Asynchronous motor turn-to-turn short circuit fault diagnosis method, intelligent equipment and storage medium

Abstract

The invention relates to the technical field of motors, in particular to an asynchronous motor turn-to-turn short circuit fault diagnosis method, intelligent equipment and a storage medium, and aims to solve the problem of how to obtain a motor state parameter which does not need to consider three-phase imbalance of power grid voltage and motor three-phase electrical parameter imbalance influence. And turn-to-turn short circuit fault diagnosis is carried out based on the motor state parameters. The method comprises the following steps: based on a voltage time domain signal and a current time domain signal of each phase, according to a preset side frequency harmonic frequency, respectively obtaining a side frequency harmonic current amplitude of each phase in a preset time length, and based on the side frequency harmonic current amplitude, determining whether a turn-to-turn short circuit fault occurs in the motor. According to the turn-to-turn short circuit fault diagnosis method based on the dispersion degree of the side frequency harmonic current amplitudes of all phases, complex modeling analysis based on an electromagnetic coupling equation is not needed, the method has the advantages of being simple and reliable, and a brand new solution is provided for asynchronous motor turn-to-turn short circuit fault diagnosis.

Description

Technical Field

[0001] The present application relates to the technical field of motors, and particularly to a method for diagnosing inter-turn short-circuit faults of an asynchronous motor, an intelligent device, and a storage medium. Background Art

[0002] Insulation problems are one of the main safety risks faced by electrical equipment. For asynchronous motors, insulation problems are mainly manifested as inter-turn short-circuit faults. Due to the characteristics of rapid development and serious consequences of inter-turn short-circuit faults, early warning of inter-turn short-circuit faults has become the focus of continuous attention of the enterprise's electrical operation and maintenance department.

[0003] Currently, the diagnosis of the initial inter-turn short-circuit fault is mainly achieved by evaluating the three-phase unbalance degree of the asynchronous motor state. The asynchronous motor state parameters used are usually negative sequence impedance and stator line current signals. However, these state parameters are significantly affected by the three-phase unbalance of the grid voltage and the inherent three-phase electrical parameter unbalance of the asynchronous motor itself. Correspondingly, there are disadvantages such as many interference factors, cumbersome processes, and low accuracy in extracting information directly related to inter-turn short-circuit faults from the negative sequence impedance and stator line current signals. Therefore, how to obtain a motor state parameter that does not need to consider the influence of the three-phase unbalance of the grid voltage and the three-phase electrical parameter unbalance of the motor, and perform inter-turn short-circuit fault diagnosis based on this motor state parameter has become an urgent problem to be solved.

[0004] Correspondingly, there is a need for a new solution for diagnosing inter-turn short-circuit faults of asynchronous motors in this field to solve the above problems. Summary of the Invention

[0005] In order to overcome the above defects, the present application is proposed 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 influence of the three-phase unbalance of the grid voltage and the three-phase electrical parameter unbalance of the motor, and perform inter-turn short-circuit fault diagnosis based on this motor state parameter.

[0006] In a first aspect, a method for diagnosing inter-turn short-circuit faults of an asynchronous motor is provided. The method includes: Obtain the voltage time-domain signals and current time-domain signals corresponding to each phase of the motor; Based on the voltage time-domain signals and current time-domain signals corresponding to each phase, and according to the preset sideband harmonic frequencies, respectively obtain the corresponding sideband harmonic current amplitudes within a preset duration; Based on the corresponding sideband harmonic current amplitudes, obtain the dispersion degree of all the sideband harmonic current amplitudes; Based on the dispersion degree of all the sideband harmonic current amplitudes, determine whether the motor has an inter-turn short-circuit fault.

[0007] In one technical solution of the above inter-turn short circuit fault diagnosis method for an asynchronous motor, the method further includes: Based on the sideband harmonic current amplitudes corresponding to each phase, obtain the mean and standard deviation corresponding to all the sideband harmonic current amplitudes; Based on the mean and standard deviation corresponding to all the sideband harmonic current amplitudes, obtain the sideband harmonic current dispersion; Compare the sideband harmonic current dispersion with a preset dispersion threshold; In response to the sideband harmonic current dispersion being greater than or equal to the dispersion threshold, determine that the motor has an inter-turn short circuit fault.

[0008] In one technical solution of the above inter-turn short circuit fault diagnosis method for an asynchronous motor, the method further includes: The preset sideband harmonic frequency is set to (1 - 2s)f e ; where s is the slip ratio and f e is the rated power frequency.

[0009] In one technical solution of the above inter-turn short circuit fault diagnosis method for an asynchronous motor, "Based on the voltage time-domain signal and current time-domain signal corresponding to each phase, and according to the preset sideband harmonic frequency, respectively obtain the sideband harmonic current amplitudes corresponding to each phase within a preset duration" includes: Based on the voltage time-domain signal and current time-domain signal corresponding to each phase, respectively obtain the current time-domain signal with the power frequency signal filtered for each phase; Through Fourier transform, respectively obtain the second current frequency-domain signal of the current time-domain signal with the power frequency signal filtered for each phase within the preset duration; According to the preset sideband harmonic frequency, respectively extract the sideband harmonic current amplitudes corresponding to each phase from the second current frequency-domain signal corresponding to each phase.

