Transformer fault detection method and device under electromechanical combined action and electronic equipment

Abstract

The invention relates to the technical field of transformer fault detection, in particular to a transformer fault detection method and device under the electromechanical joint effect and electronic equipment, and the method comprises the steps: obtaining a fusion signal of a transformer in a working process, the fusion signal comprises a vibration signal and an ultrasonic pulse signal of the transformer; filtering and denoising the fusion signal to separate the ultrasonic pulse signal and the vibration signal from the fusion signal; according to the ultrasonic pulse signal and the vibration signal, a first discharge map between the ultrasonic pulse amplitude of the ultrasonic pulse signal of the transformer and the vibration phase of the vibration signal is established, the abscissa of the first discharge map is the vibration phase, and the ordinate of the first discharge map is the ultrasonic pulse amplitude; and determining whether the transformer has a partial discharge fault according to the first discharge map and the reference value, and determining an association relationship between the partial discharge fault of the transformer and mechanical vibration of the transformer according to the first discharge map.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of transformer fault detection, and in particular to a transformer fault detection method and device under the action of electromechanical coupling and electronic equipment. BACKGROUND

[0002] In the power system, the power transformer is always in a complex multi-physical field coupling environment, not only to withstand the electric field under high voltage, but also to produce continuous mechanical vibration due to electromagnetic induction effect. Research has shown that this long-term mechanical vibration can cause significant potential damage to the oil-paper insulation structure inside the transformer. When the insulation structure has a local defect, it will cause partial discharge phenomenon. The continuous occurrence of partial discharge not only accelerates the insulation aging process, but also gradually expands the defect range, and in severe cases, it may even cause insulation breakdown.

[0003] In some scenarios, to ensure the safe operation of the transformer, the industry has developed various partial discharge detection technologies, among which the pulse current method, ultrasonic method and high-frequency electromagnetic method are the most widely used traditional detection methods. However, these traditional methods are based on single physical quantity signal detection and analysis, and are limited by the complex environment on site, which is prone to missed detection or misjudgment. More importantly, the traditional detection methods do not consider the influence of electromechanical coupling on partial discharge during transformer operation. Mechanical vibration is not only an important factor leading to insulation structure damage and inducing partial discharge, but also changes the structure of the defect area, affects the physical properties of the insulation medium, and further changes the characteristic parameters of partial discharge. Since the traditional method cannot establish the correlation between the discharge behavior and the mechanical stress, it is prone to missed detection, misjudgment and other problems when diagnosing partial discharge defects induced by mechanical vibration. The reliability and accuracy of the fault detection result of the transformer is low. SUMMARY

[0004] In order to solve the technical problem that the reliability and accuracy of the fault detection result of the transformer is low, the purpose of the present application is to provide a transformer fault detection method under the action of electromechanical coupling, and the technical scheme adopted is as follows:

[0005] In a first aspect, an embodiment of the present application provides a transformer fault detection method under electromechanical action, comprising: obtaining a fusion signal of a transformer in a working process, the fusion signal comprising a vibration signal and an ultrasonic pulse signal of the transformer; filtering and denoising the fusion signal to separate the ultrasonic pulse signal and the vibration signal from the fusion signal; establishing a first discharge map between an ultrasonic pulse amplitude of the ultrasonic pulse signal and a vibration phase of the vibration signal of the transformer according to the ultrasonic pulse signal and the vibration signal, the horizontal coordinate of the first discharge map being the vibration phase and the vertical coordinate being the ultrasonic pulse amplitude; determining whether a partial discharge fault occurs in the transformer according to the first discharge map and a reference value, and determining a correlation between the partial discharge fault of the transformer and mechanical vibration of the transformer according to the first discharge map.

[0006] In a second aspect, an embodiment of the present application provides a transformer fault detection device under electromechanical action, comprising: an acquisition module configured to obtain a fusion signal of a transformer in a working process, the fusion signal comprising a vibration signal and an ultrasonic pulse signal of the transformer; a filtering module configured to filter and denoise the fusion signal to separate the ultrasonic pulse signal and the vibration signal from the fusion signal; an establishing module configured to establish a first discharge map between an ultrasonic pulse amplitude of the ultrasonic pulse signal and a vibration phase of the vibration signal of the transformer according to the ultrasonic pulse signal and the vibration signal, the horizontal coordinate of the first discharge map being the vibration phase and the vertical coordinate being the ultrasonic pulse amplitude; and a detection module configured to determine whether a partial discharge fault occurs in the transformer according to the first discharge map and a reference value, and determine a correlation between the partial discharge fault of the transformer and mechanical vibration of the transformer according to the first discharge map.

[0007] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory; wherein the memory is configured to store a computer program capable of running on the processor; and the processor is configured to execute the program stored in the memory to implement the steps of the transformer fault detection method under electromechanical action as mentioned in the first aspect.

[0008] The present application has the following beneficial effects: by synchronously collecting the vibration and ultrasonic pulse fusion signals of the transformer, filtering and denoising to separate the ultrasonic pulse signal and the vibration signal, avoiding the misjudgment of the transformer fault caused by the interference of a single signal; establishing the discharge atlas between the ultrasonic pulse amplitude of the ultrasonic pulse signal and the vibration phase of the vibration signal, can directly capture the correlation characteristics between the pulse amplitude and the vibration phase of the partial discharge, compared with the traditional single physical signal diagnosis method of the transformer partial discharge fault, the misjudgment and misjudgment probability is greatly reduced. Further, the ultrasonic pulse amplitude and vibration phase correlation modeling of the embodiment of the present application can clearly reveal the mutual influence of the partial discharge fault and the mechanical vibration of the transformer, thereby reducing the missed detection and misjudgment rate when diagnosing the partial discharge defect induced by mechanical vibration, and improving the reliability and accuracy of the fault detection result of the transformer. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art and the advantages thereof, a brief introduction will be given to the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0010] Figure 1 A flowchart of a transformer fault detection method under the action of electromechanical coupling is provided for the embodiments of the present application.

[0011] Figure 2 A structural diagram of a mechanical vibration-ultrasonic wave fusion detection sensor is provided for the embodiments of the present application.

