Multi-element detection method for mechanical and partial discharge faults of transformer and electronic equipment

Abstract

The invention relates to the technical field of transformer fault detection, in particular to a multivariate detection method for mechanical and partial discharge faults of a transformer and electronic equipment, and the multivariate detection method for the mechanical and partial discharge faults of the transformer comprises the steps: obtaining a fusion signal of the transformer in a working process, the fusion signal comprises a vibration signal and an ultrasonic signal of the transformer; the fusion signal is filtered through a first filter and a second filter, the ultrasonic signal and the vibration signal are separated from the fusion signal, and the frequency interval of the first filter is the frequency interval of the ultrasonic signal generated by partial discharge; according to a time domain characteristic signal and a frequency domain characteristic signal in the ultrasonic signal, determining the degree of a partial discharge phenomenon in the transformer; and determining whether a mechanical fault exists in the transformer according to the time domain characteristic signal and the frequency domain characteristic signal of the vibration signal. Therefore, the reliability, the accuracy and the comprehensiveness of the fault detection result in the transformer are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer fault detection, and particularly to a multi - detection method and an electronic device for mechanical and partial discharge faults of a transformer. Background Art

[0002] In the power system, as an indispensable key hub device, the transformer undertakes the core tasks of voltage conversion and power transmission and distribution. The reliability of its operating state directly maintains the stability and safety of the entire power supply network. Any fault may cause large - scale power outages, seriously affecting industrial production and social life.

[0003] In some scenarios, transformer faults are caused by partial discharge phenomena inside the transformer. When partial discharge phenomena occur, the vibration characteristics and ultrasonic characteristics inside the transformer will change. Currently, a single vibration sensor is often used to detect mechanical faults inside the transformer. Among them, the vibration sensor, relying on its own characteristics, has a certain ability to detect vibration information caused by mechanical stress inside the transformer, and can effectively capture relevant information on the mechanical vibration of the transformer winding core, thereby detecting mechanical faults occurring inside the transformer. However, in addition to causing changes in the vibration information inside the transformer, partial discharge phenomena also generate high - frequency ultrasonic signals, which will affect the operation of the transformer. The vibration sensor is difficult to capture the high - frequency ultrasonic signals generated by partial discharge phenomena in the face of the high - frequency ultrasonic characteristics generated by partial discharge. Therefore, using a single sensor to detect faults inside the transformer has great limitations, resulting in insufficient reliability, accuracy, and comprehensiveness of the fault detection results inside the transformer, thus affecting the safe and stable operation of the power system. Summary of the Invention

[0004] In order to solve the technical problem of insufficient reliability, accuracy, and comprehensiveness of the fault detection results inside the transformer, the purpose of the present invention is to provide a multi - detection method for mechanical and partial discharge faults of a transformer. The specific technical solution adopted is as follows:

[0005] In a first aspect, an embodiment of the present invention provides a multi - detection method for mechanical and partial discharge faults of a transformer, including: obtaining a fusion signal during the operation of the transformer, where the fusion signal includes the vibration signal and the ultrasonic signal of the transformer; filtering the fusion signal respectively using a first filter and a second filter to separate the ultrasonic signal and the vibration signal from the fusion signal, and the frequency range of the first filter is the frequency range of the ultrasonic signal generated by partial discharge; determining the degree of partial discharge phenomena occurring inside the transformer according to the time - domain characteristic signal and the frequency - domain characteristic signal in the ultrasonic signal; determining whether there is a mechanical fault inside the transformer according to the time - domain characteristic signal and the frequency - domain characteristic signal of the vibration signal.

[0006] Optionally, determining the degree of partial discharge occurring inside the transformer based on the time-domain characteristic signal and the frequency-domain characteristic signal in the ultrasonic signal includes: denoising the ultrasonic signal using the DB4 wavelet basis function to obtain a pure ultrasonic signal; extracting the energy in the time-domain characteristic signal and the frequency-domain characteristic signal of the pure ultrasonic signal; determining the rise time when the starting amplitude of the signal in the time-domain characteristic signal rises to a first threshold, where the first threshold is determined based on the peak amplitude of the time-domain characteristic signal, and the rise time is related to the degree of change of the partial discharge occurring inside the transformer; determining the duration when the signal amplitude in the time-domain characteristic signal exceeds a second threshold, where the second threshold is determined based on the peak amplitude of the time-domain characteristic signal, and the duration is related to the intensity of the partial discharge occurring inside the transformer; determining the severity of the partial discharge occurring inside the transformer based on the energy in the frequency-domain characteristic signal; and determining the degree of partial discharge occurring inside the transformer based on the rise time, the duration, and the energy in the frequency-domain characteristic signal.

[0007] Optionally, denoising the ultrasonic signal using the DB4 wavelet basis function to obtain a pure ultrasonic signal includes: decomposing the ultrasonic signal into multiple layers using the DB4 wavelet basis function, where the wavelet coefficients of each layer correspond to different frequency components of the ultrasonic signal; determining a third threshold corresponding to the wavelet coefficients of each layer according to the standard deviation of the wavelet coefficients of each layer and the length of the corresponding ultrasonic signal sequence; removing the wavelet coefficients less than or equal to the third threshold as noise, and retaining the wavelet coefficients greater than the third threshold to obtain a pure ultrasonic signal.

