Fault identification method and device for variable frequency power supply system

Abstract

The invention provides a fault identification method and equipment for a variable frequency power supply system. The method comprises the following steps: constructing a relay protection controller database; acquiring a plurality of current and voltage data of the variable-frequency power supply system; obtaining a frequency component with the highest amplitude corresponding to each piece of current and voltage data; establishing a difference equation; each piece of filtered current and voltage data is obtained; the instantaneous amplitude, the instantaneous phase and the instantaneous frequency of each piece of filtered current and voltage data are obtained; taking the selected plurality of time-frequency matrixes as time-frequency matrixes of normal operation conditions; taking the constructed plurality of time-frequency matrixes as the time-frequency matrix of the actual operation condition; performing similarity calculation to obtain a normalized cross correlation coefficient corresponding to each piece of current and voltage data; and judging whether a fault occurs or not and the type of the fault. The technical problem that online fault diagnosis cannot be achieved in the prior art can be solved.

Description

Technical Field

[0001] This invention relates to the field of ultra-high-speed maglev traction relay protection technology, and in particular to a fault identification method and device for frequency conversion power supply systems. Background Technology

[0002] The technology of ultra-high-speed maglev trains is constantly developing and has received widespread attention and research both at home and abroad.

[0003] The operation of ultra-high-speed maglev trains relies on the ground traction power supply system to provide energy. The energy comes from the power grid substation, and a stable DC voltage is obtained through the input transformer and rectifier. Then, the voltage and current with controllable amplitude and frequency are obtained through the inverter, and transmitted to the stator section of the linear motor through the trackside switch station and power supply cable to realize the operation of the train.

[0004] The safe and reliable operation of high-speed maglev trains is of paramount importance. Therefore, the traction control system must function as a relay protection mechanism. This involves monitoring the three-phase voltage and current output from the inverter to the stator section, identifying any abnormal values ​​to determine if a fault or anomaly has occurred, and then executing protection procedures. High-speed maglev trains use three-phase linear motors, which are industrial-grade variable frequency motors. Common faults and anomalies include insulation degradation, three-phase parameter imbalance, short circuits, and open circuits. Since the relay protection controller can only extract three-phase voltage and current information from sensors, and the inverter's output three-phase voltage is in the form of variable frequency pulses containing numerous harmonics, and the three-phase current is also in the form of variable frequency and contains harmonics, along with interference signals during data acquisition, the extraction and processing of these signals to determine and identify faults is crucial.

[0005] Currently, frequency converters are mainly used for fault detection. This involves simulating fault circuits within the frequency converter, extracting features from waveform data of different faults, and then building a fault database. This allows for rapid identification of the cause of a fault after it occurs. However, existing fault detection solutions cannot achieve online fault diagnosis, preventing timely intervention before a fault occurs. Typically, for ultra-high-speed magnetic levitation frequency converter power supply systems, abnormal voltage or current signals will appear before a fault occurs. Therefore, online analysis and processing of these abnormal or fault signals are necessary to address them proactively and prevent faults from happening. Summary of the Invention

[0006] This invention provides a fault identification method and device for frequency conversion power supply systems, which can solve the technical problem that existing technologies cannot achieve online fault diagnosis.

[0007] According to one aspect of the present invention, a fault identification method for a variable frequency power supply system is provided, the method comprising:

[0008] Construct a relay protection controller database; wherein, the relay protection controller database includes several normal operating conditions, each normal operating condition includes several operating data, each set of operating data includes several current and voltage data, each current and voltage data corresponds to a time-frequency matrix and a set of instantaneous values, the instantaneous values ​​include instantaneous amplitude, instantaneous phase and instantaneous frequency, and each time-frequency matrix includes normalized cross-correlation coefficients of multiple fault operating conditions;

[0009] Collect several current and voltage data from the frequency converter power supply system;

[0010] Perform a Fast Fourier Transform on each current and voltage data point to obtain the frequency component with the highest amplitude corresponding to each current and voltage data point;

[0011] A difference equation is established based on the frequency component with the highest amplitude corresponding to each current and voltage data.

[0012] Each current and voltage data point is filtered using a difference equation to obtain the filtered current and voltage data.

[0013] Perform Hilbert transform on each filtered current and voltage data to obtain the instantaneous amplitude, instantaneous phase, and instantaneous frequency of each filtered current and voltage data.

