CN115372699A - Adaptive filtering method, adaptive filtering device, fault detection method, electronic device, and medium - Google Patents
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Abstract
The invention provides a self-adaptive filtering method, a self-adaptive filtering device, a fault detection method, electronic equipment and a medium, wherein the method comprises the steps of carrying out fast Fourier transform on a voltage time domain signal to obtain a voltage frequency domain signal, selecting a frequency point in a first preset sequence number range and a frequency point in a second preset sequence number range from the voltage frequency domain signal, setting the values of other frequency points to be 0, and carrying out fast Fourier inverse transform on the processed voltage frequency domain signal to obtain an estimated signal of a voltage power frequency signal; based on a wiener filter, the collected current time domain signal is filtered by utilizing the estimated signal of the voltage power frequency signal, so as to filter the power frequency signal contained in the signal. Therefore, adverse effects of direct current and higher harmonic signals in the voltage time domain signals on filtering are eliminated, so that current power frequency signals can be filtered more thoroughly, current component signals located in a rated power frequency side frequency band can be identified more accurately from a current frequency spectrum, and reliability of detecting the broken bar fault of the asynchronous induction motor rotor is improved.
Description
Technical Field
The invention relates to the technical field of filtering, and particularly provides a self-adaptive filtering method, a self-adaptive filtering device, a fault detection method, electronic equipment and a medium.
Background
After the asynchronous induction motor has a rotor breaking fault, an additional current component signal with the frequency of (1 + 2s) f (wherein s is slip and f is a rated value of a power frequency) appears in the stator current of the asynchronous induction motor, and the current component signal can be used as a characteristic signal of the rotor breaking fault.
However, in practical conditions, the value of the power frequency is not equal to its rated value, for example, for a power frequency rating of 50Hz adopted in China, there is usually a deviation of ± 0.2 to ± 0.5Hz. The frequency spectrum leakage phenomenon exists near a frequency point corresponding to the rated power frequency in the frequency spectrum of the stator current obtained through the fast Fourier transform, so that a current component signal with a frequency very close to the rated power frequency is submerged, a characteristic signal of a rotor broken bar cannot be identified, and the reliability of detecting the rotor broken bar fault based on a stator current analysis method is low.
The adaptive filtering method used in patent No. CN200710061634.8 is to perform continuous fine fourier transform on the collected current time domain signal, to find the frequency, amplitude and initial phase of the power frequency signal in the current time domain signal, then calculate the power frequency signal in the current time domain signal through a discrete sine function, and then use the power frequency signal obtained by calculation to cancel the power frequency signal in the current time domain signal, thereby realizing the adaptive filtering of the power frequency signal in the current time domain signal. However, since the power frequency in the actual working condition is not equal to the nominal value thereof, but has a deviation of ± 0.2 to ± 0.5Hz, in order to accurately determine the power frequency, a very high frequency resolution is required, and the frequency resolution of the fast fourier transform is proportional to the sampling time length of the time domain signal. Therefore, the adaptive filtering method requires a long sampling time, which is disadvantageous for real-time monitoring of the motor state.
Therefore, how to perform fast and accurate adaptive filtering on a power frequency signal in a stator current time domain signal of an asynchronous induction motor so as to improve the detection sensitivity of a characteristic current signal of a rotor bar breakage fault is a technical problem to be solved in the field.
Disclosure of Invention
In order to overcome the defects, the invention provides a self-adaptive filtering method, a self-adaptive filtering device, a fault detection method, electronic equipment and a medium, which solve or at least partially solve the technical problem that in a frequency spectrum obtained by Fourier transform of stator line current signals of an asynchronous induction motor, due to frequency spectrum leakage of power frequency current signals, current component signals which are very close to the power frequency can be submerged by the power frequency signals, so that the current component signals cannot be accurately and effectively identified, and the reliability of detecting the rotor broken bar fault of the asynchronous induction motor is low.
In a first aspect, the present invention provides an adaptive filtering method, comprising:
carrying out fast Fourier transform on the collected steady-state voltage time domain signals to obtain voltage frequency domain signals corresponding to the voltage time domain signals;
selecting a plurality of frequency points in a first preset sequence number range and a plurality of frequency points in a second preset sequence number range in the voltage frequency domain signals, and setting the real parts and the imaginary parts of the frequency points outside the first preset sequence number range and the second preset sequence number range to be 0 to obtain processed voltage frequency domain signals; the starting sequence number value of the first preset sequence number range is the difference between the first power frequency sequence number value of the first frequency point corresponding to the rated power frequency and a first preset threshold value, and the ending sequence number value of the first preset sequence number range is the sum of the first power frequency sequence number value and a second preset threshold value; the starting sequence number value of the second preset sequence number range is the difference between the second power frequency sequence number value of the second frequency point corresponding to the rated power frequency and a second preset threshold value, and the ending sequence number value of the second preset sequence number range is the sum of the second power frequency sequence number value and the first preset threshold value; the first power frequency sequence number is smaller than the second power frequency sequence number;
performing inverse fast Fourier transform on the processed voltage frequency domain signal to obtain an estimated signal of a voltage power frequency signal in the voltage time domain signal;
and based on a wiener filter, filtering the acquired current time domain signal by using the estimated signal of the voltage power frequency signal to obtain a current time domain signal of which the power frequency signal is filtered.
