Brushless doubly-fed motor power winding turn-to-turn short circuit fault diagnosis method and system

Abstract

The invention provides a brushless doubly-fed motor power winding turn-to-turn short circuit fault diagnosis method and system, and belongs to the technical field of driving equipment, and the method comprises the steps: obtaining the fault characteristic frequency corresponding to each winding according to the basic electromagnetic relation of a brushless doubly-fed motor; acquiring a signal of a fault phase; fourier decomposition is carried out on the signal of the fault phase, and the current fundamental wave content of the current signal is obtained; discrete wavelet transform is carried out on the signal of the fault phase, a detail signal is extracted according to a detail signal layer where the fault characteristic frequency is located, Fourier transform is carried out again to extract a fault characteristic signal, and the harmonic content of the fault characteristic signal is obtained; and when the current fundamental wave content of the power winding is increased and the harmonic content of the signal obtained through discrete wavelet transform is increased, determining that the turn-to-turn short circuit fault occurs in the power winding. According to the method, the turn-to-turn short circuit fault of the brushless doubly-fed motor power winding is diagnosed by using a converter signal of a control winding side on the premise of not adding an additional sensor, and the winding insulation fault of the motor caused by relatively high voltage is effectively handled.

Description

Technical Field

[0001] The present invention relates to the technical field of drive equipment, and in particular to a method and system for diagnosing inter-turn short-circuit faults of power windings of a brushless double-fed motor. Background Art

[0002] The brushless doubly-fed motor eliminates the brush slip ring and can achieve dual-port AC excitation through two sets of independent stator windings. It has the characteristics of driving high-voltage and large-capacity motors with low-voltage and small-capacity converters, and has high reliability and high system transmission efficiency. It has broad application prospects in the field of high-voltage explosion-proof drives.

[0003] However, since the power winding of the brushless doubly-fed motor needs to be directly connected to the high-voltage power grid, the insulation of its winding is subjected to high voltage for a long time, and there is a great risk of insulation failure, which is particularly prone to cause inter-turn short circuit of the winding. It will not only cause a large short-circuit current, but also have a great impact on the electrical performance of the motor, and it is very likely to cause damage to the entire coil and the motor core, affecting the normal operation of the equipment. There is an urgent need for a power winding inter-turn short circuit fault diagnosis method. Summary of the invention

[0004] To solve the above problems, the present invention provides a method and system for diagnosing the inter-turn short-circuit fault of the power winding of a brushless doubly-fed motor. Through the special electromagnetic coupling principle of the brushless doubly-fed motor, the inter-turn short-circuit fault of the power winding of the brushless doubly-fed motor is diagnosed by using the converter signal on the control winding side without adding additional sensors, thereby effectively dealing with the winding insulation fault of this type of motor caused by high voltage.

[0005] To achieve the above object, the present invention provides the following technical solutions.

[0006] The present invention provides a method for diagnosing a short-circuit fault in a power winding of a brushless double-fed motor, comprising the following steps:

[0007] 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 high-voltage insulation of the power winding has a turn-to-turn short circuit fault is obtained;

[0008] The converter is electrically connected to the control winding of the brushless double-fed motor to be diagnosed, and a current signal of a section of the converter that changes with time when the motor is running in a stable state is obtained, and the current signal of the section is a valid signal;

[0009] The root mean square value of the current signal is obtained as an effective value, the phase with an increased effective value of each phase is taken as a fault phase, and the signal of the fault phase of the current signal is extracted;

[0010] Perform Fourier decomposition on the signal of the fault phase to obtain the current fundamental content of the current signal;

[0011] Perform discrete wavelet transform on the signal of the fault phase, 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 to obtain the harmonic content of the fault characteristic signal;

[0012] When the current fundamental 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 a turn-to-turn short circuit fault occurs in the power winding, and the severity of the turn-to-turn short circuit of the power winding is determined based on the harmonic content.

[0013] Preferably, the fault characteristic frequency caused by the power winding fault characteristic frequency on the control winding side is (n / 60)(p p +p c )-kf 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 pairs of the power winding and the control winding respectively.

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

[0015]

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

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

[0018]

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

[0020] Preferably, the acquisition of the harmonic content of the fault characteristic signal comprises the following steps:

[0021] Decompose the electromagnetic characteristics of the short-circuit fault in layers and determine the number of decomposition layers n, as shown in the following formula:

[0022]

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

[0024] The frequencies are layered as shown below:

[0025]

[0026] The mother wavelet is determined according to 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] Among them, h(k) and g(k) are low-pass and high-pass filters respectively;

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

[0031]

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

[0033]

[0034] Among them, n is the number of decomposition levels, A n is the approximate signal of the nth layer, D j is the detail signal of the jth layer;

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

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

[0037] A brushless double-fed motor power winding turn-to-turn short-circuit fault diagnosis system, comprising:

[0038] processor;

[0039] a memory having stored thereon a computer program executable on the processor;

[0040] Wherein, when the computer program is executed by the processor, the steps of the brushless double-fed motor power winding turn-to-turn short-circuit fault diagnosis method are implemented.

