Adaptive filtering method, device, fault detection method, electronic device and medium

Abstract

The present invention provides an adaptive filtering method, device, fault detection method, electronic device and medium, including performing fast Fourier transform on a voltage time domain signal to obtain a voltage frequency domain signal, selecting a frequency point within a first preset sequence number range and a frequency point within a second preset sequence number range, and setting the values ​​of other frequency points to 0, performing inverse fast Fourier transform on the processed voltage frequency domain signal to obtain an estimated signal of the voltage power frequency signal; based on a Wiener filter, using the estimated signal of the voltage power frequency signal, filtering the collected current time domain signal to filter out the power frequency signal contained therein. In this way, the adverse effects of direct current and high-order harmonic signals in the voltage time domain signal on filtering are eliminated, so that the current power frequency signal can be filtered out more thoroughly, so as to more accurately identify the current component signal located in the rated power frequency sideband from the current spectrum, thereby improving the reliability of detecting the rotor broken bar fault of the asynchronous induction motor.

Description

Technical Field

[0001] The present invention relates to the field of filtering technology, and specifically provides an adaptive filtering method, a device, a fault detection method, an electronic device and a medium. Background Art

[0002] After a broken rotor bar fault occurs in an asynchronous induction motor, an additional current component signal with a frequency of (1+2s)f (where s is the slip rate and f is the rated value of the power frequency) will appear in its stator current. This current component signal can be used as a characteristic signal of the broken rotor bar fault.

[0003] However, in actual working conditions, the value of the power frequency is not equal to its rated value. For example, for the rated power frequency of 50Hz adopted in my country, there is usually a deviation of ±0.2 to ±0.5Hz. This leads to spectrum leakage near the frequency point corresponding to the rated power frequency in the spectrum of the stator current obtained by fast Fourier transform, thereby drowning out the current component signal whose frequency is very close to the rated power frequency, making it impossible to identify the characteristic signal of the broken rotor bar, and making the reliability of detecting the broken rotor bar fault based on the stator current analysis method low.

[0004] The adaptive filtering method used in the patent with patent number CN200710061634.8 is to continuously refine the Fourier transform of the collected current time domain signal, find the frequency, amplitude and initial phase of the power frequency signal in the current time domain signal, and then calculate the power frequency signal in the current time domain signal through a discrete sine function, and then use the calculated power frequency signal to offset the power frequency signal in the current time domain signal, so as to achieve 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 its rated value, but there is 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 of the time domain signal. Therefore, the adaptive filtering method requires a very long sampling time, which is not conducive to real-time monitoring of the motor state.

[0005] Therefore, how to quickly and accurately adaptively filter the industrial frequency signal in the stator current time domain signal of the asynchronous induction motor, and thereby improve the detection sensitivity of the characteristic current signal of the broken rotor bar fault, is a technical problem that needs to be solved urgently in this field. Summary of the invention

[0006] In order to overcome the above-mentioned defects, the present invention is proposed to provide an adaptive filtering method, device, fault detection method, electronic device and medium that solve or at least partially solve the technical problem that in the spectrum obtained by Fourier transform of the stator line current signal of the asynchronous induction motor, due to the spectrum leakage of the power frequency current signal, the current component signals very close to the power frequency will be submerged by the power frequency signal, resulting in the inability to accurately and effectively identify these current component signals, resulting in low reliability in detecting the rotor broken bar fault of the asynchronous induction motor.

[0007] In a first aspect, the present invention provides an adaptive filtering method, comprising:

[0008] Performing a fast Fourier transform on the collected steady-state voltage time domain signal to obtain a voltage frequency domain signal corresponding to the voltage time domain signal;

[0009] Select multiple frequency points within a first preset serial number range and multiple frequency points within a second preset serial number range in the voltage frequency domain signal, and set the real part and the imaginary part of the frequency points outside the first preset serial number range and the second preset serial number range to 0, so as to obtain a processed voltage frequency domain signal; wherein, the starting serial number value of the first preset serial number range is the difference between the first power frequency serial number value of the first frequency point corresponding to the rated power frequency and the first preset threshold value, and the ending serial number value of the first preset serial number range is the sum of the first power frequency serial number value and the second preset threshold value; the starting serial number value of the second preset serial number range is the difference between the second power frequency serial number value of the second frequency point corresponding to the rated power frequency and the second preset threshold value, and the ending serial number value of the second preset serial number range is the sum of the second power frequency serial number value and the first preset threshold value; the first power frequency serial number value is less than the second power frequency serial number value;

[0010] Performing an inverse fast Fourier transform on the processed voltage frequency domain signal to obtain an estimated signal of the voltage power frequency signal in the voltage time domain signal;

[0011] Based on the Wiener filter, the estimated signal of the voltage power frequency signal is used to filter the collected current time domain signal to obtain the current time domain signal with the power frequency signal filtered out.