Further, in the above adaptive filtering method, based on a wiener filter, the method filters the acquired current time domain signal by using the estimated signal of the voltage power frequency signal to obtain a current time domain signal with a power frequency signal filtered, and includes:
selecting N-1 elements from the estimated signal of the voltage power frequency signal according to the number N of taps of the wiener filter, and amplifying the estimated signal of the voltage power frequency signal to obtain an amplified estimated signal of the voltage power frequency signal; wherein N is an integer greater than or equal to 2;
according to the amplification estimation signal of the voltage power frequency signal, calculating an autocorrelation function of the amplification estimation signal of the voltage power frequency signal and a cross-correlation function between the amplification estimation signal of the voltage power frequency signal and a current time domain signal;
constructing an autocorrelation matrix of the amplification estimation signal of the voltage power frequency signal according to the autocorrelation function;
constructing a cross-correlation vector of the amplified estimated signal of the voltage power frequency signal and the current time domain signal according to the cross-correlation function;
determining a weight vector of the wiener filter according to the autocorrelation matrix and the cross-correlation vector;
obtaining an estimated signal of a current power frequency signal in a current time domain signal according to the weight vector and the amplified estimated signal of the voltage power frequency signal;
and filtering the current time domain signal by utilizing the estimated signal of the current power frequency signal to obtain the current time domain signal of which the power frequency signal is filtered.
Further, in the adaptive filtering method described above, N is 2;
selecting N-1 elements from the estimated signal of the voltage power frequency signal according to the number N of taps of the wiener filter, amplifying the estimated signal of the voltage power frequency signal to obtain an amplified estimated signal of the voltage power frequency signal, wherein the method comprises the following steps:
and copying a specified element from the estimated value of the power frequency signal as an amplification element, and amplifying the estimated signal of the voltage power frequency signal to obtain an amplified estimated signal of the voltage power frequency signal.
Further, in the adaptive filtering method, copying a specified element from the estimated value of the power frequency signal as an amplification element, and amplifying the estimated signal of the voltage power frequency signal to obtain an amplified estimated signal of the voltage power frequency signal, includes:
copying a first element from the estimated value of the power frequency signal as an amplification element, and placing the amplification element in front of the first element to obtain an amplification estimated signal of the voltage power frequency signal.
Further, in the adaptive filtering method, obtaining an estimation signal of a current power frequency signal in a current time domain signal according to the weight vector and the amplified estimation signal of the voltage power frequency signal includes:
according to the weight vector and the j element U in the amplification estimation signal of the voltage power frequency signal e0 [j]And j +1 th element U e0 [j+1]Calculating corresponding element I in the estimated signal of current power frequency signal e [j]Wherein j is greater than or equal to 0 and less than 2 n Any integer of (2) n The number of sampling points is the current time domain signal and the voltage time domain signal.
Further, in the adaptive filtering method, determining a weight vector of the wiener filter according to the autocorrelation matrix and the cross-correlation vector includes:
carrying out inversion operation on the autocorrelation matrix to obtain an inverse matrix of the autocorrelation matrix;
taking the product of the inverse matrix and the cross-correlation vector as the weight vector.
In a second aspect, the present invention provides an adaptive filtering apparatus comprising:
the first transformation module is used for carrying out fast Fourier transformation on the collected steady-state voltage time domain signals to obtain voltage frequency domain signals corresponding to the voltage time domain signals;
the selecting module is used for selecting a plurality of frequency points in a first preset sequence number range and a plurality of frequency points in a second preset sequence number range in the voltage frequency domain signals, and setting the real parts and the imaginary parts of the frequency points outside the first preset sequence number range and the second preset sequence number range as 0 to obtain the processed voltage frequency domain signals; the starting sequence number value of the first preset sequence number range is the difference between the first power frequency sequence number value of the first frequency point corresponding to the rated power frequency and a first preset threshold value, and the ending sequence number value of the first preset sequence number range is the sum of the first power frequency sequence number value and a second preset threshold value; the starting sequence number value of the second preset sequence number range is the difference between the second power frequency sequence number value of the second frequency point corresponding to the rated power frequency and a second preset threshold value, and the ending sequence number value of the second preset sequence number range is the sum of the second power frequency sequence number value and the first preset threshold value; the first power frequency serial number value is smaller than the second power frequency serial number value;
the second transformation module is used for performing fast Fourier inverse transformation on the processed voltage frequency domain signal to obtain an estimation signal of a voltage power frequency signal in the voltage time domain signal;
and the filtering module is used for filtering the acquired current time domain signal by using the estimated signal of the voltage power frequency signal based on the wiener filter to obtain the current time domain signal of which the power frequency signal is filtered.
In a third aspect, a fault detection method is provided, comprising:
according to any one of the self-adaptive filtering methods, a current time domain signal of a power frequency signal is filtered;
performing fast Fourier transform on the current time domain signal of which the power frequency signal is filtered out to obtain the amplitude of a component current signal with the frequency of (1 + 2s) f in a side frequency band of a rated power frequency in the current time domain signal; wherein s is slip ratio, and f is rated value of power frequency;
determining the ratio of the amplitude of the component current signal to the amplitude of the current power frequency signal;
and if the ratio is greater than a preset threshold value, determining that the asynchronous induction motor has a rotor broken bar fault.
In a fourth aspect, there is provided an electronic device comprising a processor and a memory means adapted to store a plurality of program codes, the program codes being adapted to be loaded and run by the processor to perform the adaptive filtering method of any of the above, or to perform the fault detection method of the above.
In a fifth aspect, a computer-readable storage medium is provided, having stored therein a plurality of program codes adapted to be loaded and run by a processor to perform the adaptive filtering method according to any one of the above-mentioned technical solutions, or to perform the fault detection method according to the above-mentioned technical solution.