[0014] Select time-frequency matrices from the relay protection controller database that correspond to the instantaneous phases of each current and voltage data, and use the selected time-frequency matrices as the time-frequency matrices for normal operation.

[0015] Discrete wavelet transform is performed on each filtered current and voltage data. Based on the wavelet transform results, time-frequency matrices corresponding to each filtered current and voltage data are constructed. The constructed time-frequency matrices are used as the time-frequency matrices of the actual operating conditions.

[0016] The similarity between the time-frequency matrix of the actual operating condition corresponding to each filtered current and voltage data and the time-frequency matrix of the selected normal operating condition is calculated to obtain the normalized cross-correlation coefficient corresponding to each current and voltage data.

[0017] The normalized cross-correlation coefficients corresponding to each current and voltage data are compared with the normalized cross-correlation coefficients for each fault operating condition in the relay protection controller database to determine whether a fault has occurred and the type of fault.

[0018] Preferably, the normalized cross-correlation coefficient corresponding to each current and voltage data point is compared with the normalized cross-correlation coefficient for each fault operating condition in the relay protection controller database to determine whether a fault has occurred and the type of fault, including:

[0019] S101. Obtain the absolute value of the difference between the normalized cross-correlation coefficient of the current fault operation condition in the relay protection controller database and the normalized cross-correlation coefficient corresponding to each current and voltage data.

[0020] S102. Determine whether the absolute value of each difference is less than the preset value. If so, determine that the fault identification result is the current fault. Otherwise, determine that the fault identification result is not the current fault and proceed to S103.

[0021] S103. Determine whether the normalized cross-correlation coefficients for each fault operating condition in the relay protection controller database have been traversed. If yes, determine that the fault identification result is no fault. Otherwise, obtain the absolute value of the difference between the normalized cross-correlation coefficient of the next fault operating condition and the normalized cross-correlation coefficient corresponding to each current and voltage data, and go to S102.

[0022] Preferably, constructing the relay protection controller database includes:

[0023] Through offline simulation, several current and voltage data of the ultra-high speed maglev under normal operating conditions and fault operating conditions were obtained.

[0024] Discrete wavelet transforms were performed on the current and voltage data for each normal operating condition and fault operating condition, and corresponding time-frequency matrices were constructed based on the wavelet transform results.

[0025] The time-frequency matrix corresponding to the current and voltage data of each fault operating condition is compared with the time-frequency matrix corresponding to the current and voltage data of each normal operating condition to calculate the time-frequency matrix similarity, thereby obtaining the normalized cross-correlation coefficient of each fault operating condition.

[0026] Preferably, establishing a difference equation based on the frequency component with the highest amplitude corresponding to each current and voltage data includes:

[0027] The passband cutoff frequency, stopband start frequency, passband ripple, and stopband minimum attenuation of the low-pass filter are determined based on the frequency component with the highest amplitude corresponding to each current and voltage data.

[0028] The order of a low-pass filter is obtained based on its passband cutoff frequency, stopband start frequency, passband ripple, and stopband minimum attenuation.

[0029] The minimum cutoff frequency is obtained based on the passband cutoff frequency, passband ripple, and the order of the low-pass filter; the maximum cutoff frequency is obtained based on the stopband start frequency, stopband minimum attenuation, and the order of the low-pass filter.

[0030] Choose any value within the cutoff frequency range, and obtain each pole of the low-pass filter based on the selected cutoff frequency and the order of the low-pass filter.

[0031] A difference equation is established based on the selected cutoff frequency, the order of the low-pass filter, and each pole of the low-pass filter.

[0032] Preferably, the order of the low-pass filter is obtained by the following formula:

[0033]

[0034] The minimum cutoff frequency can be obtained using the following formula:

[0035]

[0036] The maximum value of the cutoff frequency is obtained by the following formula:

[0037]

[0038] Each pole of the low-pass filter is obtained using the following formula:

[0039]

[0040] The difference equation is established using the following formula:

[0041]

[0042] In the formula, N represents the order of the low-pass filter, Rp represents the passband ripple, Rs represents the minimum stopband attenuation, Wp represents the passband cutoff frequency, Ws represents the stopband start frequency, ceil represents the floor function, W1 represents the minimum cutoff frequency, W2 represents the maximum cutoff frequency, Wc represents the selected cutoff frequency, and p k Let represent the k-th pole of the low-pass filter, j represent the imaginary part, H(s) represent the transfer function, and p1, p2, ..., p... N Let represent the 1st, 2nd, ..., Nth poles of the low-pass filter, s represent the transfer function factor, T represent the discrete sampling time, and z represent the discrete factor.