[0012] Furthermore, in the above-mentioned adaptive filtering method, based on the Wiener filter, the estimated signal of the voltage power frequency signal is used to filter the collected current time domain signal to obtain the current time domain signal with the power frequency signal filtered out, including:

[0013] According to the number of taps N of the Wiener filter, N-1 elements are selected from the estimated signal of the voltage and power frequency signal, and the estimated signal of the voltage and power frequency signal is amplified to obtain an amplified estimated signal of the voltage and power frequency signal; wherein N is an integer greater than or equal to 2;

[0014] According to the amplified estimated signal of the voltage and power frequency signal, calculating the autocorrelation function of the amplified estimated signal of the voltage and power frequency signal and the cross-correlation function between the amplified estimated signal of the voltage and power frequency signal and the current time domain signal;

[0015] According to the autocorrelation function, constructing an autocorrelation matrix of the amplified estimation signal of the voltage power frequency signal;

[0016] According to the cross-correlation function, construct a cross-correlation vector of the amplified estimated signal of the voltage power frequency signal and the current time domain signal;

[0017] Determining a weight vector of the Wiener filter according to the autocorrelation matrix and the cross-correlation vector;

[0018] 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;

[0019] The current time domain signal is filtered using the estimated signal of the current power frequency signal to obtain the current time domain signal with the power frequency signal filtered out.

[0020] Further, in the above-mentioned adaptive filtering method, N is 2;

[0021] According to the tap number N of the Wiener filter, N-1 elements are selected from the estimated signal of the voltage and power frequency signal, and the estimated signal of the voltage and power frequency signal is amplified to obtain the amplified estimated signal of the voltage and power frequency signal, including:

[0022] A designated element is 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.

[0023] Furthermore, in the above-mentioned adaptive filtering method, copying a specified element from the estimated value of the power frequency signal as an amplification element, amplifying the estimated signal of the voltage power frequency signal, and obtaining the amplified estimated signal of the voltage power frequency signal, comprises:

[0024] The first element is copied from the estimated value of the power frequency signal as an amplified element, and the amplified element is placed before the first element to obtain an amplified estimated signal of the voltage power frequency signal.

[0025] Furthermore, in the above-mentioned adaptive filtering method, 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 includes:

[0026] According to the weight vector and the amplified voltage and power frequency signal, the jth element U in the estimated signale0 [j] and the j+1th element U e0 [j+1], calculate the corresponding element I in the estimated signal of the current power frequency signal e [j], where j is greater than or equal to 0 and less than 2 n Any integer, 2 n is the number of sampling points of the current time domain signal and the voltage time domain signal.

[0027] Furthermore, in the above-mentioned adaptive filtering method, determining the weight vector of the Wiener filter according to the autocorrelation matrix and the cross-correlation vector includes:

[0028] Performing an inverse operation on the autocorrelation matrix to obtain an inverse matrix of the autocorrelation matrix;

[0029] The product of the inverse matrix and the cross-correlation vector is used as the weight vector.

[0030] In a second aspect, the present invention provides an adaptive filtering device, comprising:

[0031] A first transformation module is used to perform a fast Fourier transform on the collected steady-state voltage time domain signal to obtain a voltage frequency domain signal corresponding to the voltage time domain signal;

[0032] A selection module is used to select multiple frequency points within a first preset serial number range and multiple frequency points within a second preset serial number range in a voltage frequency domain signal, and set the real part and the imaginary part of the frequency points outside the first preset serial number range and the second preset serial number range to 0, so as to obtain a processed voltage frequency domain signal; wherein, the starting serial number value of the first preset serial number range is the difference between the first power frequency serial number value of the first frequency point corresponding to the rated power frequency and the first preset threshold value, and the ending serial number value of the first preset serial number range is the sum of the first power frequency serial number value and the second preset threshold value; the starting serial number value of the second preset serial number range is the difference between the second power frequency serial number value of the second frequency point corresponding to the rated power frequency and the second preset threshold value, and the ending serial number value of the second preset serial number range is the sum of the second power frequency serial number value and the first preset threshold value; the first power frequency serial number value is less than the second power frequency serial number value;

[0033] A second transformation module, configured to perform an inverse fast Fourier transform on the processed voltage frequency domain signal to obtain an estimated signal of the voltage power frequency signal in the voltage time domain signal;

[0034] The filtering module is used to filter the collected current time domain signal based on the Wiener filter by using the estimated signal of the voltage power frequency signal to obtain the current time domain signal with the power frequency signal filtered out.

[0035] In a third aspect, a fault detection method is provided, comprising:

[0036] According to any of the above-mentioned adaptive filtering methods, a current time domain signal with the power frequency signal filtered out is obtained;

[0037] Performing a fast Fourier transform on the current time domain signal from which the power frequency signal is filtered out, to obtain the amplitude of a component current signal with a frequency of (1+2s)f located in the sideband of the rated power frequency in the current time domain signal; wherein s is the slip rate, and f is the rated value of the power frequency;

[0038] Determining a ratio of the amplitude of the component current signal to the amplitude of the current power frequency signal;

[0039] If the ratio is greater than a preset threshold, it is determined that a rotor bar breakage fault occurs in the asynchronous induction motor.

[0040] In a fourth aspect, an electronic device is provided, comprising a processor and a storage device, wherein the storage device is suitable for storing a plurality of program codes, and the program codes are suitable for being loaded and run by the processor to execute any one of the adaptive filtering methods described above, or to execute the fault detection method described above.