[0010] In one technical solution of the above inter-turn short circuit fault diagnosis method for an asynchronous motor, "Based on the voltage time-domain signal and current time-domain signal corresponding to each phase, respectively obtain the current time-domain signal with the power frequency signal filtered for each phase" includes: 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; Select multiple frequency points within the first preset serial number range and multiple frequency points within the second preset serial number range from the voltage frequency-domain signal, and set the real part and imaginary part of the frequency points outside the first preset serial number range and the second preset serial number range to 0 to obtain the processed voltage frequency-domain signal, where 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, 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, 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, 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, and the first power frequency serial number value is less than the second power frequency serial number 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 a Wiener filter, use the estimated signal of the voltage power frequency signal to filter the current time-domain signal to obtain the current time-domain signal with the power frequency signal filtered out.

[0011] In a technical solution of the above asynchronous motor inter-turn short circuit fault diagnosis method, "Based on a Wiener filter, use the estimated signal of the voltage power frequency signal to filter the current time-domain signal to obtain the current time-domain signal with the power frequency signal filtered out" includes: According to the number of taps N of the Wiener filter, select N - 1 elements from the estimated signal of the voltage power frequency signal to amplify the estimated signal of the voltage power frequency signal to obtain an amplified estimated signal of the voltage power frequency signal, where N is an integer greater than or equal to 2; According to 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; According to the autocorrelation function, construct the autocorrelation matrix of the amplified estimated signal of the voltage power frequency signal; According to 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; According to the autocorrelation matrix and the cross-correlation vector, determine the weight vector of the Wiener filter; According to the weight vector and the amplified estimated signal of the voltage power frequency signal, obtain an estimated signal of the current power frequency signal in the current time-domain signal; Use the estimated signal of the current power frequency signal to filter the current time-domain signal to obtain the current time-domain signal with the power frequency signal filtered out.

[0012] In one technical solution of the above-mentioned inter-turn short-circuit fault diagnosis method for an asynchronous motor, N is set to 2, and "selecting N-1 elements from the estimated signal of the power frequency voltage signal according to the number of taps N of the Wiener filter, and amplifying the estimated signal of the power frequency voltage signal to obtain an amplified estimated signal of the power frequency voltage signal" includes: Copying the first element from the estimated signal of the power frequency voltage signal as an amplified element, and placing the amplified element before the first element to obtain the amplified estimated signal of the power frequency voltage signal.

[0013] In a second aspect, there is provided an intelligent device, which includes at least one processor; and a memory communicatively connected to the at least one processor; wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method described in any one of the technical solutions of the above-mentioned inter-turn short-circuit fault diagnosis method for an asynchronous motor is implemented.

[0014] In a third aspect, there is provided a storage medium, in which multiple program codes are stored, and when the computer program is executed by the at least one processor, the method described in any one of the technical solutions of the above-mentioned inter-turn short-circuit fault diagnosis method for an asynchronous motor is implemented.

[0015] One or more of the above technical solutions of the present application have at least one or more of the following beneficial effects: The method for diagnosing inter-turn short-circuit faults based on the degree of dispersion (relative magnitude) of the corresponding side-frequency harmonic current amplitudes of each phase in the present application does not require complex modeling analysis based on electromagnetic coupling equations, and factors such as three-phase imbalance of the grid voltage and three-phase electrical parameters imbalance of the motor itself can be not considered during implementation, and has the characteristics of simplicity and reliability, providing a brand-new solution for diagnosing inter-turn short-circuit faults of asynchronous motors. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Referring to the accompanying drawings, the disclosure of the present application will become easier to understand. It is easy for those skilled in the art to understand that: These drawings are only for the purpose of illustration and are not intended to limit the protection scope of the present application.

[0017] Figure 1 It is a schematic diagram of the main step flow of the inter-turn short-circuit fault diagnosis method for an asynchronous motor according to an embodiment of the present application.

[0018] Figure 2 It is a schematic diagram of the detailed step flow of step S02 according to an embodiment of the present application.

[0019] Figure 3 It is a schematic diagram of the main step flow of step S201 according to an embodiment of the present application.

[0020] Figure 4 It is a schematic diagram of a star-connected three-phase AC circuit according to an embodiment of the present application.

[0021] Figure 5 It is a detailed step flow schematic diagram of steps S03 and S04 according to an embodiment of the present application.

[0022] Figure 6 It is a schematic diagram of the main structure of an intelligent device according to an embodiment of the present application. Detailed implementation manners

[0023] The following describes some implementation manners of the present application with reference to the accompanying drawings. Those skilled in the art should understand that these implementation manners are only used to explain the technical principle of the present application and are not intended to limit the protection scope of the present application.

