Electricity: motive power systems – Plural diverse motor controls – Running-speed control
Reexamination Certificate
2002-07-18
2004-03-30
Ro, Bentsu (Department: 2837)
Electricity: motive power systems
Plural diverse motor controls
Running-speed control
C318S561000, C318S621000, C318S632000, C318S812000
Reexamination Certificate
active
06713978
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Technical Field
The present invention relates to a method and system for determining the speed of induction motors in general, and in particular to a neural network-based method and system for determining the dynamic speed of induction motors.
2. Description of the Related Art
Induction motors are commonly found in power plants and various types of manufacturing facilities throughout the world. An induction motor typically includes a stationary stator and a rotatable rotor. The ability to accurately determine the speed of a rotating rotor with respect to a stationary stator within an induction motor is vitally important to the every day operations of induction motors. However, it is generally undesirable to introduce transducers or other physical sensors for measuring the speed or the position of a rotating rotor because of the additional cabling and increased costs. For example, for a small motor, such as 5 horsepower or less, the cost of speed sensor installation could be comparable to the cost of the motor itself. Also, the probability of failure of the speed sensor is higher than the probability of failure of the small motor.
Hence, sensorless rotor speed estimation methods (or non-intrusive rotor speed estimation methods) have emerged as a cost-effective alternative over the sensor-based rotor speed estimation methods. Sensorless rotor speed estimation methods generally fall under one of the following two categories, this is, either by using an induction motor model, or by analyzing the rotor slot harmonic (RSH) content of the stator current waveform.
The model based speed estimation methods are inconvenient because they have to rely on detailed motor parameters, i.e., a priori knowledge of the motor's electrical (and in some cases mechanical) characteristics, in order to operate properly. However, those parameters are typically available only to the designer of the motor. Further, many motor based speed estimation methods assume linear motor models and time invariant parameters, which leads to poor speed estimation. The RSH based speed estimation methods are not completely acceptable either because they sometimes perform poorly at low motor speeds due to difficulty in tracking low frequency harmonics. Additionally, RSH based speed estimation methods require the calculation of the Fast Fourier Transform (FFT) of the stator current, and as a result, RSH based speed estimation methods inherit all the typical limitations of an FFT based scheme. For example, for an accurate calculation of the RSH, a relatively higher frequency resolution is required (typically in the range of 1-2 Hz), which implies longer data windows. Also, for certain rotor-stator slot combinations, primarily those at lighter loads, the RSH may not be readily detectable. While the transient speed can be calculated by using a moving window, it leads to poor time localization of the estimated speed response. Moreover, FFT based schemes are computationally burdensome for real-time implementation, and more expensive data processing equipment is needed, which would offset any cost advantages the RSH based speed estimation methods might deliver.
Consequently, it would be desirable to provide an improved sensorless method and system for determining the dynamic speed of a rotating rotor.
SUMMARY OF THE INVENTION
In accordance with a preferred embodiment of the present invention, a non-linear, semi-parametric neural network-based adaptive filter is utilized to determine the dynamic speed of a rotating rotor within an induction motor, without the explicit use of a speed sensor, such as a tachometer. The neural network-based filter is developed using actual motor current measurements, voltage measurements, and nameplate information. The neural network-based adaptive filter is trained using an estimated speed &ohgr;
RSH
calculator derived from the actual current and voltage measurements. The training of the neural network-based adaptive filter takes place off-line; thus, time lags resulting from the FFT-based estimation can be compensated. The neural network-based adaptive filter uses voltage and current measurements to determine the instantaneous speed of a rotating rotor. The neural network-based adaptive filter also includes an on-line adaptation scheme that permits the filter to be readily adapted for new operating conditions during operations and for adaptation to new motors, though the on-line adaptation scheme needs only to be used infrequently.
All objects, features, and advantages of the present invention will become apparent in the following detailed written description.