[0008] Optionally, extracting the energy in the frequency-domain characteristic signal of the pure ultrasonic signal includes: determining the maximum points and minimum points from the pure ultrasonic signal, and using the maximum points and minimum points as the node set for Hermite interpolation; determining the Hermite interpolation basis functions of each node in the node set; determining the envelope of the pure ultrasonic signal according to the Hermite interpolation basis functions of each node, the signal amplitude at each node, and the first derivative value at each node; and integrating the envelope over the duration to obtain the energy in the frequency-domain characteristic signal of the pure ultrasonic signal.

[0009] Optionally, determining the degree of partial discharge occurring inside the transformer based on the rise time, the duration, and the energy in the frequency-domain characteristic signal includes: the shorter the rise time, the greater the degree of change of the partial discharge occurring inside the transformer, where the degree of change includes the discharge speed; the longer the duration, the greater the intensity of the partial discharge occurring inside the transformer; and the greater the energy in the frequency-domain characteristic signal, the more severe the partial discharge occurring inside the transformer.

[0010] Optionally, the first threshold is 90% of the peak amplitude, and the second threshold is 10% of the peak amplitude.

[0011] Optionally, determining whether there is a mechanical fault inside the transformer based on the time-domain characteristic signal and the frequency-domain characteristic signal of the vibration signal includes: denoising the vibration signal using the sym8 wavelet basis function to obtain a pure vibration signal; storing the vibration acceleration in the pure vibration signal as a time series to obtain a vibration acceleration sequence; determining potential peaks from the vibration acceleration sequence, where the vibration accelerations corresponding to the potential peaks are all greater than the vibration accelerations adjacent to them; selecting the maximum value from all the potential peaks as the vibration peak of the vibration signal, and the vibration peak of the vibration signal indicates the maximum intensity reached by the vibration signal at the corresponding moment, and is used to evaluate the instantaneous stress borne by the mechanical structure of the transformer; calculating the variance of the time-domain characteristic signal of the vibration signal, and the variance indicates the change range of the transformer vibration; performing a Fourier transform on the time-domain characteristic signal to obtain the frequency-domain characteristic signal of the vibration signal; determining the vibration characteristics of the transformer according to the amplitude distribution and energy distribution of each frequency component in the frequency-domain characteristic signal; and determining whether there is a mechanical fault inside the transformer based on the vibration peak, variance, and vibration characteristics.

[0012] Optionally, denoising the vibration signal using the sym8 wavelet basis function to obtain a pure vibration signal includes: decomposing the vibration signal into multiple layers using the sym8 wavelet basis function, and the wavelet coefficients of each layer correspond to different frequency components of the vibration signal; determining the fourth threshold corresponding to the wavelet coefficients of each layer according to the standard deviation of the wavelet coefficients of each layer and the length of the corresponding vibration signal sequence; removing the wavelet coefficients less than or equal to the fourth threshold as noise, and retaining the wavelet coefficients greater than the fourth threshold to obtain a pure vibration signal.

[0013] Optionally, determining whether there is a mechanical fault inside the transformer based on the vibration peak, variance, and vibration characteristics includes: when the vibration peak is greater than the fifth threshold, determining that the instantaneous stress borne by the mechanical structure of the transformer exceeds the tolerable range, and there is a potential mechanical fault inside the transformer; when the variance is greater than the sixth threshold, determining that the change range of the mechanical vibration of the transformer exceeds the normal range, and there is a potential mechanical fault inside the transformer; when the amplitude distribution of the frequency components exceeds the amplitude distribution range during the normal operation of the transformer and / or the energy distribution changes compared with the energy distribution during the normal operation of the transformer, determining that there is a potential mechanical fault inside the transformer.

[0014] In a second aspect, an embodiment of the present invention provides an electronic device, including: a processor and a memory; wherein, the memory is used to store a computer program that can run on the processor; the processor is used to execute the program stored in the memory to implement the steps of the multi-source detection method for transformer mechanical and partial discharge faults as mentioned in the first aspect.

[0015] The present invention has the following beneficial effects: First, a fusion signal during the operation of the transformer is obtained, and the fusion signal includes the vibration signal and the ultrasonic signal of the transformer; then the first filter and the second filter are used to filter the fusion signal respectively to separate the ultrasonic signal and the vibration signal from the fusion signal, and the frequency range of the first filter is the frequency range of the ultrasonic signal generated by partial discharge; secondly, the degree of partial discharge occurring inside the transformer is determined according to the time-domain characteristic signal and the frequency-domain characteristic signal in the ultrasonic signal; finally, whether there is a mechanical fault inside the transformer is determined according to the time-domain characteristic signal and the frequency-domain characteristic signal of the vibration signal.

[0016] In this way, the embodiment of the present invention obtains a fusion signal that combines the vibration signal and the ultrasonic signal of the transformer, filters the fusion signal, extracts the vibration signal and the ultrasonic signal, can analyze the vibration characteristics inside the transformer, and can also analyze the ultrasonic signal generated when partial discharge occurs inside the transformer. Then, the faults inside the transformer are detected by fusing the frequency-domain characteristics and the time-domain characteristics of the vibration signal and the ultrasonic signal. It reduces the limitation of using a single sensor to detect the faults inside the transformer, improves the reliability, accuracy, and comprehensiveness of the fault detection results inside the transformer, and thus ensures the safe and stable operation of the power system. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the following-described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a schematic flow chart of a multi-element detection method for mechanical and partial discharge faults of a transformer provided by an embodiment of the present invention.