[0024] In the description of the present application, "module" and "processor" may include hardware, software, or a combination of both. A module may include a hardware circuit, various suitable sensors, communication ports, memories, and may also include a software part, such as program code, or may be 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, in hardware, or in a combination of both. The computer-readable storage medium includes any suitable medium for storing program code, such as magnetic disks, hard disks, optical discs, flash memories, read-only memories, random access memories, and so on. The term "A and / or B" represents 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 meaning similar to "A and / or B" and may include only A, only B, or A and B. The singular terms "a" and "this" may also include the plural form.

[0025] First, refer to the attached Figure 1 , Figure 1 It is a schematic diagram of the main steps of the inter-turn short circuit fault diagnosis method for an asynchronous motor according to an embodiment of the present application. As Figure 1 shown, the inter-turn short circuit fault diagnosis method for an asynchronous motor in the embodiment of the present application includes: Step S01: Obtain the voltage time-domain signal and current time-domain signal corresponding to each phase of the motor; Step S02: Based on the voltage time-domain signal and current time-domain signal corresponding to each phase, and according to the preset sideband harmonic frequency, obtain the amplitude of the sideband harmonic current corresponding to each phase within a preset time period; Step S03: Obtain the dispersion degree of all sideband harmonic current amplitudes based on the corresponding sideband harmonic current amplitudes; Step S04: Determine whether the motor has a turn-to-turn short circuit fault based on the dispersion degree of all sideband harmonic current amplitudes.

[0026] In the embodiment of the present application, the motor is an asynchronous induction motor, and the wiring method of this asynchronous motor is star connection. In step S01, according to the preset sampling frequency f s and the preset sampling duration T, the voltage time-domain signals and current time-domain signals corresponding to each phase during the steady-state operation of the motor are synchronously collected through a voltage sensor and a current sensor respectively, and the three-phase (phase a, phase b, and phase c) voltage time-domain signals {u a (n)}, {u b (n)}, and {u c (n)}, as well as the three-phase (phase a, phase b, and phase c) current time-domain signals {i a (n)}, {i b (n)}, and {i c (n)} are obtained. Wherein, n = 0, 1, 2, …, (N - 1), and N is determined by the sampling frequency f s and the sampling duration T1.

[0027] Considering that the radix-2 fast Fourier (inverse) transform requires the amount of data to be processed to be an integer power of 2, therefore, N can be selected as the value corresponding to an integer power of 2. For example, N = 2 17 = 131072. As an example, the sampling frequency f s is set to 12.8 KHz, and the sampling duration T is set to 10.24 seconds. At this time, the data lengths of the corresponding voltage time-domain signals and current time-domain signals are both T * f s = 131072, that is, they respectively include 131072 sampling points of data.

[0028] Next, in combination with Figure 2 , the detailed step flow of step S02 will be described. Figure 2 is the detailed step flow diagram of step S02 according to an embodiment of the present application.

[0029] In the embodiment of the present application, step S201 can adopt the adaptive filtering method described in the invention patent with the Chinese patent application number 202210933441.1, and based on the corresponding voltage time-domain signals and current time-domain signals, respectively obtain the current time-domain signals with the power frequency signals filtered out.

[0030] The main steps of obtaining the current time-domain signals with the power frequency signals filtered out described in the invention patent with the Chinese patent application number 202210933441.1 are as Figure 3 shown.

[0031] Step 101: Perform a fast Fourier transform on the voltage time-domain signal (obtained through the above step S01) to obtain the voltage frequency-domain signal corresponding to the voltage time-domain signal.

[0032] Step 102: Select multiple frequency points within the first preset serial number range and multiple frequency points within the second preset serial number range in the voltage frequency-domain signal, and set the real part and imaginary part of the frequency points outside the first preset serial number range and the second preset serial number range to 0 to obtain the processed voltage frequency-domain signal.

[0033] In a specific implementation process, in addition to the power frequency signal in the voltage time-domain signal, there are also a DC component signal and high-order harmonic signals of the power frequency signal. In addition, during actual operation, 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, and this fluctuation range is usually ±0.2 to ±0.5 Hz. Therefore, in order to accurately determine the power frequency, multiple frequency points including the frequency point corresponding to the rated power frequency can be selected according to this fluctuation range.

[0034] Specifically, multiple frequency points within the first preset serial number range and multiple frequency points within the second preset serial number range can be selected in the voltage frequency-domain signal, and the real part and imaginary part of the frequency points outside the first preset serial number range and the second preset serial number range are set to 0 to obtain the processed voltage frequency-domain signal. Among them, 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, 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; 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, 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; the first power frequency serial number value is less than the second power frequency serial number value.

[0035] In a specific implementation process, taking the sampling frequency of 12.8 kHz and the sampling time of 10.24 s as an example. The total number of frequency points in the voltage frequency-domain signal is 10.24×12800 = 131072.

[0036] When the sampling duration of the voltage time-domain signal to be processed or analyzed is 10.24 s, 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. Both the first preset threshold and the second preset threshold can be set to 256, so the first preset serial number range is from 256 to 768, and the second preset serial number range is from 130304 to 130816.

[0037] 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.