One or more technical schemes of the invention at least have one or more of the following beneficial effects:
in the technical scheme, after voltage frequency domain signals of the voltage time domain signals are obtained by performing fast Fourier transform on the acquired voltage time domain signals, frequency points within a first preset sequence number range and frequency points within a second preset sequence number range are selected from the voltage frequency domain signals, the values of other frequency points are set to be 0, and the processed voltage frequency domain signals are subjected to fast Fourier inverse transform to obtain estimated signals of the voltage power frequency signals; based on a wiener filter, the collected current time domain signal is filtered by utilizing the estimated signal of the voltage power frequency signal, so as to filter the power frequency signal contained in the signal. Therefore, the adverse effects of direct current and higher harmonic signals in the voltage time domain signals on filtering are eliminated, so that the current power frequency signals can be filtered more thoroughly, the current component signals located in the rated power frequency side band can be identified more accurately from the current frequency spectrum, and the reliability of detecting the broken bar fault of the asynchronous induction motor rotor is improved.
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The present disclosure will become more readily understood with reference to the accompanying drawings. As is readily understood by those skilled in the art: these drawings are for illustrative purposes only and are not intended to be a limitation on the scope of the present disclosure. Moreover, in the drawings, like numerals are used to indicate like parts, and in which:
FIG. 1 is a flow chart illustrating the main steps of an adaptive filtering method according to an embodiment of the present invention;
FIG. 2 is a graph comparing the results of filtering a current signal using the adaptive filtering method of the present invention and a conventional filtering method;
FIG. 3 is a flow chart illustrating the main steps of a fault detection method according to one embodiment of the present invention;
fig. 4 is a main block diagram of an adaptive filtering apparatus according to an embodiment of the present invention;
fig. 5 is a main configuration block diagram of an electronic apparatus according to an embodiment of the present invention.
Detailed Description
Some embodiments of the invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and are not intended to limit the scope of the present invention.
In the description of the present invention, a "module" or "processor" may include hardware, software, or a combination of both. A module may comprise hardware circuitry, various suitable sensors, communication ports, memory, may comprise software components such as program code, or may be a combination of software and hardware. The processor may be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and/or signal processing functionality. The processor may be implemented in software, hardware, or a combination thereof. Non-transitory computer readable storage media include any suitable medium that can store program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random-access memory, and the like. The term "a and/or B" denotes all possible combinations of a and B, such as a alone, B alone or a and B. The term "at least one A or B" or "at least one of A and B" means similar to "A and/or B" and may include only A, only B, or both A and B. The singular forms "a", "an" and "the" may include the plural forms as well.
After the asynchronous induction motor has the rotor breaking fault, an additional current component signal with the frequency of (1 + 2s) f (wherein s is slip and f is a rated value of a power frequency) appears in the stator current of the asynchronous induction motor, and the current component signal can be used as a characteristic signal of the rotor breaking fault.
However, in practical conditions, the power frequency value is not equal to its rated value, for example, for a 50Hz rated power frequency adopted in China, there is usually a deviation of ± 0.2 to ± 0.5Hz. The frequency spectrum leakage phenomenon exists near a frequency point corresponding to the rated power frequency in the frequency spectrum of the stator current obtained through the fast Fourier transform, so that a current component signal with a frequency very close to the rated power frequency is submerged, a characteristic signal of a rotor broken bar cannot be identified, and the reliability of detecting the rotor broken bar fault based on a stator current analysis method is low.
Therefore, in order to solve the above technical problems, the present invention provides the following technical solutions.
Referring to fig. 1, fig. 1 is a flow chart illustrating the main steps of an adaptive filtering method according to an embodiment of the present invention. As shown in fig. 1, the adaptive filtering method in the embodiment of the present invention mainly includes the following steps 101 to 104.
101, performing fast Fourier transform on the acquired steady-state voltage time domain signal to obtain a voltage frequency domain signal corresponding to the voltage time domain signal;
in a specific implementation process, voltage time domain signals of a site stable state can be continuously acquired through voltage transformers arranged at key power supply points, hub substations, bus nodes and branch nodes in a power grid. And then carrying out fast Fourier transform on the acquired steady-state voltage time domain signals, and converting the acquired steady-state voltage time domain signals into voltage frequency domain signals.
102, selecting a plurality of frequency points in a first preset sequence number range and a plurality of frequency points in a second preset sequence number range in the voltage frequency domain signals, and setting the real parts and the imaginary parts of the frequency points outside the first preset sequence number range and the second preset sequence number range as 0 to obtain processed voltage frequency domain signals;
in a specific implementation process, the voltage time domain signal also has a direct current component signal and a higher harmonic signal of the power frequency signal besides the power frequency signal. In addition, in the actual operation process, under the influence of various factors, the frequency of the power frequency signal in the voltage time domain signal is not equal to the rated value thereof, but fluctuates within a certain range, and the fluctuation range is usually +/-0.2 to +/-0.5 Hz. Therefore, in order to obtain a relatively accurate determined power frequency, a plurality of frequency points including the frequency point corresponding to the rated power frequency can be selected according to the fluctuation range.
Specifically, in the voltage frequency domain signal, a plurality of frequency points within a first preset sequence number range and a plurality of frequency points within a second preset sequence number range may be selected, and the real part and the imaginary part of the frequency points outside the first preset sequence number range and the second preset sequence number range are set to be 0, so as to obtain the processed voltage frequency domain signal. The starting sequence number value of the first preset sequence number range is the difference between the first power frequency sequence number value of the first frequency point corresponding to the rated power frequency and a first preset threshold value, and the ending sequence number value of the first preset sequence number range is the sum of the first power frequency sequence number value and a second preset threshold value; the starting sequence number value of the second preset sequence number range is the difference between the second power frequency sequence number value of the second frequency point corresponding to the rated power frequency and a second preset threshold value, and the ending sequence number value of the second preset sequence number range is the sum of the second power frequency sequence number value and the first preset threshold value; and the first power frequency sequence number value is smaller than the second power frequency sequence number value.