[0019] Figure 2 It is a schematic curve diagram of the ultrasonic wave signal of a transformer provided by an embodiment of the present invention.

[0020] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. Detailed Embodiments

[0021] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details a multi - detection method and an electronic device for transformer mechanical and partial discharge faults proposed according to the present invention, including its specific implementation manner, structure, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this invention belongs.

[0023] The following specifically describes the specific solutions of a multi - detection method and an electronic device for transformer mechanical and partial discharge faults provided by the present invention in conjunction with the accompanying drawings.

[0024] Please refer to Figures 1 to 3 , Figure 1 , which is a schematic flowchart of a multi - detection method for transformer mechanical and partial discharge faults provided by an embodiment of the present invention. Figure 2 , which is a schematic curve diagram of the ultrasonic signal of a transformer provided by an embodiment of the present invention. Figure 3 , which is a schematic structural diagram of an electronic device provided by an embodiment of the present invention.

[0025] As Figure 1 shown, the multi - detection method for transformer mechanical and partial discharge faults disclosed in the embodiments of the present invention includes:

[0026] Step S101, obtain the fusion signal during the operation of the transformer, where the fusion signal includes the vibration signal and the ultrasonic signal of the transformer.

[0027] Specifically, in the embodiments of the present invention, an ultrasonic - vibration fusion sensor is used to collect the fusion signal during the operation of the transformer, realizing synchronous and co - point measurement of vibration and ultrasonic signals. Among them, in the embodiments of the present invention, the ultrasonic - vibration fusion sensor is installed at key parts of the transformer, such as the middle, upper, and lower parts of the oil tank wall, etc., which can better capture the vibration signal and ultrasonic signal generated during partial discharge of the transformer. It should be noted that when installing the ultrasonic - vibration fusion sensor, it is necessary to ensure close contact with the surface of the transformer to ensure more accurate vibration signals and ultrasonic signals are collected.

[0028] Further, in the embodiments of the present invention, the sampling frequency of the ultrasonic-vibration fusion sensor can be determined according to the operating conditions and detection requirements of the transformer to obtain a sufficient number of effective data samples. When setting the sampling frequency in the embodiments of the present invention, the frequency range of the partial discharge signal of the transformer is comprehensively analyzed. Generally, the frequency of the ultrasonic signal generated by the partial discharge of the transformer is between 20 kHz and 100 kHz. In the embodiments of the present invention, the sampling rate of the oscilloscope is set at 500 kHz, which can collect complete spectrum information and avoid frequency aliasing. The ultrasonic-vibration fusion sensor is correctly connected to the corresponding channel of the oscilloscope to ensure that the signal transmission line is stable and well-shielded to reduce the influence of external interference on the signal. After starting the oscilloscope, 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. The fusion signal collected during the operation of the transformer is S(t), where t represents time.

[0029] Further, when collecting the fusion signal of the transformer in the embodiments of the present invention, the ultrasonic-vibration fusion sensor is closely attached to the surface of the transformer box. To ensure that the ultrasonic-vibration fusion sensor achieves a good signal collection effect, in the embodiments of the present invention, an appropriate amount of coupling agent is evenly applied to the contact part between the ultrasonic-vibration fusion sensor and the box, thereby effectively reducing the energy loss and distortion during the signal transmission, and enabling the ultrasonic-vibration fusion sensor to accurately and efficiently collect the vibration-ultrasonic fusion signal generated during the partial discharge of the transformer.

[0030] Step S102, filter the fusion signal by using a first filter and a second filter respectively to separate the ultrasonic signal and the vibration signal from the fusion signal.

[0031] Among them, the frequency range of the first filter is the frequency range of the ultrasonic signal generated by the partial discharge.

[0032] Specifically, in the embodiments of the present invention, the collected fusion signal is filtered. According to the vibration mechanism during the operation of the transformer and the frequency characteristics of the ultrasonic signal during the partial discharge, a first filter and a second filter are respectively set, and the fusion signal is filtered to obtain the ultrasonic signal and the vibration signal respectively. Among them, both the first filter and the second filter can be band-pass filters, such as Butterworth filters. The Butterworth filter has a flat response curve in the passband, small signal attenuation, and can maintain the original characteristics of the signal to the greatest extent and reduce distortion.

[0033] Furthermore, in the embodiments of the present invention, through the analysis of the vibration mechanism and experimental data during the operation of the transformer, the frequency of the ultrasonic signal generated during partial discharge in the transformer mainly concentrates in the frequency range of 20 kHz - 100 kHz, and the vibration frequency of the transformer is generally between 0 - 1000 Hz. Therefore, in the embodiments of the present invention, 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.

[0034] Furthermore, in the embodiments of the present invention, the fused signal S(t) is connected to the first filter, and the first filter performs frequency screening on the fused signal. Only the signal components with frequencies in the range of 20 kHz - 100 kHz in the fused signal can pass through the first filter, and the remaining frequency components will be effectively suppressed. The signal after passing through the first filter is the preliminarily separated ultrasonic signal S1(t).

