Electricity: motive power systems – Switched reluctance motor commutation control
Reexamination Certificate
2001-08-16
2002-12-24
Nappi, Robert E. (Department: 2837)
Electricity: motive power systems
Switched reluctance motor commutation control
C318S701000, C318S702000, C318S611000, C318S629000, C318S632000
Reexamination Certificate
active
06498447
ABSTRACT:
CROSS-REFERENCE TO RELATED APPLICATION
The subject matter of this application is related to the subject matter of British Patent Application No. GB 0020501.3, priority to which is claimed under 35 U.S.C. § 119 and which is incorporated herein by reference.
BACKGROUND OF THE INVENTION
1. Field of the invention
Embodiments of the invention relate to the control of an electronically commutated machine in which it is desired to shape an output parameter, for example torque ripple, noise or vibration. The invention is particularly, though not exclusively, applicable to switched reluctance machines.
2. Description of Related Art
In general, a reluctance machine is an electrical machine in which torque is produced by the tendency of its movable part to move into a position where the reluctance of a magnetic circuit is minimized, i.e. where the inductance of the exciting winding is maximized. Typically, circuitry is provided for detecting the angular position of the rotor and energizing the phase windings as a function of the rotor position. This type of reluctance machine is generally known as a switched reluctance machine and may be operated as a motor or a generator. The characteristics of such switched reluctance machines are well known and are described in, for example, “The Characteristics, Design and Application of Switched Reluctance Motors and Drives” by Stephenson and Blake, PCIM'93, Nürnberg, Jun. 21-24, 1993, incorporated herein by reference. This paper describes in some detail the features of the switched reluctance machine which together produce the characteristic cyclically varying inductance of the phase windings.
It is known that particular attention has to be paid to some aspects of the design of such a machine to control the torque ripple and/or noise and/or vibration and/or output current or voltage produced by the machine. For example, U.S. Pat. No. 6,072,260 (Randall) and U.S. Pat. No. 6,093,993 (McClelland), both of which are incorporated herein by reference, describe ways of choosing physical dimensions of the machine to control noise and torque ripple.
However, it may not be possible to choose all the design parameters to give the best performance in these respects, since there may be other constraints on the design. In any case, once the machine is built, it is no longer possible to vary these design parameters.
Methods of modifying control parameters to influence the operation of the machine have been studied for many years. For example, U.S. Pat. No. 5,461,295 (Horst) and U.S. Pat. No. 5,923,141 (McHugh), both of which are incorporated herein by reference, use predetermined current profiles to reduce the noise emitted. These, and similar, methods are effective with machines of known characteristics. Applying methods of this type requires careful characterization of the machine in order to achieve the best results. Individual differences between machines can reduce the effectiveness of these methods.
Some researchers have used iterative techniques to deduce an exact relationship between the input (e.g. the phase current) and an output (e.g. the torque) of the machine. Numerical techniques are invoked since there is no analytical solution to this problem, and the solution is generally given in tabular form. One such approach is reported by Sahoo et al. in “Determination of current waveforms for torque ripple minimization in switched reluctance motors using iterative learning: an investigation”, IEE Proceedings—Electric Power Applications, Vol. 146, No 4, July 99, pp. 369-377, which is incorporated herein by reference. However such a scheme requires large computing power, takes significant time to implement and is relatively inflexible, since the solution for one operating point will require recomputation for another. The same is true, in general, of schemes using artificial neural networks and neuro-fuzzy logic.
There is therefore a need for a method of control that is suitable for any electronically commutated machine and that does not require pre-knowledge of the characteristics of the machine. Preferably, such a method would be self-optimizing.
SUMMARY OF THE INVENTION
Embodiments of the invention analyze an output parameter of interest into two or more constituent harmonics and create an input signal to counter some or all of the analyzed harmonics in the machine output.
According to one embodiment of the invention, there is provided a closed-loop, self-optimizing controller for an electronically commutated machine that minimizes harmonic components in an output parameter by adjusting the magnitude and phase of harmonics in the input to the machine by treating each harmonic individually and in sequence. The sequence may be in ascending order of frequency. However, particularly significant components of the frequency spectrum of the parameter may be dealt with in preference to others by ordering them according to their magnitude, starting with the component with the largest magnitude. Preferably the controller includes an optimizing routine that iterates through the set of harmonic components to find an overall minimum torque ripple.
For a machine operating as a motor, the output parameter may be torque, vibration or acoustic noise, for example. For a machine operating as a generator, the output parameter may be current, voltage, vibration or acoustic noise, for example.
REFERENCES:
patent: 4645991 (1987-02-01), Ban et al.
patent: 5319297 (1994-06-01), Bahn
patent: 5461295 (1995-10-01), Horst
patent: 5636193 (1997-06-01), Ohmi
patent: 5923141 (1999-07-01), McHugh
patent: 6072260 (2000-06-01), Randall
patent: 6093993 (2000-07-01), McClelland
patent: 6426605 (2002-07-01), Toliyat et al.
Sahoo et al., “Determination of current waveforms for torque ripple minimisation in switched reluctance motors using iterative learning: an investigation,” Proc. IEE Electr. Power Appl., vol. 146, No. 4, Jul. 1999, pp. 369-377.
Hughes Austin
Mann Raymond
Stephenson John Michael
Dicke, Billig & Czaja P.A.
Duda Rina I.
Nappi Robert E.
Switched Reluctance Drives Ltd.
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