[0038] In a specific implementation process, a certain range of multiple frequency points 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 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 signal of the voltage power-frequency signal in the voltage time-domain signal has a high degree of fitting with the actual voltage power-frequency signal.

[0039] Step 104: Based on a Wiener filter, use the estimated signal of the voltage power-frequency signal to filter the current time-domain signal (obtained through the above step S01) to obtain a current time-domain signal with the power-frequency signal filtered out.

[0040] After obtaining the estimated signal of the voltage power-frequency signal, step 104 can be implemented according to the following steps.

[0041] (1) According to the number of taps N of the Wiener filter, select N - 1 elements from the estimated signal of the voltage power-frequency signal to amplify the estimated signal of the voltage power-frequency signal to obtain an amplified estimated signal of the voltage power-frequency signal; where N is an integer greater than or equal to 2.

[0042] In a specific implementation process, when the number of taps of the Wiener filter is different, the method of using the estimated signal of the current power-frequency signal to filter the current time-domain signal to obtain a current time-domain signal with the power-frequency signal filtered out is different. Therefore, according to the number of taps N of the Wiener filter, select N - 1 elements from the estimated signal of the voltage power-frequency signal to amplify the estimated signal of the voltage power-frequency signal to obtain an amplified estimated signal of the voltage power-frequency signal.

[0043] Specifically, taking N = 2 as an example, a specified element can be copied from the estimated signal of the power frequency voltage signal as an amplification element, and the estimated signal of the power frequency voltage signal is amplified to obtain an amplified estimated signal of the power frequency voltage signal. In this way, compared with calculating the amplification element that meets the requirements according to the sine signal, the calculation is simpler, and when obtaining the amplified estimated signal of the power frequency voltage signal by this method, the calculation accuracy is relatively high.

[0044] In a specific implementation process, the first element can be copied from the estimated signal of the power frequency voltage signal as the amplification element, and the amplification element is placed before the first element to obtain an amplified estimated signal of the power frequency voltage signal.

[0045] For example, after steps 101 - 103, the estimated signal of the power frequency voltage signal obtained includes the following multiple elements: U e [0], U e [1], U e [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.

[0046] Copy the first U e [0] and place it in front of the estimated signal of the power frequency voltage signal to obtain an amplified estimated signal of the power frequency voltage signal including the following multiple elements: U e0 [0], U e0 [1], U e0 [2]......U e0 [2 n ; where the value of U e0 [0] is equal to the value of U e0 [1], the value of U e0 [j + 1] is equal to the value of U e [j], j is any integer greater than or equal to 0 and less than 2 n , 2 n is the number of sampling points of the current time-domain signal and the voltage time-domain signal.

[0047] (2) Calculate the autocorrelation function of the amplified estimated signal of the power frequency voltage signal and the cross-correlation function between the amplified estimated signal of the power frequency voltage signal and the current time-domain signal according to the amplified estimated signal of the power frequency voltage signal.

[0048] In a specific implementation process, there are two autocorrelation functions of the amplified estimated signal of the power frequency voltage signal, which can be specifically obtained according to calculation formulas (1) and (2): where U e0 [j] is the j-th element in the amplified estimated signal of the power frequency voltage signal, and 2 n is the number of sampling points of the current time-domain signal and the voltage time-domain signal.

[0049] There can also be two cross-correlation functions between the amplified estimated signal of the power frequency voltage signal and the current time-domain signal, which can be specifically obtained according to calculation formulas (3) and (4): where U e0 [j] is the j-th element in the amplified estimated signal of the power frequency voltage signal, I[j] is the j-th element of the current time-domain signal, and 2 n is the number of sampling points of the current time-domain signal and the voltage time-domain signal.

[0050] (3) Construct the autocorrelation matrix R of the amplified estimated signal of the power frequency voltage signal according to the autocorrelation function.

[0051] In a specific implementation process, taking 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 in the first row and first column, first row and second column, second row and first column, and second row and second column of the autocorrelation matrix R in sequence. Among them, R[0][0] = R[1][1] = r0, and R[1][0] = R[0][1] = r1.

[0052] (4) Construct the cross-correlation vector P between the amplified estimated signal of the power frequency voltage signal and the current time-domain signal according to the cross-correlation function.

[0053] In a specific implementation process, taking the cross-correlation vector P as a vector with two elements as an example, where P[0][0] and P[1][0] represent the elements in the first row and first column and the second row and first column in sequence. Among them, P[0][0] = p0, and P[1][0] = p1.

[0054] (5) Determine the weight vector w of the Wiener filter according to the autocorrelation matrix R and the cross-correlation vector P.

[0055] In a specific implementation process, the weight vector w of the Wiener filter can be obtained by the process of calculating the weight vector using the steepest descent method or the LMS method. The detailed process can refer to the records of existing related technologies and will not be elaborated here.

[0056] In a specific implementation process, the inverse operation can also be performed on the autocorrelation matrix R to obtain the inverse matrix R -1 ; take the product of the inverse matrix R -1 and the cross-correlation vector P as the weight vector, that is, w = R -1 P.