[0035] Furthermore, in the embodiments of the present invention, the fused signal S(t) 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 signals with frequencies in the range of 0 - 1000 Hz in the fused signal can pass through the second filter, and the remaining frequency components will be effectively suppressed. The signal after passing through the first filter is the preliminarily separated vibration signal S2(t).

[0036] Step S103: Determine the degree of partial discharge occurrence inside the transformer according to the time-domain characteristic signal and frequency-domain characteristic signal in the ultrasonic signal.

[0037] Further, in the embodiments of the present invention, time-domain analysis and frequency-domain analysis are performed on the ultrasonic signal to extract time-domain characteristic signals and frequency-domain characteristic signals, and the time-domain characteristic signals and frequency-domain characteristic signals of the ultrasonic signal are comprehensively analyzed to determine the degree of partial discharge occurring inside the transformer. Among them, the time-domain characteristic signals can directly observe the change of the ultrasonic signal over time, including characteristics such as the amplitude, pulse width, rise time, and fall time of the signal. Abnormal pulses, distortion of the ultrasonic signal, or specific time series patterns in the time-domain signal may imply the existence of a fault. For example, the ultrasonic signal caused by partial discharge inside the transformer may be manifested as additional pulses or changes in the signal amplitude in the time domain. The frequency-domain characteristic signals can reveal the distribution of the ultrasonic signal in different frequency components, obtain the spectral characteristics of the signal, including the center frequency, bandwidth, frequency amplitude distribution, etc. The frequency-domain characteristic signals can provide more detailed information about the type and severity of the fault. Different types of faults will cause specific changes in the frequency components or spectral characteristics of the ultrasonic signal in the frequency domain. For example, when partial discharge occurs in the transformer, the ultrasonic signal will have specific changes in the frequency components in the frequency domain. The embodiments of the present invention combine the time-domain and frequency-domain characteristics of the ultrasonic signal, which can complement and verify each other to more accurately locate the fault of the transformer and reduce the possibility of misdiagnosis and missed diagnosis.

[0038] Further, in an alternative embodiment of the present invention, determining the degree of partial discharge occurring inside the transformer according to the time-domain characteristic signals and frequency-domain characteristic signals in the ultrasonic signal includes: denoising the ultrasonic signal using the DB4 wavelet basis function to obtain a pure ultrasonic signal; extracting the energies in the time-domain characteristic signals and frequency-domain characteristic signals of the pure ultrasonic signal; determining the rise time when the starting amplitude of the signal in the time-domain characteristic signal rises to the first threshold, where the first threshold is determined according to the peak amplitude of the time-domain characteristic signal, and the rise time is related to the degree of change of the partial discharge occurring inside the transformer; determining the duration when the signal amplitude in the time-domain characteristic signal exceeds the second threshold, where the second threshold is determined according to the peak amplitude of the time-domain characteristic signal, and the duration is related to the intensity of the partial discharge occurring inside the transformer; determining the severity of the partial discharge occurring inside the transformer according to the energy in the frequency-domain characteristic signal; and determining the degree of partial discharge occurring inside the transformer based on the rise time, duration, and energy in the frequency-domain characteristic signal.

[0039] Specifically, in order to improve the signal processing efficiency, the embodiments of the present invention denoise the ultrasonic signal to obtain a pure ultrasonic signal, and use the pure ultrasonic signal for subsequent data analysis. In view of the mutation characteristics of ultrasound, the embodiments of the present invention select the DB4 wavelet basis function to denoise the ultrasonic signal. The DB4 wavelet basis function can better process non-stationary ultrasonic signals and retain the signal feature details to the greatest extent, avoiding the distortion of the ultrasonic signal, and the denoising effect of the ultrasonic signal is better.

[0040] Further, as an optional embodiment of the present invention, denoising the ultrasonic signal using the DB4 wavelet basis function to obtain a pure ultrasonic signal includes: decomposing the ultrasonic signal into multiple layers using the DB4 wavelet basis function, and the wavelet coefficients of each layer correspond to different frequency components of the ultrasonic signal; determining the third threshold corresponding to the wavelet coefficients of each layer according to the standard deviation of the wavelet coefficients of each layer and the length of the corresponding ultrasonic signal sequence; removing the wavelet coefficients less than or equal to the third threshold as noise, and retaining the wavelet coefficients greater than the third threshold to obtain a pure ultrasonic signal.

[0041] Specifically, in the embodiment of the present invention, the ultrasonic signal is decomposed into 5 layers using the wavelet basis function, and the wavelet coefficients of each layer correspond to different frequency components of the ultrasonic signal, and a reasonable threshold is set for the wavelet coefficients of each decomposed layer. Among them, the following formula is used in the embodiment of the present invention to set the third threshold for the wavelet coefficients of each layer:

[0042]

[0043] In the above formula, T i represents the third threshold corresponding to the wavelet coefficients of the i-th layer. σi is the standard deviation of the wavelet coefficients of the i-th layer. length(S1(t)) is the length of the ultrasonic signal sequence. log represents taking the logarithm of length(S1(t)).