[0041] In a fifth aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored therein, wherein the program codes are suitable for being loaded and run by a processor to execute the adaptive filtering method described in any of the above technical solutions, or to execute the above-mentioned fault detection method.

[0042] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

[0043] In the technical solution of the present invention, after the voltage frequency domain signal of the voltage time domain signal is obtained by performing a fast Fourier transform on the collected voltage time domain signal, the frequency points within the first preset serial number range and the frequency points within the second preset serial number range are selected, and the values ​​of other frequency points are set to 0, and the processed voltage frequency domain signal is subjected to an inverse fast Fourier transform to obtain an estimated signal of the voltage power frequency signal; based on the Wiener filter, the estimated signal of the voltage power frequency signal is used to filter the collected current time domain signal to filter out the power frequency signal contained therein. In this way, the adverse effects of the DC and high-order harmonic signals in the voltage time domain signal on filtering are eliminated, so that the current power frequency signal can be filtered out more thoroughly, so as to more accurately identify the current component signal located in the rated power frequency sideband from the current spectrum, thereby improving the reliability of detecting the rotor broken bar fault of the asynchronous induction motor. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The disclosure of the present invention will become more easily understood with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present invention. In addition, similar numbers in the figures are used to represent similar components, among which:

[0045] Figure 1 is a schematic flow chart of main steps of an adaptive filtering method according to an embodiment of the present invention;

[0046] Figure 2 is a schematic diagram comparing the results of filtering a current signal using the adaptive filtering method of the present invention and a conventional filtering method;

[0047] Figure 3 is a schematic flow chart of main steps of a fault detection method according to an embodiment of the present invention;

[0048] Figure 4 is a main structural block diagram of an adaptive filtering device according to an embodiment of the present invention;

[0049] Figure 5 It is a main structural block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] Some embodiments of the present 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 used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0051] In the description of the present invention, "module" and "processor" may include hardware, software or a combination of the two. A module may include hardware circuits, various suitable sensors, communication ports, and memories, and may also include software parts, such as program codes, or a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, hardware, or a combination of the two. Non-temporary computer-readable storage media include any suitable medium that can store program codes, such as a magnetic disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, and the like. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and / or B", and may include only A, only B, or A and B. The singular terms "one" and "the" may also include plural forms.

[0052] After a broken rotor bar fault occurs in an asynchronous induction motor, an additional current component signal with a frequency of (1+2s)f (where s is the slip rate and f is the rated value of the power frequency) will appear in its stator current. This current component signal can be used as a characteristic signal of the broken rotor bar fault.

[0053] However, in actual working conditions, the value of the power frequency is not equal to its rated value. For example, for the rated power frequency of 50Hz adopted in my country, there is usually a deviation of ±0.2 to ±0.5Hz. This leads to spectrum leakage near the frequency point corresponding to the rated power frequency in the spectrum of the stator current obtained by fast Fourier transform, thereby drowning out the current component signal whose frequency is very close to the rated power frequency, making it impossible to identify the characteristic signal of the broken rotor bar, and making the reliability of detecting the broken rotor bar fault based on the stator current analysis method low.

[0054] Therefore, in order to solve the above technical problems, the present invention provides the following technical solutions.

[0055] See attached Figure 1 , Figure 1 FIG. 1 is a schematic flow chart of the main steps of an adaptive filtering method according to an embodiment of the present invention. Figure 1 As shown, the adaptive filtering method in the embodiment of the present invention mainly includes the following steps 101 to 104.

[0056] Step 101, performing fast Fourier transform on the collected steady-state voltage time domain signal to obtain a voltage frequency domain signal corresponding to the voltage time domain signal;

[0057] In a specific implementation process, the voltage transformers arranged at key power supply points, hub substations, bus nodes and branch nodes in the power grid can continuously collect the steady-state voltage time domain signals on site. Then, the collected steady-state voltage time domain signals are fast Fourier transformed to convert the collected steady-state voltage time domain signals into voltage frequency domain signals.

[0058] Step 102, selecting multiple frequency points within a first preset sequence number range and multiple frequency points within a second preset sequence number range in the voltage frequency domain signal, and setting the real parts and imaginary parts of the frequency points outside the first preset sequence number range and the second preset sequence number range to 0, to obtain a processed voltage frequency domain signal;

[0059] In a specific implementation process, in addition to the power frequency signal, the voltage time domain signal also contains a DC component signal and a higher harmonic signal of the power frequency signal. In addition, in the actual operation process, affected by various factors, the frequency of the power frequency signal in the voltage time domain signal is not equal to its rated value, but fluctuates within a certain range, which is usually ±0.2~±0.5Hz. Therefore, in order to obtain a more accurate determination of the power frequency, multiple frequency points including the frequency point corresponding to the rated power frequency can be selected according to the fluctuation range.

