CN108761117B - Portable current detection rotating speed tester - Google Patents
Portable current detection rotating speed tester Download PDFInfo
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- CN108761117B CN108761117B CN201810846959.5A CN201810846959A CN108761117B CN 108761117 B CN108761117 B CN 108761117B CN 201810846959 A CN201810846959 A CN 201810846959A CN 108761117 B CN108761117 B CN 108761117B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P3/00—Measuring linear or angular speed; Measuring differences of linear or angular speeds
- G01P3/42—Devices characterised by the use of electric or magnetic means
- G01P3/44—Devices characterised by the use of electric or magnetic means for measuring angular speed
- G01P3/48—Devices characterised by the use of electric or magnetic means for measuring angular speed by measuring frequency of generated current or voltage
Abstract
The invention relates to a portable current detection rotating speed tester which is convenient to carry, small in size and light in weight and can be applied to most motors. The portable current detection rotating speed tester adopts a sensorless rotating speed online identification algorithm based on stator current spectrum analysis, proposes two analysis methods based on stator current spectrum and stator current square spectrum, accurately extracts rotor frequency in a spectrogram of the rotating speed tester after fast Fourier transformation, and respectively adopts a complex modulation ZoomFFT (fast Fourier transform) spectrum refinement technology and a frequency correction technology based on a ratio method to carry out optimization analysis on an original frequency spectrum so as to save hardware cost and improve frequency resolution.
Description
Technical Field
The invention relates to a testing device, in particular to a portable current detection rotating speed tester which is used for detecting the field efficiency of the rotating speed of a motor.
Background
Along with the continuous aggravation of the problem of energy shortage and the greenhouse effect, the development and utilization of clean energy, energy conservation and emission reduction become the focus of attention of countries around the world. Energy conservation is particularly necessary for China, where resources are scarce and the population is vast. The small and medium three-phase asynchronous motor is the most widely applied high-energy-consumption product, the application range of the motor is throughout the various fields of national economy, the electricity consumption is about 50% of the total electricity consumption of the whole country, the motor operation efficiency is generally low in about 2/3 in the industrial field, and therefore, the improvement of the motor operation efficiency has important significance. There are many reasons for the low operation efficiency of the motor, including problems of the motor itself, but more importantly, problems of use, such as low load factor, aging of the motor, etc. There are many methods for solving this problem, such as popularizing and using a high-efficiency motor, replacing a motor with significantly lower operation efficiency, or improving the operation efficiency of the motor by a proper control method. To achieve this, the actual operating efficiency of the motor should be accurately detected without interfering with its normal operation. Conventional laboratory environment-based detection methods cannot be used directly for field detection because no-load tests, short-circuit tests, stator resistance detection and rotational speed detection are difficult to accomplish in field situations.
For large-sized motors, an on-line motor state monitoring system is often arranged, the running efficiency of the motor can be generally detected, and for small-and medium-capacity motors, the monitoring system is generally not arranged from the viewpoint of cost. And the middle and small capacity motors account for the vast majority of the motors in use, both in terms of number and electricity consumption. On-line monitoring of all small and medium-sized motors is almost impossible, which has to be done by on-site personnel. For this reason, it is necessary to develop a low-cost field efficiency inspection apparatus suitable for small and medium-sized motors. The field personnel generally want the detection device to be as simple to operate as possible, small in size, light in weight and portable.
The common portable detection device is a handheld infrared or laser sensor, is low in price compared with a photoelectric encoder, does not need to be arranged on a motor, is simple to operate, and is practical for small, medium and low-power motors. However, for large motors where the motor shaft cannot be observed, such sensors cannot be used, and the measurement accuracy of the infrared or laser sensor is low, so that the requirements of a high-accuracy test system are often not met. Comprehensive consideration shows that for a field portable motor test system, the sensor is unreliable to directly obtain the motor rotation speed, so that the research on the on-line motor rotation speed identification algorithm without the sensor has important significance for obtaining a high-precision motor rotation speed result.
Disclosure of Invention
In order to solve the technical problems in the background technology, the invention provides a portable current detection rotating speed tester which is light in weight, convenient, practical and high in measurement accuracy.
The technical scheme adopted for solving the technical problems is as follows:
a portable current detection rotating speed tester is characterized in that a hole is formed in the top of a shell of the rotating speed tester, a wire is connected to the top of the shell of the rotating speed tester and used for connecting with a tested motor, a processor is arranged in the rotating speed tester, the processor detects the current of the tested motor through a current sensor, and the current detected by the current sensor is input into the processor after being subjected to signal regulation and filtering in a filter;
the processor comprises a micro-processing module, wherein the micro-processing module is connected with a current data acquisition module, a current data processing module, a frequency spectrum processing module and a rotating speed processing module, the current data acquisition module is connected with a current sensor, the current data acquisition module sends the stator rotating speed of a motor to be tested to the current data processing module through the micro-processing module, the current data processing module sends a demodulated signal to the frequency spectrum processing module through the micro-processing module, and the frequency spectrum processing module sends the signal after frequency spectrum processing to the rotating speed processing module through the micro-processing module, finds the rotor frequency and calculates the rotating speed of the motor.
The surface of the shell is provided with a display screen and a plurality of buttons for control.
The processor is an ARM microcontroller.
The current sensor is a rogowski coil.
The processor is also provided with a display module and a keyboard module.
The processor is also provided with a signal processing module, the processor sends a processing signal to the control module through the signal processing module, and the control module controls the rotation of the motor to be tested.
The portable current detection rotation speed tester calculates the rotation speed of the motor based on a rotation speed identification algorithm of a stator current frequency spectrum.