[0012] Figure 3 A waveform diagram of the fusion signal and the separated vibration signal and ultrasonic signal is provided for the embodiments of the present application.

[0013] Figure 4 A curve diagram of a first discharge atlas is provided for the embodiments of the present application.

[0014] Figure 5 A curve diagram between the envelope line of the ultrasonic pulse scatter points in the first discharge atlas and the vibration reference sinusoidal waveform is provided for the embodiments of the present application.

[0015] Figure 6 A distribution diagram of the ultrasonic pulse scatter points of a second discharge atlas is provided for the embodiments of the present application.

[0016] Figure 7 A structural diagram of a transformer fault detection device under the action of electromechanical coupling is provided for the embodiments of the present application.

[0017] Figure 8 An electronic device structure schematic diagram is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined purposes, the following describes in detail the specific implementation, structure, features and effects of the method and device for detecting transformer faults under electromechanical coupling and electronic equipment according to the present application, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0020] The specific scheme of the method and device for detecting transformer faults under electromechanical coupling and electronic equipment provided by the present application is described in detail below in combination with the accompanying drawings.

[0021] As shown in Figure 1 The method for detecting transformer faults under electromechanical coupling disclosed by the present embodiment includes:

[0022] Step S101, obtaining a fusion signal of the transformer during operation, the fusion signal including a vibration signal and an ultrasonic pulse signal of the transformer.

[0023] Specifically, the fusion signal in the present embodiment can be collected by an ultrasonic-vibration fusion sensor. In the present embodiment, the ultrasonic-vibration fusion sensor is installed at key positions of the transformer, such as the middle, upper and lower parts of the oil tank wall, etc. These positions can better capture the vibration signal and ultrasonic signal generated during partial discharge of the transformer. It is worth noting that the ultrasonic-vibration fusion sensor needs to be tightly contacted with the surface of the transformer during installation to ensure that more accurate vibration signals and ultrasonic signals are collected. The fusion signal is a fusion signal containing mechanical vibration and ultrasonic output by the same channel. The mechanical vibration-ultrasonic fusion detection sensor can simultaneously and simultaneously measure the vibration signal and ultrasonic signal generated by the transformer to be detected, effectively reducing the phase error of the ultrasonic and vibration signals. For example, as shown in Figure 2 Figure 2 ​A structural schematic diagram of a mechanical vibration-ultrasonic fusion detection sensor provided by the embodiment of the present application is composed of a mass block, a backing, a matching layer, a piezoelectric ceramic, and a Sub Miniature version A (SMA) interface. The SMA interface is used for connecting the sensor with a data acquisition card to realize transmission of an electrical signal, provide electrical energy for the sensor, or lead out the electrical signal generated by the sensor. The mass block mainly functions to adjust the inertia and other characteristics of the sensor, which helps to optimize the working performance of the sensor, such as the resonance frequency and other parameters, so that the sensor can better adapt to the detection requirements. The backing can absorb the sound energy at the back of the piezoelectric ceramic, reduce sound reflection, thereby improving the sensitivity and resolution of the sensor, and making the detection result more accurate. The piezoelectric ceramic is the core functional component of the sensor, which realizes mutual conversion between electrical energy and ultrasonic energy by using the piezoelectric effect (mechanical deformation under the action of an electric field, or an electric field under mechanical deformation), and is the key to ultrasonic signal transmission and reception. The matching layer is used to improve the acoustic impedance matching between the sensor and the detection medium (such as the workpiece to be detected, human tissue, etc.), so that more ultrasonic energy can enter the detection medium, while reducing the reflection loss of ultrasonic energy at the interface, and improving the efficiency of the sensor in transmitting and receiving ultrasonic signals.

[0024] Further, after the sensor collects the fusion signal of the transformer, the fusion signal is transmitted to the acquisition card through the SMA interface, and the acquisition card is connected to the host computer of the tablet computer through a Universal Serial Bus (USB) interface. The host computer includes LabVIEW host computer software, and parameter settings can be performed in the LabVIEW host computer software, including sampling rate, sampling point number, filtering parameters, etc. of signal acquisition. According to the actual operation condition and monitoring requirement of the transformer, the sampling rate is reasonably set to ensure that the characteristic information of the signal can be completely collected. In the embodiment of the present application, when setting the sampling frequency, the frequency range of the transformer partial discharge signal is comprehensively analyzed. Generally, the frequency of the ultrasonic signal generated by the transformer partial discharge is between 20 kHz and 100 kHz, and the sampling rate of the LabVIEW host computer software is set to 500 kHz in the embodiment of the present application, so that complete frequency spectrum information can be collected, and frequency aliasing can be avoided. The ultrasonic-vibration fusion sensor and the corresponding channel of the LabVIEW host computer software are correctly connected to ensure that the signal transmission line is stable and well shielded, so as to reduce the influence of external interference on the signal. After starting the LabVIEW host computer software, it receives the analog signal from the ultrasonic-vibration fusion sensor in real time according to the set parameters, and performs high-speed sampling and digital processing, and collects the fusion signal S(t) of the transformer in the working process, wherein t represents time.

[0025] Further, when collecting the fusion signal of the transformer, the ultrasonic-vibration fusion sensor is closely attached to the surface of the transformer tank, in order to ensure that the ultrasonic-vibration fusion sensor can collect the vibration ultrasonic fusion signal generated by the partial discharge of the transformer accurately and efficiently, the contact part between the ultrasonic-vibration fusion sensor and the tank is uniformly coated with an appropriate amount of coupling agent, so as to effectively reduce the energy loss and distortion in the signal transmission process, and the ultrasonic-vibration fusion sensor can accurately and efficiently collect the vibration ultrasonic fusion signal generated by the partial discharge of the transformer.

[0026] Step S102, filtering and denoising the fusion signal to separate the ultrasonic pulse signal and the vibration signal from the fusion signal.

[0027] Specifically, after the fusion signal is collected, it needs to be separated and denoised. As an optional embodiment of the present application, filtering and denoising the fusion signal includes: filtering and denoising the fusion signal by using a wavelet packet algorithm to separate the ultrasonic pulse signal and the vibration signal from the fusion signal.