[0060] Specifically, multiple frequency points within a first preset serial number range and multiple frequency points within a second preset serial number range can be selected from the voltage frequency domain signal, and the real and imaginary parts of the frequency points outside the first preset serial number range and the second preset serial number range are set to 0 to obtain a processed voltage frequency domain signal. The starting serial number value of the first preset serial number range is the difference between the first power frequency serial number value of the first frequency point corresponding to the rated power frequency and the first preset threshold value, and the ending serial number value of the first preset serial number range is the sum of the first power frequency serial number value and the second preset threshold value; the starting serial number value of the second preset serial number range is the difference between the second power frequency serial number value of the second frequency point corresponding to the rated power frequency and the second preset threshold value, and the ending serial number value of the second preset serial number range is the sum of the second power frequency serial number value and the first preset threshold value; the first power frequency serial number value is less than the second power frequency serial number value.

[0061] In a specific implementation process, taking the sampling frequency as 12.8 kHz and the sampling time as 10.24 s as an example, the total number of frequency points in the voltage frequency domain signal is 10.24×12.8k=131072.

[0062] When the sampling time length of the voltage time domain signal being processed or analyzed 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, and 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. The first preset threshold and the second preset threshold can both be set to 256, then the first preset serial number range is 256 to 768, and the second preset serial number range is 130304 to 130816.

[0063] Step 103, performing an inverse fast Fourier transform on the processed voltage frequency domain signal to obtain an estimated signal of the voltage power frequency signal in the voltage time domain signal;

[0064] In a specific implementation process, multiple frequency points within a certain range are selected from the processed voltage frequency domain signal, and the real and imaginary parts of other frequency points are set to 0. In this way, the industrial frequency signal in the processed voltage frequency domain signal is closer to the actual industrial frequency signal. Therefore, after the processed voltage frequency domain signal is subjected to an inverse fast Fourier transform, the estimated signal of the voltage industrial frequency signal in the voltage time domain signal has a high degree of fit with the actual voltage industrial frequency signal.

[0065] Step 104 : Based on a Wiener filter, the estimated signal of the voltage power frequency signal is used to filter the collected current time domain signal to obtain a current time domain signal with the power frequency signal filtered out.

[0066] After obtaining the estimated signal of the voltage and power frequency signal, step 104 can be implemented according to the following steps:

[0067] (1) selecting N-1 elements from the estimated signal of the voltage and power frequency signal according to the number of taps N of the Wiener filter, amplifying the estimated signal of the voltage and power frequency signal, and obtaining an amplified estimated signal of the voltage and power frequency signal; wherein N is an integer greater than or equal to 2;

[0068] In a specific implementation process, when the number of taps of the Wiener filter is different, the method of filtering the current time domain signal using the estimated signal of the current industrial frequency signal to obtain the current time domain signal with the industrial frequency signal filtered out is different. Therefore, according to the number of taps N of the Wiener filter, N-1 elements are selected from the estimated signal of the voltage industrial frequency signal, and the estimated signal of the voltage industrial frequency signal is amplified to obtain the amplified estimated signal of the voltage industrial frequency signal.

[0069] Specifically, taking N=2 as an example, a specified element can 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 can be amplified to obtain an amplified estimated signal of the voltage power frequency signal. In this way, compared with calculating an amplification element that meets the requirements based on a sinusoidal signal, the calculation is simpler, and the calculation accuracy of the amplified estimated signal of the voltage power frequency signal obtained by this method is relatively high.

[0070] In a specific implementation process, the first element can be copied from the estimated value of the power frequency signal as an amplified element, and the amplified element is placed before the first element to obtain an amplified estimated signal of the voltage power frequency signal.

[0071] For example, after step 101 to step 103, the estimated signal of the voltage and power frequency signal obtained includes the following multiple elements:

[0072] U e [0], U e [1] Ue [2]......U e [2 n -1]; 2 n is the number of sampling points of the current time domain signal and the voltage time domain signal.

[0073] The first U e [0] is copied and placed in front of the estimated signal of the voltage and power frequency signal, and the amplified estimated signal of the voltage and power frequency signal is obtained, which includes the following multiple elements:

[0074] U e0 [0], U e0 [1] U e0 [2]......U e0 [2 n ]; among them, U e0 The value of [0] is equal to U e0 [1] value, U e0 The value of [j+1] is equal to U e [j] value, j is greater than or equal to 0 and less than 2 n Any integer, 2 n is the number of sampling points of the current time domain signal and the voltage time domain signal.

[0075] (2) calculating, according to the amplified estimated signal of the voltage and power frequency signal, an autocorrelation function of the amplified estimated signal of the voltage and power frequency signal and a cross-correlation function between the amplified estimated signal of the voltage and power frequency signal and the current time domain signal;

[0076] In a specific implementation process, the autocorrelation function of the amplified estimated signal of the voltage power frequency signal is two, which can be obtained according to calculation formulas (1) and (2):

[0077]

[0078]

[0079] Among them, U e0 [j] is the jth element in the amplified estimated signal of the voltage power frequency signal, 2 n is the number of sampling points of the current time domain signal and the voltage time domain signal.

[0080] The cross-correlation function between the amplified estimated signal of the voltage power frequency signal and the current time domain signal can also be two, which can be obtained according to calculation formulas (3) and (4):

[0081]

[0082]

[0083] Among them, Ue0 I[j] is the jth element in the amplified estimated signal of the voltage power frequency signal, I[j] is the jth element of the current time domain signal, 2 n is the number of sampling points of the current time domain signal and the voltage time domain signal.