In one specific implementation, the sampling frequency is 12.8kHz, and the sampling time is 10.24 s. The total number of frequency points in the voltage frequency domain signal is 10.24 × 12.8k=131072.
When the sampling duration of the processed or analyzed voltage time-domain signal is 10.24s, the first power frequency serial number value of the first frequency point corresponding to the rated power frequency in the voltage frequency-domain signal is 10.24/(1/50) =512, the first power frequency serial number value of the second frequency point corresponding to the rated power frequency in the voltage frequency-domain signal is 131072-512=130560, both the first preset threshold and the second preset threshold can be set to 256, the first preset serial number range is 256 to 768, and the second preset serial number range is 130304 to 130816.
103, performing inverse fast Fourier transform on the processed voltage frequency domain signal to obtain an estimated signal of a voltage power frequency signal in the voltage time domain signal;
in a specific implementation process, a plurality of frequency points in a certain range are selected from the processed voltage frequency domain signal, and the real part and the imaginary part of other frequency points are set to be 0, so that a power frequency signal in the processed voltage frequency domain signal is closer to an actual power frequency signal, and after the processed voltage frequency domain signal is subjected to fast inverse Fourier transform, the fitting degree of an estimated signal of the voltage power frequency signal in the voltage time domain signal and the actual voltage power frequency signal is higher.
And 104, based on a wiener filter, filtering the acquired current time domain signal by using the estimated signal of the voltage power frequency signal to obtain a current time domain signal of which the power frequency signal is filtered.
After obtaining the estimated signal of the voltage power frequency signal, step 104 may be implemented according to the following steps:
(1) Selecting N-1 elements from the estimated signal of the voltage power frequency signal according to the number N of taps of the wiener filter, and amplifying the estimated signal of the voltage power frequency signal to obtain an amplified estimated signal of the voltage power frequency signal; wherein N is an integer greater than or equal to 2;
in a specific implementation process, when the tap number of the wiener filter is different, the current time domain signal is filtered by utilizing the estimation signal of the current power frequency signal, and the current time domain signal of which the power frequency signal is filtered is obtained in different modes, so that N-1 elements are selected from the estimation signal of the voltage power frequency signal according to the tap number N of the wiener filter, and the estimation signal of the voltage power frequency signal is amplified to obtain an amplified estimation signal of the voltage power frequency signal.
Specifically, taking N =2 as an example, a specified element may be copied from the estimated value of the power frequency signal as an amplification element, and the estimated signal of the voltage power frequency signal may be amplified to obtain an amplified estimated signal of the voltage power frequency signal. Therefore, compared with the method for calculating the amplification elements meeting the requirements according to the sinusoidal signals, the method is simpler in calculation, and the calculation accuracy is relatively higher when the amplification estimation signals of the voltage power frequency signals are obtained by the method.
In a specific implementation process, a first element can be copied from the estimated value of the power frequency signal as an amplification element, and the amplification element is placed in front of the first element to obtain an amplification estimated signal of the voltage power frequency signal.
For example, after the steps 101 to 103, the obtained estimated signal of the voltage power frequency signal includes the following elements:
U e [0]、U e [1]、U e [2]......U e [2 n -1]；2 n the number of sampling points of the current time domain signal and the voltage time domain signal.
The first U e [0]The amplified signal is copied and placed in front of the estimated signal of the voltage power frequency signal, and the amplified estimated signal of the obtained voltage power frequency signal comprises the following elements:
U e0 [0]、U e0 [1]、U e0 [2]......U e0 [2 n ](ii) a Wherein, U e0 [0]Is equal to U e0 [1]Value of (A), U e0 [j+1]Is equal to U e [j]J is 0 or more and less than 2 n Any integer of (2) n The number of sampling points of the current time domain signal and the voltage time domain signal.
(2) Calculating an autocorrelation function of the amplified estimation signal of the voltage power frequency signal and a cross-correlation function between the amplified estimation signal of the voltage power frequency signal and the current time domain signal according to the amplified estimation signal of the voltage power frequency signal;
in a specific implementation process, the autocorrelation functions of the amplified estimated signal of the voltage power frequency signal are two, which can be specifically obtained according to the calculation formulas (1) and (2):
wherein, U e0 [j]For estimating the jth element of the signal for amplification of the voltage power frequency signal, 2 n The number of sampling points of the current time domain signal and the voltage time domain signal.
The cross-correlation function between the amplified estimated signal of the voltage power frequency signal and the current time-domain signal can also be 2, and can be obtained according to the calculation formulas (3) and (4):
wherein, U e0 [j]Estimating the jth element, ij, of the signal for amplification of a voltage power frequency signal]Is the j-th element of the current time domain signal, 2 n The number of sampling points of the current time domain signal and the voltage time domain signal.
(3) And constructing an autocorrelation matrix R of the amplification estimation signal of the voltage power frequency signal according to the autocorrelation function.
In one embodiment, the autocorrelation matrix R is a second order matrix, where R [0 ]][0]、R[0][1]、R[1][0]、R[1][1]The elements in the autocorrelation matrix R are in sequence one row and one column, one row and two columns, two rows and one column, two rows and two columns. Wherein, R < 0 >][0]＝R[1][1]＝r 0 ，R[1][0]＝R[0][1]＝r 1 。
(4) Constructing a cross-correlation vector P of the amplified estimated signal of the voltage power frequency signal and the current time domain signal according to the cross-correlation function;
in one implementation, the cross-correlation vector P is taken as a two-element vector, where P [0 ]][0]、P[1][0]Sequentially represents a row element, a column element, a two-row element and a column element. Wherein, P [0 ]][0]＝p 0 ，P[1][0]＝p 1 。
(5) Determining a weight vector w of the wiener filter according to the autocorrelation matrix R and the cross-correlation vector P;
in a specific implementation process, the weight vector w of the wiener filter can be obtained by a process of calculating the weight vector by a steepest descent method or an LMS method. The detailed process can refer to the records of the prior related art, and is not described herein again.