[0057] 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 the autocorrelation matrix R as a 2nd-order matrix as an example, R[0][0], R[0][1], R[1][0], and R[1][1] are the elements of the first row and first column, the first row and second column, the second row and first column, and the second row and second column in the autocorrelation matrix R in sequence. Let x = R[0][0] * R[1][1] - R[0][1] * R[1][0], and the inverse matrix of the autocorrelation matrix R is R -1 , then the elements in the inverse matrix R -1 are 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; in this way, each element of R -1 is determined.

[0058] When the autocorrelation matrix R is a 2nd-order matrix and the cross-correlation vector P is a column vector with two elements, the obtained weight vector is a column vector containing 2 elements. w0 and w1 represent the elements of the first row and first column and the second row and first column in sequence. Among them, w0 = R -1 [0][0] * p0 + R -1 [0][1] * p1, w1 = R -1 [1][0] * p0 + R -1 [1][1] * p1.

[0059] 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 this weight vector, only the inverse matrix R of the autocorrelation matrix R with an order of 2 needs to be obtained -1 , and this inverse matrix can be directly calculated 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.

[0060] (6) According to the weight vector and the amplified estimated signal of the voltage power frequency signal, obtain the estimated signal of the current power frequency signal in the current time domain signal.

[0061] In a specific implementation process, according to the weight vector, the j-th element U e0 [j] and the (j + 1)-th element U e0 [j + 1] in the amplified estimated signal of the voltage power frequency signal, calculate the corresponding element I e [j] in the estimated signal of the current power frequency signal; where j is greater than or equal to 0 and less than 2n 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): I e [j] = w0 * U e0 [j + 1] + w1 * U e0 [j] (5).

[0062] (7) Filter the current time-domain signal by using the estimated signal of the power frequency current signal to obtain the current time-domain signal with the power frequency signal filtered out.

[0063] Specifically, the current time-domain signal can be subtracted from the estimated signal of the power frequency current signal, that is, the current time-domain signal with the power frequency signal filtered out is 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 to say, the current component signal located in the rated power frequency sideband in the current time-domain signal can be more accurately identified.

[0064] In the embodiment of the present application, the voltage time-domain signal {u a (n)} and the current time-domain signal {i a (n)} corresponding to phase a are substituted into the above steps 101 to 104, and according to calculation formulas (1) to (5), the current time-domain signal {i′ a (n)} with the power frequency signal filtered out corresponding to phase a can be obtained.

[0065] Similarly, the voltage time-domain signal {u b (n)} and the current time-domain signal {i b (n)} corresponding to phase b are substituted into the above steps 101 to 104, and according to calculation formulas (1) to (5), the current time-domain signal {i′ b (n)} with the power frequency signal filtered out corresponding to phase b can be obtained.

[0066] The voltage time-domain signal {u c (n)} and the current time-domain signal {i c (n)} corresponding to phase c are substituted into the above steps 101 to 104, and according to calculation formulas (1) to (5), the current time-domain signal {i′ c (n)} with the power frequency signal filtered out corresponding to phase c can be obtained.

[0067] The adaptive filtering method of the above embodiment performs a fast Fourier transform on the collected voltage time-domain signal. After obtaining the voltage frequency-domain signal of the 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 therefrom, and the values of other frequency points are set to 0. Then, an inverse fast Fourier transform is performed on the processed voltage frequency-domain signal 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 excluded, so that the current power frequency signal can be filtered more thoroughly, so as to more accurately identify the current component signal located in the rated power frequency sideband from the current spectrum.

[0068] In step S202, through Fourier transform, the frequency-domain signals (second current frequency-domain signals) of the current time-domain signals of each corresponding filtered power frequency signal within a preset duration are respectively obtained.

[0069] Specifically, within the preset duration (such as the above-mentioned 10.24 seconds), the a-phase second current frequency-domain signal {F a (ω)} corresponding to the a-phase current time-domain signal {i′ a (n)} of the filtered power frequency signal, the b-phase second current frequency-domain signal {F b (ω)} corresponding to the b-phase current time-domain signal {i′ b (n)} of the filtered power frequency signal, and the c-phase second current frequency-domain signal {F c (ω)} corresponding to the c-phase current time-domain signal {i′ c (n)} of the filtered power frequency signal are obtained.

[0070] The sideband harmonic signals include signals of multiple 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, while the other sideband harmonics with frequencies (including (1 + 2s)fe and (1 ± 2ks)fe, where k is an integer greater than or equal to 2) are sideband harmonics 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.

[0071] In the embodiment of the present 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 amplitudes are respectively extracted from the corresponding second current frequency-domain signals.

[0072] Specifically, the a-phase sideband harmonic current amplitude I is extracted from the a-phase second current frequency-domain signal {F a (ω)}a , the side - frequency harmonic current amplitude I of phase b is extracted from the second current frequency - domain signal {F b (ω)} of phase b b , the side - frequency harmonic current amplitude I of phase c is extracted from the second current frequency - domain signal {F c (ω)} of phase c c .