The invention has the beneficial effects that:
this patent rotational speed tester conveniently carries, and is small, light in weight, and can be applied to most motors, this kind of portable electric current detects rotational speed tester when detecting the rotational speed of motor, measures the electric current of being surveyed the motor through contactless rogowski coil earlier, obtains stator current original signal, sends it in carrying out current demodulation, spectral analysis, spectral refinement, spectral correction, frequency search to it in the treater after handling through the wave filter later, and the rotational speed of motor is finally calculated to the accuracy, and the rotational speed error that obtains is less, and the precision is higher.
In addition, the identification algorithm adopted by the portable current detection rotating speed tester has the following advantages:
(1) The portable current detection rotating speed tester utilizes a sensorless rotating speed on-line identification method to replace the function of an original rotating speed sensor to obtain the rotating speed parameter required by calculating the motor efficiency.
(2) A sensorless rotating speed online identification algorithm based on stator current spectrum analysis is adopted, two analysis methods based on stator current spectrum and stator current square spectrum are provided, and rotor frequency is accurately extracted from a spectrogram of the rotor frequency after fast Fourier transform. In order to save hardware cost and improve frequency resolution, a spectrum refining technology based on complex modulation ZoomFFT and a spectrum correction technology based on a ratio method are respectively adopted to carry out optimization analysis on an original spectrum, and experimental results show that after the optimization algorithm is applied, the identification accuracy of the rotating speed is improved from 5r/min to 2r/min, and the method is close to the international advanced level.
Drawings
The invention will be further described with reference to the drawings and examples.
FIG. 1 is a diagram showing the configuration of a portable current detecting rotational speed tester according to the present invention.
Fig. 2 is a schematic diagram of a portable current sensing rotational speed tester of the present invention.
Fig. 3 is a schematic diagram of the internal modules of the processor of fig. 2.
FIG. 4 is a flowchart of a speed identification algorithm used in the portable current sensing speed tester of the present invention.
Fig. 5 is a graph showing the amplitude-phase characteristics of a butterworth low-pass filter of the low-pass filter according to the present invention.
Fig. 6 is a flow chart of a complex modulation refinement spectrum analysis method in the stator current spectrum analysis of the present invention.
Fig. 7 is a diagram of the simulation results of original spectrum and refined spectrum based on Zoom FFT with relatively close frequency components of the signal in the complex modulation refined spectrum analysis method in the stator current spectrum analysis of the present invention.
Fig. 8 is a diagram of simulation results of original spectrum and refined spectrum based on Zoom FFT with far distance between frequency components of signal in the complex modulation refined spectrum analysis method in stator current spectrum analysis of the present invention.
Fig. 9 is a graph of a hanning window function spectrum at the time of spectrum correction based on the ratio method of the present invention.
FIG. 10 is a graph showing the frequency versus amplitude correction for spectral correction based on the ratio method of the present invention.
Fig. 11 is a graph showing the spectrum analysis results before and after the spectrum correction of the hanning window with relatively close frequency components of the signal according to the present invention.
Fig. 12 is a graph showing the spectrum analysis results before and after the spectrum correction of the hanning window with the frequency components far apart.
Fig. 13 is a graph of actual rotational speed of a motor acquired by a motor measurement module during online simulation according to the present invention.
Fig. 14 is a graph of the analysis result of the motor speed on-line identification algorithm based on the rotor frequency to the stator current.
Fig. 15 is a schematic diagram of the method for on-line identification of the rotational speed of a motor based on stator current spectrum analysis according to the present invention.
In the figure: the device comprises a processor 1, a micro-processing module 1.1, a current data acquisition module 1.2, a current data processing module 1.3, a frequency spectrum processing module 1.4, a rotating speed processing module 1.5, a current sensor 2, a filter 3, a display module 4, a keyboard module 5, a signal processing module 6, a control module 7 and a motor 8 to be tested.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The invention relates to a portable current detection rotating speed tester, referring to fig. 1, a hole is formed in the top of a shell of the portable current detection rotating speed tester, wires are connected out and used for connecting wires with a tested motor, and a display screen and various buttons are arranged on the surface of the shell for control.
The portable current detection rotating speed tester is internally provided with a microcontroller, stator current of a motor to be detected is collected through a current sensor, current demodulation, spectrum analysis, spectrum refinement and spectrum correction are carried out on the current sensor through an ARM microcontroller, and finally, the rotating speed of the motor is accurately calculated.
Referring to fig. 2, the portable current detection rotation speed tester mainly comprises a processor 1, wherein the processor 1 is an ARM microcontroller, and has functions of current demodulation spectrum analysis, spectrum refinement, spectrum correction, frequency search, rotation speed calculation and the like.
The processor 1 detects the current of the tested motor 8 through the current sensor 2, the tested motor 8 is a three-phase alternating current asynchronous motor which is most widely used, the current sensor 2 is a rogowski coil, the rogowski coil is also called a current measuring coil, the measuring principle is based on Faraday electromagnetic induction law, the output signal is the differentiation of the current to time, and the real current signal can be restored through an integrating circuit.
The motor stator current measured by the current sensor 2 is subjected to signal conditioning and filtering in the filter 3 and then is input into the processor 1.
Referring to fig. 3 and fig. 4, the processor 1 includes a micro-processing module 1.1, the micro-processing module 1.1 is connected with a current data acquisition module 1.2, a current data processing module 1.3, a frequency spectrum processing module 1.4 and a rotation speed processing module 1.5, wherein the current data acquisition module 1.2 is connected with a current sensor 2 to obtain the stator rotation speed of the tested motor 8, the current data acquisition module 1.2 sends the stator rotation speed of the tested motor 8 to the current data processing module 1.3 through the micro-processing module 1.1, the current data processing module 1.3 demodulates the current, the current data processing module 1.3 sends the demodulated signal to the frequency spectrum processing module 1.4 through the micro-processing module 1.1, the frequency spectrum processing module 1.4 performs frequency spectrum analysis, frequency spectrum refinement and frequency spectrum correction on the signal, the frequency spectrum processing module 1.4 sends the corrected signal to the rotation speed processing module 1.5 to find the rotor frequency and calculate the motor rotation speed, and the obtained motor rotation speed precision is higher.