In a specific implementation process, the autocorrelation matrix R may be subjected to an inversion operation to obtain an inverse matrix R of the autocorrelation matrix -1 (ii) a Transforming the inverse matrix R -1 The product with the cross-correlation vector P serves as the weight vector. I.e., w = R -1 P。
Specifically, a adjoint of the autocorrelation matrix R may be calculated, and then the inverse of the autocorrelation matrix R may be calculated from the autocorrelation matrix R and the adjoint -1 . Taking the autocorrelation matrix R as a 2-degree matrix as an example, R0][0]、R[0][1]、R[1][0]、R[1][1]Sequentially comprises elements of a row, a column, a row and a column and a row and a column in the autocorrelation matrix R, and x = R [0 ]][0]*R[1][1]-R[0][1]*R[1][0]The inverse of the autocorrelation matrix R is R -1 Then inverse matrix R -1 Wherein each element is R -1 [0][0]＝R[1][1]/x、R -1 [0][1]＝R[1][0]/x、R -1 [1][0]＝R[0][1]/x、R -1 [1][1]＝R[0][0]X; such that R -1 Each element of (a) is determined.
When the autocorrelation matrix R is a 2-order matrix and the cross-correlation vector P is a two-element column vector, the obtained weight vector is a column vector containing 2 elements. w is a 0 、w 1 Sequentially represents a row and a column element, two rows and a column element. Wherein, w 0 ＝R -1 [0][0]*p 0 +R -1 [0][1]*p 1 ，w 1 ＝R -1 [1][0]*p 0 +R -1 [1][1]*p 1 。
It should be noted that when the number of taps of the wiener filter is small, especially when the number of taps is 2, when the weight vector is obtained, only the inverse matrix R of the autocorrelation matrix R with the order of 2 is required -1 The inverse matrix can be directly calculated by definition of the inverse matrix, and the process of calculating the weight vector relative to the steepest descent method or LMS methodThe amount of calculation is greatly reduced.
(6) Obtaining an estimated signal of a current power frequency signal in a current time domain signal according to the weight vector and the amplified estimated signal of the voltage power frequency signal;
in a specific implementation process, the jth element U in the signal can be estimated according to the weight vector and the amplification of the voltage power frequency signal e0 [j]And j +1 th element U e0 [j+1]Calculating corresponding element I in estimated signal of current power frequency signal e [j](ii) a Wherein j is more than or equal to 0 and less than 2 n Any integer of (2) n The number of sampling points of the current time domain signal and the voltage time domain signal. Specifically, the calculation formula may refer to calculation formula (5):
I e [j]＝w 0 *U e0 [j+1]+w 1 *U e0 [j] (5)
(7) And filtering the current time domain signal by utilizing the estimated signal of the current power frequency signal to obtain the current time domain signal of which the power frequency signal is filtered.
Specifically, the current time domain signal may be subtracted from the estimated current power frequency signal to obtain a current time domain signal with the power frequency signal filtered. Therefore, in the frequency spectrum of the current time domain signal of the filtered power frequency signal, the current component signal positioned in the rated power frequency side band is not submerged due to frequency spectrum leakage, namely, the current component signal positioned in the rated power frequency side band in the current time domain signal can be more accurately identified, and the reliability of detecting the broken bar fault of the asynchronous induction motor rotor is further improved.
Fig. 2 is a diagram comparing results of filtering a current signal by using the adaptive filtering method of the present invention and a conventional filtering method. In fig. 2, (a) is an amplitude spectrum obtained by performing fast fourier transform on an original current time domain signal, (b) is an amplitude spectrum obtained by performing fast fourier transform on a current time domain signal in which a power frequency signal is filtered, which is obtained by directly performing wiener filtering on the original current time domain signal using an original voltage time domain signal in a conventional filtering method, and (c) is an amplitude spectrum obtained by performing wiener filtering on the original current time domain signal using an estimated signal of a voltage power frequency signal in the voltage time domain signal obtained by the adaptive filtering method of the present invention, and (c) is an amplitude spectrum obtained by performing fast fourier transform on a current time domain signal in which a power frequency signal is filtered, which is obtained by performing wiener filtering on the original current time domain signal using an estimated signal of a voltage power frequency signal in the voltage time domain signal. In the parts (a) to (c), the ordinate is the amplitude, the abscissa below is the frequency, and the abscissa above is the serial number corresponding to each frequency point.
As shown in fig. 2, the power frequency signal amplitude is indeed reduced, and at the left side of 50HZ, an amplitude peak is found, but due to frequency leakage, the current component signal close to 50HZ is still indistinguishable. (c) The amplitude at 50Hz in the section is almost 0, and the frequency leakage phenomenon is hardly observed in the vicinity of 50 Hz. In addition, because the influence of frequency leakage of the power frequency signal is eliminated, an amplitude peak is respectively found at 49.7Hz and 50.3Hz, which shows that by adopting the technical scheme of the invention, the power frequency signal in the current time domain signal can be filtered more thoroughly, so that the current component signal which is very close to 50Hz can be more clearly distinguished.