[0028] In the present embodiment, the fusion signal is separated and denoised by using a wavelet packet algorithm based on LabVIEW software, so that the mechanical vibration signal and the ultrasonic pulse signal are respectively extracted and analyzed, and the smoothness and clarity of the signal are ensured.

[0029] Further, the wavelet packet definition function is as follows:

[0030]

[0031] In the above formula, d j,k (j) represents the wavelet packet decomposition coefficient at the jth layer and the kth node. h(k), g(k) are expansion coefficients. m and n are index variables in the summation process. n is the serial number of the parent jth layer, and m is the serial number of the j+1th layer.

[0032] Calculate the energy E j,k

[0033]

[0034] In the above formula: E j,k represents the energy of the jth layer and the kth frequency band, N is the number of data points in the frequency band, d j,k (i) is the corresponding wavelet packet decomposition coefficient of the ith data point in the jth layer and the kth frequency band.

[0035] Further, the first filter and the second filter are set according to the vibration mechanism during the operation of the transformer and the frequency characteristics of the ultrasonic signal during the partial discharge by using the wavelet packet algorithm, the fusion signal is filtered, and the ultrasonic signal and the vibration signal are obtained respectively.

[0036] The first filter and the second filter can both be band-pass filters, such as Butterworth filters, which have a flat response curve in the passband, small signal attenuation, and can maximize the original characteristics of the signal and reduce distortion. The frequency range of the first filter is the frequency range of the ultrasonic signal generated by partial discharge.

[0037] Further, according to the vibration mechanism and experimental data analysis of the transformer during operation, the ultrasonic signal generated by partial discharge in the transformer is mainly concentrated in the frequency range of 20 kHz-100 kHz, and the vibration frequency of the transformer is generally between 0-1000 Hz. Therefore, the frequency range of the first filter is set to 20 kHz-100 kHz, and the frequency range of the second filter is set to 0-1000 Hz.

[0038] Further, the fusion signal is connected to the first filter, and the first filter performs frequency screening on the fusion signal. Only the signal components with a frequency in the range of 20 kHz-100 kHz in the fusion signal can pass through the first filter, and the remaining frequency components are effectively suppressed. The signal after the first filter is the preliminary separated ultrasonic signal.

[0039] Further, the fusion signal is connected to the second filter, and the second filter screens the signal according to the set frequency range of 0-1000 Hz. Only the signal with a frequency in the range of 0-1000 Hz in the fusion signal can pass through the second filter, and the remaining frequency components are effectively suppressed. The signal after the first filter is the preliminary separated vibration signal.

[0040] As shown in FIG. 1, Figure 3 As shown in FIG. 1, Figure 3 is a waveform diagram of the fusion signal and the separated vibration signal and ultrasonic signal provided by the embodiment of the present application, Figure 3 The original fusion signal of the transformer and the separated vibration signal and ultrasonic signal are clearly presented. Figure 3 The signal characteristics of vibration and ultrasonic can be clearly presented.

[0041] In step S103, a first discharge map between the ultrasonic pulse amplitude of the ultrasonic pulse signal of the transformer and the vibration phase of the vibration signal is established according to the ultrasonic pulse signal and the vibration signal. The abscissa of the first discharge map is the vibration phase, and the ordinate is the ultrasonic pulse amplitude.

[0042] Specifically, after wavelet packet decomposition of the fusion signal, time domain features of the ultrasonic pulse signal and the vibration signal are obtained, and the corresponding relationship between the ultrasonic pulse amplitude of the ultrasonic pulse signal and the vibration phase of the electric vibration signal is further counted to obtain a first discharge spectrum, and the correlation between the discharge behavior and the mechanical vibration can be analyzed through the first discharge spectrum.

[0043] Therefore, the ultrasonic pulse waveform and the vibration signal waveform of the partial discharge are obtained through processing of the ultrasonic pulse signal and the vibration signal, the ultrasonic pulse amplitude is corresponded to the vibration phase, and the correlation between the partial discharge and the vibration phase is obtained.

[0044] For example, as shown in FIG. 2, after processing of the fusion signal, the vibration signal and the ultrasonic pulse signal are obtained, the ultrasonic pulse amplitude of the ultrasonic pulse signal and the corresponding time are obtained, the voltage phase and the vibration phase at the time are obtained according to the waveform data of the vibration signal and the ultrasonic pulse signal corresponding to the time, and the ultrasonic pulse amplitude at the time is corresponded to the vibration phase. Figure 3 For example, as shown in FIG. 2, after processing of the fusion signal, the vibration signal and the ultrasonic pulse signal are obtained, the ultrasonic pulse amplitude of the ultrasonic pulse signal and the corresponding time are obtained, the voltage phase and the vibration phase at the time are obtained according to the waveform data of the vibration signal and the ultrasonic pulse signal corresponding to the time, and the ultrasonic pulse amplitude at the time is corresponded to the vibration phase. Figure 4 For example, as shown in FIG. 2, after processing of the fusion signal, the vibration signal and the ultrasonic pulse signal are obtained, the ultrasonic pulse amplitude of the ultrasonic pulse signal and the corresponding time are obtained, the voltage phase and the vibration phase at the time are obtained according to the waveform data of the vibration signal and the ultrasonic pulse signal corresponding to the time, and the ultrasonic pulse amplitude at the time is corresponded to the vibration phase. Figure 4 For example, as shown in FIG. 2, after processing of the fusion signal, the vibration signal and the ultrasonic pulse signal are obtained, the ultrasonic pulse amplitude of the ultrasonic pulse signal and the corresponding time are obtained, the voltage phase and the vibration phase at the time are obtained according to the waveform data of the vibration signal and the ultrasonic pulse signal corresponding to the time, and the ultrasonic pulse amplitude at the time is corresponded to the vibration phase. Figure 4 For example, as shown in FIG. 2, after processing of the fusion signal, the vibration signal and the ultrasonic pulse signal are obtained, the ultrasonic pulse amplitude of the ultrasonic pulse signal and the corresponding time are obtained, the voltage phase and the vibration phase at the time are obtained according to the waveform data of the vibration signal and the ultrasonic pulse signal corresponding to the time, and the ultrasonic pulse amplitude at the time is corresponded to the vibration phase.