[0043] Preferably, a Hilbert transform is performed on each filtered current-voltage data point to obtain the instantaneous amplitude, instantaneous phase, and instantaneous frequency of each filtered current-voltage data point, including:

[0044] Perform an n-point Discrete Fourier Transform on each filtered current and voltage data to obtain each current and voltage data after the Discrete Fourier Transform, where n is the number of data points.

[0045] Each current and voltage data after discrete Fourier transform is multiplied by a vector function and then subjected to inverse discrete Fourier transform to obtain the instantaneous amplitude, instantaneous phase, and instantaneous frequency of each filtered current and voltage data.

[0046] Preferably, the instantaneous amplitude, instantaneous phase, and instantaneous frequency of each filtered current and voltage data are obtained using the following formula:

[0047]

[0048]

[0049]

[0050] In the formula, A(t) represents the instantaneous amplitude, φ(t) represents the instantaneous phase, ω(t) represents the instantaneous frequency, and x(t) represents the filtered current and voltage data. The denot represents the imaginary part of the filtered current and voltage data, and t represents time.

[0051] Preferably, the normalized cross-correlation coefficient corresponding to each current and voltage data is obtained by the following formula:

[0052]

[0053] In the formula, S ab The normalized cross-correlation coefficients corresponding to the current and voltage data are represented by M, N, m, n, and E respectively. a (m,n) represents the time-frequency matrix under normal operating conditions, E b (m,n) represents the time-frequency matrix under actual operating conditions.

[0054] Preferably, the acquisition of several current and voltage data of the frequency converter power supply system includes: acquiring several current and voltage data of the frequency converter power supply system using a relay protection controller.

[0055] Preferably, the relay protection controller is used to collect several current and voltage data from the inverter output side and the trackside switch station output to the stator section.

[0056] According to another aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the methods described above.

[0057] Applying the technical solution of this invention, when a train is in operation, the three-phase voltage and current information collected by the voltage and current sensors of the relay protection controller is processed through signal preprocessing to obtain an effective acquisition signal. Then, a fault identification algorithm is used to obtain the instantaneous values, instantaneous phase, and instantaneous frequency of the fundamental and harmonic frequencies of the acquisition signal. This information is compared with an existing fault database to determine whether a fault has occurred and what type of fault it is, providing a basis for the relay protection controller to perform its protection actions. This method is mainly applied to high-speed magnetic levitation variable frequency power supply systems, particularly for online fault identification between the frequency converter and the motor. Attached Figure Description

[0058] The accompanying drawings, which form part of this specification, are provided to further illustrate embodiments of the invention and, together with the textual description, explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0059] Figure 1 A flowchart of a fault identification method for a frequency converter power supply system according to an embodiment of the present invention is shown;

[0060] Figure 2 A structural diagram of a traction control system according to an embodiment of the present invention is shown;

[0061] Figure 3 A schematic diagram of discrete wavelet transform according to an embodiment of the present invention is shown;

[0062] Figure 4 A schematic diagram of the time-frequency matrix for normal operation of offline simulation provided according to an embodiment of the present invention is shown;

[0063] Figure 5 A schematic diagram of the time-frequency matrix for offline simulation of fault operation is shown according to an embodiment of the present invention;

[0064] Figure 6 The diagram shows a waveform of the acquired current data provided according to an embodiment of the present invention;

[0065] Figure 7 The diagram shows a filtered current data waveform according to an embodiment of the present invention.

[0066] Figure 8a The instantaneous amplitude of the acquired current data provided according to an embodiment of the present invention is shown;

[0067] Figure 8bThe instantaneous phase of the acquired current data provided according to an embodiment of the present invention is shown;

[0068] Figure 8c The instantaneous frequency of the acquired current data provided according to an embodiment of the present invention is shown;

[0069] Figure 9 The time-frequency matrix corresponding to the acquired current data provided according to an embodiment of the present invention is shown. Detailed Implementation

[0070] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0072] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0073] like Figure 1 As shown, the present invention provides a fault identification method for a variable frequency power supply system, the method comprising:

[0074] S10. Construct a relay protection controller database; wherein, the relay protection controller database includes several normal operating conditions, each normal operating condition includes several operating data, each set of operating data includes several current and voltage data, each current and voltage data corresponds to a time-frequency matrix and a set of instantaneous values, the instantaneous values ​​include instantaneous amplitude, instantaneous phase and instantaneous frequency, and each time-frequency matrix includes normalized cross-correlation coefficients of multiple fault operating conditions;

[0075] S20: Collect several current and voltage data from the frequency converter power supply system;

[0076] S30. Perform a fast Fourier transform on each current and voltage data to obtain the frequency component with the highest amplitude corresponding to each current and voltage data.

