Multi-source partial discharge positioning method and system for power equipment

Abstract

The invention belongs to the technical field of power equipment detection, and particularly relates to a multi-source partial discharge positioning method and system for power equipment. The method comprises the following steps: collecting a partial discharge pulse signal through a very high frequency sensor; establishing a three-dimensional simulation model of the power equipment to be tested; performing time reversal operation on the pulse signal, and reversely injecting the signal subjected to time reversal transformation into the three-dimensional simulation model to obtain distribution of an inversion electric field; processing the inversion electric field signal to obtain a windowed phase spectrum; calculating the cross-correlation between the windowed phase spectrums of the inversion electric fields of the pair of sensors to obtain the distribution condition of the cross-correlation of the windowed phase spectrums; and based on the distribution condition of the windowed phase spectrum cross-correlation, determining the position of one or more maximum cross-correlation, and positioning the partial discharge position. According to the time reversal method based on the phase spectrum cross-correlation criterion, multi-source partial discharge positioning is achieved, the method is not prone to being influenced by signal amplitude attenuation, and the specific position of partial discharge in power equipment can be accurately determined.

Description

Technical Field

[0001] The invention belongs to the technical field of power equipment detection, and in particular relates to a method and system for locating multi-source partial discharge of power equipment. Background Art

[0002] There are a large number of power equipment running in the power grid, such as cables, transformers, switchgear, gas insulated switches, etc. However, due to the harsh operating environment of the equipment and the long operation time, the insulation materials of these power equipment will inevitably age and produce local defects. These local insulation defects will continue to develop with the increase of operation time, and may eventually lead to insulation breakdown of the equipment, which may cause power outages or other safety accidents. Therefore, regular inspection of the insulation condition of power equipment plays a very important role in maintaining reliable operation of the equipment and ensuring the quality of power supply.

[0003] Partial discharge (PD) detection is a commonly used insulation condition detection method for power equipment. It can effectively detect early insulation defects, timely discover the deterioration and defects of equipment insulation, and evaluate and repair them on this basis. Localizing partial discharge can provide clearer guidance for equipment maintenance. At present, the localization methods commonly used in the industry are mainly based on electromagnetic waves or ultrasonic signals, combined with arrival time difference, arrival angle or machine learning methods. The shortcomings of these methods are that the arrival time difference method requires precise extraction of pulse time, resulting in weak anti-attenuation and noise capabilities, and is not suitable for internal partial discharge of complex structures. For example, the winding shielding in the transformer will affect the determination of the arrival time. The arrival angle method is suitable for outdoor open scenes, but is also not suitable for internal discharge of complex equipment structures. The machine learning method requires a large amount of discharge data set and training time, is not universal, and cannot be promoted and applied in the power grid.

[0004] In summary, the industry currently lacks a method that has strong resistance to attenuation and noise and is suitable for accurately locating partial discharges in power equipment with complex internal structures. Summary of the invention

[0005] The purpose of the present invention is to provide a method for locating multi-source partial discharge of electric equipment, which solves the problem that partial discharge in electric equipment with complex structure is difficult to locate and is greatly affected by noise and attenuation.

[0006] The present invention is achieved through the following technical solutions:

[0007] The present invention discloses a method for locating multi-source partial discharge of electric power equipment, comprising the following steps:

[0008] S1. Collect the partial discharge pulse signal of the power equipment to be tested through a very high frequency sensor;

[0009] S2. Establish a three-dimensional simulation model of the power equipment to be tested;

[0010] S3, performing a time reversal transformation on the collected partial discharge pulse signal, and reversely injecting the time reversal transformed signal into the three-dimensional simulation model to obtain the distribution of the inversion electric field;

[0011] S4, processing the inverted electric field to obtain a windowed phase spectrum, calculating the cross-correlation between the windowed phase spectra of the inverted electric field of a pair of sensors, and obtaining the distribution of the cross-correlation of the windowed phase spectra;

[0012] S5. Based on the distribution of the cross-correlation of the windowed phase spectrum, determine the location of a single or multiple maximum cross-correlations, which is the location of the partial discharge.

[0013] Further, S1 specifically includes: installing at least one pair of very high frequency sensors on the power equipment to be tested, and collecting partial discharge pulse signals generated by partial discharge through the very high frequency sensors;

[0014] In S2, the three-dimensional simulation model of the power equipment to be tested is established as follows:

[0015] According to the type and specific model of the power equipment to be tested, obtain the material and size information of the power equipment to be tested, establish a three-dimensional simulation model, and set the electromagnetic characteristic parameters of the solid, liquid and gas materials in the three-dimensional simulation model;

[0016] The electromagnetic boundary condition of the three-dimensional simulation model space is set as an absorption boundary, and the electromagnetic signal is completely refracted from the boundary to the outside; the metal edge of the three-dimensional simulation model is set as an electrical boundary to save calculation amount.

