Flicker Attenuation-Part I: Response of Three-Phase Induction Motors to Regular Voltage Fluctuations

Voltage fluctuations leading to lamp flicker that originate in one place in a power system tend to propagate to other parts of the network with some level of attenuation depending on the network impedances and the loads connected. Numerous subsynchronous-type frequency components exist in these volt...

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Bibliographic Details
Published in:IEEE transactions on power delivery 2008-04, Vol.23 (2), p.1207-1214
Main Authors: Tennakoon, S., Perera, S., Robinson, D.
Format: Article
Language:English
Subjects:
A.c. Machines
Amplitude modulation
Applied sciences
Attenuation
Australia
Disturbances. Regulation. Protection
Electrical engineering. Electrical power engineering
Electrical machines
Electrical power engineering
Exact sciences and technology
Flicker
flicker attenuation
Frequency
Impedance
Induction machines
Induction motors
Lamps
Miscellaneous
Motors
Power networks and lines
small-signal models
Stators
Voltage fluctuations
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Summary:Voltage fluctuations leading to lamp flicker that originate in one place in a power system tend to propagate to other parts of the network with some level of attenuation depending on the network impedances and the loads connected. Numerous subsynchronous-type frequency components exist in these voltage fluctuations that are responsible for lamp flicker. The rudimentary theory and the experimental measurements support the idea that industrial load bases, which contain a large percentage of mains-connected induction motors, tend to attenuate flicker better compared to residential load bases having mainly passive loads. This paper reports on the response of three-phase induction motors of several sizes when subjected to low-frequency voltage fluctuations: 1) the case where a balanced single-frequency component is superimposed on the mains voltage and 2) the case where the mains voltage is sinusoidally amplitude modulated, a scheme that is frequently used in the flicker-related work. Small-signal models are presented that will enable systematic understanding of the behavior which is verified using large signal models.
ISSN:0885-8977
1937-4208
DOI:10.1109/TPWRD.2007.908788