[0077] S40. Establish a difference equation based on the frequency component with the highest amplitude corresponding to each current and voltage data.

[0078] S50. Filter each current and voltage data using the difference equation to obtain the filtered current and voltage data.

[0079] S60. Perform Hilbert transform on each filtered current and voltage data to obtain the instantaneous amplitude, instantaneous phase, and instantaneous frequency of each filtered current and voltage data.

[0080] S70. Select the time-frequency matrix corresponding to the instantaneous phase that is equal to the instantaneous phase of each current and voltage data from the relay protection controller database, and use the selected time-frequency matrices as the time-frequency matrix for normal operation.

[0081] S80. Perform discrete wavelet transform on each filtered current and voltage data respectively, and construct the time-frequency matrix corresponding to each filtered current and voltage data according to the wavelet transform results respectively. Use the constructed time-frequency matrices as the time-frequency matrix of the actual operating conditions.

[0082] S90. Calculate the similarity between the time-frequency matrix of the actual operating condition corresponding to each filtered current and voltage data and the time-frequency matrix of the selected normal operating condition to obtain the normalized cross-correlation coefficient corresponding to each current and voltage data.

[0083] S100: The normalized cross-correlation coefficient corresponding to each current and voltage data is compared with the normalized cross-correlation coefficient of each fault operating condition in the relay protection controller database to determine whether a fault has occurred and the type of fault.

[0084] When the train is in operation, the three-phase voltage and current information collected by the voltage and current sensors of the relay protection controller is preprocessed to obtain effective acquisition signals. Then, a fault identification algorithm is used to obtain the instantaneous values, instantaneous phase, and instantaneous frequency of the fundamental and harmonic frequencies of the acquisition signals. These are compared with an existing fault database to determine whether a fault has occurred and what type of fault it is, providing a basis for the relay protection controller to execute protection actions. This invention's method is mainly applied to high-speed magnetic levitation variable frequency power supply systems, particularly for online fault identification between the frequency converter and the motor.

[0085] The relay protection controllers of the traction control frequency converter power supply system are located in the trackside switchyard and on the inverter output side. Each relay protection controller is equipped with corresponding voltage and current sensors to extract voltage and current signals. Compared to other controllers in the traction control system, the relay protection controllers avoid excessively long communication distances and possess rapid execution capabilities. Therefore, they are a crucial component for the safe and reliable operation of ultra-high-speed maglev trains. The structure of its traction control system is as follows: Figure 2 As shown. When the ultra-high-speed maglev train is running, it monitors the three-phase voltage, current, and other status quantities at the inverter output and (trackside switch station output to the stator section). Based on the presence of abnormal values, it determines whether the system has experienced a fault or abnormality, and then executes the protection program.

[0086] According to one embodiment of the present invention, in S10 of the present invention, constructing the relay protection controller database includes:

[0087] S11. Through offline simulation, obtain several current and voltage data for several normal operation conditions and fault operation conditions of the ultra-high speed maglev.

[0088] S12. Perform discrete wavelet transform on the current and voltage data for each normal operating condition and fault operating condition, such as... Figure 3 As shown, the corresponding time-frequency matrices are constructed based on their respective wavelet transform results;

[0089] S13. The time-frequency matrix corresponding to the current and voltage data of each fault operating condition is compared with the time-frequency matrix corresponding to the current and voltage data of each normal operating condition to calculate the time-frequency matrix similarity, and the normalized cross-correlation coefficient of each fault operating condition is obtained.

