Methods for diagnosing inter-turn short circuit faults in asynchronous motors, intelligent devices and storage media

Abstract

This application relates to the field of motor technology, specifically to a method, intelligent device, and storage medium for diagnosing inter-turn short-circuit faults in asynchronous motors. It aims to solve the technical problem of how to obtain motor state parameters that do not need to consider the effects of three-phase imbalance of grid voltage and imbalance of three-phase electrical parameters of the motor, and to diagnose inter-turn short-circuit faults based on these motor state parameters. To this end, this application includes: based on the voltage time-domain and current time-domain signals of each phase, obtaining the amplitude of the side-frequency harmonic current of each phase within a preset time period according to a preset side-frequency harmonic frequency, and determining whether an inter-turn short-circuit fault has occurred in the motor based on the amplitude of the side-frequency harmonic current of all phases. The method of this application for diagnosing inter-turn short-circuit faults based on the dispersion of the amplitude of the side-frequency harmonic current of all phases does not require complex modeling and analysis based on electromagnetic coupling equations, and has the characteristics of simplicity and reliability, providing a completely new solution for diagnosing inter-turn short-circuit faults in asynchronous motors.

Description

Technical Field

[0001] This application relates to the field of motor technology, specifically to a method for diagnosing inter-turn short-circuit faults in asynchronous motors, an intelligent device, and a storage medium. Background Technology

[0002] Insulation issues are one of the main safety risks faced by electrical equipment. For asynchronous motors, insulation problems mainly manifest as inter-turn short-circuit faults. Due to the rapid development and serious consequences of inter-turn short-circuit faults, early warning of these faults has always been a key focus for enterprise electrical maintenance departments.

[0003] Currently, the diagnosis of initial inter-turn short-circuit faults is mainly achieved by assessing the three-phase imbalance of the asynchronous motor, typically using negative-sequence impedance and stator current signals as the motor's state parameters. However, these state parameters are significantly affected by the three-phase imbalance of the grid voltage and the inherent three-phase electrical parameter imbalance of the asynchronous motor itself. Consequently, extracting information directly related to inter-turn short-circuit faults from the negative-sequence impedance and stator current signals suffers from drawbacks such as numerous interference factors, cumbersome processes, and low accuracy. Therefore, obtaining motor state parameters that do not need to consider the influence of the grid voltage's three-phase imbalance and the motor's three-phase electrical parameter imbalance, and performing inter-turn short-circuit fault diagnosis based on these motor state parameters, has become an urgent problem to be solved.

[0004] Accordingly, there is a need in the field for a new inter-turn short-circuit fault diagnosis scheme for asynchronous motors to solve the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned deficiencies, this application is made to solve, or at least partially solve, the technical problem of how to obtain a motor state parameter that does not need to consider the effects of three-phase imbalance of grid voltage and imbalance of motor three-phase electrical parameters, and to perform inter-turn short-circuit fault diagnosis based on the motor state parameter.

[0006] In a first aspect, a method for diagnosing inter-turn short-circuit faults in an asynchronous motor is provided, the method comprising:

[0007] Obtain the corresponding voltage time-domain signals and current time-domain signals of the motor;

[0008] Based on the corresponding voltage time-domain signal and current time-domain signal, the amplitude of the corresponding side-frequency harmonic current within the preset time period is obtained according to the preset side-frequency harmonic frequency.

[0009] Based on the corresponding sideband harmonic current amplitude, the dispersion of all the sideband harmonic current amplitudes is obtained;

[0010] Based on the dispersion of the amplitude of all the aforementioned side-frequency harmonic currents, it is determined whether the motor has experienced an inter-turn short-circuit fault.

[0011] In one technical solution of the above-mentioned method for diagnosing inter-turn short-circuit faults in asynchronous motors, the method further includes:

[0012] Based on the corresponding sideband harmonic current amplitude, the mean and standard deviation of all the sideband harmonic current amplitudes are obtained;

[0013] Based on the mean and standard deviation of all the sideband harmonic current amplitudes, the dispersion of the sideband harmonic current is obtained.

[0014] Compare the dispersion of the sideband harmonic current with a preset dispersion threshold;

[0015] In response to the side-frequency harmonic current dispersion being greater than or equal to the dispersion threshold, it is determined that the motor has an inter-turn short-circuit fault.

[0016] In one technical solution of the above-mentioned method for diagnosing inter-turn short-circuit faults in asynchronous motors, the method further includes:

[0017] The preset sideband harmonic frequency is set to (1-2s)f e ;

[0018] Where s is the slip ratio, f e This is the rated power frequency.

[0019] In one technical solution of the above-mentioned asynchronous motor inter-turn short-circuit fault diagnosis method, "based on the corresponding voltage time-domain signal and current time-domain signal, according to the preset side-frequency harmonic frequency, the amplitude of the corresponding side-frequency harmonic current within a preset time period is obtained" includes:

[0020] Based on the corresponding voltage time-domain signal and current time-domain signal, the corresponding current time-domain signal after filtering out the power frequency signal is obtained respectively.

[0021] By using Fourier transform, the second current frequency domain signal of the current time domain signal of the filtered power frequency signal within the preset time period is obtained respectively.

[0022] Based on the preset sideband harmonic frequency, the amplitude of each corresponding sideband harmonic current is extracted from the corresponding second current frequency domain signal.