[0084] (3) Based on the autocorrelation function, construct an autocorrelation matrix R of the amplified estimation signal of the voltage power frequency signal.

[0085] In a specific implementation process, take the autocorrelation matrix R as a second-order matrix as an example, where R[0][0], R[0][1], R[1][0], and R[1][1] are the elements of one row and one column, one row and two columns, two rows and one column, and two rows and two columns in the autocorrelation matrix R, respectively. Among them, R[0][0]=R[1][1]=r0, R[1][0]=R[0][1]=r1.

[0086] (4) constructing a cross-correlation vector P between the amplified estimated signal of the voltage power frequency signal and the current time domain signal according to the cross-correlation function;

[0087] In a specific implementation process, the cross-correlation vector P is a vector of two elements as an example, where P[0][0] and P[1][0] represent one row and one column of elements and two rows and one column of elements respectively. Among them, P[0][0]=p0, P[1][0]=p1.

[0088] (5) determining a weight vector w of the Wiener filter according to the autocorrelation matrix R and the cross-correlation vector P;

[0089] In a specific implementation process, the weight vector w of the Wiener filter can be obtained by calculating the weight vector using the steepest descent method or the LMS method. The detailed process can be referred to the records of the existing related technology, which will not be repeated here.

[0090] In a specific implementation process, the autocorrelation matrix R can also be inverted to obtain the inverse matrix R of the autocorrelation matrix: -1 ; The inverse matrix R -1 The product of the cross-correlation vector P is used as the weight vector. That is, w = R -1 P.

[0091] Specifically, the adjoint matrix of the autocorrelation matrix R can be calculated, and then the inverse matrix R of the autocorrelation matrix can be calculated based on the autocorrelation matrix R and the adjoint matrix. -1Taking the autocorrelation matrix R as a second-order matrix as an example, R[0][0], R[0][1], R[1][0], and R[1][1] are the elements of one row and one column, one row and two columns, two rows and one column, and two rows and two columns in the autocorrelation matrix R, respectively. Let x = R[0][0]*R[1][1]-R[0][1]*R[1][0]. The inverse matrix of the autocorrelation matrix R is R -1 , then the inverse matrix R -1 Each element in 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; so R -1 Each element of is determined.

[0092] When the autocorrelation matrix R is a 2nd-order matrix and the cross-correlation vector P is a column vector of two elements, the weight vector obtained is a column vector containing two elements. w0 and w1 represent one row and one column of elements and two rows and one column of elements respectively. Where w0 = R -1 [0][0]*p0+R -1 [0][1]*p1,w1=R -1 [1][0]*p0+R -1 [1][1]*p1.

[0093] 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 obtaining the weight vector, it is only necessary to obtain the inverse matrix R of the autocorrelation matrix R of order 2. -1 , this inverse matrix can be calculated directly through the definition of the inverse matrix. Compared with the process of calculating the weight vector by the steepest descent method or the LMS method, the amount of calculation will be greatly reduced.

[0094] (6) 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;

[0095] In a specific implementation process, the jth element U in the signal can be estimated based on the weight vector and the amplified voltage and power frequency signal. e0 [j] and the j+1th element U e0 [j+1], calculate the corresponding element I in the estimated signal of the current power frequency signal e [j]; where j is greater than or equal to 0 and less than 2 n Any integer, 2 n is the number of sampling points of the current time domain signal and the voltage time domain signal. Specifically, the calculation formula can refer to calculation formula (5):

[0096] I e [j] = w0*U e0 [j+1]+w1*U e0 [j] (5)

[0097] (7) Using the estimated signal of the current industrial frequency signal, the current time domain signal is filtered to obtain the current time domain signal with the industrial frequency signal filtered out.

[0098] Specifically, the estimated signal of the current power frequency signal can be subtracted from the current time domain signal, that is, the current time domain signal with the power frequency signal filtered out can be obtained. In this way, in the spectrum of the current time domain signal with the power frequency signal filtered out, the current component signal located in the rated power frequency sideband is no longer submerged due to spectrum leakage, that is, the current component signal located in the rated power frequency sideband in the current time domain signal can be more accurately identified, thereby improving the reliability of detecting the broken rotor bar fault of the asynchronous induction motor.

[0099] Figure 2 It is a schematic diagram comparing the results of filtering current signals using the adaptive filtering method of the present invention and a conventional filtering method. Figure 2 Part (a) is the amplitude spectrum obtained by fast Fourier transform of the original current time domain signal, part (b) is the current time domain signal obtained by filtering out the power frequency signal by directly using the original voltage time domain signal in the conventional filtering method and performing Wiener filtering on the original current time domain signal, and the amplitude spectrum obtained by fast Fourier transform, and part (c) is the current time domain signal obtained by filtering out the power frequency signal by performing Wiener filtering on the original current time domain signal using the estimated signal of the voltage power frequency signal in the voltage time domain signal obtained by the adaptive filtering method of the present invention, and the amplitude spectrum obtained by fast Fourier transform. Among them, in parts (a) to (c), the ordinate is the amplitude, the lower abscissa is the frequency, and the upper abscissa is the serial number value corresponding to each frequency point.