[0090] In this embodiment, since the ultra-high-speed maglev train has various normal operating conditions, the relay protection controller database can be classified. The first level is the operating condition; the second level is the operating data, which is extracted from the relay protection controllers in the trackside switch station and on the inverter output side. Therefore, each operating condition in the first level has multiple sets of operating data; the third level is the time-frequency matrix. For each set of operating data in the second level, there are several current and voltage data. A time-frequency matrix is ​​constructed for each parameter of current and voltage data. Therefore, each set of operating data in the second level has multiple time-frequency matrices; the fourth level is the normalized cross-correlation coefficient of fault operating conditions. Since the ultra-high-speed maglev train may have various faults, different fault operating conditions are simulated offline to obtain the time-frequency matrix. For each time-frequency matrix of the normal operating condition in the third level, there are multiple time-frequency matrices of fault operating conditions. Then, similarity calculations are performed on them respectively. Therefore, each time-frequency matrix in the third level has multiple normalized cross-correlation coefficients of fault operating conditions.

[0091] According to one embodiment of the present invention, in S20 of the present invention, collecting several current and voltage data of the frequency converter power supply system includes: collecting several current and voltage data of the frequency converter power supply system using a relay protection controller.

[0092] Specifically, the relay protection controller is used to collect several current and voltage data from the inverter output side and the trackside switch station output to the stator section.

[0093] According to one embodiment of the present invention, in S30 of the present invention, establishing a difference equation based on the frequency component with the highest amplitude corresponding to each current and voltage data includes:

[0094] S31. Determine the passband cutoff frequency, stopband start frequency, passband ripple, and stopband minimum attenuation of the low-pass filter based on the frequency component with the highest amplitude corresponding to each current and voltage data.

[0095] S32. Obtain the order of the low-pass filter based on the passband cutoff frequency, stopband start frequency, passband ripple, and stopband minimum attenuation of the low-pass filter.

[0096] S33. Obtain the minimum cutoff frequency based on the passband cutoff frequency, passband ripple, and the order of the low-pass filter; obtain the maximum cutoff frequency based on the stopband start frequency, stopband minimum attenuation, and the order of the low-pass filter.

[0097] S34. Select any value within the range of cutoff frequencies, and obtain each pole of the low-pass filter based on the selected cutoff frequency and the order of the low-pass filter.

[0098] S35. Establish a difference equation based on the selected cutoff frequency, the order of the low-pass filter, and each pole of the low-pass filter.

[0099] Specifically, the order of the low-pass filter is obtained using the following formula:

[0100]

[0101] The minimum cutoff frequency can be obtained using the following formula:

[0102]

[0103] The maximum value of the cutoff frequency is obtained by the following formula:

[0104]

[0105] Each pole of the low-pass filter is obtained using the following formula:

[0106]

[0107] The difference equation is established using the following formula:

[0108]

[0109] In the formula, N represents the order of the low-pass filter, Rp represents the passband ripple (in dB), Rs represents the minimum stopband attenuation (in dB), Wp represents the passband cutoff frequency (in rad / s), Ws represents the stopband start frequency (in rad / s), ceil represents the floor function, W1 represents the minimum cutoff frequency (in rad / s), W2 represents the maximum cutoff frequency (in rad / s), Wc represents the selected cutoff frequency (in rad / s), and p k Let represent the k-th pole of the low-pass filter, j represent the imaginary part, H(s) represent the transfer function, and p1, p2, ..., p... N Let represent the 1st, 2nd, ..., Nth poles of the low-pass filter, s represent the transfer function factor, T represent the discrete sampling time, and z represent the discrete factor.

[0110] According to one embodiment of the present invention, in S60 of the present invention, performing a Hilbert transform on each filtered current-voltage data to obtain the instantaneous amplitude, instantaneous phase, and instantaneous frequency of each filtered current-voltage data includes:

[0111] S61. Perform an n-point Discrete Fourier Transform on each filtered current and voltage data to obtain each current and voltage data after the Discrete Fourier Transform, where n is the number of data.

[0112] S62. Multiply each current and voltage data after discrete Fourier transform by a vector function and then perform inverse discrete Fourier transform to obtain the instantaneous amplitude, instantaneous phase and instantaneous frequency of each filtered current and voltage data.

[0113] Specifically, the expression for the vector function is as follows:

[0114]

[0115] The instantaneous amplitude, instantaneous phase, and instantaneous frequency of each filtered current and voltage data can be obtained using the following formula:

[0116]

[0117]

[0118]

[0119] In the formula, A(t) represents the instantaneous amplitude, φ(t) represents the instantaneous phase, ω(t) represents the instantaneous frequency, and x(t) represents the filtered current and voltage data. Let represent the imaginary part of the filtered current and voltage data, t represent time, and h(i) represent the vector function.