[0045] In step S104, whether the transformer has a partial discharge fault is determined according to the first discharge spectrum and a reference value, and the correlation between the partial discharge fault of the transformer and the mechanical vibration of the transformer is determined according to the first discharge spectrum.

[0046] Specifically, the ordinate of the scatter point in the first discharge spectrum in the embodiment of the present application is the ultrasonic pulse amplitude. When the transformer is in a normal state without a partial discharge phenomenon, the ultrasonic pulse amplitude of the transformer is lower than a standard pulse threshold. Therefore, as an optional embodiment of the present application, whether the transformer has a partial discharge fault is determined according to the first discharge spectrum and a reference value, including: in the case that any ultrasonic pulse amplitude in the first discharge spectrum exceeds the pulse threshold, it is determined that the transformer has a partial discharge fault.

[0047] Specifically, the reference value in the embodiment of the present application is the pulse threshold, and the pulse threshold can be obtained by counting a large number of ultrasonic pulse amplitudes of the transformer in a normal working state, which can be determined according to an actual scene, and the embodiment of the present application is not limited thereto.

[0048] Further, after determining that the transformer has a partial discharge fault, it is determined whether the partial discharge fault is caused by mechanical vibration of the transformer. As an optional embodiment of the present application, the correlation between the partial discharge fault of the transformer and the mechanical vibration of the transformer is determined according to the first discharge map, including: the ultrasonic pulse points in the first discharge map are regularly distributed along the vibration sine reference waveform, and it is determined that the partial discharge fault of the transformer is correlated with the mechanical vibration of the transformer; the ultrasonic pulse points in the first discharge map are not regularly distributed along the vibration sine reference waveform, and the distribution patterns of the ultrasonic pulse points in the first discharge map under different vibration conditions are similar, and it is determined that the partial discharge fault of the transformer is not correlated with the mechanical vibration of the transformer.

[0049] Specifically, the ultrasonic pulse points of the partial discharge are regularly distributed along the vibration reference sine waveform in the first discharge map, such as gathering around some phases of the vibration reference sine waveform, or the distribution range of the ultrasonic pulse points is systematically expanded with the increase of the vibration amplitude of the mechanical vibration under different conditions, which indicates that the generation of the partial discharge fault is significantly affected by the mechanical vibration of the transformer, and the two have a strong correlation. When the ultrasonic pulse points in the first discharge map are not regularly distributed, and the distribution patterns of the ultrasonic pulse points under different vibration conditions change little, the correlation between the partial discharge fault and the mechanical vibration of the transformer is weak, that is, there is no correlation. For example, as shown in FIG. 2, Figure 5 Figure 5 FIG. 2 is a curve diagram between the envelope of the ultrasonic pulse points in the first discharge map and the vibration reference sine waveform, provided by an embodiment of the present application, wherein, Figure 5 In the figure, the blue curve represents the envelope of the ultrasonic pulse points in the first discharge map, and the red curve represents the vibration reference sine waveform of 100 Hz. The blue curve and the red curve have a relatively obvious phase correspondence, and the ultrasonic pulse points are regularly distributed along the vibration reference sine waveform in the first discharge map, which indicates that the partial discharge fault has a strong correlation with the mechanical vibration of the transformer.

[0050] Further, the embodiment of the present application can also combine the ultrasonic pulse amplitude of the ultrasonic pulse signal of the transformer and the voltage phase of the ultrasonic pulse signal to determine whether the transformer has an insulation defect. As an optional embodiment of the present application, after determining whether the transformer has a partial discharge fault according to the first discharge map and the reference value, and determining the correlation between the partial discharge fault of the transformer and the mechanical vibration of the transformer according to the first discharge map, the method further includes: establishing a second discharge map between the ultrasonic pulse amplitude of the ultrasonic pulse signal of the transformer and the voltage phase of the ultrasonic pulse signal, the horizontal coordinate of the second discharge map is the voltage phase, and the vertical coordinate is the ultrasonic pulse amplitude.​

[0051] According to the distribution characteristics of the ultrasonic pulse scatter points in the second discharge map, the insulation defect of the transformer can be detected.

[0052] Specifically, the embodiment of the present application can correspond the ultrasonic pulse amplitude of the ultrasonic pulse signal to the voltage phase of the ultrasonic pulse signal, so as to intuitively reflect the distribution characteristics of the partial discharge signal with the voltage phase. The ultrasonic pulse data points caused by different insulation defects will show different regular distribution on the second discharge map, so that the type of the insulation defect of the transformer can be determined according to the distribution of the partial discharge signal on the second discharge map. For example, the partial discharge caused by the needle-plate insulation defect is often concentrated near the voltage peak. As shown in the example of FIG. 4, Figure 6 Figure 6 A distribution diagram of ultrasonic pulse scatter points of a second discharge map provided by the embodiment of the present application, Figure 6 In the distribution diagram of the ultrasonic pulse scatter points of the second discharge map, the ultrasonic pulse scatter points of the partial discharge caused by the insulation defect are mostly concentrated near the voltage peak of the discharge reference sinusoidal waveform, so that the type of the needle-plate insulation defect of the transformer can be determined according to the distribution regularity of the partial discharge pulse scatter points caused by the plate insulation defect. Figure 6 The distribution characteristics of the ultrasonic pulse scatter points in the second discharge map are consistent with the type of the needle-plate insulation defect of the transformer. When the defect is further deteriorated and developed, the amplitude, number and distribution range of the ultrasonic pulse scatter points in the second discharge map will also change obviously, so as to reflect the insulation deterioration degree and development trend. Therefore, the type and degree of the insulation defect of the transformer can be determined by the second discharge map, and the accuracy and comprehensiveness of the fault recognition of the transformer are further improved.