[0044] Further, after determining the third threshold, the wavelet coefficients less than or equal to the third threshold are set to 0, thereby removing the wavelet coefficients as noise, and the wavelet coefficients greater than the third threshold are retained as useful information. In the embodiment of the present invention, the wavelet coefficients are denoted as ω i,j , where i is the number of decomposition layers and j is the serial number of the wavelet coefficients of this layer. If |ω i,j | > T i , then the wavelet coefficient is retained, and the processed wavelet coefficient at this time If |ω i,j | ≤ T i , then the wavelet coefficient is set to 0, that is, the processed wavelet coefficient at this time

[0045] Further, in the embodiment of the present invention, the first threshold is 90% of the peak amplitude, and the second threshold is 10% of the peak amplitude.

[0046] Further, in the embodiment of the present invention, the time-domain characteristic signal in the pure ultrasonic signal is extracted. Exemplarily, such as Figure 2As shown, the rise time corresponding to the amplitude A at which the starting amplitude of the ultrasonic signal rises to 90% of the peak amplitude can reflect the change characteristics of partial discharge in the transformer. The shorter the rise time, the greater the degree of change in the partial discharge occurring inside the transformer. This degree of change can include the discharge speed, that is, the shorter the rise time, the faster the discharge speed, and the stronger the energy released in a short time.

[0047] Further, as Figure 2 shown, the second threshold is set to 10% of the peak amplitude in the embodiment of the present invention, and the duration t d of the ultrasonic signal whose amplitude exceeds 10% of the peak amplitude is the duration of the ultrasonic pulse whose amplitude exceeds 10% of the peak amplitude. The duration can reflect the intensity of partial discharge in the transformer. The longer the duration, the greater the intensity of the partial discharge occurring inside the transformer.

[0048] Further, the embodiment of the present invention uses the Hermite interpolation method to extract the energy in the frequency-domain characteristic signal of the pure ultrasonic signal. As an optional embodiment of the present invention, extracting the energy in the frequency-domain characteristic signal of the pure ultrasonic signal includes: determining the maximum points and minimum points from the pure ultrasonic signal, and using the maximum points and minimum points as the node set for Hermite interpolation; determining the Hermite interpolation basis functions of each node in the node set; determining the envelope of the pure ultrasonic signal according to the Hermite interpolation basis functions of each node, the signal amplitudes at each node, and the first derivative values at each node; integrating the envelope over the duration to obtain the energy in the frequency-domain characteristic signal of the pure ultrasonic signal.

[0049] Specifically, the embodiment of the present invention finds the maximum points and minimum points in the ultrasonic signal S1(t). These maximum points and minimum points constitute the node set for Hermite interpolation. These nodes are the key positions where the amplitude of the ultrasonic signal changes, and are crucial for accurately depicting the envelope shape of the ultrasonic signal. Among them, the specific process of determining the Hermite interpolation basis functions of each node in the node set can refer to the well-known technology, and the embodiment of the present invention will not elaborate here.

[0050] Further, the embodiment of the present invention calculates the envelope of the pure ultrasonic signal using the following formula:

[0051]

[0052] In the above formula, H(x) represents the envelope of the pure ultrasonic signal. x represents the pure ultrasonic signal. y i represents the signal amplitude of the ultrasonic signal at the i-th node. and h i (x) represents the Hermite interpolation basis function of the i-th node. m iIt represents the first derivative value of the i-th node. n represents the number of nodes in the node set.

[0053] So far, after obtaining the envelope of the pure ultrasonic signal, integrate it within the duration t d to obtain the energy E in the frequency-domain characteristic signal of the pure ultrasonic signal. The energy E in the frequency-domain characteristic signal of the pure ultrasonic signal reflects the severity of the partial discharge occurring inside the transformer. Generally, the larger the energy E in the frequency-domain characteristic signal of the pure ultrasonic signal, the more severe the partial discharge occurring inside the transformer.

[0054] Furthermore, as an optional embodiment of the present invention, determining the degree of partial discharge occurring inside the transformer based on the rise time, duration, and energy in the frequency-domain characteristic signal includes: the shorter the rise time, the greater the degree of change of the partial discharge occurring inside the transformer, and the degree of change includes the discharge speed; the longer the duration, the greater the intensity of the partial discharge occurring inside the transformer; the larger the energy in the frequency-domain characteristic signal, the more severe the partial discharge occurring inside the transformer.

[0055] Specifically, the embodiments of the present invention can evaluate the degree of partial discharge occurring inside the transformer by setting thresholds. For example, the embodiments of the present invention set a first time threshold, a second time threshold, and an energy threshold. If the rise time, duration, and energy satisfy any one of the following conditions, it indicates that the degree of partial discharge occurring inside the transformer is relatively light. If the rise time, duration, and energy satisfy any two of the following conditions, it indicates that the degree of partial discharge occurring inside the transformer is medium. If the rise time, duration, and energy satisfy all of the following conditions, it indicates that the degree of partial discharge occurring inside the transformer is relatively severe.

[0056] The above conditions include:

[0057] The rise time is less than or equal to the first time threshold;

[0058] The duration is greater than or equal to the second time threshold;

[0059] The energy is greater than or equal to the energy threshold.

[0060] Step S104, determine whether there is a mechanical fault inside the transformer according to the time-domain characteristic signal and the frequency-domain characteristic signal of the vibration signal.