[0120] According to one embodiment of the present invention, in S80 of the present invention, the discrete wavelet transform is performed by the following formula:

[0121] [C,L]=wavedec(x(t),m,wname)

[0122] In the formula, C is the value of the wavelet transform, L is the length, m is the wavelet series, wname is the wavelet signal used, and wavedec is the wavelet function.

[0123] According to one embodiment of the present invention, in S90 of the present invention, the normalized cross-correlation coefficient corresponding to each current and voltage data is obtained by the following formula:

[0124]

[0125] In the formula, S ab The normalized cross-correlation coefficients corresponding to the current and voltage data are represented by M, N, m, n, and E respectively. a (m,n) represents the time-frequency matrix under normal operating conditions, E b (m,n) represents the time-frequency matrix under actual operating conditions.

[0126] According to one embodiment of the present invention, in S100, the normalized cross-correlation coefficient corresponding to each current and voltage data is compared with the normalized cross-correlation coefficient of each fault operating condition in the relay protection controller database to determine whether a fault has occurred and the type of fault, including:

[0127] S101. Obtain the absolute value of the difference between the normalized cross-correlation coefficient of the current fault operation condition in the relay protection controller database and the normalized cross-correlation coefficient corresponding to each current and voltage data.

[0128] S102. Determine whether the absolute value of each difference is less than the preset value. If so, determine that the fault identification result is the current fault. Otherwise, determine that the fault identification result is not the current fault and proceed to S103.

[0129] S103. Determine whether the normalized cross-correlation coefficients for each fault operating condition in the relay protection controller database have been traversed. If yes, determine that the fault identification result is no fault. Otherwise, obtain the absolute value of the difference between the normalized cross-correlation coefficient of the next fault operating condition and the normalized cross-correlation coefficient corresponding to each current and voltage data, and go to S102.

[0130] For example, when fault A occurs, the normalized cross-correlation coefficient S corresponding to the simulated voltage and current data can be obtained. A_k Where k = 1, 2, ..., n, these normalized cross-correlation coefficients may deviate from their normal values. Therefore, the normalized cross-correlation coefficients S obtained in actual operation... ab_k Where k = 1, 2, ..., n, if there is a value that deviates from the normal value, a fault is considered to have occurred. Then, a database search is performed, and if the value deviates from the normal value by S... ab_k With S A_k If the values ​​can be matched and are within the allowable error range, then fault A is considered to have occurred.

[0131] During the operation of ultra-high-speed maglev trains, the relay protection controller of the traction control system needs to monitor the voltage and current online in real time. Through signal processing and data analysis within the controller, online diagnostics are achieved during operation, providing a basis for the controller to execute safety protection measures and addressing issues before they occur.

[0132] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the methods described above.

[0133] To gain a further understanding of the present invention, the following description is provided in conjunction with... Figures 4-9 The fault identification method for frequency conversion power supply systems of the present invention will be described in detail.

[0134] In this embodiment, taking the acquisition of current data as an example, the specific fault identification method is as follows:

[0135] Step 1: Obtain waveform data for normal operation and fault operation conditions through offline simulation, and construct the time-frequency matrix for both conditions, such as... Figures 4-5 As shown, the normalized cross-correlation coefficient is obtained, and the waveform data of normal operation, the time-frequency matrix, and the normalized cross-correlation coefficient are stored in the relay protection controller database.

[0136] Step 2: The current data collected by the relay protection controller is as follows Figure 6 As shown;

[0137] Step 3: Obtain the main frequency through FFT, design the filter transfer function, and derive the filter's difference equation;

[0138] Step 4: Filter the collected current data. The filtered current data is as follows: Figure 7 As shown;

[0139] Step 5: Perform Hilbert transform on the filtered current data to obtain the instantaneous value, as shown in Figure 8;

[0140] Step 6: Construct a time-frequency matrix from the filtered current data, such as... Figure 9 As shown, the normalized cross-correlation coefficients are obtained by comparing the similarity between the time-frequency matrix of the offline simulation and the time-frequency matrix of the offline simulation running normally. The two are similar, indicating that there is no fault. However, the normalized cross-correlation coefficients obtained by comparing the similarity between the time-frequency matrix of the offline simulation running with the time-frequency matrix of the offline simulation running normally are very different from the former.