[0073] It should be noted that those skilled in the art can also, according to the actual situation, select side - frequency harmonics of other frequencies. As an example, side - frequency harmonics with a frequency of (1 + 2s)fe, or side - frequency harmonics with a frequency of (1 - 3s)fe, etc. Without departing from the principle of this application, these technical solutions after such changes or replacements will fall within the protection scope of this application.

[0074] In other embodiments, step S02 can also adopt the method described in the invention patent with the Chinese patent application number 202310916262.1: "Obtain synchronous voltage signals and current signals when an asynchronous induction motor operates stably; perform recursive processing on the voltage signals based on an extended Kalman filter to obtain a real - time estimated value of the power - grid fundamental frequency; perform recursive processing on the current signals based on a Kalman filter and the real - time estimated value of the power - grid fundamental frequency to obtain an estimated signal of the fundamental - frequency signal in the current signals; use 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; based on the spectrum of the current signal with the fundamental - frequency signal filtered out, obtain the amplitude of the side - frequency signal with a frequency of (1±2s)fe" to obtain the side - frequency harmonic current amplitude required by this application. Or adopt the method described in the invention patent with the Chinese patent application number 202410804841.1: "Obtain the fault range of the side - frequency signal in the current spectrum according to the motor parameters" to obtain the side - frequency harmonic current amplitude required by this application. For the specific implementation methods and steps, please refer to the relevant literature materials and will not be elaborated here.

[0075] Continue reading Figure 4 , combined with Figure 4 explain the principle of judging whether the motor has an inter - turn short - circuit fault based on the side - frequency harmonic current.

[0076] When an asynchronous motor is actually operating, it will inevitably be affected by multiple objectively existing three - phase unbalance factors. For example, the three - phase unbalance of the power - grid voltage, the unbalance of the three - phase electrical parameters caused by manufacturing process deviations, etc. Affected by these three - phase unbalance factors, the magnetomotive force generated by the rotor due to electromagnetic induction will also be three - phase unbalanced, thus generating a magnetomotive force (reverse magnetomotive force) whose rotation direction is opposite to the rotation direction of the rotor. This reverse magnetomotive force cuts the stator winding and will induce the above - mentioned multiple side - frequency harmonic signals with different frequencies in the stator winding.

[0077] When analyzing the turn - to - turn short - circuit fault of an induction motor through side - frequency harmonics based on the equivalent circuit model of the induction motor, the applicant found that when the equivalent circuit model only considers the stator winding inductance and does not consider the stator winding resistance, the three - phase side - frequency harmonics contained in the stator line current of the induction motor with a turn - to - turn short - circuit fault are symmetric. Thus, it is impossible or very difficult to diagnose the turn - to - turn short - circuit fault of the induction motor based on side - frequency harmonics. 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 induction motor with a turn - to - turn short - circuit fault are asymmetric. Thus, the turn - to - turn short - circuit fault of the induction motor can be diagnosed through the degree of dispersion of the amplitudes of the three - phase side - frequency harmonic currents contained in the stator line current. The relevant modeling analysis is as follows.

[0078] As Figure 4 shown, for the equivalent circuit model of a star - connected induction motor, when the reverse magnetomotive force cuts the three - phase stator windings, reverse magnetomotive - force - induced voltages and will be induced in the three - phase stator windings respectively. The magnitudes of these three - phase induced voltages are proportional to the number of turns of the three - phase windings, that is, U a :U b :U c =n a :n b :n c .

[0079] There is a relationship in the three - phase AC circuit of this equivalent circuit model as shown in calculation formula (6): Where is the reverse magnetomotive - force - induced voltage corresponding to phase a, and Z a is the stator winding impedance corresponding to phase a; is the reverse magnetomotive - force - induced voltage corresponding to phase b, and Z b is the stator winding impedance corresponding to phase b; is the reverse magnetomotive - force - induced voltage corresponding to phase c, and Z c is the stator winding impedance corresponding to phase c; is the reverse magnetomotive - force - induced voltage corresponding to the common point.

[0080] From calculation formula (6), it can be obtained that:

[0081] According to calculation formula (7) and combined with calculation formula (6), the side - frequency harmonic current corresponding to phase a is:

[0082] According to calculation formula (7) and combined with calculation formula (6), the side-frequency harmonic current corresponding to b is:

[0083] According to calculation formula (7) and combined with calculation formula (6), the side-frequency harmonic current corresponding to c is:

[0084] 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.

[0085] Let Z a = r + jn a X, Z b = r + jn b X, Z c = r + jn c X, where represents the proportional base of the stator winding induced voltage phasor, n a is the number of turns of the a-phase stator winding, n b is the number of turns of the b-phase stator winding, n c is the number of turns of the c-phase stator winding, r represents the ohmic resistance of the stator winding, and X represents the proportional base of the stator winding inductive reactance. At this time, calculation formulas (8), (9) and (10) will be transformed into the following forms.