[0061] Specifically, the time-domain characteristic signals and frequency-domain characteristic signals of the vibration signal can reflect the vibration characteristics of the mechanical structure of the transformer. Through the comprehensive analysis of the time-domain characteristic signals and frequency-domain characteristic signals of the vibration signal, the mechanical faults occurring in the transformer can be more accurately located. Among them, the time-domain characteristic signals of the vibration signal include, but are not limited to, characteristic parameters such as peak value and variance, to reflect the intensity and variation amplitude of the vibration signal. The frequency-domain characteristic signals of the vibration signal include, but are not limited to, frequency distribution, characteristic distribution, etc., to reflect the frequency distribution and energy distribution of the vibration signal, etc.

[0062] Further, as an optional embodiment of the present invention, determining whether there is a mechanical fault inside the transformer according to the time-domain characteristic signals and frequency-domain characteristic signals of the vibration signal includes: denoising the vibration signal by using the sym8 wavelet basis function to obtain a pure vibration signal; storing the vibration acceleration in the pure vibration signal in a time series to obtain a vibration acceleration sequence; determining potential peaks from the vibration acceleration sequence, and the vibration accelerations corresponding to the potential peaks are all greater than the vibration accelerations adjacent to them; selecting the maximum value from all the potential peaks as the vibration peak of the vibration signal, and the vibration peak of the vibration signal indicates the maximum intensity reached by the vibration signal at the corresponding moment, and is used to evaluate the instantaneous stress borne by the mechanical structure of the transformer; calculating the variance of the time-domain characteristic signals of the vibration signal, and the variance indicates the variation amplitude of the transformer vibration; performing Fourier transform on the time-domain characteristic signals to obtain the frequency-domain characteristic signals of the vibration signal; determining the vibration characteristics of the transformer according to the amplitude distribution and energy distribution of each frequency component in the frequency-domain characteristic signals; determining whether there is a mechanical fault inside the transformer based on the vibration peak, variance and vibration characteristics.

[0063] Specifically, in the embodiment of the present invention, the vibration signal S2(t) reflecting the vibration acceleration collected is stored in a time series, denoted as a(t) in the embodiment of the present invention. By traversing the entire vibration acceleration sequence, the value of each data point is compared with the values of the two adjacent data points. For example, for the i-th data point a(ti), if a(ti), if a(ti) > a(ti - 1) and a(ti) > a(ti + 1), then a(ti) is taken as a potential peak. After the traversal is completed, the maximum value is selected from all the potential peaks as the vibration peak P of the vibration signal. The vibration peak P can intuitively reflect the maximum intensity reached by the vibration signal at a certain moment, and it is used to evaluate the instantaneous stress borne by the mechanical structure of the transformer.

[0064] Further, calculate the variance σ of the time-domain characteristic signals of the vibration signal V 2 and the mean value μ V . The variance indicates the variation amplitude of the transformer vibration, and the mean value μ V indicates the vibration level of the transformer.

[0065] Further, as an optional embodiment of the present invention, determining whether there is a mechanical fault inside the transformer based on the vibration peak value, variance, and vibration characteristics includes: when the vibration peak value is greater than the fifth threshold, it is determined that the instantaneous stress borne by the mechanical structure of the transformer exceeds the tolerable range, and there is a potential mechanical fault inside the transformer; when the variance is greater than the sixth threshold, it is determined that the change amplitude of the mechanical vibration of the transformer exceeds the normal range, and there is a potential mechanical fault inside the transformer; when the amplitude distribution of the frequency components exceeds the amplitude distribution range during the normal operation of the transformer and / or the energy distribution changes compared with the energy distribution during the normal operation of the transformer, it is determined that there is a potential mechanical fault inside the transformer.

[0066] Specifically, in the embodiments of the present invention, the fifth threshold and the sixth threshold can be determined according to the actual situation, and the embodiments of the present invention do not limit this here. Among them, the larger the vibration peak value, the greater the instantaneous stress borne by the mechanical structure of the transformer, and there may be potential mechanical faults. If the variance of the transformer is larger, it means that the mechanical vibration of the transformer has large fluctuations, and the transformer may have potential mechanical faults. If the amplitude distribution of the frequency components of the transformer exceeds the amplitude distribution range during the normal operation of the transformer and / or the energy distribution changes compared with the energy distribution during the normal operation of the transformer, it means that the transformer may not be in a normal operating state, and it may have potential mechanical faults and needs to be processed in time.

[0067] Further, in order to improve the signal processing efficiency, the embodiments of the present invention perform denoising processing on the vibration signal to obtain a pure vibration signal, and use the pure vibration signal for subsequent data analysis. For relatively stable vibration signals, the embodiments of the present invention select the sym8 wavelet basis function to denoise the vibration signal. The sym8 wavelet basis function can better process stable vibration signals and retain the signal feature details to the greatest extent, avoiding the distortion of the vibration signal, and the denoising effect of the vibration signal is better.

[0068] Further, as an optional embodiment of the present invention, the sym8 wavelet basis function is used to decompose the vibration signal into multiple layers, and the wavelet coefficients of each layer correspond to different frequency components of the vibration signal; according to the standard deviation of the wavelet coefficients of each layer and the length of the corresponding vibration signal sequence, the fourth threshold corresponding to the wavelet coefficients of each layer is determined; the wavelet coefficients less than or equal to the fourth threshold are removed as noise, and the wavelet coefficients greater than the fourth threshold are retained to obtain a pure vibration signal.