The processor 1 carries out current demodulation, spectrum analysis, spectrum refinement, spectrum correction and frequency search on the motor, and finally, the rotating speed of the motor is accurately calculated.
The processor 1 is further provided with a display module 4, a keyboard module 5 and a signal processing module 6, the processor 1 can send the calculated motor rotation speed adjustment value to the control module 7 through the signal processing module 6, and the control module 7 is connected with the motor 8 to be tested, so that the rotation speed of the motor 8 to be tested can be controlled.
The motor rotation speed can be calculated by each module in the processor 1 through an optimized rotation speed identification algorithm, and the obtained motor rotation speed error is smaller and the precision is higher.
The rotating speed identification algorithm is a rotating speed identification algorithm based on a stator current frequency spectrum or a rotating speed identification algorithm based on a stator current square frequency spectrum, and according to Fourier analysis, any periodic signal can be regarded as superposition of sine waves with different amplitudes and different frequencies, namely, a sine signal with a certain frequency spectrum in the periodic signal can find out the corresponding frequency component in the frequency spectrum.
After the symmetrical three-phase current is introduced into the stator winding, a rotating magnetic field, namely an electromagnetic generating process, is generated due to the electromagnetic induction field. Meanwhile, the closed rotor winding coil cuts the magnetic induction wire under the action of the rotating magnetic field generated by the stator winding, and induced electromotive force and induced current are generated in the winding, namely a 'magneto electricity generation' process. The electrified conductor rotates under the action of electromagnetic force in the magnetic field, meanwhile, the rotor winding can generate a changed magnetic field after electrification due to the electromagnetic induction phenomenon, the stator winding induces corresponding induction current under the action of the rotor induced magnetic field, and the induction current is related to the rotating speed of the rotor, so that the rotating speed of the motor can be calculated by detecting the current component. The schematic flow chart is shown in fig. 15.
1) Stator current demodulation
(1) A rotational speed identification algorithm based on a stator current spectrum:
the motor stator current is typically collected by a current sensor. Only one phase of three-phase current is needed in the algorithm. Demodulation of the stator current is a core part of the algorithm. According to fourier analysis, any periodic signal can be decomposed into sinusoidal signals with different frequency components, so that the acquired stator current can be demodulated to obtain the frequency components of the stator current, and the required rotor frequency can be extracted.
The stator current can be expressed as:
i(t)＝[k 1 +k 2 cos(2πf 2 t)+k 3 cos(2πf 3 t)+...+k n cos(2πf n t)]cos(2πf 1 t) 3.1
wherein k is n And f n The amplitude and frequency of the n harmonics, respectively. K (K) 1 =50hz is the frequency of the ac power supply.
The motor stator current can also be expressed as:
i(t)＝m(t)cos(2πf 1 t) 3.2
wherein order
m(t)＝k 1 +k 2 cos(2πf 2 t)+k 3 cos(2πf 3 t)+...+k n cos(2πf n t) 3.3
m (t) is the magnitude of the stator current.
Then square the stator current to obtain
Considering that the signal frequency components in the signal m (t) are all smaller than f 1 The stator current is passed through a low pass filter, which typically has a cut-off frequency of less than 2f, to obtain the m (t) signal from the stator current 1 . The signal passing through the low pass filter is then square-cut, thereby enabling demodulation of the stator current. To prevent square openingPost signal distortion typically begins by dc biasing the stator current before demodulation.
After the signal represented by the formula 3.4 passes through the low-pass filter designed in the above way, the m (t) signal is extracted, and the low-pass filtered signal is subjected to squaring processing, so that the stator current signal finally used for spectrum analysis can be obtained, as shown in the following formula.
i(t)＝0.707m(t) 3.5
(2) A rotating speed identification algorithm based on a stator current square spectrum:
the algorithm mainly comprises three parts of phase current acquisition, phase current analysis and spectrum analysis. Wherein the first and third parts are identical to the first method, the second part-phase amperometric analysis is mainly described herein.
Considering an ideal working condition of an asynchronous motor, the phase current expression is as follows:
i(t)＝I max cos(ωt) 3.6
wherein I is max Is the amplitude of the phase current.
The expression of the square of the phase current is:
analysis of its frequency will see that there will be a DC component first, and in addition there will be a 2f s Frequency components. If the motor rotor eccentricity is considered, in this case, the expression of the square of the stator current is:
wherein I is max Is the amplitude of the phase current, I lsb Is the amplitude of the low sideband component of the phase current, I lsb Is the amplitude of the high sideband component of the phase current. It can be seen from the expression that the frequency spectrum of the square of the current comprises a DC component, 2f s Frequency component, two sidebands 2f s -f r And 2f s +f r And (b)Additional frequency component f r ，2f r ，2f s -2f r And 2f s +2f r . So by performing spectral analysis on the stator current square, f can be accurately extracted from the spectrum r The frequency component, i.e., the rotor frequency, identifies the motor speed.
2) Low pass filter design
Since low-pass filtering is required for the signal before the final signal is obtained, the filtering technology is the most basic and important technology in signal processing, and useful signal components can be extracted from complex signals through a filtering means, and useless components are filtered. The filters are signal processing devices with corresponding transmission characteristics, and are mainly classified as follows:
(1) According to the range, the filter can be divided into a low-pass filter, a high-pass filter, a band-pass filter and a band-stop filter;
(2) Time-domain characteristics can be divided into IIR and FIR filters;
(3) The signal type can be classified into an analog filter and a digital filter.