[0100] like Figure 2 As shown in section (b), the amplitude of the power frequency signal is indeed reduced, and an amplitude peak can be found on the left side of 50HZ, but due to frequency leakage, the current component signal very close to 50Hz cannot be distinguished. The amplitude at 50Hz in section (c) is almost 0, and the frequency leakage phenomenon is almost not observed near 50Hz. In addition, since the influence of the power frequency signal leakage is eliminated, an amplitude peak is found at 49.7Hz and 50.3Hz, respectively, indicating that the technical solution of the present invention can more thoroughly filter out the power frequency signal in the current time domain signal, so that the current component signal very close to 50Hz can be more clearly distinguished.

[0101] The adaptive filtering method of this embodiment performs fast Fourier transform on the collected voltage time domain signal to obtain the voltage frequency domain signal of the voltage time domain signal, selects the frequency points within the first preset serial number range and the frequency points within the second preset serial number range, and sets the values ​​of other frequency points to 0, performs inverse fast Fourier transform on the processed voltage frequency domain signal to obtain the estimated signal of the voltage power frequency signal; based on the Wiener filter, the estimated signal of the voltage power frequency signal is used to filter the collected current time domain signal to filter out the power frequency signal contained therein. In this way, the adverse effects of the DC and high-order harmonic signals in the voltage time domain signal on filtering are eliminated, so that the current power frequency signal can be filtered out more thoroughly, so as to more accurately identify the current component signal located in the rated power frequency sideband from the current spectrum, thereby improving the reliability of detecting the broken rotor bar fault of the asynchronous induction motor.

[0102] Furthermore, the present invention also provides a fault detection method.

[0103] Figure 3 FIG. 1 is a schematic flow chart of the main steps of a fault detection method according to an embodiment of the present invention. The fault detection method is used to detect a broken bar fault in a motor rotor. Figure 3 As shown, the fault detection method of this embodiment may include the following steps 301 to 304.

[0104] Step 301: Obtain a current time domain signal from which the power frequency signal is filtered out according to a preset adaptive filtering method;

[0105] In a specific implementation process, the detailed process of the adaptive filtering method can refer to the records of the above-mentioned related embodiments, which will not be repeated here.

[0106] Step 302, performing a fast Fourier transform on the current time domain signal after filtering out the power frequency signal, to obtain the amplitude of the component current signal with a frequency of (1+2s)f located in the sideband of the rated power frequency in the current time domain signal; wherein s is the slip rate, and f is the rated value of the power frequency;

[0107] Step 303, determining the ratio of the amplitude of the component current signal to the amplitude of the current power frequency signal;

[0108] Step 304: If the ratio is greater than a preset threshold, it is determined that a rotor bar broken fault occurs in the asynchronous induction motor.

[0109] In a specific implementation process, the implementation process of the above steps 302 to 304 can refer to the records of existing related technologies and will not be repeated here.

[0110] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effects of the present invention, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present invention.

[0111] Furthermore, the present invention also provides a control device for a smart home device.

[0112] See attached Figure 4 , Figure 4 FIG. 1 is a main structural block diagram of an adaptive filtering device according to an embodiment of the present invention. Figure 4 As shown, the adaptive filtering device in the embodiment of the present invention may include a first transform module 40 , a selection module 41 , a second transform module 42 and a filtering module 43 .

[0113] A first transformation module 40 is used to perform a fast Fourier transform on the collected steady-state voltage time domain signal to obtain a voltage frequency domain signal corresponding to the voltage time domain signal;

[0114] A selection module 41 is used to select multiple frequency points within a first preset serial number range and multiple frequency points within a second preset serial number range in a voltage frequency domain signal, and set the real part and the imaginary part of the frequency points outside the first preset serial number range and the second preset serial number range to 0, so as to obtain a processed voltage frequency domain signal; wherein, the starting serial number value of the first preset serial number range is the difference between the first power frequency serial number value of the first frequency point corresponding to the rated power frequency and the first preset threshold value, and the ending serial number value of the first preset serial number range is the sum of the first power frequency serial number value and the second preset threshold value; the starting serial number value of the second preset serial number range is the difference between the second power frequency serial number value of the second frequency point corresponding to the rated power frequency and the second preset threshold value, and the ending serial number value of the second preset serial number range is the sum of the second power frequency serial number value and the first preset threshold value; the first power frequency serial number value is less than the second power frequency serial number value;

[0115] A second transformation module 42, configured to perform an inverse fast Fourier transform on the processed voltage frequency domain signal to obtain an estimated signal of the voltage power frequency signal in the voltage time domain signal;

[0116] The filtering module 43 is used to filter the collected current time domain signal based on a Wiener filter using the estimated signal of the voltage power frequency signal to obtain the current time domain signal with the power frequency signal filtered out.

