Dissertation
Incipient soft-fault detection in DC-DC converters through switching observers
Doctor of Philosophy (Ph.D.), Drexel University
15 Dec 2022
DOI:
https://doi.org/10.17918/00001458
Abstract
An observer or state estimator can be programmed into a microcontroller to operate an observer-based fault detection system to detect faults in dc-dc switching converters or other closed loop controlled linear systems. A non-zero difference between the output of the observer and the dc-dc converter can be used to indicate a fault. This difference is defined as an observer residual. The performance of the observer-based fault detection system is dependent on selection of the observer gains. The observer gains are a vector of constants that control the placement of the poles in the differential equations of the observer system model. The location of the poles in the observer model affects the magnitude of the observer residual. A step by step design method to calculate the observer gains and adjust the pole placement of the observer model is presented. The dc-dc observer-based fault detection system is simulated to empirically select observer gains that provide a larger magnitude observer residual. The empirically selected observer gains are used in a microcontroller controlled dc-dc converter with an observer-based fault detection system to demonstrate how the proper selection of the observer gains can produce observer residuals with larger magnitudes, which will more easily detect a fault.
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Details
- Title
- Incipient soft-fault detection in DC-DC converters through switching observers
- Creators
- John Tsinetakes
- Contributors
- Leonid Hrebien (Advisor)Moshe Kam (Advisor)
- Awarding Institution
- Drexel University
- Degree Awarded
- Doctor of Philosophy (Ph.D.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Number of pages
- xxii, 284 pages
- Resource Type
- Dissertation
- Language
- English
- Academic Unit
- College of Engineering (1970-2026); Electrical (and Computer) Engineering (1970-2026); Drexel University
- Other Identifier
- 991020034314604721