Comprehensively considering the requirements of the patent, a Butterworth low-pass filter is designed by utilizing Matlab, and the Butterworth low-pass filter is characterized by having the largest flat amplitude-frequency characteristic in a passband and smoothly and monotonically decreasing along with the increase of frequency; the higher the order, the closer the characteristics are to rectangular, and the narrower the excess bandwidth.
The amplitude square function of the butterworth low-pass filter is:
wherein omega c N is the order of the filter, which is the cut-off frequency of the filter. Where N is an integer, called a filter coefficient, the larger N, the better the approximation of the passband and stopband, and the steeper the transition band.
The function of providing a butterworth low pass filter in Matlab signal processing toolbox is as follows
[n,wn]＝buttord(wp,ws,rp,rs)；
[b,a]＝butter(n,wn)；
Where n is the filter order, wn is the system filter cut-off frequency, b, a is the designed filter coefficient. wp is the normalized passband cut-off frequency; ws is normalized stop band cut-off frequency; rp is the maximum ripple attenuation within the passband; rs is the minimum attenuation in the stop band.
Power supply frequency f of three-phase power in China 1 The passband cut-off frequency of the low pass filter was set to 100Hz, the stopband cut-off frequency was set to 200Hz, and the butterworth low pass filter amplitude-phase characteristic designed in this patent is shown in fig. 5.
3) Frequency spectrum analysis technology based on stator current
(1) Fast Fourier Transform (FFT) based spectrum analysis
The filtered signal is subjected to a Fast Fourier Transform (FFT) to obtain a spectrum diagram thereof, which necessarily contains the required rotor frequency components.
Fourier transform, i.e. the representation of a signal that varies over a period as a linear combination of innumerable sine (or cosine) signals, can transform the signal from the time domain into the frequency domain, so that features that are not visible in the time domain can be found. In different research fields, fourier transforms have different forms, mainly divided into continuous fourier transforms and discrete fourier transforms.
Discrete Fourier Transform (DFT), i.e., a fourier transform, is a form of discrete form in both time and frequency domains, and the sequences of the time and frequency domains at both ends of the fourier transform are finite in form, and when transforming, it is necessary to extend the period of the signal into a periodic signal and then transform it.
In general, when performing fourier transform on a finite-length sequence in a time domain by using butterfly operation, the calculation amount is often large, and it is generally difficult to process the problem in real time. In 1965, cooley and Turkey improved traditional discrete fourier transform algorithm according to the characteristics of discrete fourier transform such as odd, even, virtual and real, and a high-efficiency and fast algorithm for calculating discrete fourier transform (fast fourier transform (FFT) is proposed, the number of multiplications is greatly reduced when the algorithm is adopted to calculate discrete fourier transform, the computation amount of DFT is reduced by several orders of magnitude, and the more the number of points of fourier transform is, the more the FFT is significantly reduced compared with the computation amount of DFT. Various FFT algorithms are generated according to different sequence decomposition and selection methods and are mainly divided into a base 2DIT and a base 2 DIF. The method has the advantages that the fast Fourier transform calculation amount is small, so that the method is widely applied to the field of signal processing, the real-time processing of signals can be realized through hardware, the digital signal processing technology also rapidly develops along with the appearance of a fast Fourier transform algorithm, the development of the digital signal processing technology plays a vital role, the operation speed is improved, and the hardware cost is reduced. For example, FFT is used in the fields of noise signal analysis, time division multiplexing (TDM/FDM) conversion in a communication system, signal filtering analysis in a frequency domain, spectrum analysis, extraction of a useful signal, improvement of signal resolution, and the like.
When the fast Fourier transform is used for harmonic analysis, harmonic parameters such as frequency, amplitude and phase of a signal can be accurately obtained only under synchronous sampling and whole period cutting conditions, but the conditions are difficult to meet in actual environments, so that the frequency spectrum leakage and the fence effect exist when the FFT is carried out on the signal, the accuracy of the signal analysis is seriously affected, and the actual requirements are difficult to meet. However, since spectrum leakage is unavoidable, it is necessary to design different window functions to reduce spectrum leakage of the signal. In order to reduce the influence of spectrum leakage during harmonic analysis, a specific window function is multiplied on a signal before fast Fourier transformation is carried out, so that the generation of side lobe component due to signal interception can be reduced, the spectrum leakage is reduced, but the adding of the window function can reduce the accuracy of main lobe frequency, and measures are needed to be taken for compensation subsequently. The window functions commonly used mainly include rectangular window, hanning window (hanning), hamming window (hanming), blackman window, etc., and the influence on reducing spectrum leakage is different from different window functions with different spectrum characteristics [55,56]. The most widely used in practice are hanning windows and rectangular windows. A hanning window (hanning) is used in this patent.
The frequency spectrum of the hanning window can be regarded as being formed by superposing the frequency spectrums of three rectangular windows, and the amplitude of the first side lobe is 0.027% of the amplitude of the main lobe, so that the side lobes can cancel each other, thereby eliminating high-frequency interference, achieving the effect of strengthening the main lobe and effectively inhibiting frequency spectrum leakage. The spectra of the hanning window and the rectangular window are compared, and the main lobe of the hanning window is widened, the amplitude is reduced, the side lobe is obviously reduced, the attenuation speed is higher, and the hanning window is superior to the rectangular window from the aspect of reducing the spectrum leakage, but the spectrum resolution is correspondingly reduced due to the widened main lobe.