[0141] Step 7: The fault diagnosis is complete. If there is no fault, proceed to the next fault diagnosis.

[0142] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the methods described above.

[0143] In summary, this invention provides a fault identification method and device for variable frequency power supply systems. The method collects three-phase voltage and current information from voltage and current sensors of the relay protection controller, preprocesses the signals to obtain effective acquisition signals, and then uses a fault identification algorithm to obtain the instantaneous values, instantaneous phase, and instantaneous frequency of the fundamental and harmonic frequencies of the acquisition signals. These are then compared with an existing fault database to determine whether a fault has occurred and what type of fault it is, providing a basis for the relay protection controller to perform protection actions. This invention is mainly applied to high-speed magnetic levitation variable frequency power supply systems, particularly for online fault identification of the frequency converter-motor system.

[0144] The parts of this invention not described in detail are techniques known to those skilled in the art.

[0145] In the description of this invention, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is generally based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this invention and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this invention; the directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0146] For ease of description, spatial relative terms such as "above," "on top of," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation beyond the orientation of the device as described in the figures. For example, if the device in the figures were inverted, a device described as "above" or "on top of" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0147] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, the above terms have no special meaning and therefore should not be construed as limiting the scope of protection of this invention.

[0148] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for fault identification for a variable frequency power supply system, characterized by, The method comprises: constructing a relay protection controller database; wherein the relay protection controller database comprises a plurality of normal operating conditions, each normal operating condition comprises a plurality of operating data, each set of operating data comprises a plurality of current and voltage data, each current and voltage data corresponds to a time-frequency matrix and a set of instantaneous values, the instantaneous values comprise instantaneous amplitude, instantaneous phase and instantaneous frequency, and each time-frequency matrix comprises a plurality of normalized cross-correlation coefficients of fault operating conditions; collecting a plurality of current and voltage data of the variable frequency power supply system; performing fast Fourier transform on each current and voltage data to obtain a frequency component with the highest amplitude corresponding to each current and voltage data; establishing a difference equation based on the frequency component with the highest amplitude corresponding to each current and voltage data; filtering each current and voltage data by using the difference equation to obtain filtered current and voltage data; performing Hilbert transform on each filtered current and voltage data to obtain the instantaneous amplitude, instantaneous phase and instantaneous frequency of each filtered current and voltage data; selecting, from the relay protection controller database, time-frequency matrices corresponding to the instantaneous phases equal to the instantaneous phase of each current and voltage data, and taking the selected time-frequency matrices as time-frequency matrices of normal operating conditions; performing discrete wavelet transform on each filtered current and voltage data, and constructing a time-frequency matrix corresponding to each filtered current and voltage data according to the respective wavelet transform results, and taking the constructed time-frequency matrices as time-frequency matrices of actual operating conditions; performing similarity calculation on the time-frequency matrices of actual operating conditions corresponding to each filtered current and voltage data and the selected time-frequency matrices of normal operating conditions to obtain the normalized cross-correlation coefficients corresponding to each current and voltage data; comparing the normalized cross-correlation coefficients corresponding to each current and voltage data with the normalized cross-correlation coefficients of each fault operating condition in the relay protection controller database respectively, so as to judge whether a fault occurs and the type of the fault.

2. The method of claim 1, wherein, comparing the normalized cross-correlation coefficients corresponding to each current and voltage data with the normalized cross-correlation coefficients of each fault operating condition in the relay protection controller database respectively, so as to judge whether a fault occurs and the type of the fault comprises: S101, obtaining the absolute values of the differences between the normalized cross-correlation coefficients of the current fault operating condition in the relay protection controller database and the normalized cross-correlation coefficients corresponding to each current and voltage data; S102, judging whether the absolute values of each difference are less than a preset value, if yes, judging that the fault identification result is the current fault, otherwise, judging that the fault identification result is not the current fault, and turning to S103; S103, judging whether the normalized cross-correlation coefficients of each fault operating condition in the relay protection controller database have been traversed, if yes, judging that the fault identification result is no fault, otherwise, obtaining the absolute values of the differences between the normalized cross-correlation coefficients of the next fault operating condition and the normalized cross-correlation coefficients corresponding to each current and voltage data, and turning to S102.