[0023] In one technical solution of the above-mentioned asynchronous motor inter-turn short-circuit fault diagnosis method, "obtaining the corresponding current time-domain signal after filtering out the power frequency signal based on the corresponding voltage time-domain signal and current time-domain signal" includes:

[0024] Perform a fast Fourier transform on the voltage time-domain signal to obtain the voltage frequency-domain signal corresponding to the voltage time-domain signal;

[0025] Multiple frequency points within a first preset sequence range and multiple frequency points within a second preset sequence range are selected in the voltage frequency domain signal. The real and imaginary parts of the frequency points outside the first and second preset sequence ranges are set to 0 to obtain the processed voltage frequency domain signal. The starting sequence value of the first preset sequence range is the difference between the first power frequency sequence value of the first frequency point corresponding to the rated power frequency and the first preset threshold. The ending sequence value of the first preset sequence range is the sum of the first power frequency sequence value and the second preset threshold. The starting sequence value of the second preset sequence range is the difference between the second power frequency sequence value of the second frequency point corresponding to the rated power frequency and the second preset threshold. The ending sequence value of the second preset sequence range is the sum of the second power frequency sequence value and the first preset threshold. The first power frequency sequence value is less than the second power frequency sequence value.

[0026] 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;

[0027] Based on the Wiener filter, the estimated signal of the voltage power frequency signal is used to filter the current time domain signal to obtain the current time domain signal after filtering out the power frequency signal.

[0028] In one technical solution of the above-mentioned asynchronous motor inter-turn short-circuit fault diagnosis method, "based on the Wiener filter, using the estimated signal of the voltage power frequency signal to filter the current time domain signal to obtain the current time domain signal after filtering out the power frequency signal" includes:

[0029] Based on the number of taps N of the Wiener filter, N-1 elements are selected from the estimated signal of the voltage power frequency signal to amplify the estimated signal of the voltage power frequency signal, thereby obtaining the amplified estimated signal of the voltage power frequency signal, wherein N is an integer greater than or equal to 2.

[0030] Based on the amplified estimated signal of the voltage power frequency signal, calculate 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;

[0031] Based on the autocorrelation function, construct the autocorrelation matrix of the amplified estimated signal of the voltage power frequency signal;

[0032] Based on the cross-correlation function, construct the cross-correlation vector between the amplified estimated signal of the voltage power frequency signal and the current time domain signal;

[0033] The weight vector of the Wiener filter is determined based on the autocorrelation matrix and the cross-correlation vector.

[0034] Based on the weight vector and the amplified estimated signal of the voltage power frequency signal, the estimated signal of the current power frequency signal in the current time domain signal is obtained;

[0035] The current time-domain signal is filtered using the estimated signal of the current power frequency signal to obtain the current time-domain signal after the power frequency signal has been filtered out.

[0036] In one technical solution of the above-mentioned asynchronous motor inter-turn short-circuit fault diagnosis method, N is set to 2. "Based on the number of taps N 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 the amplified estimated signal of the voltage power frequency signal" includes:

[0037] The first element is copied from the estimated signal of the voltage power frequency signal as an amplification element, and the amplification element is placed before the first element to obtain the amplified estimated signal of the voltage power frequency signal.

[0038] In a second aspect, a smart device is provided, the smart device comprising at least one processor; and a memory communicatively connected to said at least one processor;

[0039] The memory stores a computer program, which, when executed by the at least one processor, implements the method described in any of the above-described technical solutions for diagnosing inter-turn short-circuit faults in asynchronous motors.

[0040] In a third aspect, a storage medium stores a plurality of program codes, wherein the computer program, when executed by the at least one processor, implements the method described in any of the above-described technical solutions for diagnosing inter-turn short-circuit faults in asynchronous motors.

[0041] The above-mentioned technical solutions of this application have at least one or more of the following beneficial effects: The method of this application for diagnosing inter-turn short-circuit faults based on the dispersion (relative magnitude) of the amplitude of each corresponding side-frequency harmonic current does not require complex modeling and analysis based on electromagnetic coupling equations, and can disregard the influence of factors such as three-phase imbalance of grid voltage and imbalance of the motor's own three-phase electrical parameters during implementation. It has the characteristics of simplicity and reliability, and provides a brand-new solution for the diagnosis of inter-turn short-circuit faults in asynchronous motors. Attached Figure Description

[0042] The disclosure of this application will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0043] Figure 1 This is a schematic flowchart of the main steps of an asynchronous motor inter-turn short circuit fault diagnosis method according to an embodiment of this application.

[0044] Figure 2 This is a detailed flowchart illustrating step S02 according to an embodiment of this application.

[0045] Figure 3 This is a schematic flowchart of the main steps of step S201 according to an embodiment of this application.

[0046] Figure 4 This is a schematic diagram of a star-connected three-phase AC circuit according to an embodiment of this application.

[0047] Figure 5 This is a detailed flowchart illustrating steps S03 and S04 according to an embodiment of this application.

[0048] Figure 6 This is a schematic diagram of the main structure of a smart device according to an embodiment of this application. Detailed Implementation

[0049] Some embodiments of this application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of this application and are not intended to limit the scope of protection of this application.

[0050] In the description of this application, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and may also include software components, such as program code, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor can be implemented in software, in hardware, or a combination of both. Computer-readable storage media includes any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. 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 terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular terms "a" or "this" can also include plural forms.