(2) Spectrum refinement based on complex modulation ZoomFFT
In general, the mainstream spectrum analysis method is to perform fast fourier transform on a computer, but sometimes we will encounter a signal with a relatively dense spectrum, and only a very narrow frequency band is often concerned, and frequency resolution in this frequency band is expected to be very high. Frequency resolution refers to the smallest separation between two spectra that can be resolved in a spectrum, i.e
Δf＝f s /N 3.10
Where fs is the sampling frequency and N is the number of fourier transform points. From the above equation, there are two methods to improve the frequency resolution:
(1) Reducing the sampling frequency fs, wherein the too low sampling frequency cannot reflect the real signal, so that the original signal is distorted; (2) Increasing the number of fourier transform points N, taking into account the butterfly operation employed during fourier transform, increases the number of transform points exponentially, which greatly increases the hardware cost.
Therefore, the two methods are not preferable in practical application, and in order to solve the problem, a learner proposes a concept of spectrum refinement, namely, locally amplifying a certain frequency band of interest in the spectrum, increasing the spectral line density in the frequency band, and achieving the purpose of spectrum refinement. In recent years, the spectrum refinement technology is rapidly developed, and the common methods mainly comprise: the Zoom FFT method based on complex modulation, YIp-ZOOM transformation, chirp-Z transformation and the like [57-61], wherein the Zoom FFT method based on complex modulation has the advantages of analysis precision, calculation efficiency, frequency resolution and the like compared with other methods, and is widely applied.
The complex modulation refinement spectrum analysis method adopts: the principle process of the frequency shift (complex modulation) -low-pass digital filtering-resampling-FFT and spectral analysis-frequency adjustment is shown in fig. 6.
Let the analog signal x (t), after a/D sampling, get its discrete time sequence x0 (N), (n=0, 1..and N-1), fs is the sampling frequency of the analog signal, fe is the center frequency of the frequency band to be thinned, D is the thinning multiple, N is the number of fast fourier transform points. X0 (k) is the output discrete spectrum sequence. The method comprises the following steps:
(1) complex modulation frequency shift
The complex modulation frequency shift refers to shifting the frequency domain coordinates to the left or right so that the starting point of the thinned frequency band is positioned at the zero position of the coordinates. Discrete Fourier transform of discrete signal x0 (n) into
Assuming that the range of the frequency band to be thinned is f 1-f 2, the center frequency fe is
Multiplying x0 (n)Complex modulation is carried out to obtain complex modulation frequency shift signal x (n)
F in s =nΔf is the sampling frequency, Δf is the frequency resolution, L 0 ＝f e The/. DELTA.f is the center shift of the frequency, i.e. the number of the center frequency fe in the frequency spectrum, so f e ＝L 0 Δf。
From the nature of the discrete fourier transform, the discrete spectrum X (k) of X (n) and the discrete spectrum X0 (k) of X0 (n) satisfy the following equation
X(k)＝X 0 (k+L 0 ) 3.14
From the above equation, complex modulation shifts the center frequency fe in the original X0 (n) to the zero point of X (n), i.e., the L0 th spectral line in the X0 (k) spectrum to the zero point of the X (k) spectrum. Then, resampling is performed on the original discrete signal to reduce the sampling frequency to fs' =fs/D, where D is a refinement multiple. In order to prevent the spectrum aliasing phenomenon of the thinned spectrum, a low-pass filtering process is required before sampling.
(2) Digital low pass filtering
In order to ensure that the resampled frequency spectrum is not aliased, anti-aliasing filtering is needed, and if the thinning multiple is set as D, the cut-off frequency of the low-pass filter should meet fc < fs/2D.
(3) Resampling
After complex modulation frequency shift and low-pass filtering, the analyzed frequency band is narrowed, and when resampling is carried out at a lower sampling frequency fs' =fs/D, namely, a point is acquired at every D points, a new discrete signal is obtained.
(4) Complex FFT
The new discrete signal is subjected to zero padding treatment to ensure that the number N of original Fourier analysis points is unchanged, FFT is carried out, and the discrete spectrum is obtained, and the frequency resolution is the same
Δf'＝f s '/N＝f s /DN＝Δf/D 3.15
Therefore, the frequency resolution is improved by a factor of D before being thinned.
(5) Frequency shift
The spectrum obtained by the above process is not the actual spectrum, but needs to be shifted to the actual frequency, i.e
Through the 5 steps, the frequency resolution of the frequency band to be analyzed is improved by D times under the condition that the number of the Fourier transform analysis points is unchanged.
In summary, it is known that, although the frequency resolution of the corresponding frequency band can be improved by D times through the zoofft algorithm under the condition that the number of fourier transform points is fixed, the method is based on sacrificing the data length, when the zoofft transform is performed, the original signal data point required by the fourier transform of the N point should be DN, and if the fast fourier transform of the DN point is directly performed on the original signal data point, the frequency resolution satisfied by the zoofft algorithm can be achieved. Therefore, the zoofft does not fundamentally solve the problem of frequency resolution, but only uses fewer fourier transform points to achieve the frequency resolution that can be achieved by using huge fourier transform points, and increasing the transform points increases the calculation amount of fourier transform exponentially, which is definitely undesirable because of greatly increasing the hardware requirements and costs. The ZoomFFT algorithm realizes that the requirement and the cost of hardware are reduced under the condition of meeting the requirement of specified frequency resolution, and is worth widely applying in the market today.
(6) ZoomFFT spectrum refinement algorithm simulation
The section simulates the spectrum refinement method based on complex modulation introduced above, adopts two different signal types, one is that each frequency component is relatively close, and the other is that each frequency component is relatively far away, so as to verify the effectiveness of the algorithm.
Let an analog current signal expression be
i＝sin(2π·9.1·t)+sin(2π·9.2·t)+sin(2π·9.5·t) 3.17
The original spectrum and the refined spectrum simulation result based on the Zoom FFT are shown in fig. 7.