[0086] The side-frequency harmonic current corresponding to a is:

[0087] The side-frequency harmonic current corresponding to b is:

[0088] The side-frequency harmonic current corresponding to c is:

[0089] It can be seen from calculation formulas (11), (12) and (13) that when there is no inter-turn short circuit, n a , n b and n c are equal, and are a set of symmetric three-phase currents. When an inter-turn short circuit occurs, n a , n b and n c will no longer be equal. At this time and will become a set of asymmetric three - phase currents, with a relative difference in their amplitude magnitudes. Therefore, it is possible to and the degree of discreteness of the amplitudes to reflect and the relative difference between the amplitudes, and then diagnose the turn - to - turn short - circuit fault.

[0090] In steps S03 and S04, first, based on the amplitudes of the corresponding side - frequency harmonic currents, obtain the degree of discreteness of all the side - frequency harmonic current amplitudes. Then, based on the degree of discreteness of all the side - frequency harmonic current amplitudes, determine whether the motor has a turn - to - turn short - circuit fault. Next, a specific implementation method of steps S03 and S04 will be described in combination with Figure 5 Explain a specific implementation method of steps S03 and S04.

[0091] First, based on the amplitudes of the corresponding side - frequency harmonic currents, obtain the mean and standard deviation corresponding to all the side - frequency harmonic current amplitudes. That is, based on the side - frequency harmonic current amplitude I a of phase a, the side - frequency harmonic current amplitude I b of phase b, and the side - frequency harmonic current amplitude I c of phase c, obtain the mean and standard deviation corresponding to this set of numbers of I a , I b , and I c .

[0092] Then, based on the mean and standard deviation corresponding to all the side - frequency harmonic current amplitudes, obtain the side - frequency harmonic current discreteness, where the side - frequency harmonic current discreteness = standard deviation / mean.

[0093] Since I a , I b , and I c are the statistical values of the side - frequency harmonic current amplitudes within a preset time duration, when there is no turn - to - turn short - circuit in phases a, b, and c (n a = n b = n c ), I a , I b , and I c can be considered equal, and at this time, the degree of discreteness between I a , I b , and I c is the smallest.

[0094] When a turn - to - turn short - circuit occurs, the relationship of n a = n b = n c is destroyed. Correspondingly, I a , I b , and I cThe equal relationship will no longer exist. When the number of turns of the stator winding of one phase differs more from the number of turns of the stator windings of the other two phases, generally, the difference between I a 、I b and I c will be greater. At this time, the dispersion degree between I a 、I b and I c will become larger.

[0095] Therefore, it is possible to judge whether an inter-turn short-circuit fault has occurred by the dispersion degree between I a 、I b and I c . Specifically, compare the dispersion degree of the sideband harmonic current with a preset dispersion degree threshold. In response to the dispersion degree of the sideband harmonic current being greater than or equal to the dispersion degree threshold, judge that the motor has an inter-turn short-circuit fault.

[0096] It should be noted that for the dispersion degree thresholds of different models of asynchronous motors, their values may vary. This dispersion degree threshold can be obtained based on the actual measurement and calculation data of multiple motors of the same model that have experienced (or simulated in the laboratory) inter-turn short-circuit faults.

[0097] As an example, the dispersion degree threshold of a certain model of asynchronous motor is set to 10%. During the operation of this model of motor, synchronously collect the corresponding voltage time-domain signal and current time-domain signal of the three phases of the motor regularly according to a preset sampling frequency f s (such as 12.8 KHz) and a preset sampling duration T (such as 10.24 seconds); through the method described in the above embodiment, calculate the amplitudes of the sideband harmonic currents corresponding to the three phases of the motor (I a 、I b and I c ); after calculating the dispersion degree of the sideband harmonic current based on I a 、I b and I c , compare the dispersion degree of the sideband harmonic current with the dispersion degree threshold. When the dispersion degree of the sideband harmonic current is greater than or equal to 10%, judge that the motor has an inter-turn short-circuit fault and send a prompt message to the user.

[0098] In other embodiments, the dispersion degree of all the sideband harmonic current amplitudes can also be represented by calculating statistical indicators such as the average deviation or variance of the sideband harmonic current amplitudes of each phase. Correspondingly, the dispersion degree threshold also needs to be recalibrated according to the selected statistical indicator.

[0099] As can be seen from the above embodiments, the method for diagnosing the inter-turn short circuit fault of the asynchronous motor in this application does not require complex modeling and analysis based on electromagnetic coupling equations, and when implemented, it does not need to consider the influence of factors such as three-phase imbalance of the grid voltage and imbalance of the three-phase electrical parameters of the motor itself. It has the characteristics of simplicity and reliability, and provides a brand-new solution for diagnosing the inter-turn short circuit fault of the asynchronous motor.

[0100] On the other hand, this application also provides an intelligent device.

[0101] In an embodiment of an intelligent device according to this application, the intelligent device may include at least one processor; and a memory communicatively connected to the at least one processor; wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, it implements the method for diagnosing the inter-turn short circuit fault of the asynchronous motor described in any of the above embodiments. Refer to the attached Figure 6 , Figure 6 It is exemplarily shown in the figure that the intelligent device 6 includes a memory 61 and a processor 62, and the memory 61 and the processor 62 are communicatively connected through a bus.

