavahi Venkata (Electrical and Computer Engineering)
Bhattacharya,Kankar (Electrical and Computer Engineering, University of Waterloo)
Department of Electrical and Computer Engineering
Energy Systems
Date accepted
Graduation date
Doctor of Philosophy
Degree level
In recent years, the increased public awareness of energy conservation has attracted serious attention to detailed energy consumption monitoring and management for the end users of power system. Load decomposition is a technique that can extract detailed sub-load information from compound load information. This technique decomposes a compound load such as an entire residential house into specific sub-load levels such as different home appliances by using only the aggregated metering data of the compound load. Through load decomposition, users can better understand the usage patterns of individual loads or load groups and therefore decide on how to save energy. On the utility side, load decomposition can be very helpful for load forecast, demand response program development, and Time-Of-Use price design. In the past, traditional methods are either too costly or inaccurate. Therefore, some researchers proposed a non-intrusive load monitoring (NILM) approach that can identify and track major sub-loads based on only the total signal collected from the meter-side with acceptable error. Recently, the vast deployment of smart meters has raised considerable interests in this approach. However, many critical problems still need to be solved before it truly becomes technically available for ordinary end-users. To solve the above problems, at the beginning, this thesis presents a novel NILM method based on event detection and load signature studies. The key idea is to model the entire operating cycle of a load and make identification based on event-window candidates. The proposed technique makes NILM more applicable for complex loads, more robust for load inventory change and can also simplify the training process; on the other hand, the thesis addresses a new and critical problem that previous researchers ignored---the non-intrusive extraction of load signatures. The proposed approach is an unsupervised non-intrusive approach which can automatically extract load signatures by using the meter-side data and requires almost zero effort from users. This thesis also discusses how to estimate the energy for several key components in a residential house such as the NILM identified appliances, load groups and background power. Based on estimation, residential energy characteristics are discussed with respect to the Time-of-use price.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
M.Dong, H.E.Mazin and W.Xu, "Data segmentation algorithms for a time-domain harmonic source modeling method," Electrical Power & Energy Conference (EPEC), 2009 IEEE , vol., no., pp.1-6, 22-23 Oct. 2009M.Dong, Paulo C.M.Meria, W.Xu and Walmir Freitas, “An Event Window Based Load Monitoring Technique for Smart Meters”, IEEE Transactions on Smart Grid, vol.3,No.2, pp 787-796, June 2012M.Dong, Paulo C.M.Meria and W.Xu , “Non-intrusive Signature Extraction for Major Residential Loads”,IEEE Transactions on Smart Grid, accepted and in press , Feb. 2013

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