[0051] First, please refer to the appendix. Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of an asynchronous motor inter-turn short-circuit fault diagnosis method according to an embodiment of this application. Figure 1 As shown, the asynchronous motor inter-turn short-circuit fault diagnosis method in this application embodiment includes:

[0052] Step S01: Obtain the corresponding voltage time-domain signal and current time-domain signal of the motor;

[0053] Step S02: Based on the corresponding voltage time-domain signal and current time-domain signal, obtain the corresponding side-frequency harmonic current amplitude within the preset time period according to the preset side-frequency harmonic frequency.

[0054] Step S03: Based on the corresponding sideband harmonic current amplitudes, obtain the dispersion of all sideband harmonic current amplitudes;

[0055] Step S04: Based on the dispersion of the amplitude of all sideband harmonic currents, determine whether the motor has an inter-turn short circuit fault.

[0056] In this embodiment, the motor is an asynchronous induction motor, and the asynchronous motor is connected in a star configuration. In step S01, according to the preset sampling frequency f... s With a preset sampling duration T, voltage and current time-domain signals corresponding to the motor's steady-state operation are synchronously acquired using voltage and current sensors, respectively, to obtain the three-phase (phase a, phase b, and phase c) voltage time-domain signals {u a (n)}、{u b (n)} and {u c (n)}, and the three-phase (a-phase, b-phase, and c-phase) current time-domain signals {i a (n)}、{i b (n)} and {i c (n)}. Where n=0,1,2,…,(N-1), N is determined by the sampling frequency f. s The sampling duration T1 is determined.

[0057] Considering that the radix-2 Fast Fourier Transform (inverse) requires the amount of data to be processed to be an integer power of 2, N can be selected as a value corresponding to an integer power of 2, for example, N = 2. 17 =131072. As an example, the sampling frequency f... s The frequency was set to 12.8 kHz, and the sampling duration T was set to 10.24 seconds. At this time, the data length of the corresponding voltage time-domain signal and current time-domain signal was T*f. s =131072, meaning it includes data from 131072 sampling points.

[0058] Next, combine Figure 2 This describes the detailed steps of step S02. Figure 2 This is a detailed flowchart illustrating step S02 according to an embodiment of this application.

[0059] In this embodiment of the application, step S201 can adopt the adaptive filtering method described in the invention patent with Chinese patent application number 202210933441.1, which obtains the corresponding current time domain signal after filtering out the power frequency signal based on the corresponding voltage time domain signal and current time domain signal.

[0060] The main steps for obtaining the current time-domain signal for filtering out power frequency signals, as described in Chinese patent application number 202210933441.1, are as follows: Figure 3 As shown.

[0061] Step 101: Perform a fast Fourier transform on the voltage time-domain signal (obtained through step S01 above) to obtain the voltage frequency-domain signal corresponding to the voltage time-domain signal.

[0062] Step 102: Select multiple frequency points within the first preset number range and multiple frequency points within the second preset number range in the voltage frequency domain signal, and set the real and imaginary parts of the frequency points outside the first and second preset number ranges to 0 to obtain the processed voltage frequency domain signal.

[0063] In a specific implementation process, the voltage time-domain signal includes not only the power frequency signal but also a DC component and higher harmonics of the power frequency signal. Furthermore, during actual operation, due to 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, typically ±0.2 to ±0.5 Hz. Therefore, to obtain a more accurate determination of the power frequency, multiple frequency points, including those corresponding to the rated power frequency, can be selected based on this fluctuation range.

[0064] Specifically, multiple frequency points within a first preset sequence range and multiple frequency points within a second preset sequence range can be selected in the voltage frequency domain signal. The real and imaginary parts of the frequency points outside the first and second preset sequence ranges are set to 0 to obtain the processed voltage frequency domain signal. The starting sequence value of the first preset sequence range is the difference between the first power frequency sequence value of the first frequency point corresponding to the rated power frequency and a first preset threshold, and the ending sequence value of the first preset sequence range is the sum of the first power frequency sequence value and the second preset threshold. The starting sequence value of the second preset sequence range is the difference between the second power frequency sequence value of the second frequency point corresponding to the rated power frequency and a second preset threshold, and the ending sequence value of the second preset sequence range is the sum of the second power frequency sequence value and the first preset threshold. The first power frequency sequence value is less than the second power frequency sequence value.

[0065] In a specific implementation, taking a sampling frequency of 12.8kHz and a sampling time of 10.24s as an example, the total number of frequency points in the voltage frequency domain signal is 10.24 × 12800 = 131072.

[0066] When the sampling duration of the voltage time-domain signal being processed or analyzed is 10.24s, the first power frequency index 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 index 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, so the range of the first preset index is 256 to 768, and the range of the second preset index is 130304 to 130816.

[0067] Step 103: 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.

[0068] In one specific implementation, multiple frequency points within a certain range are selected in the processed voltage frequency domain signal, and the real and imaginary parts of other frequency points are set to 0. In this way, the power frequency signal in the processed voltage frequency domain signal is closer to the actual power frequency signal. Therefore, after performing an inverse fast Fourier transform on the processed voltage frequency domain signal, the estimated voltage power frequency signal in the voltage time domain signal has a high degree of fit with the actual voltage power frequency signal.