Let another analog signal be:
i＝4cos(2π·91.356·t+π/6)+3·cos(2π·101.232·t+2π/9)+cos(2π·113.565·t+4π/3) 3.18
the original spectrum and the spectrum obtained after the refining based on the Zoom FFT are shown in figure 8.
Through comparing fig. 7 and fig. 8, it is found that the spectrum refining method based on the Zoom FFT provided by the present patent can meet the requirement of improving the frequency resolution, and can distinguish different frequency components, and the closer each frequency component in the signal is, the better the spectrum refining effect is, and the better the refining effect is for weaker signal components.
(3) Spectrum correction based on ratio method
In general, a discrete spectrum can only be analyzed on a limited sample length, which causes spectrum leakage, so that a spectrum obtained after fast fourier transformation has larger errors in frequency, amplitude and phase, and although the window function can be added before transformation to inhibit spectrum leakage, the accuracy obtained by only the window function is far insufficient for some occasions with high requirements on the harmonic parameters. In addition, in modern digital signal processing, the spectrum obtained by FFT or DFT is a discrete spectral line, and is generally obtained by sampling the spectrum at equal intervals after the spectrum convolution processing of the spectrum of the target signal and the window function. If the frequency of the target signal is exactly on a certain spectral line, the obtained frequency, amplitude and phase are accurate. However, in general, the frequency of the target signal is not on the spectral line, but is between two spectral lines, and the corresponding spectral line is not on the main lobe center A, so that the frequency, amplitude and phase of the reaction of the two peak spectral lines are inaccurate.
The phenomenon greatly influences the application of the spectrum analysis technology, so that a plurality of professionals at home and abroad develop the research of the discrete spectrum correction technology to reduce the identification error of harmonic parameters and improve the identification precision.
Current theory of discrete spectrum correction for single frequency component or far away multi-frequency component signals mainly includes: an energy center correction method, a ratio correction method, an FFT+DFT continuous refinement analysis Fourier variation method and a phase difference method [62-71]. The four methods have advantages and disadvantages and are applied to practical engineering. When the ratio correction method is applied to a hanning window, the correction precision is very high, the frequency correction error is smaller than 0.0001 frequency resolution, the amplitude error is smaller than one ten thousandth, and the phase difference is smaller than 1 degree. Because the frequency accuracy requirement is very high in the rotation speed identification algorithm, the discrete stator current spectrum obtained after FFT is corrected by adopting a ratio correction method and a hanning window so as to obtain accurate rotor frequency.
The frequency correction is to accurately find the abscissa of the center of the main lobe. Let the spectral function of the window function be f (x), f (x) being symmetrical about the Y-axis, as shown in fig. 9.
As can be seen from fig. 9, y=f (x), y1=f (x+1), x now needs to be solved by y and y1, i.e. the spectral correction is Δx= -x. Now construct a function V
That is, V is determined by the ratio of the ordinate of two points with an abscissa interval of 1 and is also a function of x, and the inverse function thereof can be solved
x＝g(v) 3.20
Will beSubstituted into to obtain
So deltax = -x can be solved, this method is called the ratio correction method.
As shown in fig. 10, in the actual discrete spectrum calculation, the real frequency of the signal is located at the center x0 of the main lobe, where yk and yk+1 are two spectral lines adjacent to each other on both sides of the center of the main lobe of the discrete spectrum, so thatSubstituting x=g (v), the spectral correction amount is Δk= -x.
If corrected by spectral lines yk-1, yk, letSubstituting x=g (v), where the spectral correction amount Δk= -x-1
Last correction frequency is
Wherein K is a discrete spectral line serial number (0-N/2-1), N is the number of Fourier transform analysis points, and fs is the signal sampling frequency
However, for different window functions, there will be completely different frequency correction functions, and this patent mainly deals with the frequency correction effect when adding hanning windows.
The functional expression of the hanning window is:
the expression of the spectrum function is
Wherein the method comprises the steps ofFor the died Li He kernel, yu Hanning windows correspond to a=0.5, and a=0.54 corresponds to a hamming window. The phase difference of the three items in brackets is +.>Taking the sum of these three modulo functions as the modulo function of W (omega), the main lobe function of the Hanning window is
/>
Order theThen 3.25 is reduced to
Substitution of formula 3.26 into formula 3.19
When a=0.5, c→infinity, formula 3.24 can be simplified to
Solving the inverse function of F (x) to obtain
This is the frequency correction function when adding the hanning window, and the correction frequency can be obtained by substitution.
(4) Spectral correction algorithm simulation
In this section, a spectrum correction algorithm based on a ratio method is simulated, and the accuracy and the effectiveness of the algorithm are verified by comparing different simulation signals.
(1) The signal determined by equation 3.18 is subjected to spectral correction, and the results of spectral analysis before and after correction are shown in fig. 11.
(2) Increasing the interval between each frequency spectrum component, and setting the analog signal as
i＝4cos(2π·81.356·t+π/6)+3·cos(2π·101.232·t+2π/9)+cos(2π·153.565·t+4π/3) 3.30
The results of the spectral analysis before and after the correction are shown in fig. 12.
As can be seen from comparing the spectrum correction results of fig. 11 and fig. 12, the degree of density of each frequency component affects the frequency correction accuracy, and the larger the interval between each component is, the higher the frequency correction accuracy is, so the frequency correction algorithm based on the ratio method is suitable for the correction of single frequency signals or multi-frequency signals with far distance, and considering that the rotor frequency component to be extracted in the present patent is far away from the power frequency, the rotor frequency can be accurately obtained by adopting the correction algorithm.