REFERENCES:
patent: 5296794 (1994-03-01), Lang et al.
patent: 5298847 (1994-03-01), Kerkman et al.
patent: 5467025 (1995-11-01), Ray
patent: 5745382 (1998-04-01), Vilim et al.
patent: 5811957 (1998-09-01), Bose et al.
patent: 6008618 (1999-12-01), Bose et al.
patent: 6069467 (2000-05-01), Jansen
patent: 6137258 (2000-10-01), Jansen
patent: 6288513 (2001-09-01), Green
patent: 6301572 (2001-10-01), Harrison
patent: 6353302 (2002-03-01), Ramachandran et al.
patent: 6388420 (2002-05-01), Jansen et al.
patent: 6577096 (2003-06-01), Cho
patent: 6590362 (2003-07-01), Parlos et al.
First Author: Bharadwaj Abstract entitled “A Neural Network-Based Speed Filter for Induction Motors: Adapting to Motor Load Changes” from IJCNN (1999).
First Author: Bharadwaj Paper entitled “A Neural Network-Based Speed Filter for Induction Motors: Adapting to Motor Load Changes” from IJCNN (1999).
First Author: Bharadwaj Presentation document entitled “A Neural Network-Based Speed Filter for Induction Motors: Adapting to Motor Condition Changes” (1999).
First Author: Bharadwaj Paper entitled “Adaptive Neural Network-Based State Filter for Induction Motor speed Estimation” from IECON (1999).
First Author: Bharadwaj Presentation paper entitled “Adaptive Neural Network-Based State Filter for Induction Motor Speed Estimation” from IECON (1999).
First Author Zamora “Rotor Speed Estimator for Induction Motors Using Voltage and Current Measurements” from Control Engineering Practice 6 (1998) 369-383.
First Author Schauder “Adaptive Speed Identification for Vector Control of Induction Motors without Rotational Transducers” from IEEE Transactions of Industry Applications, vol. 28, No. 5 (Sep./Oct. 1992).
First Author Buja “Accuracy of the Speed Estimation in the Sensorless Induction Motor Drives Based on the MRAS Technique”.
First Author Armstrong “A Comparison of Estimation Techniques for Sensorless Vector Controlled Induction Motor Drives,” IEEE (1997).
First Author: Hurst “Sensorless Speed Measurement Using Current Harmonic Spectral Estimation in Induction Machine Drives,” IEEE Transactions on Power Electronics, vol. 11, No. 1 (Jan. 1996).
First Author Blasco-Gimenez “Performance of FFT-Rotor Slot Harmonic Speed Detector for Sensorless Induction Motor Drives,” IE Proceedings Elctr. Power Appl. vol. 143, No. 3 (May 1996).
First Author Orlowska-Kowalska “Neural network Application for Flux and Speed Estimation in the Sensorless Induction Motor Drive,” IEEE Catalog No. 97TH82890 (1997).
First Author Mehrotra “Speed Estimation of Induction Motor Using Artificial Neural Networks,” IEEE (1996).
First Author Ben-Brahim “Identification of Induction Motor speed Using Neural Networks,” TH0406 (1993) IEEE.
First Author Kazmierkowski “Sensorless Control of Induction Motor Using a Neural network for speed Estimation,” IEEE Catalog No. 97TH8280 (1997).
First Author Stronach “Design, DSP Implementation and Performance of Artificial-Intelligence-Based sped Estimator for Electromechanical Drives,” IEEE Proc. Control Theory Appl. vol. 145, No. 2 (Mar. 1998).
First Author Ferrah “A Speed Identifier for Induction Motor Drives Using Real-Time Adaptive Digital Filtering,” IEEE Transactions on Industry Applications, vol. 34, No. 1 (Jan./Feb. 1998).
First Author Benbouzid “A Review of Induction Motors Signature Analysis as a Medium for Faults Detection,” IEEE (1998).
First Author Schoen “Effects of Time Varying Loads on Rotor Fault Detection in Induction Machin
Bharadwaj Raj M.
Parlos Alexander G.
Bracewell & Patterson LLP
Ro Bentsu
Texas A&M University System
LandOfFree
Method and system for determining induction motor speed does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Method and system for determining induction motor speed, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method and system for determining induction motor speed will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3244604