[0102] Furthermore, this application also provides a storage medium.

[0103] In an embodiment of a storage medium according to this application, the storage medium may be configured to store a program for executing the method for diagnosing the inter-turn short circuit fault of the asynchronous motor in the above method embodiment, and this program can be loaded and run by a processor to implement the above method for diagnosing the inter-turn short circuit fault of the asynchronous motor. For the sake of convenience of description, only the parts related to the embodiments of this application are shown. For the specific technical details not disclosed, please refer to the method part of the embodiments of this application. The storage medium may be a storage device formed by various electronic devices. As an example, the storage medium in the embodiments of this application is a non-transitory storage medium.

[0104] Those skilled in the art should be able to realize that although the above steps are described in a specific order, those skilled in the art can understand that in order to achieve the effects of this application, it is not necessary for different steps to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders, and these adjusted solutions are equivalent technical solutions to the technical solutions described in this application, and therefore will also fall within the protection scope of this application.

[0105] So far, the technical solutions of this application have been described in conjunction with an embodiment shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of this application is obviously not limited to these specific embodiments. Without departing from the principle 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 protection scope of this application.

Claims

1. A method for diagnosing an asynchronous motor turn-to-turn short-circuit fault, characterized in that: The method comprises: Obtaining the voltage time domain signal and current time domain signal corresponding to each motor; Based on the corresponding voltage time domain signals and current time domain signals, and according to the preset sideband harmonic frequencies, the corresponding sideband harmonic current amplitudes within the preset time length are respectively obtained; Based on the corresponding sideband harmonic current amplitudes, obtaining the discreteness of all the sideband harmonic current amplitudes; Based on the discreteness of all the sideband harmonic current amplitudes, it is determined whether a turn-to-turn short circuit fault occurs in the motor.

2. The method for diagnosing an asynchronous motor turn-to-turn short-circuit fault according to claim 1, characterized in that: The method further comprises: Based on the corresponding sideband harmonic current amplitudes, obtaining the mean and standard deviation corresponding to all the sideband harmonic current amplitudes; Based on the mean and standard deviation corresponding to all the sideband harmonic current amplitudes, the sideband harmonic current dispersion is obtained; Comparing the sideband harmonic current dispersion with a preset dispersion threshold; In response to the sideband harmonic current dispersion being greater than or equal to the dispersion threshold, it is determined that a turn-to-turn short circuit fault occurs in the motor.

3. The method for diagnosing an asynchronous motor turn-to-turn short-circuit fault according to any one of claims 1 to 2, characterized in that: The method further comprises: The preset sideband harmonic frequency is set to (1-2s)f e ; Among them, s is the slip rate, f e is the rated power frequency.

4. The method for diagnosing an asynchronous motor turn-to-turn short-circuit fault 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 sideband harmonic frequencies, respectively obtaining the corresponding sideband harmonic current amplitudes within the preset time length" includes: Based on the corresponding voltage time domain signals and current time domain signals, respectively obtaining the corresponding current time domain signals with the power frequency signal filtered out; By Fourier transform, second current frequency domain signals of the current time domain signals corresponding to the current time domain signals from which the power frequency signal is filtered out within the preset time length are respectively obtained; According to the preset sideband harmonic frequency, the corresponding sideband harmonic current amplitudes are extracted from the corresponding second current frequency domain signals respectively.

5. The method for diagnosing an asynchronous motor turn-to-turn short-circuit fault according to claim 4 is characterized in that , "Based on the corresponding voltage time domain signals and current time domain signals, respectively obtaining the corresponding current time domain signals after filtering out the power frequency signals" includes: Performing fast Fourier transform on the 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 parts and imaginary parts of the frequency points outside the first preset serial number range and the second preset serial number range to 0, 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, 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, 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, and 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, 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 with the power frequency signal filtered out.

6. The method for diagnosing an asynchronous motor turn-to-turn short-circuit fault according to claim 5, characterized in that "Based on the Wiener filter, using the estimated signal of the voltage power frequency signal, filtering the current time domain signal to obtain the current time domain signal with the power frequency signal filtered out" includes: 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; 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 of 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.

7. The method for diagnosing an asynchronous motor turn-to-turn short-circuit fault according to claim 6, characterized in that , the N is set to 2, "according to the tap number N of the Wiener filter, selecting N-1 elements from the estimated signal of the voltage and power frequency signal, amplifying the estimated signal of the voltage and power frequency signal, and obtaining the amplified estimated signal of the voltage and power frequency signal" includes: The first element is copied from the estimated signal of the voltage and power frequency signal as an amplified element, and the amplified element is placed before the first element to obtain the amplified estimated signal of the voltage and power frequency signal.

8. A smart device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; Wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method for diagnosing the inter-turn short-circuit fault of an asynchronous motor according to any one of claims 1 to 7 is implemented.

9. A 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 asynchronous motor turn-to-turn short-circuit fault diagnosis method according to any one of claims 1 to 7.

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