(5) Rotor frequency search and rotational speed calculation
After the current spectrum is obtained, the most critical step is to extract the rotor frequency in the spectrum. As can be seen from analysis of mechanical characteristics of the motor, the motor generally operates at or below a rated state, so that the minimum value of the rotor frequency is determined by the rotor frequency at the rated rotational speed, the maximum value is the rotational magnetic field frequency, the frequency search area is between the minimum value and the maximum value, and the frequency corresponding to the maximum amplitude in the search area is the rotor frequency.
/>
Where p is the pole pair number of the motor and nR is the rated rotational speed of the motor.
So the rotor frequency is between flow and fupp.
After the rotor frequency fr is obtained, the rotational speed n of the motor can be easily obtained
n＝f r ×60 3.32
Online simulation of rotational speed identification algorithm
In order to verify the rotating speed online identification algorithm provided by the patent, an asynchronous motor simulation model based on Matlab/Simulink is established. The motor parameters used in the simulation model are shown in table 1.
Table 1 simulation motor parameters
The simulation model is characterized in that the motor is powered by a 220V alternating current power supply, and a motor measuring module in the Simulink is used for collecting the rotating speed and stator current information of the motor during operation and used for subsequent analysis.
Because the dynamic model of the asynchronous motor provided in the Matlab/Simulink simulation system is an ideal model, the stator current only contains the component of the main frequency (50 Hz) of the power supply source when the motor operates
i a ＝I m sin(ωt) 3.33
Since the rotor frequency, the eccentric frequency, and other components generated in the non-ideal state do not exist, the stator current collected in the Simulink cannot be used as an original signal when the performance of the algorithm is simulated, so in order to evaluate the performance of the motor rotation speed online identification algorithm studied in the patent when the motor is stably operated, an analog motor stator current signal [72] comprising a main frequency (50 Hz), a rotor frequency (taking the rotation speed of the motor into consideration at 1450 rpm) and a double-sided eccentric component (3 Hz) needs to be artificially generated, as shown in formula 3.31.
i a ＝i o +0.1·sin(2π·24.2·t)+0.1·sin(2π·47·t+π/3)+0.1·sin(2π·53·t) 3.34
Where ia is the actual stator current and i0 is the ideal stator current.
The actual rotational speed of the motor collected by the motor measuring module is shown in fig. 13, and it can be seen from the graph that the motor reaches a steady operation state after starting for 0.5 s.
The analysis result of the stator current by adopting the motor rotating speed on-line identification algorithm based on the rotor frequency provided by the patent is shown in fig. 14.
From the analysis results of fig. 14, it can be seen that in the frequency spectrum of the analog stator current, the rotor frequency is precisely identified by the algorithm of this patent, and the magnitude is f r Error analysis is shown in table 2, = 24.151 Hz.
TABLE 2 rotational speed identification algorithm error analysis
As can be seen from Table 2, in the simulation experiment, the actual measured rotating speed of the motor is 1450r/min, the rotor frequency obtained through the rotating speed on-line identification algorithm is 24.151Hz, the rotating speed is 1449.1r/min, the error between the rotor frequency and the actual rotating speed is 0.9r/min and is lower than 1r/min, and the simulation result shows that the rotating speed identification algorithm provided by the patent is very high in reliability.
Therefore, the motor rotation speed obtained by the optimized online identification method has high accuracy and small error.
With the above-described preferred embodiments according to the present invention as an illustration, the above-described descriptions can be used by persons skilled in the relevant art to make various changes and modifications without departing from the scope of the technical idea of the present invention. The technical scope of the present invention is not limited to the description, but must be determined according to the scope of claims.
Claims (6)
1. A portable current detection rotational speed tester is characterized in that:
the top of the shell of the rotating speed tester is provided with a hole for connecting a wire out and is used for connecting a wire with a tested motor (8), a processor (1) is arranged in the rotating speed tester, the processor (1) detects the current of the tested motor (8) through a current sensor (2), and the current detected by the current sensor (2) is input into the processor (1) after being subjected to signal regulation and filtering in a filter (3);
the processor (1) comprises a micro-processing module (1.1), wherein the micro-processing module (1.1) is connected with a current data acquisition module (1.2), a current data processing module (1.3), a frequency spectrum processing module (1.4) and a rotating speed processing module (1.5), the current data acquisition module (1.2) is connected with a current sensor (2), the current data acquisition module (1.2) sends the stator rotating speed of a tested motor (8) to the current data processing module (1.3) through the micro-processing module (1.1), the current data processing module (1.3) sends a demodulated signal to the frequency spectrum processing module (1.4) through the micro-processing module (1.1), and the frequency spectrum processing module (1.4) sends the frequency spectrum processed signal to the rotating speed processing module (1.5) through the micro-processing module (1.1), finds the rotor frequency and calculates the motor rotating speed;
the rotating speed tester realizes a rotating speed identification algorithm based on a stator current frequency spectrum, and comprises the following specific steps:
1) Stator current demodulation
The stator current is expressed as:
i(t)＝[k 1 +k 2 cos(2πf 2 t)+k 3 cos(2πf 3 t)+...+k n cos(2πf n t)]cos(2πf 1 t) 3.1
the motor stator current can also be expressed as:
i(t)＝m(t)cos(2πf 1 t)--------------------------------3.2
wherein order
m(t)＝k 1 +k 2 cos(2πf 2 t)+k 3 cos(2πf 3 t)+...+k n cos(2πf n t)----------------3.3
Squaring the stator current to obtain
After the signal represented by formula 3.4 passes through the low-pass filter, the m (t) signal is extracted, and the low-pass filtered signal is subjected to squaring processing to obtain a stator current signal finally used for spectrum analysis:
i(t)＝0.707m(t)--------------------------------3.5
2) Frequency spectrum analysis technology based on stator current
The complex modulation based refined spectrum analysis method adopts: frequency shift-low pass digital filtering-resampling-FFT-spectral analysis-frequency adjustment;
y=f (x), y1=f (x+1), a function V is constructed
Solving the inverse function thereof
The method comprises the steps ofSubstituted into to obtain
Resolvable Δx= -x;
order theSubstituting x=g (v), the spectral correction amount is Δk= -x;
order theSubstituting x=g (v), where the spectral correction amount Δk= -x-1, the correction frequency is:
and frequency correction when a hanning window is added, wherein the function expression of the hanning window is as follows:
the expression of the spectrum function is as follows:
taking the sum of the three modulo functions as the modulo function of W (omega), the main lobe function of the Hanning window is:
order theThen 3.25 is reduced to
Substitution of formula 3.26 into formula 3.19
When a=0.5, c→infinity, formula 3.24 can be simplified to
Solving the inverse function of F (x) to obtain the final correction frequency:
3) Rotor frequency search and rotational speed calculation
The frequency corresponding to the maximum amplitude in the search area is the rotor frequency:
rotor frequency is between f low And f upp Between them, obtain the rotor frequency f r Then, the rotation speed n of the motor is obtained:
n＝f r ×60------------------------------------------------3.32
thereby obtaining the rotating speed n of the motor, the rotating speed n of the motor is sent to the control module (7), and the control module (7) controls the motor (8) to be tested.