[0069] Step 104: Based on the Wiener filter, the estimated signal of the voltage power frequency signal is used to filter the current time domain signal (obtained through step S01 above) to obtain the current time domain signal with the power frequency signal filtered out.

[0070] After obtaining the estimated voltage frequency signal, step 104 can be implemented according to the following steps.

[0071] (1) Based on the number of taps N of the Wiener filter, select N-1 elements from the estimated signal of the voltage power frequency signal and amplify the estimated signal of the voltage power frequency signal to obtain the amplified estimated signal of the voltage power frequency signal; wherein, N is an integer greater than or equal to 2.

[0072] In a specific implementation, when the number of taps of the Wiener filter is different, the way to filter the current time domain signal using the estimated signal of the current power frequency signal to obtain the current time domain signal after filtering out the power frequency signal 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 power frequency signal to amplify the estimated signal of the voltage power frequency signal to obtain the amplified estimated signal of the voltage power frequency signal.

[0073] Specifically, taking N=2 as an example, a specified element can be copied from the estimated signal of the voltage power frequency signal as an amplification element to amplify the estimated signal of the voltage power frequency signal, thereby obtaining the amplified estimated signal of the voltage power frequency signal. In this way, compared with calculating the amplification element that meets the requirements based on the sine 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 also relatively high.

[0074] In one specific implementation, the first element can be copied from the estimated signal of the voltage power frequency signal as an amplification element, and the amplification element can be placed before the first element to obtain the amplified estimated signal of the voltage power frequency signal.

[0075] For example, after steps 101-103, the estimated voltage frequency signal obtained includes the following elements: U e [0]、U e [1]、U e [2]......U e [2 n -1];2 n This represents the number of sampling points for the current time-domain signal and the voltage time-domain signal.

[0076] The first U e [0] is copied and placed before the estimated signal of the voltage power frequency signal to obtain the amplified estimated signal of the voltage power frequency signal, which includes the following elements: 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 The value of [1], U e0 The value of [j+1] is equal to U. e The value of [j], where j is greater than or equal to 0 and less than 2. n For any integer, 2 n This represents the number of sampling points for the current time-domain signal and the voltage time-domain signal.

[0077] (2) Based on the amplified estimated signal of the voltage power frequency signal, calculate 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.

[0078] In a specific implementation process, the autocorrelation function of the amplified estimation signal of the voltage power frequency signal is two, which can be obtained from calculation formulas (1) and (2):

[0079]

[0080] Among them, U e0 [j] represents the j-th element in the amplified estimated signal of the voltage power frequency signal, 2 n This represents the number of sampling points for the current time-domain signal and the voltage time-domain signal.

[0081] 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 from calculation formulas (3) and (4):

[0082]

[0083] Among them, U e0 [j] represents the j-th element in the amplified estimated signal of the voltage power frequency signal, and I[j] represents the j-th element in the current time domain signal. n This represents the number of sampling points for the current time-domain signal and the voltage time-domain signal.

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

[0085] In a specific implementation, taking 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 the autocorrelation matrix R in the first row and first column, the second row and second column, the second row and first column, and the second row and second column, respectively. Among them, R[0][0] = R[1][1] = r0, and R[1][0] = R[0][1] = r1.

[0086] (4) Based on the cross-correlation function, construct the cross-correlation vector P between the amplified estimated signal of the voltage power frequency signal and the current time domain signal.

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

[0088] (5) Determine the weight vector w of the Wiener filter based on the autocorrelation matrix R and the cross-correlation vector P.

[0089] In a specific implementation, 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. Detailed procedures can be found in existing technical documentation and will not be elaborated upon here.

[0090] In a specific implementation, the autocorrelation matrix R can also be inverted to obtain the inverse matrix Rinverse of the autocorrelation matrix.-1 ; the inverse matrix R -1 The product of the cross-correlation vector P and the weight vector 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. -1 Taking an autocorrelation matrix R as a 2x2 matrix as an example, R[0][0], R[0][1], R[1][0], and R[1][1] are the elements of the autocorrelation matrix R in the first row and first column, the second row and second column, the second row and first column, and the second row and second column, 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 the equation 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; thus R -1 Each element is then determined.

[0092] When the autocorrelation matrix R is a 2x2 matrix and the cross-correlation vector P is a two-element column vector, the resulting weight vector is a two-element column vector. w0 and w1 represent elements in one row and one column, and elements in two rows and one column, 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 in the Wiener filter is small, especially when the number of taps is 2, in order to obtain the weight vector, it is only necessary to find the inverse matrix R of the autocorrelation matrix R of order 2. -1 The inverse matrix can be calculated directly using the definition of the inverse matrix. Compared to the steepest descent method or LMS method for calculating the weight vector, the computational cost is significantly reduced.

[0094] (6) Based on the weight vector and the amplified estimated signal of the voltage power frequency signal, the estimated signal of the current power frequency signal in the current time domain signal is obtained.

[0095] In a specific implementation, the j-th element U in the amplified signal of the voltage power frequency signal can be estimated based on the weight vector. e0 [j] and the (j+1)th element Ue0 [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 For any integer, 2 n This represents the number of sampling points for the current time-domain signal and the voltage time-domain signal. Specifically, the calculation formula can be found in formula (5):

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

[0097] (7) 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.