[0017] Further, in S3, the collected partial discharge pulse signal is subjected to a time reversal transformation, and the expression of the time reversal transformation is:

[0018] U TR (t) = U(Tt);

[0019] Where t is time, U is the voltage signal sensed by the sensor antenna, and U TR is the signal after time reversal transformation, T is the time length of the voltage signal U;

[0020] Tt means that after the original voltage signal U is flipped on the time axis, it is shifted in the positive direction of the time axis by a time length T of the voltage signal, so as to ensure that the analysis process time remains positive.

[0021] Further, in S3, the signal after the time reversal transformation is reversely injected into the three-dimensional simulation model to obtain the distribution of the inversion electric field, specifically:

[0022] After receiving the partial discharge signal through a certain sensor, in the three-dimensional simulation model, the signal after time reversal transformation is added to the sensor antenna as an excitation source;

[0023] The signals after each time reversal transformation are sequentially added from the corresponding sensor antenna positions to obtain the electromagnetic time inversion results when each sensor is used;

[0024] Based on the finite element or finite difference analysis method, the time-varying electric field intensity distribution is obtained, and then the inversion electric field distribution is obtained.

[0025] Furthermore, the analysis method based on finite element or finite difference obtains the distribution of electric field intensity varying with time, specifically:

[0026] In finite element or finite difference analysis software, the established three-dimensional simulation model is divided into many units; the added electrical signal excitation is radiated into the model of the device, causing the electromagnetic field of each unit in the three-dimensional simulation model to change;

[0027] Use finite element or finite difference analysis methods to solve partial differential equations related to electromagnetic fields, that is, solve the three-dimensional Maxwell equations to obtain numerical approximate solutions of the electric field signal and magnetic field signal of each unit in the process of electromagnetic field changing with time;

[0028] The numerical approximate solutions of the electric field signals of each unit in the process of the electromagnetic field changing with time are summarized to obtain the distribution of the electric field intensity changing with time in the three-dimensional simulation model.

[0029] Further, in S4, the inverted electric field is processed to obtain a windowed phase spectrum, and the cross-correlation between the windowed phase spectra of the inverted electric field of a pair of sensors is calculated, specifically:

[0030] Perform Fourier transform on the inverted electric field to obtain the frequency domain expression;

[0031] Based on the frequency domain expression, the amplitude spectrum and phase spectrum of the electric field intensity signal are obtained;

[0032] A threshold is set for the amplitude spectrum to select a frequency band, and the frequency band is added to the phase spectrum as a window to obtain a windowed phase spectrum;

[0033] Based on the windowed phase spectrum, the cross-correlation between the windowed phase spectra of the inverted electric field of a pair of sensors is calculated.

[0034] Furthermore, the inverted electric field is subjected to Fourier transform to obtain a frequency domain expression, specifically:

[0035] After obtaining the distribution of the electric field intensity that changes with time in the three-dimensional simulation model, taking a certain point r in the three-dimensional simulation model as an example, after reverse injection using the i-th sensor, the electric field intensity signal of the point is E TR Represents the electric field strength after the sensor signal is reversely injected;

[0036] right Perform Fourier transform to obtain its frequency domain expression Where ω represents the frequency;

[0037] Based on the frequency domain expression, the amplitude spectrum and phase spectrum of the electric field intensity signal are obtained, specifically:

[0038] According to the frequency domain expression, the amplitude spectrum of the electric field strength signal is obtained respectively: And the phase spectrum

[0039]

[0040] The frequency band is selected by setting a threshold value for the amplitude spectrum, and the frequency band is added to the phase spectrum as a window to obtain the windowed phase spectrum, specifically:

[0041] Amplitude spectrum Set threshold A TH , get the frequency subband whose amplitude exceeds the threshold as the window;

[0042] In the phase spectrum Add a window in the middle, retain only the phase of the frequency subband whose amplitude exceeds the threshold, and obtain the windowed phase spectrum

[0043] Based on the windowed phase spectrum, the cross-correlation between the windowed phase spectra of the inverted electric field of a pair of sensors is calculated, specifically:

[0044] Find the windowed phase spectrum of one sensor in a pair of sensors and the windowed phase spectrum of another sensor The cross-correlation between them is used to obtain the cross-correlation distribution of the windowed phase spectrum; the cross-correlation is calculated using the Pearson correlation coefficient.