In the adaptive filtering method of this embodiment, after voltage frequency domain signals of the voltage time domain signals are obtained by performing fast fourier transform on the acquired voltage time domain signals, frequency points within a first preset sequence number range and frequency points within a second preset sequence number range are selected from the voltage frequency domain signals, values of other frequency points are set to be 0, and the processed voltage frequency domain signals are subjected to fast inverse fourier transform to obtain estimated signals of the voltage power frequency signals; based on a wiener filter, the collected current time domain signal is filtered by utilizing the estimated signal of the voltage power frequency signal, so as to filter the power frequency signal contained in the signal. Therefore, the adverse effects of direct current and higher harmonic signals in the voltage time domain signals on filtering are eliminated, so that the current power frequency signals can be filtered more thoroughly, the current component signals located in the rated power frequency side band can be identified more accurately from the current frequency spectrum, and the reliability of detecting the broken bar fault of the asynchronous induction motor rotor is improved.
Further, the invention also provides a fault detection method.
Fig. 3 is a flow chart illustrating the main steps of a fault detection method according to an embodiment of the present invention. The fault detection method is used for detecting the broken bar fault of the motor rotor. As shown in fig. 3, the fault detection method of the present embodiment may include the following steps 301 to 304.
301, obtaining a current time domain signal of a filtered power frequency signal according to a preset adaptive filtering method;
in a specific implementation process, the detailed process of the adaptive filtering method may refer to the description of the related embodiments, and is not described herein again.
and step 304, if the ratio is larger than a preset threshold value, determining that the asynchronous induction motor has a rotor broken bar fault.
In a specific implementation process, the implementation processes of the above steps 302 to 304 may refer to the records of the related art, and are not described herein again.
It should be noted that, although the foregoing embodiments describe each step in a specific sequence, those skilled in the art can understand that, in order to achieve the effect of the present invention, different steps do not have to be executed in such a sequence, and they may be executed simultaneously (in parallel) or in other sequences, and these changes are all within the scope of the present invention.
Further, the invention also provides a control device of the intelligent household equipment.
Referring to fig. 4, fig. 4 is a main structural block diagram of an adaptive filtering apparatus according to an embodiment of the present invention. As shown in fig. 4, the adaptive filtering apparatus in the embodiment of the present invention may include a first transformation module 40, a selection module 41, a second transformation module 42, and a filtering module 43.
The first transformation module 40 is configured to perform fast fourier transformation on the acquired steady-state voltage time-domain signal to obtain a voltage frequency-domain signal corresponding to the voltage time-domain signal;
a selecting module 41, configured to select multiple frequency points in a first preset sequence number range and multiple frequency points in a second preset sequence number range from the voltage frequency domain signals, and set a real part and an imaginary part of the frequency points outside the first preset sequence number range and the second preset sequence number range to 0, so as to obtain processed voltage frequency domain signals; the starting sequence number value of the first preset sequence number range is the difference between the first power frequency sequence number value of the first frequency point corresponding to the rated power frequency and a first preset threshold value, and the ending sequence number value of the first preset sequence number range is the sum of the first power frequency sequence number value and a second preset threshold value; the starting sequence number value of the second preset sequence number range is the difference between the second power frequency sequence number value of the second frequency point corresponding to the rated power frequency and a second preset threshold value, and the ending sequence number value of the second preset sequence number range is the sum of the second power frequency sequence number value and the first preset threshold value; the first power frequency sequence number is smaller than the second power frequency sequence number;
a second transform module 42, configured to perform inverse fast fourier transform on the processed voltage frequency domain signal to obtain an estimated signal of a voltage power frequency signal in the voltage time domain signal;
and a filtering module 43, configured to filter the acquired current time-domain signal based on a wiener filter by using the estimated signal of the voltage power-frequency signal, so as to obtain a current time-domain signal with the power-frequency signal filtered.
Specifically, according to the number N of taps of the wiener filter, N-1 elements are selected from the estimated signal of the voltage power frequency signal, and the estimated signal of the voltage power frequency signal is amplified to obtain an amplified estimated signal of the voltage power frequency signal; wherein N is an integer greater than or equal to 2; calculating an autocorrelation function of the amplified estimation signal of the voltage power frequency signal and a cross-correlation function between the amplified estimation signal of the voltage power frequency signal and the current time domain signal according to the amplified estimation signal of the voltage power frequency signal; constructing an autocorrelation matrix of the amplification estimation signal of the voltage power frequency signal according to the autocorrelation function; constructing a cross-correlation vector of the amplified estimated signal of the voltage power frequency signal and the current time domain signal according to the cross-correlation function; determining a weight vector of the wiener filter according to the autocorrelation matrix and the cross-correlation vector; obtaining an estimation signal of a current power frequency signal in a current time domain signal according to the weight vector and the amplification estimation signal of the voltage power frequency signal; and filtering the current time domain signal by using the estimated signal of the current power frequency signal to obtain the current time domain signal of which the power frequency signal is filtered.
In a specific implementation process, when N is 2, a specified element may be copied from the estimated value of the power frequency signal as an amplification element, and the estimated signal of the voltage power frequency signal is amplified to obtain an amplified estimated signal of the voltage power frequency signal. For example, a first element is copied from the estimated value of the power frequency signal as an amplification element, and the amplification element is placed in front of the first element, so that an amplification estimated signal of the voltage power frequency signal is obtained.
In a specific implementation process, the jth element U in the signal can be estimated according to the weight vector and the amplification of the voltage power frequency signal e0 [j]And j +1 th element U e0 [j+1]Calculating corresponding element I in estimated signal of current power frequency signal e [j]Wherein j is greater than or equal to 0 and less than 2 n Any integer of (2) n The number of sampling points of the current time domain signal and the voltage time domain signal.
In a specific implementation process, the inverse operation may be performed on the autocorrelation matrix to obtain an inverse matrix of the autocorrelation matrix; taking the product of the inverse matrix and the cross-correlation vector as the weight vector.