3. The method of claim 1, wherein, constructing a relay protection controller database comprises: Obtaining current and voltage data of several normal operation conditions and fault operation conditions of the super-speed magnetic suspension through offline simulation; Discrete wavelet transform is performed on the current and voltage data of each normal operation condition and fault operation condition, and a corresponding time-frequency matrix is constructed according to the respective wavelet transform results; Time-frequency matrix similarity calculation is performed on the time-frequency matrix corresponding to the current and voltage data of each fault operation condition and the time-frequency matrix corresponding to the current and voltage data of each normal operation condition, to obtain a normalized cross-correlation coefficient of each fault operation condition.

4. The method of claim 1, wherein, The establishment of the difference equation based on the frequency component with the highest amplitude of each current and voltage data includes: Based on the frequency component with the highest amplitude of each current and voltage data, the passband cutoff frequency, the stopband starting frequency, the passband fluctuation and the stopband minimum attenuation of the low-pass filter are determined; Based on the passband cutoff frequency, the stopband starting frequency, the passband fluctuation and the stopband minimum attenuation of the low-pass filter, the order of the low-pass filter is obtained; Based on the passband cutoff frequency, the passband fluctuation and the order of the low-pass filter, the minimum value of the cutoff frequency is obtained, and based on the stopband starting frequency, the stopband minimum attenuation and the order of the low-pass filter, the maximum value of the cutoff frequency is obtained; Within the range of the cutoff frequency, any value is selected, and based on the selected cutoff frequency, the order of the low-pass filter, each pole of the low-pass filter is obtained; The establishment of the difference equation based on the selected cutoff frequency, the order of the low-pass filter and each pole of the low-pass filter.

5. The method according to any one of claims 1 to 4, characterized in that, The order of the low-pass filter is obtained by the following formula: The minimum value of the cutoff frequency is obtained by the following formula: The maximum value of the cutoff frequency is obtained by the following formula: Each pole of the low-pass filter is obtained by the following formula: The difference equation is established by the following formula: In the formula, N represents the order of the low-pass filter, Rp represents the passband fluctuation, Rs represents the minimum attenuation of the stop band, Wp represents the passband cutoff frequency, Ws represents the start frequency of the stop band, ceil represents the rounding function, W1 represents the minimum value of the cutoff frequency, W2 represents the maximum value of the cutoff frequency, Wc represents the selected cutoff frequency, p k represents the kth pole of the low-pass filter, j represents the imaginary part, H(s) represents the transfer function, p1, p2, …, p N respectively represent the 1st, 2nd, …, Nth poles of the low-pass filter, s represents the transfer function factor, T represents the discrete sampling time, and z represents the discrete factor.

6. The method of claim 1, wherein, The Hilbert transform is performed on each filtered current and voltage data to obtain the instantaneous amplitude, instantaneous phase and instantaneous frequency of each filtered current and voltage data, including: The n-point discrete Fourier transform is performed on each filtered current and voltage data to obtain each current and voltage data after discrete Fourier transform, and n is the number of data; The instantaneous amplitude, instantaneous phase and instantaneous frequency of each filtered current and voltage data are obtained by multiplying each current and voltage data after discrete Fourier transform by a vector function and then performing inverse discrete Fourier transform.

7. The method of claim 6, wherein, The instantaneous amplitude, instantaneous phase and instantaneous frequency of each filtered current and voltage data are obtained by the following formula: In the formula, A(t) represents an instantaneous amplitude, φ(t) represents an instantaneous phase, ω(t) represents an instantaneous frequency, x(t) represents filtered current-voltage data, represents an imaginary part of the filtered current-voltage data, and t represents time.

8. The method of claim 1, wherein, The normalized cross-correlation coefficient corresponding to each current and voltage data is obtained by the following formula: In the formula, S ab The normalized cross-correlation coefficient corresponding to the current current-voltage data is represented by S a The time-frequency matrix of normal operation condition is represented by E b The time-frequency matrix of actual operation condition is represented by E 9. The method of claim 1, wherein, The current and voltage data of the frequency conversion power supply system are collected, including collecting the current and voltage data of the frequency conversion power supply system by a relay protection controller.

10. The method of claim 9, wherein, The relay protection controller is used to collect the current and voltage data of the output side of the inverter and the output of the trackside switch station to the stator section.

11. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the method of any one of claims 1 to 10. The processor executes the computer program to realize the method of any one of claims 1 to 10.

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