[0045] Furthermore, the threshold A TH Determined by the absolute median difference method; the absolute median difference method is specifically:

[0046] For the amplitude spectrum of the inverted electric field signal, the calculation formula of the absolute median difference MAD is:

[0047] MAD=median(x i -x m |);

[0048] Among them, median means to find the median operation, x i is the amplitude of the ith frequency point x m is the median of the amplitudes of all frequency points;

[0049] If the amplitude x at a certain frequency point i Greater than x m +α·MAD, then the frequency point is considered to be prominent;

[0050] Among them, α is an arbitrary multiple, and the multiple α is adjusted according to the specific application scenario and data distribution;

[0051] The cross-correlation is calculated using the Pearson correlation coefficient, specifically:

[0052] Windowed phase spectrum of the inverted electric field of two sensors and Considered as two discrete signal sequences x(n) and y(n), the Pearson correlation coefficient rho(x,y) is calculated as:

[0053]

[0054] Where n is the number of frequency points in the phase spectrum, i = 1, 2, ..., n; j = 1, 2, ..., n; x(n) and y(n) are the windowed phase spectra of the inverted electric field of the two sensors, respectively. and x(i) and y(i) are the phases of the windowed phase spectra of the inverted electric fields of the two sensors at the i-th frequency point; are the mean values ​​of the phases of all frequency points of the windowed phase spectrum of the inverted electric field of the two sensors respectively; after calculating the cross-correlation for each point, the cross-correlation of the inverted electric field is summarized.

[0055] Further, in S5, the location of the maximum cross-correlation is determined, specifically:

[0056] First determine the position of the maximum cross-correlation and the size of the maximum cross-correlation, the maximum cross-correlation position is a maximum cross-correlation position;

[0057] Find all positions other than the maximum cross-correlation and nearby positions where the cross-correlation is greater than or equal to the adjacent points, that is, all the maximum value points;

[0058] If the cross-correlation of a certain maximum point is greater than or equal to 80% of the maximum cross-correlation, it is also considered to be a maximum cross-correlation position.

[0059] The present invention also discloses a multi-source partial discharge positioning system for electric power equipment, comprising:

[0060] A very high frequency acquisition module is used to collect partial discharge pulse signals of the power equipment to be tested through a very high frequency sensor;

[0061] A model building module is used to build a three-dimensional simulation model of the power equipment to be tested;

[0062] The time reversal module performs time reversal transformation on the collected partial discharge pulse signal, and injects the time reversal transformed signal back into the three-dimensional simulation model to obtain the distribution of the inverted electric field;

[0063] The cross-correlation module is used to process the inverted electric field to obtain a windowed phase spectrum, calculate the cross-correlation between the windowed phase spectra of the inverted electric field of a pair of sensors, and obtain the distribution of the cross-correlation of the windowed phase spectra;

[0064] The multi-source partial discharge positioning module is used to determine the location of a single or multiple maximum cross-correlations based on the distribution of the cross-correlations of the windowed phase spectrum, which is the location of the partial discharge.

[0065] Compared with the prior art, the present invention has the following beneficial technical effects:

[0066] The present invention discloses a method for locating multi-source partial discharge of electric power equipment. The method adopts a partial discharge locating technology based on an electromagnetic time inversion method and a windowed phase spectrum cross-correlation criterion. Compared with the arrival time difference, arrival angle or machine learning locating methods of traditional partial discharge of electric power equipment, the method has strong anti-noise interference capability and is less affected by noise. The method can accurately locate the three-dimensional coordinates of multi-source partial discharge points inside complex structures, and can provide clearer guidance for the maintenance of electric power equipment.

[0067] The present invention adopts the electromagnetic time inversion method to solve the problem that the traditional time difference method requires at least 4 sensors for three-dimensional local discharge positioning, and reduces the number of sensors to 2. At the same time, the present method combines the phase spectrum cross-correlation criterion instead of the amplitude criterion used in the traditional time inversion method, effectively reducing the influence of amplitude attenuation during signal propagation. The proposed phase spectrum windowing method based on the absolute median difference method can better select the main frequency band, reduce the influence of the noise frequency band, and improve the anti-noise ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a flow chart of a method for locating multi-source partial discharge of electric power equipment according to embodiment 1 of the present invention;

[0069] Figure 2 This is a schematic diagram of performing a time reversal (TR) transformation on a signal according to Embodiment 3 of the present invention;

[0070] Figure 3A schematic diagram of a phase spectrum windowing method and its effect on a noisy signal provided as a verification example of the present invention; wherein, Figure a is an amplitude spectrum of a noisy discharge signal; Figure b is an amplitude spectrum of a noise-free original signal corresponding to the noisy signal; Figure c is a phase spectrum of a noisy discharge signal; Figure d is a phase spectrum of a noise-free original signal corresponding to the noisy signal; Figure e is a windowed phase spectrum of a noisy discharge signal; and Figure f is a windowed phase spectrum of a noise-free original signal corresponding to the noisy signal;