[0098] Specifically, the estimated signal of the current power frequency signal can be subtracted from the current time-domain signal to obtain the current time-domain signal after filtering out the power frequency signal. In this way, the current component signal located in the rated power frequency sideband is no longer submerged due to spectral leakage in the spectrum of the current time-domain signal after filtering out the power frequency signal. That is to say, the current component signal located in the rated power frequency sideband in the current time-domain signal can be identified more accurately.

[0099] In this embodiment of the application, the voltage time-domain signal {u} corresponding to a is... a (n)} and current time-domain signal {i a Substituting (n)} into steps 101 to 104 above, and according to calculation formulas (1) to (5), the current time-domain signal {i′} corresponding to a for filtering out the power frequency signal can be obtained. a (n)}.

[0100] Similarly, the voltage time-domain signal {u} corresponding to b b (n)} and current time-domain signal {i b Substituting (n)} into steps 101 to 104 above, and according to calculation formulas (1) to (5), the current time-domain signal {i′} corresponding to b after filtering out the power frequency signal can be obtained. b (n)}.

[0101] The voltage time-domain signal {u} corresponding to c c (n)} and current time-domain signal {i c Substituting (n)} into steps 101 to 104 above, and according to calculation formulas (1) to (5), the current time-domain signal {i′} corresponding to c after filtering out the power frequency signal can be obtained. c (n)}.

[0102] The adaptive filtering method in the above embodiment performs a Fast Fourier Transform (FFT) on the acquired voltage time-domain signal to obtain the voltage frequency-domain signal. Then, it selects frequency points within a first preset index range and a second preset index range, setting the values ​​of other frequency points to 0. Finally, it performs an Inverse Fast Fourier Transform on the processed voltage frequency-domain signal to obtain an estimated voltage power frequency signal. Based on a Wiener filter, the estimated voltage power frequency signal is used to filter the acquired current time-domain signal, removing the power frequency signal it contains. This eliminates the adverse effects of DC and higher harmonic signals in the voltage time-domain signal on the filtering, thereby more thoroughly filtering out the current power frequency signal and more accurately identifying the current component signal located in the rated power frequency sideband from the current spectrum.

[0103] In step S202, the frequency domain signals (second current frequency domain signals) of the corresponding current time domain signals after filtering out the power frequency signal are obtained by Fourier transform within a preset time period.

[0104] Specifically, the current time-domain signal {i′} of phase a, after filtering out the power frequency signal, is obtained within a preset time period (e.g., 10.24 seconds as mentioned above). a The frequency domain signal {F} corresponding to phase a of (n)} is the second current frequency domain signal of phase a. a (ω)}, b-phase filter removes the current time-domain signal {i′ b The frequency domain signal of the second current in phase b corresponding to (n)} is {F}. b (ω)}, and the current time-domain signal {i′} of phase c after filtering out the power frequency signal. c The frequency domain signal of the second current in phase c corresponding to (n)} is {F} c (ω)}.

[0105] Sideband harmonic signals include multiple signals of different frequencies. Among them, the sideband harmonic with a frequency of (1-2s)fe is the sideband harmonic directly induced when the reverse magnetomotive force cuts the stator winding. The sideband harmonics of other frequencies (including those with frequencies of (1+2s)fe and (1±2ks)fe, where k is an integer greater than or equal to 2) are derived from the sideband harmonic with a frequency of (1-2s)fe due to torque oscillation. Therefore, the sideband harmonic with a frequency of (1-2s)fe is the most representative.

[0106] In this embodiment of the application, the preset sideband harmonic frequency is selected as (1-2s)fe. In step S203, according to the sideband harmonic frequency (1-2s)fe, the corresponding sideband harmonic current amplitude is extracted from the corresponding second current frequency domain signal.

[0107] Specifically, from the frequency domain signal {F} of phase a second current a The amplitude of the sideband harmonic current I of phase a is extracted from (ω)}.a From the frequency domain signal {F} of the second current in phase b b The amplitude of the sideband harmonic current I of phase b is extracted from (ω)}. b From the frequency domain signal {F} of the second current in phase c c The amplitude of the sideband harmonic current I of phase c is extracted from (ω)}. c .

[0108] It should be noted that those skilled in the art may also select other frequency sideband harmonics as examples, such as sideband harmonics with a frequency of (1+2s)fe or sideband harmonics with a frequency of (1-3s)fe, etc. Without deviating from the principle of this application, the technical solutions after these modifications or substitutions will fall within the protection scope of this application.

[0109] In other embodiments, step S02 can also employ the method described in Chinese Patent Application No. 202310916262.1, which involves "obtaining the synchronous voltage and current signals of the asynchronous induction motor during stable operation; recursively processing the voltage signals based on an extended Kalman filter to obtain a real-time estimate of the grid fundamental frequency; recursively processing the current signals based on the Kalman filter and the real-time estimate of the grid fundamental frequency to obtain an estimated signal of the fundamental frequency signal in the current signals; using the estimated signal of the fundamental frequency signal in the current signals to suppress the fundamental frequency signal in the current signals to obtain a current signal with the fundamental frequency signal filtered out; and obtaining the amplitude of the sideband signal with a frequency of (1±2s)fe based on the spectrum of the current signal with the fundamental frequency signal filtered out," to obtain the sideband harmonic current amplitude required in this application. Alternatively, the method described in Chinese Patent Application No. 202410804841.1, which involves "obtaining the fault range of the sideband signal in the current spectrum based on motor parameters," can be used to obtain the sideband harmonic current amplitude required in this application. For detailed implementation methods and steps, please refer to relevant literature; they will not be elaborated here.