2. The portable current sensing rotational speed tester of claim 1, wherein: the surface of the shell is provided with a display screen and a plurality of buttons for control.
3. The portable current sensing rotational speed tester of claim 1, wherein: the processor (1) is an ARM microcontroller.
4. The portable current sensing rotational speed tester of claim 1, wherein: the current sensor (2) is a rogowski coil.
5. The portable current sensing rotational speed tester of claim 1, wherein: the processor (1) is also provided with a display module (4) and a keyboard module (5).
6. The portable current sensing rotational speed tester of claim 1, wherein: the processor (1) is also provided with a signal processing module (6), the processor (1) sends a processing signal to the control module (7) through the signal processing module (6), and the control module (7) controls the tested motor (8) to rotate.
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| CN110346594B (en) * | 2019-05-29 | 2022-04-08 | 浙江工业大学 | Portable totally-enclosed compressor high-precision rotation speed measuring method and device |
| CN110470212B (en) * | 2019-07-10 | 2021-04-20 | 哈尔滨工业大学（威海） | Dynamic eccentric fault detection method for hub motor |
| CN115586345B (en) * | 2022-12-09 | 2023-03-21 | 深圳市双合电气股份有限公司 | Motor self-adaptive rotating speed estimation method and system based on stator current |
| CN116203419A (en) * | 2023-05-05 | 2023-06-02 | 江苏省特种设备安全监督检验研究院 | Motor energy efficiency detecting system based on multi-force field coupling action algorithm |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1894292A1 (en) * | 2005-06-09 | 2008-03-05 | Philips Intellectual Property & Standards GmbH | Sensorless measurement of the rotation frequency of a rotor of an asynchronous machine |
| CN102072779A (en) * | 2010-11-17 | 2011-05-25 | 哈尔滨工业大学 | Rotor-groove harmonic analysis based method for identifying temperature of sensorless rotor for submersible motor |
| CN202330454U (en) * | 2011-11-17 | 2012-07-11 | 黑龙江科技学院 | Rotation speed measurement device of motor |
| CN103308706A (en) * | 2013-05-14 | 2013-09-18 | 江苏大学 | Method and device for detecting rotating speed of vane pump |
| CN103454495A (en) * | 2013-09-13 | 2013-12-18 | 电子科技大学 | Self-adaptive high-precision fast spectral analysis method |
| CN205061228U (en) * | 2015-09-16 | 2016-03-02 | 江苏省特种设备安全监督检验研究院 | Integrated comprehensive tester of elevator inspection |
| CN107561306A (en) * | 2017-08-17 | 2018-01-09 | 湖南纬拓信息科技有限公司 | Winding motor rotating speed measurement method based on rotor current |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB0908112D0 (en) * | 2009-05-12 | 2009-06-24 | Peto Raymond J | Apparatus and method for determining speed of a motor, generator or alternator |
| GB201503930D0 (en) * | 2015-03-09 | 2015-04-22 | Rolls Royce Plc | Fault detection and diagnosis in an induction motor |
-
2018
- 2018-07-27 CN CN201810846959.5A patent/CN108761117B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1894292A1 (en) * | 2005-06-09 | 2008-03-05 | Philips Intellectual Property & Standards GmbH | Sensorless measurement of the rotation frequency of a rotor of an asynchronous machine |
| CN102072779A (en) * | 2010-11-17 | 2011-05-25 | 哈尔滨工业大学 | Rotor-groove harmonic analysis based method for identifying temperature of sensorless rotor for submersible motor |
| CN202330454U (en) * | 2011-11-17 | 2012-07-11 | 黑龙江科技学院 | Rotation speed measurement device of motor |
| CN103308706A (en) * | 2013-05-14 | 2013-09-18 | 江苏大学 | Method and device for detecting rotating speed of vane pump |
| CN103454495A (en) * | 2013-09-13 | 2013-12-18 | 电子科技大学 | Self-adaptive high-precision fast spectral analysis method |
| CN205061228U (en) * | 2015-09-16 | 2016-03-02 | 江苏省特种设备安全监督检验研究院 | Integrated comprehensive tester of elevator inspection |
| CN107561306A (en) * | 2017-08-17 | 2018-01-09 | 湖南纬拓信息科技有限公司 | Winding motor rotating speed measurement method based on rotor current |
Non-Patent Citations (1)
| Title |
|---|
| 基于MCSA的离心泵转速测量与试验研究;石洋 等;《排灌机械工程学报》;第35卷(第11期);第929-932页 * |
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