[0117] Specifically, according to the number of taps N of the Wiener filter, N-1 elements can be selected from the estimated signal of the voltage and power frequency signal, and the estimated signal of the voltage and power frequency signal can be amplified to obtain the amplified estimated signal of the voltage and power frequency signal; wherein, N is an integer greater than or equal to 2; according to the amplified estimated signal of the voltage and power frequency signal, the autocorrelation function of the amplified estimated signal of the voltage and power frequency signal and the cross-correlation function between the amplified estimated signal of the voltage and power frequency signal and the current time domain signal are calculated; according to the autocorrelation function, the autocorrelation matrix of the amplified estimated signal of the voltage and power frequency signal is constructed; according to the cross-correlation function, the cross-correlation vector of the amplified estimated signal of the voltage and power frequency signal and the current time domain signal is constructed; according to the autocorrelation matrix and the cross-correlation vector, the weight vector of the Wiener filter is determined; according to the weight vector and the amplified estimated signal of the voltage and power frequency signal, the estimated signal of the current power frequency signal in the current time domain signal is obtained; and the current time domain signal is filtered using the estimated signal of the current power frequency signal to obtain the current time domain signal with the power frequency signal filtered out.

[0118] 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 amplified 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. For example, the first element may be copied from the estimated value of the power frequency signal as an amplified element, and the amplified element may be placed before the first element to obtain an amplified estimated signal of the voltage power frequency signal.

[0119] In a specific implementation process, the jth element U in the signal can be estimated based on the weight vector and the amplified voltage and power frequency signal. e0 [j] and the j+1th element U e0 [j+1], calculate the corresponding element I in the estimated signal of the current power frequency signal e [j], where j is greater than or equal to 0 and less than 2 n Any integer, 2 n is the number of sampling points of the current time domain signal and the voltage time domain signal.

[0120] In a specific implementation process, the autocorrelation matrix may be inverted to obtain an inverse matrix of the autocorrelation matrix; and the product of the inverse matrix and the cross-correlation vector is used as the weight vector.

[0121] The above-mentioned adaptive filtering device is used to execute the adaptive filtering method embodiment of the above-mentioned embodiment. The technical principles, technical problems solved and technical effects produced by the two are similar. Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process and related instructions of the adaptive filtering device can refer to the contents described in the embodiment of the adaptive filtering method, which will not be repeated here.

[0122] It is understood by those skilled in the art that the present invention implements all or part of the processes in the method of the above embodiment, and can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium that can carry the computer program code. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0123] Furthermore, the present invention also provides an electronic device.

[0124] See attached Figure 5 , Figure 5 1 is a main structural block diagram of an electronic device according to an embodiment of the present invention. Figure 5 As shown, the electronic device in the embodiment of the present invention may include a processor 50 and a storage device 51. The storage device 51 may be configured to store a program for executing the adaptive filtering method of the above method embodiment, or a program for executing the fault detection method of the above method embodiment. The processor 50 may be configured to execute the program in the storage device 51, which includes but is not limited to a program for executing the adaptive filtering method of the above method embodiment, or a program for executing the fault detection method of the above method embodiment. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The electronic device may be a control device formed by various electronic devices.

[0125] Furthermore, the present invention also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present invention, the computer-readable storage medium can be configured to store a program for executing the adaptive filtering method of the above method embodiment, or a program for executing the fault detection method of the above method embodiment, and the program can be loaded and run by a processor to implement the above adaptive filtering method, or a fault detection method for executing the above method embodiment. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present invention is a non-temporary computer-readable storage medium.

[0126] Further, it should be understood that since the setting of each module is only for illustrating the functional units of the device of the present invention, the physical devices corresponding to these modules may be the processor itself, or a part of the software in the processor, a part of the hardware, or a part of the combination of software and hardware. Therefore, the number of each module in the figure is only schematic.

[0127] Those skilled in the art will appreciate that the modules in the device can be adaptively split or merged. Such splitting or merging of specific modules will not cause the technical solution to deviate from the principle of the present invention, and therefore, the technical solutions after splitting or merging will fall within the protection scope of the present invention.

[0128] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. An adaptive filtering method based on Wiener filter, characterized in that: include: Performing a fast Fourier transform on the collected steady-state voltage time domain signal to obtain a voltage frequency domain signal corresponding to the voltage time domain signal; Select multiple frequency points within a first preset serial number range and multiple frequency points within a second preset serial number range in the voltage frequency domain signal, and set the real part and the imaginary part of the frequency points outside the first preset serial number range and the second preset serial number range to 0, so as to obtain a processed voltage frequency domain signal; wherein, the starting serial number value of the first preset serial number range is the difference between the first power frequency serial number value of the first frequency point corresponding to the rated power frequency and the first preset threshold value, and the ending serial number value of the first preset serial number range is the sum of the first power frequency serial number value and the second preset threshold value; the starting serial number value of the second preset serial number range is the difference between the second power frequency serial number value of the second frequency point corresponding to the rated power frequency and the second preset threshold value, and the ending serial number value of the second preset serial number range is the sum of the second power frequency serial number value and the first preset threshold value; the first power frequency serial number value is less than the second power frequency serial number value; Performing an inverse fast Fourier transform on the processed voltage frequency domain signal to obtain an estimated signal of the voltage power frequency signal in the voltage time domain signal; Based on the Wiener filter, a specified element is 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; wherein the number of taps of the Wiener filter is 2; According to the amplified estimated signal of the voltage and power frequency signal, calculating the autocorrelation function of the amplified estimated signal of the voltage and power frequency signal and the cross-correlation function between the amplified estimated signal of the voltage and power frequency signal and the current time domain signal; According to the autocorrelation function, constructing an autocorrelation matrix of the amplified estimation signal of the voltage power frequency signal; According to the cross-correlation function, construct a cross-correlation vector between the amplified estimated signal of the voltage power frequency signal and the current time domain signal; Determining a weight vector of the Wiener filter according to the autocorrelation matrix and the cross-correlation vector; 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; The current time domain signal is filtered using the estimated signal of the current power frequency signal to obtain the current time domain signal with the power frequency signal filtered out.