[0041] Beneficial effects of the present invention:

[0042] The present invention proposes a method and system for diagnosing short-circuit faults in power windings of brushless double-fed motors. The method uses the converter current signal on the control winding side of the brushless double-fed motor for analysis. When the power winding current is missing, the characteristic frequency of the control winding current fault directly connected to the converter can be calculated through the electromagnetic coupling relationship of the brushless double-fed motor when the power winding short-circuit fault occurs. The relatively weak fault signal is extracted through data processing methods such as discrete wavelet transform, and the turn-to-turn short-circuit fault is determined through comprehensive comparison. The present invention is proposed to address the potential problem of high voltage of the power winding in the brushless double-fed explosion-proof motor system and the easy occurrence of insulation faults. It is unnecessary to install an electrical signal sensor on the high-voltage winding side, and the system reliability is greatly improved under the premise of effectively reducing the system cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of the diagnosis of short-circuit fault between turns of power winding of brushless double-fed explosion-proof drive motor based on control winding converter signal according to an embodiment of the present invention;

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

[0045] Figure 3 is a wavelet decomposition time domain diagram of a fault current and a normal current signal obtained by discrete wavelet transform in an embodiment of the present invention;

[0046] Figure 4 4 is a diagram showing the Fourier analysis result of the D4 layer detail signal according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0048] Example 1

[0049] The present invention provides a method for diagnosing the inter-turn short-circuit fault of the power winding of a brushless double-fed explosion-proof drive motor. By using the special electromagnetic coupling principle of the brushless double-fed motor, the inter-turn short-circuit fault of the power winding of the brushless double-fed motor is diagnosed by using the converter signal on the control winding side without adding an additional sensor, so as to effectively deal with the winding insulation fault of this type of motor caused by the high voltage.

[0050] Figure 2A structural block diagram of a brushless doubly-fed explosion-proof drive motor system provided for an embodiment of the present invention includes a brushless doubly-fed motor, a converter, and a transformer; wherein the brushless doubly-fed motor includes a stator and a rotor; the stator of the brushless doubly-fed motor has two sets of independent windings, namely a power winding and a control winding, wherein the power winding is directly connected to the high-voltage power grid, and the control winding is connected in series with the converter and connected to the power grid through the transformer, so the control winding generally has a lower voltage, and the current signal in the control winding is consistent with the current signal in the converter.

[0051] Figure 1 The present invention is a flow chart of a method for diagnosing a short-circuit fault in a power winding of a brushless double-fed motor, which specifically includes the following steps:

[0052] S1: According to the basic electromagnetic relationship of the brushless doubly-fed motor, obtain the power winding fault characteristic frequency kf when the high-voltage insulation of the power winding has a turn-to-turn short circuit fault p The characteristic frequency of the fault caused on the control winding side is (n / 60)(p p +p c )-kf p , where n is the motor rotor speed, k = ±1, ±3, ±5…, p p and p c are the pole pairs of the power winding and the control winding respectively.

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

[0054] S2: The converter is electrically connected to the control winding of the brushless double-fed motor to be diagnosed, and a current signal that varies with time in the converter when the motor is running in a stable state is obtained. This current signal is a valid signal.

[0055] S3: The root mean square value of the current signal is obtained as the effective value. When the effective value of one phase increases, it is determined that a turn-to-turn short circuit fault may occur on the power winding side. Therefore, the phase with the increased effective value of each phase is taken as the fault phase, and the signal of the fault phase of the current signal is extracted.

[0056] The solution of the effective value is as follows:

[0057]

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

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

[0060] The discrete Fourier decomposition coefficients are shown below:

[0061]

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

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

[0064] Specifically include:

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

[0066]

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

[0068] The frequencies are layered as shown below:

[0069]

[0070] For data with a signal sampling frequency of 1250 Hz, when the lowest fault frequency is around 50 Hz, the decomposition level can be selected as 5. The specific detailed signal division frequency bands are shown in Table 1.

[0071] Table 1 Details signal frequency 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 of winding current of brushless doubly-fed motor, Daubechies wavelet can be selected as mother wavelet, which can provide good time and frequency localization, and db30 is specifically selected as mother wavelet of discrete wavelet transform.

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

[0077]

[0078] Among them, 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 and is decomposed into multiple levels corresponding to different frequency bandwidths. At each level, the signal is divided into approximate coefficients and detail coefficients:

[0080] a j+1 [k] = ∑ n (h[n-2k]a j [n])

[0081]

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

[0083]

[0084] Among them, n is the number of decomposition levels, A n is the approximate signal of the nth layer, D j is the detail signal of the jth layer. S5.6: Perform discrete wavelet transform based on the mother wavelet, 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 signal with a fault frequency of 50Hz, the wavelet decomposition time domain diagram of the fault current obtained by discrete wavelet transform and the normal current signal is compared. Figure 3 As shown in the D4 layer, it can be seen that the waveforms and amplitudes of the fault time domain and the normal time domain are quite different. The detail signal D4 is extracted and Fourier transformed. The spectrum is shown in Figure 4 shown.