[0053] The present application has the following beneficial effects: by synchronously collecting the vibration and ultrasonic pulse fusion signals of the transformer, the ultrasonic pulse signal and the vibration signal are separated by filtering and denoising, so as to avoid the misjudgment of the transformer fault caused by the interference of a single signal; the discharge map between the ultrasonic pulse amplitude of the ultrasonic pulse signal and the vibration phase of the vibration signal is established, so as to intuitively capture the correlation characteristics between the pulse amplitude of the partial discharge and the vibration phase. Compared with the traditional single physical signal diagnosis method of the partial discharge fault of the transformer, the probability of missed judgment and misjudgment is greatly reduced. Further, the embodiment of the present application models the correlation between the ultrasonic pulse amplitude and the vibration phase, so as to clearly reveal the mutual influence between the partial discharge fault and the mechanical vibration of the transformer, thereby reducing the missed detection and misjudgment rate when diagnosing the partial discharge defect induced by the mechanical vibration, and improving the reliability and accuracy of the fault detection result of the transformer.

[0054] ​Furthermore, embodiments of the present invention can also identify mechanical faults in transformers, thereby further improving the comprehensiveness of transformer fault identification. As an optional embodiment of the present invention, after determining whether a transformer has a partial discharge fault based on the first discharge spectrum and reference values, and the correlation between the partial discharge fault and the transformer's mechanical vibration, the method further includes: extracting vibration features from the vibration signal, the vibration features including: dominant vibration frequency, vibration entropy, frequency proportion, and odd-even harmonic ratio. The dominant vibration frequency characterizes the maximum value of the vibration amplitude in the vibration signal; the vibration entropy characterizes the complexity of the frequency components of the vibration signal; the frequency proportion characterizes the proportion of harmonics at the target frequency of the vibration signal; and the odd-even harmonic ratio characterizes the ratio of odd-frequency energy to even-frequency energy. Based on the dominant vibration frequency, vibration entropy, frequency proportion, and odd-even harmonic ratio, it is determined whether a mechanical fault has occurred in the transformer.

[0055] Specifically, in this embodiment of the invention, the dominant vibration frequency is the frequency with the highest amplitude in the vibration spectrum of the vibration signal. Frequency weight P f This characterizes the proportion of harmonics at frequency f. For example, based on the vibration characteristics of transformers, it is generally assumed that the vibration of the transformer tank has a fundamental frequency of twice the voltage frequency, with a frequency range generally between 100Hz and 1000Hz. The frequency proportion can be calculated using the following formula:

[0056]

[0057] In the above formula, P f This indicates the proportion of harmonics at frequency f. S f Let f be the vibration harmonic component at frequency f.

[0058] Furthermore, the odd-even harmonic ratio P is the ratio of odd-frequency energy to even-frequency energy, which reflects whether the transformer experiences DC bias. In this embodiment of the invention, the odd-even harmonic ratio P is calculated using the following formula:

[0059]

[0060]

[0061] In the above formula, P is the odd-even harmonic ratio. A is the vibration amplitude of the vibration signal. E e Energy of even frequencies. E o This refers to the energy at odd frequencies. When DC bias exists, the DC component increases, leading to an increase in the odd frequency components, i.e., the odd-even harmonic ratio P increases.

[0062] Furthermore, the vibration entropy H primarily characterizes the complexity of the frequency components in the vibration spectrum of a vibration signal. A lower value indicates that the energy in the vibration spectrum is more concentrated at certain characteristic frequencies, while a higher value indicates that the energy in the vibration spectrum is more dispersed. Specifically, this embodiment of the invention uses the following formula to calculate the vibration entropy H:

[0063]

[0064] In the above formula, H represents the vibration entropy, and P f This indicates the proportion of harmonics at frequency f. A is the vibration amplitude of the vibration signal.

[0065] Furthermore, as an optional embodiment of the present invention, determining whether a transformer has a mechanical fault based on the dominant vibration frequency, vibration entropy, frequency proportion, and odd-even harmonic ratio includes: when the dominant vibration frequency exceeds the transformer's standard dominant frequency, determining that the instantaneous stress borne by the transformer's mechanical structure exceeds the tolerable range, indicating a potential mechanical fault within the transformer; determining whether the transformer's mechanical structure has a mechanical fault and the degree of fault when a mechanical fault occurs based on the rate of change of vibration entropy; if the frequency proportion of the newly added frequency in the vibration signal exceeds a first threshold, determining that the transformer has a potential mechanical fault; if the frequency proportion of the original frequency in the vibration signal exceeds a second threshold, determining that the mechanical component in the transformer corresponding to the original frequency has a fault; and if the odd-even harmonic ratio exceeds a third threshold, determining that a DC bias fault has occurred within the transformer.

[0066] Specifically, the first threshold, the second threshold, and the third threshold in the embodiments of the present invention can be determined according to the actual situation, and the embodiments of the present invention are not limited thereto.

[0067] Furthermore, when determining potential mechanical faults based on the dominant vibration frequency, the dominant vibration frequency is the frequency component with the most concentrated energy in the transformer's vibration signal, and its magnitude is directly related to the inherent characteristics of key mechanical structures such as the internal core and windings. Under normal operating conditions, the dominant vibration frequency of a transformer will stabilize within a standard frequency range based on design parameters and material properties (for example, for a transformer with a power frequency of 50Hz, the normal dominant frequency usually fluctuates around 100Hz (dominated by core magnetostriction) or its multiples, depending on the specific equipment model calibration). When the dominant vibration frequency is detected to exceed this standard frequency range, it can be determined that the instantaneous stress borne by the transformer's mechanical structure has exceeded its tolerance range, indicating a potential internal mechanical fault. For example, when a transformer winding becomes loose, the winding stiffness will decrease, and its natural frequency will shift towards lower frequencies. If the original standard main frequency was 100Hz, it may drop to below 90Hz after the fault. At this time, the potential mechanical fault of winding looseness can be detected by monitoring the main frequency. Another example is that insulation damage between iron core laminations can lead to short circuits in the laminations, which will cause an abnormal increase in local magnetic flux density of the iron core, exacerbate the magnetostriction effect, and cause the vibration main frequency to shift towards higher frequencies, exceeding the standard range, indicating that there is a potential fault in the iron core.