[0110] Continue reading Figure 4 , combined Figure 4 This explains the principle of determining whether a motor has an inter-turn short circuit fault based on side-frequency harmonic current.

[0111] In actual operation, asynchronous motors are inevitably affected by several objectively existing three-phase imbalance factors, such as three-phase voltage imbalance in the power grid and imbalance of three-phase electrical parameters caused by manufacturing process deviations. Affected by these three-phase imbalance factors, the magnetomotive force generated by the rotor due to electromagnetic induction will also exhibit three-phase imbalance, thus producing a magnetomotive force with a rotational direction opposite to the rotor's rotation direction (reverse magnetomotive force). This reverse magnetomotive force cuts the stator windings, inducing the aforementioned multiple sideband harmonic signals of different frequencies in the stator windings.

[0112] Based on the equivalent circuit model of the asynchronous motor, when analyzing the diagnosis of inter-turn short-circuit faults in the asynchronous motor using side-frequency harmonics, the applicant found that when the equivalent circuit model only considers the stator winding inductance and not the stator winding resistance, the three-phase side-frequency harmonics contained in the stator line current of the asynchronous motor with an inter-turn short-circuit fault are symmetrical. Therefore, it is impossible or very difficult to diagnose inter-turn short-circuit faults in the asynchronous motor based on side-frequency harmonics. However, when the equivalent circuit model considers both the stator winding inductance and the stator winding resistance, the three-phase side-frequency harmonics contained in the stator line current of the asynchronous motor with an inter-turn short-circuit fault are asymmetrical. Therefore, the inter-turn short-circuit fault of the asynchronous motor can be diagnosed by the dispersion of the amplitude of the three-phase side-frequency harmonic current contained in the stator line current. The relevant modeling analysis is shown below.

[0113] like Figure 4 As shown in the equivalent circuit model of a star-connected asynchronous motor, when the reverse magnetomotive force cuts the three-phase stator windings, reverse magnetomotive force induced voltages will be generated in the three-phase stator windings respectively. and The magnitude of the three-phase induced voltage is proportional to the number of turns of the three-phase winding, i.e., U a :U b :U c =n a :n b :n c .

[0114] The three-phase AC circuit of this equivalent circuit model has the relationship shown in calculation formula (6):

[0115]

[0116] in, Z is the induced voltage of the reverse magnetomotive force corresponding to a. a Let a be the stator winding impedance corresponding to 'a'. Z is the induced voltage of the reverse magnetomotive force corresponding to b. b Here is the stator winding impedance corresponding to b; Z is the inverted magnetomotive force induced voltage corresponding to c. c The stator winding impedance corresponding to c; The voltage induced by the reverse magnetomotive force at the common point is denoted as .

[0117] From the calculation formula (6), we can obtain:

[0118]

[0119] Based on the calculation formula (7) and combined with the calculation formula (6), the corresponding sideband harmonic current of a is... for:

[0120]

[0121] Based on the calculation formula (7) and combined with the calculation formula (6), the corresponding sideband harmonic current of b is... for:

[0122]

[0123] Based on the calculation formula (7) and combined with the calculation formula (6), the corresponding side-frequency harmonic current of c is... for:

[0124]

[0125] Considering that the inductive reactance of the stator winding generated by the magnetic core and winding is usually much greater than the capacitive reactance of the stator winding, the influence of the capacitive reactance of the stator winding on the impedance of the stator winding can be ignored in actual engineering calculations.

[0126] set up Z a =r+jn a X, Z b =r+jn b X, Z c =r+jn c X, where, n represents the proportional base of the phasor of the induced voltage in the stator winding. a n is the number of turns of the stator winding in phase a. b n is the number of turns of the b-phase stator winding. c Let be the number of turns of the c-phase stator winding, r represent the ohmic resistance of the stator winding, and X represent the proportional base of the stator winding reactance. Then, equations (8), (9), and (10) will be transformed into the following forms.

[0127] a Corresponding side-frequency harmonic current for:

[0128]

[0129] b corresponds to the sideband harmonic current for:

[0130]

[0131] c corresponds to the side-frequency harmonic current for:

[0132]

[0133] From the calculation formulas (11), (12) and (13), it can be seen that when no inter-turn short circuit occurs, n a n b and n cequal, and This is a set of symmetrical three-phase currents. When an inter-turn short circuit occurs, n a n b and n c They will no longer be equal, at this point and This will become a set of asymmetrical three-phase currents, with relative differences in their amplitudes. Therefore, it can be achieved through... and The degree of dispersion of amplitude, to reflect and The relative differences between amplitudes are used to diagnose inter-turn short-circuit faults.

[0134] In steps S03 and S04, firstly, based on the corresponding side-frequency harmonic current amplitudes, the dispersion of all side-frequency harmonic current amplitudes is obtained. Then, based on the dispersion of all side-frequency harmonic current amplitudes, it is determined whether the motor has experienced an inter-turn short-circuit fault. Next, combined with... Figure 5 Explain a specific implementation method for steps S03 and S04.