2. The adaptive filtering method according to claim 1, characterized in that: Copying a designated element from the estimated value of the power frequency signal as an amplified element, amplifying the estimated signal of the voltage power frequency signal, and obtaining an amplified estimated signal of the voltage power frequency signal, including: The first element is copied from the estimated value of the power frequency signal as an amplified element, and the amplified element is placed before the first element to obtain an amplified estimated signal of the voltage power frequency signal.

3. The adaptive filtering method according to claim 1, characterized in that: 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, including: According to the weight vector and the amplified voltage and power frequency signal, the jth element U in the estimated signal e0 [j] and the j+1th element U e0 [j+1], calculate the corresponding element I in the estimated signal of the current power frequency signal e [j], where j is greater than or equal to 0 and less than 2 n Any integer, 2 n is the number of sampling points of the current time domain signal and the voltage time domain signal.

4. The adaptive filtering method according to claim 1, characterized in that: Determining a weight vector of the Wiener filter according to the autocorrelation matrix and the cross-correlation vector includes: Performing an inverse operation on the autocorrelation matrix to obtain an inverse matrix of the autocorrelation matrix; The product of the inverse matrix and the cross-correlation vector is used as the weight vector.

5. An adaptive filtering device based on a Wiener filter, characterized in that: include: A first transformation module is used to perform a fast Fourier transform on the collected steady-state voltage time domain signal to obtain a voltage frequency domain signal corresponding to the voltage time domain signal; A selection module is used to select multiple frequency points within a first preset serial number range and multiple frequency points within a second preset serial number range in a voltage frequency domain signal, and set the real part and the imaginary part of the frequency points outside the first preset serial number range and the second preset serial number range to 0, so as to obtain a processed voltage frequency domain signal; wherein, the starting serial number value of the first preset serial number range is the difference between the first power frequency serial number value of the first frequency point corresponding to the rated power frequency and the first preset threshold value, and the ending serial number value of the first preset serial number range is the sum of the first power frequency serial number value and the second preset threshold value; the starting serial number value of the second preset serial number range is the difference between the second power frequency serial number value of the second frequency point corresponding to the rated power frequency and the second preset threshold value, and the ending serial number value of the second preset serial number range is the sum of the second power frequency serial number value and the first preset threshold value; the first power frequency serial number value is less than the second power frequency serial number value; A second transformation module, configured to perform an inverse fast Fourier transform on the processed voltage frequency domain signal to obtain an estimated signal of the voltage power frequency signal in the voltage time domain signal; A filtering module, for copying a designated element from the estimated value of the power frequency signal as an amplified element based on a Wiener filter, amplifying the estimated signal of the voltage power frequency signal, and obtaining an amplified estimated signal of the voltage power frequency signal; wherein the number of taps of the Wiener filter is 2; according to the amplified estimated signal of the voltage power frequency signal, calculating the autocorrelation function of the amplified estimated signal of the voltage power frequency signal and the cross-correlation function between the amplified estimated signal of the voltage power frequency signal and the current time domain signal; according to the autocorrelation function, constructing the autocorrelation matrix of the amplified estimated signal of the voltage power frequency signal; according to the cross-correlation function, constructing the cross-correlation vector of the amplified estimated signal of the voltage power frequency signal and the current time domain signal; according to the autocorrelation matrix and the cross-correlation vector, determining the weight vector of the Wiener filter; according to the weight vector and the amplified estimated signal of the voltage power frequency signal, obtaining the estimated signal of the current power frequency signal in the current time domain signal; using the estimated signal of the current power frequency signal to filter the current time domain signal, obtaining the current time domain signal with the power frequency signal filtered out.

6. A fault detection method, characterized in that: For detecting a broken rotor bar fault in an asynchronous induction motor, the method comprises: According to the adaptive filtering method according to any one of claims 1 to 4, a current time domain signal with the power frequency signal filtered out is obtained; Performing a fast Fourier transform on the current time domain signal from which the power frequency signal is filtered out, to obtain the amplitude of a component current signal with a frequency of (1+2s)f located in the sideband of the rated power frequency in the current time domain signal; wherein s is the slip rate, and f is the rated value of the power frequency; Determining a ratio of the amplitude of the component current signal to the amplitude of the current power frequency signal; If the ratio is greater than a preset threshold, it is determined that a rotor bar breakage fault occurs in the asynchronous induction motor.

7. An electronic device comprising a processor and a storage device, wherein the storage device is suitable for storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by the processor to execute the adaptive filtering method according to any one of claims 1 to 4, or to execute the fault detection method according to claim 6.

8. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the adaptive filtering method according to any one of claims 1 to 4, or to execute the fault detection method according to claim 6.

Images