[0068] Furthermore, when determining whether a transformer's mechanical structure has experienced a mechanical fault and the severity of such faults based on the rate of change of vibration entropy, vibration entropy is a quantitative indicator describing the complexity and disorder of the transformer's vibration signal. Essentially, it reflects the stability of the internal mechanical system's operating state. Under normal operating conditions, the vibrations of the various mechanical components inside the transformer are coordinated, the frequency and amplitude distribution of the vibration signal are relatively stable, and the vibration entropy remains at a low and gradual level. However, when a mechanical fault occurs, the abnormal vibration of the faulty component disrupts the stability of the original signal, leading to a significant change in vibration entropy, and the magnitude of this change is positively correlated with the severity of the fault. To determine the fault and its severity based on the rate of change of vibration entropy, a baseline curve of vibration entropy under normal transformer operation must first be established. This involves real-time monitoring of vibration entropy values ​​and calculating their rate of change per unit time (i.e., the ratio of the difference in vibration entropy between adjacent monitoring periods to the time interval). This rate of change is then compared with a preset health threshold. If the rate of change of vibration entropy is within the health threshold range, it indicates that the transformer's internal mechanical structure is operating stably without significant faults. If the rate of change of vibration entropy exceeds the health threshold and shows a slow upward trend, it indicates that the mechanical structure has experienced an initial fault. At this point, the fault has a relatively small impact on equipment operation, but subsequent changes need close monitoring. If the rate of change of vibration entropy increases sharply, far exceeding the health threshold, and continues to climb in a short period, it indicates that the mechanical fault has entered a severe stage, and the abnormal vibration of the faulty component has seriously interfered with the stable operation of the overall mechanical system. For example, when a transformer is operating normally, its vibration entropy is stable between 0.8 and 1.0, with a change rate of less than 0.05 / day. When the core clamping bolts begin to loosen slightly, the vibration entropy gradually rises to 1.2, with a change rate of 0.1 / day, exceeding the health threshold (0.08 / day), indicating an initial fault. As the bolts loosen further, the vibration entropy soars from 1.2 to 2.5 within 3 days, with a change rate as high as 0.43 / day, indicating that the fault has become serious and an emergency shutdown is required.

[0069] Furthermore, when determining mechanical faults based on the frequency proportion of newly added frequencies in the vibration signal, if a new frequency not present during normal operation appears in the vibration signal, and the proportion of this new frequency exceeds a preset first threshold (usually set based on historical equipment data and industry standards, generally 5%-10%, but specific calibration is required based on equipment operating conditions), a potential mechanical fault in the transformer can be identified. The generation of new frequencies typically stems from abnormal vibration modes of faulty components. For example, a broken support at the bottom of the transformer tank can cause additional shaking during operation, generating a low-frequency new frequency of 2-5 Hz. If the proportion of this frequency reaches 8% (exceeding the first threshold of 6%), it indicates a potential breakage of the support component. Furthermore, the original frequency refers to the characteristic frequency that already exists during normal transformer operation and is directly related to specific mechanical components (e.g., 100 Hz corresponds to magnetostriction of the iron core, 50 Hz corresponds to the fundamental electromagnetic force of the winding, etc.). When the proportion of a certain original frequency exceeds a preset second threshold (which needs to be set according to the characteristics of the component corresponding to that frequency; for example, the second threshold for core-related frequencies can be set to 30%, and the second threshold for winding-related frequencies can be set to 25%), it indicates that the mechanical component corresponding to that original frequency has malfunctioned. The malfunction causes an abnormal increase in the vibration energy of the component, thereby increasing the proportion of the corresponding frequency. For example, if the proportion of 100Hz (the dominant magnetostriction frequency of the iron core) rises from the normal 20% to 35%, exceeding the second threshold of 30%, it can be determined that the iron core has malfunctioned (such as a short circuit in the iron core laminations, poor iron core grounding leading to local overheating, etc.), because the iron core malfunction will exacerbate the magnetostriction effect, significantly increasing the vibration energy at the 100Hz frequency.

[0070] Furthermore, when judging transformer faults based on the odd-even harmonic ratio, under normal operating conditions, the transformer's magnetic field distribution is relatively symmetrical, and the even harmonics (mainly generated by the magnetostriction of the iron core) account for a higher proportion in the vibration signal, with the odd-even harmonic ratio within a relatively low standard range (usually 0.3-0.6). However, when a DC bias fault occurs in the transformer, the DC current is superimposed on the AC excitation current, causing unidirectional saturation of the iron core magnetic circuit, disrupting the symmetry of the magnetic field distribution, and significantly increasing the energy of the odd harmonics (mainly generated by electromagnetic force vibrations induced by the asymmetrical magnetic field), thus causing the odd-even harmonic ratio to exceed the standard range. When the odd-even harmonic ratio is detected to exceed the preset third threshold (generally set according to the equipment's rated parameters and test data, usually 1.0), a DC bias fault can be directly determined to have occurred in the transformer. The main causes of DC bias faults include: single-pole operation of the DC transmission system in the power grid, increased ground potential near the grounding electrode leading to DC current flowing into the transformer, and open circuit or poor grounding of the transformer neutral point. For example, when a transformer is operating normally, its odd-even harmonic ratio is stable between 0.4 and 0.5. However, when a single-pole fault occurs in the DC transmission system of the power grid, some DC current flows into the transformer, causing the core magnetic circuit to saturate. This increases the odd-harmonic energy, raising the odd-even harmonic ratio to 1.2, exceeding the third threshold of 1.0. At this point, it can be clearly determined that the transformer has experienced a DC bias fault. If not addressed promptly, DC bias can lead to increased transformer losses, excessive temperature rise, and long-term operation may cause mechanical damage to the core and windings, even affecting the equipment's lifespan.

