System identification techniques and modeling for nonintrusive load diagnostics

This thesis addresses the requirements of a system that can detect on/off transients and identify physical parameters of loads connected to a power distribution network. The thesis emphasizes three areas; a transient classifier that recognizes load transients using a pattern matching scheme, parameter estimation techniques suited for use with this classifier, and case studies of modeling and identification motivated by diagnostics and performance monitoring. Together, these areas support applications that can extract detailed load information from centralized, easily accessible parts of a distribution network. A new approach and implementation of pattern-based nonintrusive transient classification is presented. The classifier is nonintrusive in the sense that it uses aggregated measurements at a central location and does not require instrumentation of individual loads. The classifier implementation includes a framework that integrates preprocessors for AC and DC environments, programs that present results, and load-specific parameter identification modules that are executed as their associated transients are classified. An obstacle for these parameter identification programs is that a good initial guess is needed for the iterative optimization routines typically used to find parameter estimates. Two approaches are given to overcome this problem for certain systems. The first extends conventional optimization methods to identify model parameters given a poor initial guess. The second approach treats the identification as a modeling problem and suggests ways to construct "inverse" models that map observations to parameter estimates without iteration. The techniques presented in the thesis are demonstrated with simulation data and in real world scenarios including a dormitory, an automobile, and an experimental building. Thesis Supervisor: Steven B. Leeb Title: Carl Richard Soderberg Associate Professor of Power Engineering Acknowledgments I would like to thank Professor Steven Leeb for his guidance and patience. Professor Leeb's enthusiasm is unwavering and inspiring, and his support over the last few years is much appreciated. I am also thankful for the many contributions of my thesis readers, Professors Kirtley, Norford, and White. The implementation reported in this thesis depends largely on freely distribed software. Tools such as gcc, g77, Octave, Perl, and routines from packages like ODEPACK, MINPACK, LAPACK and the BLAS were essential. I was consistently amazed by the rapid and effective advice provided by members of the free software community. In particular, Matt Welsh's das1200 driver and email suggestions convinced me that the project would work as envisioned. Thanks are due to Andy Suby and the crew at the Iowa Energy Center for their essential help in setting up experiments and collecting data. Students Craig Abler, Chris Laughman and Chris Salthouse provided important support throughout. Professors Bernard Lesieutre and George Verghese deserve thanks for their many useful suggestions. Intel, Tektronix and Hewlett-Packard generously donated equipment used in this work. Other support was provided by the National Science Foundation; AMP incorporated, including Mr. Joseph P. Sweeney, Mr. Mike LeVan, Mr. Tom Davis, and Dr. Howard Peiffer; and Dr. Emanuel Landsman.