[0086] S6: Comprehensively analyze the results of direct Fourier analysis and discrete wavelet transform analysis. When the current fundamental 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 a turn-to-turn short circuit fault occurs in the power winding, and the severity of the turn-to-turn short circuit of the power winding is determined based on the harmonic content.

[0087] The above is a brushless doubly-fed motor power winding turn-to-turn short-circuit fault diagnosis method provided by one embodiment of this embodiment. Based on the same idea, this embodiment also provides a corresponding brushless doubly-fed motor power winding turn-to-turn short-circuit fault diagnosis system. The specific definition of the brushless doubly-fed motor power winding turn-to-turn short-circuit fault diagnosis system can refer to the definition of the brushless doubly-fed motor power winding turn-to-turn short-circuit fault diagnosis method in the above text, which will not be repeated here. Each module in the above-mentioned brushless doubly-fed motor power winding turn-to-turn short-circuit fault diagnosis system can be fully or partially implemented by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

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

[0089] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0090] The above are only 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 in the protection scope of the present invention.

Claims

1. A method for diagnosing short-circuit faults in power windings of a brushless double-fed motor, characterized in that: The following steps are involved: 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 high-voltage insulation of the power winding has a turn-to-turn short circuit fault is obtained; The converter is electrically connected to the control winding of the brushless double-fed motor to be diagnosed, and a current signal of a section of the converter that changes with time when the motor is running in a stable state is obtained, and the current signal of the section is a valid signal; The root mean square value of the current signal is obtained as an effective value, the phase with an increased effective value of each phase is taken as a fault phase, and the signal of the fault phase of the current signal is extracted; Perform Fourier decomposition on the signal of the fault phase to obtain the current fundamental content of the current signal; Perform discrete wavelet transform on the signal of the fault phase, 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 to obtain 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 by discrete wavelet transform increases, it is determined that a turn-to-turn short circuit fault occurs in the power winding, and the severity of the turn-to-turn short circuit of the power winding is determined based on the harmonic content.

2. The method for diagnosing short-circuit faults of power windings of a brushless double-fed motor according to claim 1, characterized in that: The fault characteristic frequency caused by the power winding fault characteristic frequency on the control winding side is (n / 60)(p p +p c )-kf 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 pairs of the power winding and the control winding respectively.

3. The method for diagnosing a short-circuit fault in a power winding of a brushless double-fed motor according to claim 1, characterized in that: The solution of the effective value is shown in the following formula: Among them, I rms is the effective value of the current signal, i(t) is the signal of the current changing with time, and m corresponds to the number of discrete current samples collected in the time period.

4. The method for diagnosing a short-circuit fault in a power winding of a brushless double-fed motor according to claim 1, characterized in that: The discrete Fourier decomposition coefficients are shown below: Where m is the number of discrete current samples collected in the corresponding time period; i(k) is the time domain signal, and λ is the frequency index.

5. The method for diagnosing a short-circuit fault in a power winding of a brushless double-fed motor according to claim 1, characterized in that: The acquisition of the harmonic content of the fault characteristic signal comprises the following steps: Decompose the electromagnetic characteristics of the short-circuit fault in layers and determine the number of decomposition layers n, as shown in the following formula: Among them, f n is the extracted component frequency, f is the sampling frequency of the signal; The frequencies are layered as shown below: The mother wavelet is determined according to the conditions of high peak signal-to-noise ratio, low mean square error and maximum tolerance; Determine the scaling function φ(t) and the wavelet function ψ(t): Among them, h(k) and g(k) are low-pass and high-pass filters respectively; Discrete wavelet transform is based on multi-resolution analysis and is decomposed into multiple levels corresponding to different frequency bandwidths. At each level, the signal is divided into approximate coefficients and detail coefficients: a j+1 [k]=∑ n (h[n-2k]a j [n]) At each level of decomposition, the signal i(t) is expressed as: Among them, n is the number of decomposition levels, A n is the approximate signal of the nth layer, D j is the detail signal of the jth layer; Discrete wavelet transform is performed based on the mother wavelet. According to the detail signal layer where the fault characteristic frequency is located, the detail signal is extracted and Fourier transform is performed again to extract the fault characteristic signal.

6. The method for diagnosing short-circuit faults of power windings of a brushless double-fed motor according to claim 5, characterized in that: The mother wavelet can be selected as a Daubechies wavelet.

7. A brushless double-fed motor power winding turn-to-turn short-circuit fault diagnosis system, characterized in that: include: processor; a memory having stored thereon a computer program executable on the processor; Wherein, when the computer program is executed by the processor, the steps of the method for diagnosing inter-turn short-circuit fault of the power winding of a brushless doubly-fed motor as described in any one of claims 1 to 6 are implemented.

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