[0071] Corresponding to the transformer fault detection method under electromechanical combined action provided in the above embodiments, based on the same technical concept, this embodiment of the invention also provides a transformer fault detection device under electromechanical combined action. This device is used to execute the above-described transformer fault detection method under electromechanical combined action, such as... Figure 7 As shown, Figure 7 This is a schematic diagram of a transformer fault detection device under electromechanical combined action provided in an embodiment of the present invention. The transformer fault detection device under electromechanical combined action includes: an acquisition module 701, used to acquire a fused signal of the transformer during operation, the fused signal including the transformer's vibration signal and ultrasonic pulse signal; a filtering module 702, used to filter and denoise the fused signal to separate the ultrasonic pulse signal and vibration signal from the fused signal; an establishment module 703, used to establish a first discharge spectrum between the ultrasonic pulse amplitude of the transformer's ultrasonic pulse signal and the vibration phase of the vibration signal based on the ultrasonic pulse signal and vibration signal, the horizontal axis of the first discharge spectrum being the vibration phase and the vertical axis being the ultrasonic pulse amplitude; and a detection module 704, used to determine whether the transformer has a partial discharge fault based on the first discharge spectrum and reference values, and to determine the correlation between the transformer's partial discharge fault and the transformer's mechanical vibration based on the first discharge spectrum.

[0072] Optionally, the detection module 704 includes: an extraction unit for extracting vibration features from the vibration signal, the vibration features including: vibration dominant frequency, vibration entropy, frequency proportion, and odd-even harmonic ratio, the vibration dominant frequency representing the maximum value of the vibration amplitude in the vibration signal, the vibration entropy representing the complexity of the frequency components of the vibration signal, the frequency proportion representing the proportion of harmonics at the target frequency of the vibration signal, and the odd-even harmonic ratio representing the ratio of odd frequency energy to even frequency energy; and a determination unit for determining whether the transformer has a mechanical fault based on the vibration dominant frequency, vibration entropy, frequency proportion, and odd-even harmonic ratio.

[0073] Optionally, the determining unit is also used to determine, when the vibration dominant frequency exceeds the transformer's standard dominant frequency, that the instantaneous stress borne by the transformer's mechanical structure exceeds the tolerable range, indicating a potential mechanical fault inside the transformer; based on the rate of change of vibration entropy, to determine whether a mechanical fault has occurred in the transformer's mechanical structure and the degree of the fault when it occurs; if the frequency proportion of the newly added frequency in the vibration signal exceeds a first threshold, to determine that a potential mechanical fault has occurred in the transformer; if the frequency proportion of the original frequency in the vibration signal exceeds a second threshold, to determine that a fault has occurred in the mechanical component in the transformer corresponding to the original frequency; and if the odd-even harmonic ratio exceeds a third threshold, to determine that a DC bias fault has occurred inside the transformer.

[0074] Optionally, the filtering module 702 is also used to filter and denoise the fused signal using a wavelet packet algorithm to separate the ultrasonic pulse signal and the vibration signal from the fused signal.

[0075] Optionally, the determining unit is also used to determine that the transformer has a partial discharge fault when any ultrasonic pulse amplitude in the first discharge spectrum exceeds the pulse threshold.

[0076] Optionally, the determining unit is also used to determine that if the ultrasonic pulse scatter points in the first discharge spectrum show a regular distribution with the vibration sinusoidal reference waveform, there is a correlation between the partial discharge fault of the transformer and the mechanical vibration of the transformer; if the ultrasonic pulse scatter points in the first discharge spectrum do not show a regular distribution with the vibration sinusoidal reference waveform and the distribution pattern of the ultrasonic pulse scatter points in the first discharge spectrum is similar under different vibration conditions, there is no correlation between the partial discharge fault of the transformer and the mechanical vibration of the transformer.

[0077] Optionally, the establishment module 703 includes: an establishment unit for establishing a second discharge spectrum between the ultrasonic pulse amplitude and the voltage phase of the ultrasonic pulse signal of the transformer, wherein the horizontal axis of the second discharge spectrum is the voltage phase and the vertical axis is the ultrasonic pulse amplitude; and a detection unit for detecting insulation defects of the transformer based on the distribution characteristics of the ultrasonic pulse scatter points in the second discharge spectrum.

[0078] It should be noted that the transformer fault detection device under electromechanical combined action provided in the embodiments of the present invention and the transformer fault detection method under electromechanical combined action provided in the embodiments of the present invention are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned transformer fault detection method under electromechanical combined action, and has the same or similar beneficial effects. Repeated parts will not be repeated.

[0079] Corresponding to the transformer fault detection method under electromechanical combined action provided in the above embodiments, based on the same technical concept, this embodiment of the invention also provides an electronic device for executing the above-described transformer fault detection method under electromechanical combined action. Figure 8 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present invention, as shown below. Figure 8 As shown. Electronic devices can vary considerably due to differences in configuration or performance, and may include one or more processors 801 and memories 802. The memory 802 stores computer programs that can run on the processor 801, and the processor 801 executes the programs stored in the memory 802 to achieve the above. Figure 1 The various steps in the method embodiment are described. The memory 802 can be temporary or persistent storage. The application stored in the memory 802 may include one or more modules (not shown), each module may include a series of computer-executable instructions for the electronic device.

[0080] Furthermore, the processor 801 may be configured to communicate with the memory 802 and execute a series of computer-executable instructions stored in the memory 802 on the electronic device. The electronic device may also include one or more power supplies 803, one or more wired or wireless network interfaces 804, one or more input / output interfaces 805, and one or more keyboards 806.

[0081] Specifically, in this embodiment, the electronic device includes a processor, a communication interface, a memory, and a communication bus; wherein, the processor, the communication interface, and the memory communicate with each other via the bus; the memory is used to store computer programs; and the processor is used to execute the programs stored in the memory to achieve the above. Figure 1 The various steps in the method embodiments are the same as those in the above method embodiments, and have the same beneficial effects. To avoid repetition, the embodiments of the present invention will not be described again here.

[0082] It should be noted that the electronic device provided in this embodiment of the invention and the transformer fault detection method under electromechanical combined action provided in this embodiment of the invention are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned transformer fault detection method under electromechanical combined action, and has the same or similar beneficial effects. Repeated parts will not be described again.