The technical principles, the solved technical problems, and the generated technical effects of the adaptive filtering apparatus for implementing the adaptive filtering method embodiments of the above embodiments are similar, and it can be clearly understood by those skilled in the art that, for convenience and conciseness of description, the contents described in the embodiments of the adaptive filtering method may be referred to for the specific working process and related descriptions of the adaptive filtering apparatus, and are not repeated herein.
It will be understood by those skilled in the art that all or part of the flow of the method according to the above-described embodiment may be implemented by a computer program, which may be stored in a computer-readable storage medium and used to implement the steps of the above-described embodiments of the method when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying said computer program code, media, usb disk, removable hard disk, magnetic diskette, optical disk, computer memory, read-only memory, random access memory, electrical carrier wave signals, telecommunication signals, software distribution media, etc. It should be noted that the computer readable storage medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable storage media that does not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
Furthermore, the invention also provides electronic equipment.
Referring to fig. 5, fig. 5 is a main structural block diagram of an electronic apparatus according to an embodiment of the present invention. As shown in fig. 5, the electronic device in the embodiment of the present invention may include a processor 50 and a storage device 51, where the storage device 51 may be configured to store a program for executing the adaptive filtering method of the above-described method embodiment, or a program for executing the fault detection method of the above-described method embodiment, and the processor 50 may be configured to execute a program in the storage device 51, where the program includes, but is not limited to, a program for executing the adaptive filtering method of the above-described method embodiment, or a program for executing the fault detection method of the above-described method embodiment. For convenience of explanation, only the parts related to the embodiments of the present invention are shown, and details of the specific techniques are not disclosed. The electronic apparatus may be a control device formed including various electronic devices.
Further, the invention also provides a computer readable storage medium. In one computer-readable storage medium embodiment according to the present invention, a computer-readable storage medium may be configured to store a program that executes the adaptive filtering method of the above-described method embodiment, or a program that executes the fault detection method of the above-described method embodiment, which may be loaded and executed by a processor to implement the above-described adaptive filtering method, or the fault detection method of the above-described method embodiment. For convenience of explanation, only the parts related to the embodiments of the present invention are shown, and details of the specific techniques are not disclosed. The computer readable storage medium may be a storage device formed by including various electronic devices, and optionally, the computer readable storage medium is a non-transitory computer readable storage medium in the embodiment of the present invention.
Further, it should be understood that, since the modules are only configured to illustrate the functional units of the apparatus of the present invention, the corresponding physical devices of the modules may be the processor itself, or a part of software, a part of hardware, or a part of a combination of software and hardware in the processor. Thus, the number of individual modules in the figures is merely illustrative.
Those skilled in the art will appreciate that the various modules in the apparatus may be adaptively split or combined. Such splitting or combining of specific modules does not cause the technical solutions to deviate from the principle of the present invention, and therefore, the technical solutions after splitting or combining will fall within the protection scope of the present invention.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
Claims (10)
1. An adaptive filtering method, comprising:
carrying out fast Fourier transform on the collected steady-state voltage time domain signals to obtain voltage frequency domain signals corresponding to the voltage time domain signals;
selecting a plurality of frequency points in a first preset sequence number range and a plurality of frequency points in a second preset sequence number range in the voltage frequency domain signals, and setting the real parts and the imaginary parts of the frequency points outside the first preset sequence number range and the second preset sequence number range to be 0 to obtain processed voltage frequency domain signals; the starting sequence number value of the first preset sequence number range is the difference between a first power frequency sequence number value of a first frequency point corresponding to the rated power frequency and a first preset threshold value, and the ending sequence number value of the first preset sequence number range is the sum of the first power frequency sequence number value and a second preset threshold value; the starting sequence number value of the second preset sequence number range is the difference between the second power frequency sequence number value of the second frequency point corresponding to the rated power frequency and a second preset threshold value, and the ending sequence number value of the second preset sequence number range is the sum of the second power frequency sequence number value and the first preset threshold value; the first power frequency serial number value is smaller than the second power frequency serial number value;
performing inverse fast Fourier transform on the processed voltage frequency domain signal to obtain an estimated signal of a voltage power frequency signal in the voltage time domain signal;
and based on a wiener filter, filtering the acquired current time domain signal by using the estimated signal of the voltage power frequency signal to obtain a current time domain signal of which the power frequency signal is filtered.
2. The adaptive filtering method according to claim 1, wherein the step of filtering the acquired current time domain signal by using the estimated signal of the voltage power frequency signal based on a wiener filter to obtain a current time domain signal with a power frequency signal filtered out comprises:
selecting N-1 elements from the estimated signal of the voltage power frequency signal according to the number N of taps of the wiener filter, and amplifying the estimated signal of the voltage power frequency signal to obtain an amplified estimated signal of the voltage power frequency signal; wherein N is an integer greater than or equal to 2;
calculating an autocorrelation function of the amplified estimation signal of the voltage power frequency signal and a cross-correlation function between the amplified estimation signal of the voltage power frequency signal and the current time domain signal according to the amplified estimation signal of the voltage power frequency signal;
constructing an autocorrelation matrix of an amplification estimation signal of the voltage power frequency signal according to the autocorrelation function;
constructing a cross-correlation vector of an amplification estimation signal of the voltage power frequency signal and a current time domain signal according to the cross-correlation function;
determining a weight vector of the wiener filter according to the autocorrelation matrix and the cross-correlation vector;
obtaining an estimated signal of a current power frequency signal in a current time domain signal according to the weight vector and the amplified estimated signal of the voltage power frequency signal;
and filtering the current time domain signal by utilizing the estimated signal of the current power frequency signal to obtain the current time domain signal of which the power frequency signal is filtered.
