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Harmonic compensation using advanced electric machines

Authors: M.T. Abolhassani; H.A. Toliyat; P. Enjeti;

Harmonic compensation using advanced electric machines

Abstract

In this paper, a new electromechanical active harmonic filter is proposed. The proposed electromechanical harmonic filter is essentially a rotating asymmetric air gap concentrated winding synchronous machine (AACWSM) with AC and DC excitation field. It is shown that the AACWSM with its unique design can be used to cancel the most dominant current harmonics of the adjacent nonlinear load. The filter is connected in a shunt manner between a nonlinear load and utility. This new AACWSM with its nonlinear current waveform can generate the nonlinear load required harmonics current. By proper control of the field excitation, the proposed electromechanical harmonic filter can compensate for the 5/sup th/ and 7/sup th/ harmonic currents in the electric power distribution system. In addition, 11/sup th/ and 13/sup th/ harmonics magnitudes are also significantly reduced. Analysis and simulation are presented to demonstrate the effectiveness of the proposed harmonic compensation method.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Top 10%
Average
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