Since the onset of automation, industry has relied on adjustable
speed drives to accurately control the speed of motors. Recent
advances have increased the number of adjustable speed drives
hitting the market. The proper operation of the speed drives
requires electrical supply with relatively high power quality which
is not the case in most industrial facilities. Power quality
problems such as harmonic, sag, swell, flicker, and unbalance can
trip the speed drive with a wrong message, which is referred as a
premature tripping. Although the power quality problems can be
mitigated by using custom power devices, they are bulky and costly.
Moreover, they themselves might adversely affect the operation of
the adjustable speed drive. A comprehensive study done in this
thesis presents the overlooked effect of the custom power devices on
the speed drive stability. It is found that the speed drive system
might trip due to its interaction with custom power devices.
Obviously, it is vital to increase ASD immunity to premature
tripping because of poor power quality or custom power.
This thesis offers fast, efficient and robust algorithms to achieve
this immunity by retrofitting the ASD control unit and integrating
the power conditioning function with the adjustable speed drive.
Therefore, the power quality problem is mitigated and the drive
system performance is significantly enhamced. Such integration
requires the modification of the control unit by considering various
elements such as envelope tracking, phase-locked loop, symmetrical
component extraction, and the controller. Simple but robust and fast
algorithms are proposed for such elements based on a newly developed
energy operator algorithm. The developed energy operator and the
developed algorithms overcome the drawbacks of the existing
algorithms.
| Identifer | oai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/3088 |
| Date | 14 May 2007 |
| Creators | Eldery, Mohamed |
| Source Sets | University of Waterloo Electronic Theses Repository |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | 3875041 bytes, application/pdf |
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