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Signal Processing and Robust Statistics for Fault Detection in Photovoltaic Arrays


Abstract Photovoltaics (PV) is an important and rapidly growing area of research. With the advent of power system monitoring and communication technology collectively known as the "smart grid," an opportunity exists to apply signal processing techniques to monitoring and control of PV arrays. In this paper a monitoring system which provides real-time measurements of each PV module's voltage and current is considered. A fault detection algorithm formulated as a clustering problem and addressed using the robust minimum covariance determinant (MCD) estimator is described; its performance on simulated instances of arc and ground faults is evaluated. The algorithm is found to perform well on many types of faults commonly occurring in PV arr... (more)
Created Date 2012
Contributor Braun, Henry Carlton (Author) / Tepedelenlioglu, Cihan (Advisor) / Spanias, Andreas (Advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / Engineering / Alternative energy / Anomaly Detection / Robust Statistics / Smart Grid
Type Masters Thesis
Extent 73 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note M.S. Electrical Engineering 2012
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis