A brushless doubly-fed machine power winding inter-turn short circuit fault diagnosis method and system

Abstract

The application provides a brushless doubly-fed motor power winding inter-turn short circuit fault diagnosis method and system, and belongs to the technical field of driving equipment, which comprises the following steps: obtaining the fault characteristic frequency corresponding to each winding according to the basic electromagnetic relationship of the brushless doubly-fed motor; obtaining the signal of the fault phase; performing Fourier decomposition on the signal of the fault phase to obtain the current fundamental content of the current signal; performing discrete wavelet transform on the signal of the fault phase, extracting the detail signal according to the detail signal layer where the fault characteristic frequency is located, and performing Fourier transform again to extract the fault characteristic signal, thereby obtaining the harmonic content of the fault characteristic signal; when the current fundamental content of the power winding increases and the signal harmonic content obtained through the discrete wavelet transform increases, it is determined that the power winding has an inter-turn short circuit fault. The method utilizes the converter signal on the control winding side to diagnose the inter-turn short circuit fault of the power winding of the brushless doubly-fed motor without adding additional sensors, and effectively deals with the winding insulation fault of the motor caused by high voltage.

Description

Technical Field

[0001] This invention relates to the field of drive equipment technology, specifically to a method and system for diagnosing inter-turn short-circuit faults in the power windings of a brushless doubly fed motor. Background Technology

[0002] Brushless doubly fed motors eliminate brushes and slip rings, and can achieve dual-port AC excitation through two sets of independent stator windings. They feature the characteristic of driving high-voltage, high-capacity motors with low-voltage, small-capacity converters, and have high reliability and high system transmission efficiency. They have broad application prospects in the field of high-voltage explosion-proof drives.

[0003] However, the power windings of brushless doubly fed motors need to be directly connected to the high-voltage power grid. The insulation of the windings is subjected to high voltage for a long time, which poses a significant risk of insulation failure. This can easily cause inter-turn short circuits in the windings, which not only cause large short-circuit currents but also have a great impact on the electrical performance of the motor. It may even damage the entire coil and the motor core, affecting the normal operation of the equipment. Therefore, there is an urgent need for a method to diagnose inter-turn short circuit faults in the power windings. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides a method and system for diagnosing inter-turn short-circuit faults in the power winding of a brushless doubly-fed motor. By utilizing the unique electromagnetic coupling principle of the brushless doubly-fed motor, the method diagnoses inter-turn short-circuit faults in the power winding of the brushless doubly-fed motor using the converter signal on the control winding side without the need for additional sensors. This effectively addresses winding insulation faults caused by high voltage in this type of motor.

[0005] To achieve the above objectives, the present invention provides the following technical solution.

[0006] This invention provides a method for diagnosing inter-turn short-circuit faults in the power windings of a brushless doubly-fed motor, comprising the following steps:

[0007] Based on the basic electromagnetic relationship of a brushless doubly fed motor, the fault characteristic frequency caused by the fault characteristic frequency of the power winding on the control winding side when an inter-turn short circuit fault occurs in the high-voltage insulation of the power winding is obtained.

[0008] Connect the converter to the control winding of the brushless doubly fed motor to be diagnosed, and obtain a current signal that varies with time in the converter when the motor is running smoothly. This current signal is the valid signal.

[0009] The root mean square value of the current signal is obtained as the effective value. The phase with the increased effective value among all phases is identified as the fault phase, and the signal of the fault phase of the current signal is extracted.

[0010] Fourier decomposition is performed on the signal of the faulty phase to obtain the fundamental current content of the current signal;

[0011] Discrete wavelet transform is performed on the signal of the fault phase. The detail signal is extracted according to the detail signal layer where the fault characteristic frequency is located. Then, Fourier transform is performed again to extract the fault characteristic signal and obtain the harmonic content of the fault characteristic signal.

[0012] When the fundamental current content of the power winding increases and the harmonic content of the fault characteristic signal obtained by discrete wavelet transform increases, it is determined that an inter-turn short circuit fault has occurred in the power winding, and the severity of the inter-turn short circuit in the power winding is determined based on the harmonic content.

[0013] Preferably, the fault characteristic frequency caused by the power winding fault on the control winding side is (n / 60)(p p +p c )-kf p Among them, kf p The fault characteristic frequency of the power winding is n, where n is the motor rotor speed, k = ±1, ±3, ±5…, p p and p c These are the number of pole pairs for the power winding and the control winding, respectively.

[0014] Preferably, the effective value is calculated as shown in the following formula:

[0015]

[0016] Among them, I rms Let i(t) be the effective value of the current signal, i(t) be the signal of current changing with time, and m be the number of discrete current samples collected within the time period.

[0017] Preferably, the discrete Fourier decomposition coefficients are as follows:

[0018]

[0019] Where m is the number of discrete current samples collected within the corresponding time period; i(k) is the time-domain signal; and λ is the frequency index.

[0020] Preferably, obtaining the harmonic content of the fault characteristic signal includes the following steps:

[0021] The electromagnetic characteristics of a short-circuit fault are decomposed into layers, and the number of layers n is determined as shown in the following formula:

[0022]

[0023] Among them, f n It is the extracted component frequency, and f is the signal sampling frequency;

[0024] The frequencies are layered as shown in the following formula:

[0025]

[0026] The mother wavelet is determined based on the conditions of high peak signal-to-noise ratio, low mean square error, and maximum tolerance.

[0027] Determine the scaling function φ(t) and the wavelet function ψ(t):

[0028]

[0029] Where h(k) and g(k) are low-pass and high-pass filters, respectively;

[0030] Discrete wavelet transform is based on multi-resolution analysis, decomposing the signal into multiple levels corresponding to different frequency bandwidths. At each level, the signal is divided into approximation coefficients and detail coefficients.

[0031]

[0032] In each level of decomposition, the signal i(t) is represented as:

[0033]

[0034] Where n is the number of decomposition layers, A n It is an approximate signal of the nth layer, D j It is the detail signal of the j-th layer;

[0035] Discrete wavelet transform is performed based on the mother wavelet. The detail signal is extracted according to the detail signal layer where the fault characteristic frequency is located, and then Fourier transform is performed again to extract the fault characteristic signal.

[0036] Preferably, the mother wavelet is a Daubechies wavelet.

[0037] A brushless doubly-fed motor power winding inter-turn short-circuit fault diagnosis system includes:

[0038] processor;

[0039] A memory on which computer programs that can run on the processor are stored;

[0040] When the computer program is executed by the processor, it implements the steps of the method for diagnosing inter-turn short-circuit faults in the power winding of a brushless doubly fed motor.

[0041] The beneficial effects of this invention are:

[0042] This invention proposes a method and system for diagnosing inter-turn short-circuit faults in the power winding of a brushless doubly-fed motor. This method analyzes the converter current signal on the control winding side of the brushless doubly-fed motor. Even in the case of missing power winding current, it calculates the fault characteristic frequency of the control winding current directly connected to the converter during an inter-turn short-circuit fault by leveraging the electromagnetic coupling relationship of the brushless doubly-fed motor. It then extracts relatively weak fault signals using data processing methods such as discrete wavelet transform, and determines the inter-turn short-circuit fault through comprehensive comparison. This invention addresses the potential problem of high power winding voltage and susceptibility to insulation faults in brushless doubly-fed explosion-proof motor systems. It eliminates the need for electrical signal sensors on the high-voltage winding side, significantly improving system reliability while effectively reducing system costs. Attached Figure Description

[0043] Figure 1 This is a flowchart of the fault diagnosis process for inter-turn short circuit in the power winding of a brushless doubly fed explosion-proof drive motor based on the control winding converter signal, according to an embodiment of the present invention.

[0044] Figure 2 This is a structural block diagram of a brushless doubly fed explosion-proof drive motor system according to an embodiment of the present invention;

[0045] Figure 3 This is a time-domain diagram of the wavelet decomposition of the fault current and normal current signals obtained by discrete wavelet transform according to an embodiment of the present invention.

[0046] Figure 4 This is a Fourier analysis result of the detail signal of layer D4 in an embodiment of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0048] Example 1

[0049] This invention provides a method for diagnosing inter-turn short circuit faults in the power winding of a brushless doubly fed explosion-proof drive motor. By utilizing the special electromagnetic coupling principle of the brushless doubly fed motor, the method diagnoses inter-turn short circuit faults in the power winding of the brushless doubly fed motor using the converter signal on the control winding side without the need for additional sensors. This effectively addresses winding insulation faults caused by high voltage in this type of motor.

[0050] Figure 2The present invention provides a structural block diagram of a brushless doubly-fed explosion-proof drive motor system, including a brushless doubly-fed motor, a converter, and a transformer. The brushless doubly-fed motor includes a stator and a rotor. The stator of the brushless doubly-fed motor has two independent windings: a power winding and a control winding. The power winding is directly connected to the high-voltage power grid, while the control winding is connected in series with the converter and connected to the power grid through the transformer. Therefore, the voltage of the control winding is generally lower, and the current signal in the control winding is consistent with the current signal in the converter.

[0051] Figure 1 The flowchart illustrates a method for diagnosing inter-turn short-circuit faults in the power windings of a brushless doubly-fed motor, specifically including the following steps:

[0052] S1: Based on the basic electromagnetic relationships of a brushless doubly-fed motor, obtain the fault characteristic frequency kf of the power winding when an inter-turn short-circuit fault occurs in the high-voltage insulation of the power winding. p The fault characteristic frequency caused on the control winding side is (n / 60)(p p +p c )-kf p Where n is the rotor speed of the motor, k = ±1, ±3, ±5…, p p and p c These are the number of pole pairs for the power winding and the control winding, respectively.

[0053] The aforementioned fault characteristic frequencies can serve as an important basis for determining inter-turn short-circuit faults. For power windings directly connected to the power grid, the fundamental frequency is 50Hz, therefore the characteristic frequency on the power winding side is mainly k50Hz, and the fault characteristic frequency of the control winding is [(n / 60)(p p +p c )-k50]Hz.

[0054] S2: Connect the converter to the control winding of the brushless doubly fed motor to be diagnosed, and obtain a current signal that varies with time in the converter when the motor is running smoothly. This current signal is a valid signal.

[0055] S3: Obtain the root mean square (RMS) value of the current signal as the effective value. When the RMS value of one phase increases, it is determined that an inter-turn short circuit fault may occur on the power winding side. Therefore, the phase with the increased RMS value is taken as the fault phase, and the signal of the fault phase of the current signal is extracted.

[0056] The effective value is calculated as shown in the following formula:

[0057]

[0058] Among them, I rms Let i(t) be the effective value of the current signal, i(t) be the signal of current changing with time, and m be the number of discrete current samples collected within the time period.

[0059] S4: Perform Fourier decomposition on the signal of the faulty phase to obtain the fundamental current content of the current signal.

[0060] The discrete Fourier decomposition coefficients are shown in the following equation:

[0061]

[0062] Where m is the number of discrete current samples collected within the corresponding time period; i(k) is the time-domain signal; and λ is the frequency index.

[0063] S5: Due to the influence of noise and harmonics, the harmonic spectrum obtained by Fourier transform of the inter-turn short circuit fault of the brushless doubly fed motor may not be obvious. It is necessary to further adopt the method based on discrete wavelet transform to decompose the electromagnetic characteristics of the short circuit fault into layers, and then use Fourier transform to effectively detect the fault characteristics.

[0064] Specifically, it includes:

[0065] S5.1: Decompose the electromagnetic characteristics of short-circuit faults into layers and determine the number of layers n, as shown in the following formula:

[0066]

[0067] Among them, f n is the extracted component frequency, and f is the signal sampling frequency.

[0068] The frequencies are layered as shown in the following formula:

[0069]

[0070] For data with a sampling frequency of 1250Hz, when the lowest fault frequency is around 50Hz, the number of decomposition layers can be set to 5. The specific signal frequency band division details are shown in Table 1.

[0071] Table 1 Detailed Signal Band Division

[0072]

[0073]

[0074] S5.2: Determine the mother wavelet based on the conditions of high peak signal-to-noise ratio, low mean square error and maximum tolerance to provide good time and frequency localization.

[0075] For the control winding current of a brushless doubly fed motor, the Daubechies wavelet is often chosen as the mother wavelet, as it can provide good time and frequency localization. Specifically, db30 is chosen as the mother wavelet for discrete wavelet transform.

[0076] S5.3: Determine the scaling function φ(t) and the wavelet function ψ(t):

[0077]

[0078] Where h(k) and g(k) are low-pass and high-pass filters, respectively.

[0079] S5.4: Discrete wavelet transform is based on multi-resolution analysis, decomposing it into multiple levels corresponding to different frequency bandwidths. At each level, the signal is divided into approximation coefficients and detail coefficients.

[0080] a j+1 [k]=∑ n (h[n-2k]a j [n])

[0081]

[0082] S5.5: In each level of decomposition, the signal i(t) is represented as:

[0083]

[0084] Where n is the number of decomposition layers, A n It is an approximate signal of the nth layer, D j This is the detail signal of the j-th layer. S5.6: Based on the mother wavelet, perform discrete wavelet transform, extract the detail signal according to the detail signal layer where the fault characteristic frequency is located, and perform Fourier transform again to extract the fault characteristic signal.

[0085] For a fault frequency of 50Hz, the wavelet decomposition time-domain plots of the fault current and the normal current signal obtained by discrete wavelet transform are compared as follows: Figure 3 As shown, in layer D4, it can be seen that the waveforms and amplitudes in the fault time domain and normal time domain differ significantly. Extracting the detail signal D4 and performing a Fourier transform, the spectrum is as follows... Figure 4 As shown.

[0086] S6: By combining the results of direct Fourier analysis and discrete wavelet transform analysis, when the fundamental current content of the power winding increases and the harmonic content of the fault characteristic signal obtained by discrete wavelet transform increases, it is determined that an inter-turn short circuit fault has occurred in the power winding, and the severity of the inter-turn short circuit in the power winding is determined based on the harmonic content.