[0069] Specifically, in the embodiments of the present invention, the sym8 wavelet basis function is used to decompose the vibration signal into 4 layers, and the wavelet coefficients of each layer correspond to different frequency components of the vibration signal, and reasonable thresholds are set for the wavelet coefficients of each decomposed layer. Among them, the embodiments of the present invention use the following formula to set the fourth threshold for the wavelet coefficients of each layer:

[0070]

[0071] In the above formula, R i represents the fourth threshold corresponding to the wavelet coefficients of the i-th layer. ωi is the standard deviation of the wavelet coefficients of the i-th layer. length(S2(t)) is the length of the vibration signal sequence. log represents taking the logarithm of length(S2(t)).

[0072] Furthermore, after determining the fourth threshold, the wavelet coefficients less than or equal to the fourth threshold are set to 0, thereby removing these wavelet coefficients as noise, and the wavelet coefficients greater than the fourth threshold are retained as useful information.

[0073] In the embodiment of the present invention, a fusion signal of an ultrasonic signal and a vibration signal of a transformer is obtained, and the fusion signal is filtered to extract the vibration signal and the ultrasonic signal, so as to analyze the vibration characteristics inside the transformer and also analyze the ultrasonic signal generated during partial discharge inside the transformer. Then, the faults inside the transformer are detected by fusing the frequency-domain characteristics and time-domain characteristics of the vibration signal and the ultrasonic signal. The limitation of using a single sensor to detect faults inside the transformer is reduced, and the reliability, accuracy, and comprehensiveness of the fault detection result inside the transformer are improved, thereby ensuring the safe and stable operation of the power system.

[0074] Corresponding to the multi-element detection method for transformer mechanical and partial discharge faults provided in the above embodiment, based on the same technical concept, the embodiment of the present invention also provides an electronic device, which is used to execute the above multi-element detection method for transformer mechanical and partial discharge faults, Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention, as Figure 3 shown. The electronic device may vary greatly due to configuration or performance differences, and may include one or more processors 301 and a memory 302. The memory 302 is used to store a computer program that can run on the processor 301. The processor 301 is used to execute the program stored in the memory 302 to implement each step in the above Figure 1 method embodiment. Among them, the memory 302 can be a transient storage or a persistent storage. The application programs stored in the memory 302 may include one or more modules (not shown in the figure), and each module may include a series of computer-executable instructions for the electronic device.

[0075] Further, the processor 301 can be configured to communicate with the memory 302 and execute a series of computer-executable instructions in the memory 302 on the electronic device. The electronic device may further include one or more power supplies 303, one or more wired or wireless network interfaces 304, one or more input / output interfaces 305, and one or more keyboards 306.

[0076] 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 complete communication with each other through the bus; the memory is used to store computer programs; the processor is used to execute the programs stored on the memory to implement each of the steps in the method embodiments above Figure 1 and has the beneficial effects of the above method embodiments. To avoid repetition, the embodiments of the present invention will not be described in detail here.

[0077] It should be noted that the electronic device provided in the embodiments of the present invention and the multi-source detection method for transformer mechanical and partial discharge faults 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 above-mentioned multi-source detection method for transformer mechanical and partial discharge faults, and has the same or similar beneficial effects. The repeated parts will not be described again.

[0078] It should be noted that the above-mentioned sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0079] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A multi - detection method for mechanical and partial discharge faults of a transformer, characterized in that, Including: Obtaining a fusion signal during the operation of a transformer, where the fusion signal includes the vibration signal and the ultrasonic signal of the transformer; Filtering the fusion signal respectively by using a first filter and a second filter to separate the ultrasonic signal and the vibration signal from the fusion signal, and the frequency range of the first filter is the frequency range of the ultrasonic signal generated by partial discharge; Determining the degree of partial discharge occurring inside the transformer according to the time-domain characteristic signal and the frequency-domain characteristic signal in the ultrasonic signal; Determining whether there is a mechanical fault inside the transformer according to the time-domain characteristic signal and the frequency-domain characteristic signal of the vibration signal.

2. The multi - detection method for transformer mechanical and partial discharge faults according to claim 1, characterized in that, The determining the degree of partial discharge occurring inside the transformer according to the time-domain characteristic signal and the frequency-domain characteristic signal in the ultrasonic signal includes: Denosing the ultrasonic signal by using the DB4 wavelet basis function to obtain a pure ultrasonic signal; Extracting the energy in the time-domain characteristic signal and the frequency-domain characteristic signal of the pure ultrasonic signal; Determining the rise time when the starting amplitude of the signal in the time-domain characteristic signal rises to a first threshold, where the first threshold is determined according to the peak amplitude of the time-domain characteristic signal, and the rise time is related to the change degree of the partial discharge occurring inside the transformer; Determining the duration when the signal amplitude in the time-domain characteristic signal exceeds a second threshold, where the second threshold is determined according to the peak amplitude of the time-domain characteristic signal, and the duration is related to the intensity of the partial discharge occurring inside the transformer; Determining the severity of the partial discharge occurring inside the transformer according to the energy in the frequency-domain characteristic signal; Determining the degree of partial discharge occurring inside the transformer based on the rise time, the duration, and the energy in the frequency-domain characteristic signal.