[0083] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0084] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for detecting transformer faults under electromechanical combined action, characterized in that, include: Acquire the fusion signal of the transformer during operation, which includes the transformer's vibration signal and ultrasonic pulse signal; The fused signal is filtered and denoised to separate the ultrasonic pulse signal and vibration signal from the fused signal; Based on the ultrasonic pulse signal and the vibration signal, a first discharge spectrum is established between the ultrasonic pulse amplitude of the ultrasonic pulse signal and the vibration phase of the vibration signal of the transformer. The horizontal axis of the first discharge spectrum is the vibration phase, and the vertical axis is the ultrasonic pulse amplitude. The first discharge spectrum and reference values ​​are used to determine whether the transformer has a partial discharge fault, and the correlation between the partial discharge fault and the mechanical vibration of the transformer is determined based on the first discharge spectrum.

2. The transformer fault detection method under electromechanical combined action according to claim 1, characterized in that, After determining whether the transformer has a partial discharge fault based on the first discharge spectrum and reference values, and the correlation between the partial discharge fault and the transformer's mechanical vibration, the method further includes: Vibration features are extracted from vibration signals. These features include: dominant vibration frequency, vibration entropy, frequency proportion, and odd-even harmonic ratio. The dominant vibration frequency represents the maximum value of the vibration amplitude in the vibration signal. The vibration entropy represents the complexity of the frequency components of the vibration signal. The frequency proportion represents the proportion of harmonics at the target frequency of the vibration signal. The odd-even harmonic ratio represents the ratio of odd frequency energy to even frequency energy. Based on the dominant vibration frequency, vibration entropy, frequency proportion, and odd-even harmonic ratio, it is determined whether the transformer has a mechanical fault.

3. The transformer fault detection method under electromechanical combined action according to claim 2, characterized in that, The method of determining whether a transformer has a mechanical fault based on the dominant vibration frequency, vibration entropy, frequency proportion, and odd-even harmonic ratio includes: When the vibration frequency exceeds the transformer's standard frequency, it is determined that the instantaneous stress on the transformer's mechanical structure exceeds the acceptable range, indicating a potential mechanical fault inside the transformer. Based on the rate of change of vibration entropy, determine whether the transformer's mechanical structure has mechanical faults and the degree of faults when they occur. If the frequency proportion of the newly added frequency in the vibration signal exceeds the first threshold, it is determined that the transformer has a potential mechanical fault. If the frequency proportion of the original frequency in the vibration signal exceeds the second threshold, it is determined that the mechanical component in the transformer corresponding to the original frequency has a fault. When the odd-even harmonic ratio exceeds the third threshold, a DC bias fault is determined to have occurred in the transformer.

4. The transformer fault detection method under electromechanical combined action according to claim 1, characterized in that, The filtering and denoising of the fused signal includes: The wavelet packet algorithm is used to filter and denoise the fused signal in order to separate the ultrasonic pulse signal and the vibration signal from the fused signal.

5. The transformer fault detection method under electromechanical combined action according to claim 1, characterized in that, The step of determining whether the transformer has a partial discharge fault based on the first discharge spectrum and reference values ​​includes: If the amplitude of any ultrasonic pulse in the first discharge spectrum exceeds the pulse threshold, a partial discharge fault is determined to have occurred in the transformer.

6. The transformer fault detection method under electromechanical combined action according to any one of claims 1-5, characterized in that, The determination of the correlation between partial discharge faults and mechanical vibrations of the transformer based on the first discharge spectrum includes: In the first discharge spectrum, the ultrasonic pulse scatter points show a regular distribution with the vibration sinusoidal reference waveform, which confirms the correlation between the partial discharge fault of the transformer and the mechanical vibration of the transformer. In the first discharge spectrum, the ultrasonic pulse scatter points did not show a regular distribution with the vibration sinusoidal reference waveform, and the distribution pattern of the ultrasonic pulse scatter points in the first discharge spectrum was similar under different vibration conditions. Therefore, it was determined that there was no correlation between the partial discharge fault of the transformer and the mechanical vibration of the transformer.

7. The transformer fault detection method under electromechanical combined action according to any one of claims 1-5, characterized in that, After determining whether the transformer has a partial discharge fault based on the first discharge spectrum and reference values, and determining the correlation between the partial discharge fault and the transformer's mechanical vibration based on the first discharge spectrum, the method further includes: A second discharge spectrum is established between the ultrasonic pulse amplitude and the voltage phase of the ultrasonic pulse signal of the transformer. The horizontal axis of the second discharge spectrum is the voltage phase, and the vertical axis is the ultrasonic pulse amplitude. Insulation defects in transformers are detected based on the distribution characteristics of ultrasonic pulse scatter points in the second discharge spectrum.

8. A transformer fault detection device under electromechanical combined action, characterized in that, include: The acquisition module is used to acquire the fusion signal of the transformer during operation. The fusion signal includes the transformer's vibration signal and ultrasonic pulse signal. The filtering module is used to filter and denoise the fused signal to separate the ultrasonic pulse signal and the vibration signal from the fused signal; A module is established to create a first discharge spectrum between the ultrasonic pulse amplitude of the ultrasonic pulse signal and the vibration phase of the vibration signal of the transformer, based on the ultrasonic pulse signal and the vibration signal. The horizontal axis of the first discharge spectrum is the vibration phase, and the vertical axis is the ultrasonic pulse amplitude. The detection module is used to determine whether the transformer has a partial discharge fault based on the first discharge spectrum and reference values, and to determine the correlation between the partial discharge fault of the transformer and the mechanical vibration of the transformer based on the first discharge spectrum.

9. The transformer fault detection device under electromechanical combined action according to claim 8, characterized in that, The module is also used to establish a second discharge spectrum between the ultrasonic pulse amplitude and the voltage phase of the ultrasonic pulse signal of the transformer. The horizontal axis of the second discharge spectrum is the voltage phase, and the vertical axis is the ultrasonic pulse amplitude. The detection module is also used to determine the insulation defects of the transformer based on the distribution characteristics of the ultrasonic pulse scatter points in the second discharge spectrum.

10. An electronic device, characterized in that, include: Processor and memory; wherein the memory is used to store computer programs that can run on the processor; A processor is used to execute a program stored in memory to implement the steps of the transformer fault detection method under electromechanical combined action as described in any one of claims 1-7.

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