[0135] First, based on the corresponding side-frequency harmonic current amplitudes, the mean and standard deviation of all side-frequency harmonic current amplitudes are obtained. That is, based on the side-frequency harmonic current amplitude I of phase a... a The amplitude of the sideband harmonic current I of phase b b The amplitude of the sideband harmonic current I of phase c c , get I a I b and I c The mean and standard deviation of this set of numbers.

[0136] Then, based on the mean and standard deviation of the amplitude of all side-frequency harmonic currents, the dispersion of side-frequency harmonic currents is obtained, where the dispersion of side-frequency harmonic currents = standard deviation / mean.

[0137] Because of I a I b and I c This is a statistical value of the side-frequency harmonic current amplitude within a preset time period. Therefore, when no inter-turn short circuit occurs in phases a, b, and c (n a =n b =n c ), I a I b and I c They can be considered equal, at which point I a I b and I c The degree of dispersion between them is minimal.

[0138] When an inter-turn short circuit occurs, na =n b =n c The relationship was broken, and accordingly, I a I b and I c The equality relationship will also no longer exist. When the difference between the number of turns of a stator winding in one phase and the number of turns of the stator windings in the other two phases is greater, usually I a I b and I c The greater the difference between them, the more I... a I b and I c The degree of dispersion between them will increase.

[0139] Therefore, it can be done through I a I b and I c The degree of dispersion between the two signals determines whether an inter-turn short-circuit fault has occurred. Specifically, the dispersion of the side-frequency harmonic current is compared with a preset dispersion threshold. If the dispersion of the side-frequency harmonic current is greater than or equal to the dispersion threshold, it is determined that an inter-turn short-circuit fault has occurred in the motor.

[0140] It should be noted that the dispersion threshold may vary for different models of asynchronous motors. This dispersion threshold can be obtained based on actual measurement and calculation data of multiple motors of the same model that have experienced (or been simulated in the laboratory) inter-turn short circuit faults.

[0141] As an example, the dispersion threshold for a certain model of asynchronous motor is set to 10%. During operation, this model of motor is periodically sampled according to a preset sampling frequency f. s (e.g., 12.8 kHz) and a preset sampling duration T (e.g., 10.24 seconds) are used to synchronously acquire the voltage time-domain signal and current time-domain signal corresponding to the three motors; using the method described in the above embodiment, the amplitude of the sideband harmonic current (I) corresponding to the three motors is calculated. a I b and I c According to I a I b and I c After calculating the dispersion of the side-frequency harmonic current, the dispersion of the side-frequency harmonic current is compared with the dispersion threshold. When the dispersion of the side-frequency harmonic current is greater than or equal to 10%, it is determined that the motor has an inter-turn short circuit fault, and a prompt message is sent to the user.

[0142] In other embodiments, the dispersion of all sideband harmonic current amplitudes can also be represented by statistical indicators such as the average deviation or variance of the sideband harmonic current amplitudes of each phase. Accordingly, the dispersion threshold also needs to be recalibrated according to the selected statistical indicators.

[0143] As can be seen from the above embodiments, the method for diagnosing inter-turn short-circuit faults in asynchronous motors in this application does not require complex modeling and analysis based on electromagnetic coupling equations, and does not need to consider the influence of factors such as three-phase imbalance of grid voltage and imbalance of the motor's own three-phase electrical parameters during implementation. It has the characteristics of simplicity and reliability, and provides a brand-new solution for diagnosing inter-turn short-circuit faults in asynchronous motors.

[0144] Another aspect of this application provides a smart device.

[0145] In one embodiment of a smart device according to this application, the smart device may include at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program, which, when executed by the at least one processor, implements the asynchronous motor inter-turn short-circuit fault diagnosis method described in any of the above embodiments. (See appendix) Figure 6 , Figure 6 The example shows a smart device 6 including a memory 61 and a processor 62, which are connected in communication via a bus.

[0146] Furthermore, this application also provides a storage medium.

[0147] In one embodiment of the storage medium according to this application, the storage medium may be configured to store a program for performing the asynchronous motor inter-turn short-circuit fault diagnosis method of the above-described method embodiments. This program may be loaded and run by a processor to implement the asynchronous motor inter-turn short-circuit fault diagnosis method. For ease of explanation, only the parts related to the embodiments of this application are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of this application. The storage medium may be a storage device comprising various electronic devices. As an example, in the embodiments of this application, the storage medium is a non-transitory storage medium.

[0148] Those skilled in the art should realize that although the steps in the above embodiments are described in a specific order, they can understand that in order to achieve the effect of this application, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders. These adjusted solutions are equivalent to the technical solutions described in this application and therefore fall within the protection scope of this application.

[0149] The technical solution of this application has been described above with reference to one embodiment shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, 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 all fall within the scope of protection of this application.