3. The multi - detection method for transformer mechanical and partial discharge faults according to claim 2, wherein, The denosing the ultrasonic signal by using the DB4 wavelet basis function to obtain a pure ultrasonic signal includes: Decomposing the ultrasonic signal into multiple layers by using the DB4 wavelet basis function, and the wavelet coefficients of each layer correspond to different frequency components of the ultrasonic signal; Determining a third threshold corresponding to the wavelet coefficients of each layer according to the standard deviation of the wavelet coefficients of each layer and the length of the corresponding ultrasonic signal sequence; Removing the wavelet coefficients less than or equal to the third threshold as noise, and retaining the wavelet coefficients greater than the third threshold to obtain the pure ultrasonic signal.

4. The multi - detection method for transformer mechanical and partial discharge faults according to claim 2, characterized in that, Extracting the energy in the frequency-domain characteristic signal of the pure ultrasonic signal includes: Determining the maximum points and the minimum points from the pure ultrasonic signal, and taking the maximum points and the minimum points as the node set for Hermite interpolation; Determining the Hermite interpolation basis functions of each node in the node set; Determining the envelope of the pure ultrasonic signal according to the Hermite interpolation basis functions of each node, the signal amplitude at each node, and the first derivative value at each node; Integrating the envelope within the duration to obtain the energy in the frequency-domain characteristic signal of the pure ultrasonic signal.

5. The multi - detection method for mechanical and partial discharge faults of a transformer according to claim 2, wherein, Determining the degree of partial discharge occurring inside the transformer based on the rise time, the duration, and the energy in the frequency-domain characteristic signal includes: The shorter the rise time, the greater the degree of change in the partial discharge occurring inside the transformer, and the degree of change includes the discharge speed; The longer the duration, the greater the intensity of the partial discharge occurring inside the transformer; The greater the energy in the frequency-domain characteristic signal, the more intense the partial discharge occurring inside the transformer.

6. The multi - detection method for mechanical and partial discharge faults of a transformer according to claim 2, characterized in that, The first threshold is 90% of the peak amplitude, and the second threshold is 10% of the peak amplitude.

7. The multi - detection method for mechanical and partial discharge faults of a transformer according to claim 1, characterized in that, Determining whether there is a mechanical fault inside the transformer based on the time-domain characteristic signal and the frequency-domain characteristic signal of the vibration signal includes: Denosing the vibration signal using the sym8 wavelet basis function to obtain a pure vibration signal; Storing the vibration acceleration in the pure vibration signal in a time series to obtain a vibration acceleration sequence; Determining potential peaks from the vibration acceleration sequence, where the vibration accelerations corresponding to the potential peaks are all greater than the vibration accelerations adjacent to them; Selecting the maximum value from all the potential peaks as the vibration peak of the vibration signal, and the vibration peak of the vibration signal indicates the maximum intensity reached by the vibration signal at the corresponding moment and is used to evaluate the instantaneous stress borne by the mechanical structure of the transformer; Calculating the variance of the time-domain characteristic signal of the vibration signal, and the variance indicates the change range of the transformer vibration; Performing a Fourier transform on the time-domain characteristic signal to obtain the frequency-domain characteristic signal of the vibration signal; Determining the vibration characteristics of the transformer according to the amplitude distribution and energy distribution of each frequency component in the frequency-domain characteristic signal; Determining whether there is a mechanical fault inside the transformer based on the vibration peak, the variance, and the vibration characteristics.

8. The multi - detection method for mechanical and partial discharge faults of a transformer according to claim 7, wherein, The denosing the vibration signal using the sym8 wavelet basis function to obtain a pure vibration signal includes: Decomposing the vibration signal into multiple layers using the sym8 wavelet basis function, and the wavelet coefficients of each layer correspond to different frequency components of the vibration signal; Determining the fourth threshold corresponding to the wavelet coefficients of each layer according to the standard deviation of the wavelet coefficients of each layer and the length of the corresponding vibration signal sequence; Removing the wavelet coefficients less than or equal to the corresponding fourth threshold as noise, and retaining the wavelet coefficients greater than the fourth threshold to obtain the pure vibration signal.

9. The multi - detection method for mechanical and partial discharge faults of a transformer according to claim 7, characterized in that, Determining whether there is a mechanical fault inside the transformer based on the vibration peak, the variance, and the vibration characteristics includes: When the vibration peak is greater than the fifth threshold, it is determined that the instantaneous stress borne by the mechanical structure of the transformer exceeds the tolerable range, and there is a potential mechanical fault inside the transformer; When the variance is greater than the sixth threshold, it is determined that the change range of the mechanical vibration of the transformer exceeds the normal range, and there is a potential mechanical fault inside the transformer; When the amplitude distribution of the frequency components exceeds the amplitude distribution range during the normal operation of the transformer and / or the energy distribution changes compared with the energy distribution during the normal operation of the transformer, it is determined that there is a potential mechanical fault inside the transformer.

10. An electronic device, characterized in that, It includes: a processor and a memory; wherein, the memory is used to store a computer program that can run on the processor; the processor is used to execute the program stored on the memory to implement the steps of the multi - parameter detection method for mechanical and partial discharge faults of the transformer as described in any one of claims 1 - 9.

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