[0087] The above is an embodiment of the brushless doubly-fed induction generator (DFIG) power winding inter-turn short-circuit fault diagnosis method. Based on the same idea, this embodiment also provides a corresponding brushless DFIG power winding inter-turn short-circuit fault diagnosis system. Specific limitations of the brushless DFIG power winding inter-turn short-circuit fault diagnosis system can be found in the limitations of the brushless DFIG power winding inter-turn short-circuit fault diagnosis method described above, and will not be repeated here. Each module in the above-described brushless DFIG power winding inter-turn short-circuit fault diagnosis system can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in hardware or independently of the processor in a computer device, or stored in software in the memory of a computer device, so that the processor can call and execute the operations corresponding to each module.

[0088] This embodiment also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 A method for diagnosing inter-turn short-circuit faults in the power windings of a brushless doubly fed motor is provided.

[0089] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0090] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method of diagnosing a power winding inter-turn short circuit fault in a brushless doubly-fed machine, characterized in that, The method comprises the following steps: According to the basic electromagnetic relationship of the brushless doubly-fed motor, the fault characteristic frequency caused by the power winding fault characteristic frequency on the control winding side when the turn-to-turn short circuit fault occurs in the high-voltage insulation of the power winding is obtained; The converter is electrically connected with the control winding of the brushless doubly-fed motor to be diagnosed, and a current signal in the converter varying with time is obtained when the motor runs stably, and the current signal is an effective signal; The root mean square value of the current signal is obtained as an effective value, one phase with an increased effective value is taken as a fault phase, and the signal of the fault phase of the current signal is extracted; Fourier decomposition is performed on the signal of the fault phase to obtain the current fundamental content of the current signal; Discrete wavelet transform is performed on the signal of the fault phase, the detail signal is extracted according to the detail signal layer where the fault characteristic frequency is located, and Fourier transform is performed again to extract the fault characteristic signal, thereby obtaining the harmonic content of the fault characteristic signal; When the current fundamental content of the power winding increases and the harmonic content of the fault characteristic signal obtained through the discrete wavelet transform increases, it is determined that the turn-to-turn short circuit fault occurs in the power winding, and the severity of the turn-to-turn short circuit fault in the power winding is determined according to the harmonic content.

2. The method of claim 1, wherein, The power winding fault characteristic frequency causes a fault characteristic frequency at the control winding side of (n / 60)(p p + k c f p ), where kf p is the fault characteristic frequency of the power winding, n is the motor rotor speed, k = ±1, ±3, ±5…, p p and p c are the pole pair numbers of the power winding and the control winding, respectively.

3. The method of claim 1, wherein the method further comprises: The effective value is solved as shown in the following formula: where I rms is the effective value of the current signal, i(t) is the signal of the current change over time, and m corresponds to the number of sample collections of discrete currents in the time period.

4. The method of claim 1, wherein, The discrete Fourier decomposition coefficient is shown in the following formula: Wherein, m is the sample collection quantity of the discrete current in the corresponding time period; i(k) is a time domain signal, and λ is a frequency index.

5. The method of claim 1, wherein, The harmonic content of the fault characteristic signal is obtained, comprising the following steps: The electromagnetic characteristics of the short circuit fault are hierarchically decomposed to determine the decomposition layer number n, as shown in the following formula: where f n is the extracted component frequency, f is the sampling frequency of the signal; The frequency is layered, as shown in the following formula: The mother wavelet is determined according to the conditions of high peak value signal-to-noise ratio, low mean square error and maximum tolerance; The scale function φ(t) and the wavelet function ψ(t) are determined: Wherein, h(k) and g(k) are low-pass and high-pass filters respectively; The discrete wavelet transform is based on multi-resolution analysis, and is decomposed into multiple levels corresponding to different frequency bandwidths, and at each level, the signal is divided into approximation coefficients and detail coefficients: a j+1 [k] = ∑ n (h[n - 2k]a j [n]) In each level of decomposition, the signal i(t) is expressed as: wherein n is the number of decomposition layers, A n is the approximation signal of the n-th layer, D j is the detail signal of the j-th layer; The discrete wavelet transform is performed based on the mother wavelet, the detail signal is extracted according to the detail signal layer where the fault characteristic frequency is located, and Fourier transform is performed again to extract the fault characteristic signal.

6. The method of claim 5, wherein the method further comprises: The mother wavelet can be selected as a Daubechies wavelet.

7. A brushless doubly-fed machine power winding inter-turn short circuit fault diagnostic system characterised in that, It comprises: a processor; a memory having a computer program stored thereon and executable on the processor; When the computer program is executed by the processor, the steps of the brushless doubly-fed motor power winding turn-to-turn short circuit fault diagnosis method in any one of claims 1 to 6 are implemented.

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