Claims

1. A method for diagnosing inter-turn short-circuit faults in an asynchronous motor, characterized in that, The method includes: Obtain the corresponding voltage time-domain signals and current time-domain signals of the motor; Based on the corresponding voltage time-domain signal and current time-domain signal, the amplitude of the corresponding side-frequency harmonic current within the preset time period is obtained according to the preset side-frequency harmonic frequency. Based on the corresponding sideband harmonic current amplitude, the dispersion of all the sideband harmonic current amplitudes is obtained; Based on the dispersion of the amplitude of all the aforementioned side-frequency harmonic currents, determine whether the motor has experienced an inter-turn short circuit fault. Specifically, based on the corresponding sideband harmonic current amplitudes, the dispersion of all sideband harmonic current amplitudes is obtained, including: Based on the corresponding sideband harmonic current amplitude, the mean and standard deviation of all the sideband harmonic current amplitudes are obtained; Based on the mean and standard deviation of all the sideband harmonic current amplitudes, the dispersion of the sideband harmonic current is obtained. Based on the dispersion of the amplitudes of all the aforementioned side-frequency harmonic currents, it is determined whether the motor has experienced an inter-turn short-circuit fault, including: Compare the dispersion of the sideband harmonic current with a preset dispersion threshold; In response to the side-frequency harmonic current dispersion being greater than or equal to the dispersion threshold, it is determined that the motor has an inter-turn short-circuit fault.

2. The method for diagnosing inter-turn short-circuit faults in asynchronous motors according to claim 1, characterized in that, The method further includes: The preset sideband harmonic frequency is set to ; Where s is the slip ratio. This is the rated power frequency.

3. The method for diagnosing inter-turn short-circuit faults in asynchronous motors according to claim 1, characterized in that, Based on the corresponding voltage time-domain signals and current time-domain signals, and according to the preset side-frequency harmonic frequencies, the amplitudes of the corresponding side-frequency harmonic currents within a preset time period are obtained, including: Based on the corresponding voltage time-domain signal and current time-domain signal, the corresponding current time-domain signal after filtering out the power frequency signal is obtained respectively. By using Fourier transform, the second current frequency domain signal of the current time domain signal of the filtered power frequency signal within the preset time period is obtained respectively. Based on the preset sideband harmonic frequency, the amplitude of each corresponding sideband harmonic current is extracted from the corresponding second current frequency domain signal.

4. The method for diagnosing inter-turn short-circuit faults in asynchronous motors according to claim 3, characterized in that... Based on the corresponding voltage time-domain signals and current time-domain signals, the corresponding current time-domain signals for filtering out power frequency signals are obtained, including: Perform a fast Fourier transform on the voltage time-domain signal to obtain the voltage frequency-domain signal corresponding to the voltage time-domain signal; Multiple frequency points within a first preset sequence range and multiple frequency points within a second preset sequence range are selected in the voltage frequency domain signal. The real and imaginary parts of the frequency points outside the first and second preset sequence ranges are set to 0 to obtain the processed voltage frequency domain signal. The starting sequence value of the first preset sequence range is the difference between the first power frequency sequence value of the first frequency point corresponding to the rated power frequency and the first preset threshold. The ending sequence value of the first preset sequence range is the sum of the first power frequency sequence value and the second preset threshold. The starting sequence value of the second preset sequence range is the difference between the second power frequency sequence value of the second frequency point corresponding to the rated power frequency and the second preset threshold. The ending sequence value of the second preset sequence range is the sum of the second power frequency sequence value and the first preset threshold. The first power frequency sequence value is less than the second power frequency sequence value. 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; Based on the Wiener filter, the estimated signal of the voltage power frequency signal is used to filter the current time domain signal to obtain the current time domain signal after filtering out the power frequency signal.

5. The method for diagnosing inter-turn short-circuit faults in asynchronous motors according to claim 4, characterized in that... Based on the Wiener filter, the estimated signal of the voltage power frequency signal is used to filter the current time domain signal to obtain the current time domain signal after filtering out the power frequency signal, including: Based on the number of taps N of the Wiener filter, N-1 elements are selected from the estimated signal of the voltage power frequency signal to amplify the estimated signal of the voltage power frequency signal, thereby obtaining the amplified estimated signal of the voltage power frequency signal, wherein N is an integer greater than or equal to 2. Based on the amplified estimated signal of the voltage power frequency signal, calculate 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; Based on the autocorrelation function, construct the autocorrelation matrix of the amplified estimated signal of the voltage power frequency signal; Based on the cross-correlation function, construct the cross-correlation vector between the amplified estimated signal of the voltage power frequency signal and the current time domain signal; The weight vector of the Wiener filter is determined based on the autocorrelation matrix and the cross-correlation vector. Based on the weight vector and the amplified estimated signal of the voltage power frequency signal, the estimated signal of the current power frequency signal in the current time domain signal is obtained; The current time-domain signal is filtered using the estimated signal of the current power frequency signal to obtain the current time-domain signal after the power frequency signal has been filtered out.

6. The method for diagnosing inter-turn short-circuit faults in asynchronous motors according to claim 5, characterized in that... The N is set to 2. Based on the number of taps N of the Wiener filter, N-1 elements are selected from the estimated signal of the voltage power frequency signal to amplify the estimated signal of the voltage power frequency signal, resulting in an amplified estimated signal of the voltage power frequency signal, including: The first element is copied from the estimated signal of the voltage power frequency signal as an amplification element, and the amplification element is placed before the first element to obtain the amplified estimated signal of the voltage power frequency signal.

7. A smart device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores a computer program, which, when executed by the at least one processor, implements the asynchronous motor inter-turn short-circuit fault diagnosis method according to any one of claims 1 to 6.

8. A storage medium storing a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by a processor to perform the asynchronous motor inter-turn short-circuit fault diagnosis method according to any one of claims 1 to 6.

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