3. The adaptive filtering method according to claim 2, wherein N is 2;
selecting N-1 elements from the estimated signal of the voltage power frequency signal according to the number N of taps of the wiener filter, amplifying the estimated signal of the voltage power frequency signal to obtain an amplified estimated signal of the voltage power frequency signal, wherein the method comprises the following steps:
and copying a specified element from the estimated value of the power frequency signal as an amplification element, and amplifying the estimated signal of the voltage power frequency signal to obtain an amplified estimated signal of the voltage power frequency signal.
4. The adaptive filtering method according to claim 3, wherein the step of copying a specified element from the estimated value of the power frequency signal as an amplification element and amplifying the estimated signal of the voltage power frequency signal to obtain an amplified estimated signal of the voltage power frequency signal comprises:
copying a first element from the estimated value of the power frequency signal as an amplification element, and placing the amplification element in front of the first element to obtain an amplification estimated signal of the voltage power frequency signal.
5. The adaptive filtering method according to claim 2, wherein obtaining an estimated signal of the current power frequency signal in the current time domain signal according to the weight vector and the amplified estimated signal of the voltage power frequency signal comprises:
according to the weight vector and the j element U in the amplification estimation signal of the voltage power frequency signal e0 [j]And j +1 th element U e0 [j+1]Calculating corresponding element I in estimated signal of current power frequency signal e [j]Wherein j is greater than or equal to 0 and less than 2 n Any integer of (2) n The number of sampling points of the current time domain signal and the voltage time domain signal.
6. The adaptive filtering method according to claim 2, wherein determining the weight vector of the wiener filter from the autocorrelation matrix and the cross-correlation vector comprises:
carrying out inversion operation on the autocorrelation matrix to obtain an inverse matrix of the autocorrelation matrix;
taking the product of the inverse matrix and the cross-correlation vector as the weight vector.
7. An adaptive filtering apparatus, comprising:
the first transformation module is used for carrying out fast Fourier transformation on the collected steady-state voltage time domain signals to obtain voltage frequency domain signals corresponding to the voltage time domain signals;
the selecting module is used for selecting a plurality of frequency points in a first preset sequence number range and a plurality of frequency points in a second preset sequence number range in the voltage frequency domain signals, and setting the real parts and the imaginary parts of the frequency points outside the first preset sequence number range and the second preset sequence number range as 0 to obtain processed voltage frequency domain signals; the starting sequence number value of the first preset sequence number range is the difference between the first power frequency sequence number value of the first frequency point corresponding to the rated power frequency and a first preset threshold value, and the ending sequence number value of the first preset sequence number range is the sum of the first power frequency sequence number value and a second preset threshold value; the starting sequence number value of the second preset sequence number range is the difference between the second power frequency sequence number value of the second frequency point corresponding to the rated power frequency and a second preset threshold value, and the ending sequence number value of the second preset sequence number range is the sum of the second power frequency sequence number value and the first preset threshold value; the first power frequency sequence number is smaller than the second power frequency sequence number;
the second transformation module is used for performing inverse fast Fourier transform on the processed voltage frequency domain signal to obtain an estimation signal of a voltage power frequency signal in the voltage time domain signal;
and the filtering module is used for filtering the acquired current time domain signal by using the estimated signal of the voltage power frequency signal based on the wiener filter to obtain the current time domain signal of which the power frequency signal is filtered.
8. A fault detection method for detecting asynchronous induction motor rotor bar break faults, the method comprising:
the adaptive filtering method according to any one of claims 1 to 6, obtaining a current time domain signal from which the power frequency signal is filtered;
performing fast Fourier transform on the current time domain signal of which the power frequency signal is filtered to obtain the amplitude of a component current signal with the frequency of (1 + 2s) f in the current time domain signal and located at a side frequency band of a rated power frequency; wherein s is slip ratio, and f is rated value of power frequency;
determining the ratio of the amplitude of the component current signal to the amplitude of the current power frequency signal;
and if the ratio is larger than a preset threshold value, determining that the asynchronous induction motor has a rotor broken bar fault.
9. An electronic device comprising a processor and a memory means adapted to store a plurality of program codes, characterized in that said program codes are adapted to be loaded and run by said processor to perform the adaptive filtering method of any of claims 1 to 6 or to perform the fault detection method of claim 8.
10. A computer readable storage medium having stored therein a plurality of program codes, characterized in that the program codes are adapted to be loaded and run by a processor to perform the adaptive filtering method of any one of claims 1 to 6 or to perform the fault detection method of claim 8.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115955161A (en) * | 2023-03-15 | 2023-04-11 | 清华大学无锡应用技术研究院 | Method, apparatus, device and medium for estimating slip of adaptive asynchronous induction motor |
| CN116626490A (en) * | 2023-07-25 | 2023-08-22 | 清华大学无锡应用技术研究院 | Motor fault diagnosis method and device based on Kalman filter |
| CN117388693A (en) * | 2023-12-06 | 2024-01-12 | 北京汇思慧能科技有限公司 | Fault detection method, device and storage medium |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115955161A (en) * | 2023-03-15 | 2023-04-11 | 清华大学无锡应用技术研究院 | Method, apparatus, device and medium for estimating slip of adaptive asynchronous induction motor |
| CN116626490A (en) * | 2023-07-25 | 2023-08-22 | 清华大学无锡应用技术研究院 | Motor fault diagnosis method and device based on Kalman filter |
| CN116626490B (en) * | 2023-07-25 | 2023-10-10 | 清华大学无锡应用技术研究院 | Motor fault diagnosis method and device based on Kalman filter |
| CN117388693A (en) * | 2023-12-06 | 2024-01-12 | 北京汇思慧能科技有限公司 | Fault detection method, device and storage medium |
| CN117388693B (en) * | 2023-12-06 | 2024-04-02 | 北京汇思慧能科技有限公司 | Fault detection method, device and storage medium |
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