[0071] Figure 4 Schematic diagram of a gas-insulated switch of a verification example of the present invention; wherein FIG. a is a schematic diagram of the actual structure of the gas-insulated switch; FIG. b is a three-dimensional simulation model established based on FIG. a;

[0072] Figure 5 It is a schematic diagram of the result of locating a single partial discharge source by a method for locating multi-source partial discharge of power equipment in a verification example of the present invention;

[0073] Figure 6 This is a schematic diagram of the results of locating multiple local discharge sources using a method for locating multiple local discharge sources in a verification example of the present invention. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solution and advantages of the present invention more clear, the following is further described in detail with reference to the accompanying drawings and embodiments. It should be noted that the specific embodiments described herein are only used to explain the present invention and do not limit the present invention, that is, the embodiments described herein are only some embodiments of the present invention, not all embodiments.

[0075] The model components described and shown in the drawings and embodiments of the present invention may vary depending on the type or model of different power equipment. Therefore, the detailed description of the embodiments of the present invention provided in the following drawings is not intended to limit the scope of the invention claimed for protection, but merely represents a selected embodiment of the present invention. Based on the drawings and embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0076] It should be noted that the terms "comprises", "includes" or any other variations are intended to cover non-exclusive inclusion, so that a process, element, method, article or equipment that includes a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to the process, element, method, article or equipment.

[0077] Example 1

[0078] like Figure 1 As shown, the present invention discloses a method for locating multi-source partial discharge of power equipment, comprising the following steps:

[0079] S1. Install at least one pair of very high frequency sensors on the power equipment to be tested, and collect partial discharge pulse signals through the very high frequency sensors;

[0080] S2. Establish a three-dimensional simulation model of the power equipment to be tested;

[0081] S3, performing electromagnetic time reversal (EMTR): using each sensor in turn, performing time reversal (TR) transformation on the collected partial discharge pulse signal, and reversely injecting the time reversal transformed signal into the three-dimensional simulation model of the power equipment to obtain the distribution of the inversion electric field;

[0082] S4, adding windows to the inverted electric field phase spectrum, and calculating the cross-correlation of the windowed phase spectrum: processing the inverted electric field to obtain a windowed phase spectrum, calculating the cross-correlation between the windowed phase spectra of the inverted electric field of a pair of sensors, and then obtaining the distribution of the cross-correlation of the windowed phase spectra;

[0083] S5. Localizing partial discharge using a windowed phase spectrum cross-correlation criterion: Based on the distribution of the windowed phase spectrum cross-correlation, determine the location of a single or multiple maximum cross-correlations, which is the location of the partial discharge.

[0084] Example 2

[0085] This section mainly introduces the specific operations of S1 and S2.

[0086] In S1, at least one pair of very high frequency sensors is installed on the power equipment to be tested, specifically:

[0087] At least two very high frequency sensors are installed at any position where the electromagnetic wave radiation of partial discharge inside the equipment can be sensed.

[0088] Specifically, the installation location may be inside of a transformer, inside of a switch cabinet, inside of a gas-insulated switch, or at a pot-type insulator.

[0089] In S2, a three-dimensional simulation model of the power equipment to be tested is established as:

[0090] According to the type and specific model of the power equipment to be tested, obtain its material and size information, establish a three-dimensional simulation model, and set the electromagnetic characteristic parameters such as conductivity, magnetic permeability, dielectric constant, etc. of the solid, liquid and gas materials in the model;

[0091] Set the electromagnetic boundary condition of the model space to an absorbing boundary, and the electromagnetic signal will be completely refracted from the boundary to the outside; the metal edge of the model can be set as an electrical boundary to save calculation.

[0092] Example 3

[0093] This section mainly introduces the specific operations of S3.

[0094] like Figure 2 As shown, each sensor is used in turn to perform a time reversal (TR) transformation on the collected partial discharge pulse signal. The method for performing a time reversal transformation on the pulse signal is:

[0095] U TR (t) = U(Tt);

[0096] Where t is time, U is the voltage signal sensed by the sensor antenna, and U TR It is the voltage signal after time reversal transformation, T is the time length of the voltage signal U, and -t means the original signal is flipped on the time axis. U(Tt) is used for analysis and solution, and Tt means the original signal is flipped on the time axis and then shifted in the positive direction of the time axis by a signal time length T to ensure that the time of the analysis process remains positive, which is convenient for importing into commercial finite element or finite difference analysis software.

[0097] Further, in S3, the time-reversed transformed signal is reversely injected into the three-dimensional simulation model of the power equipment, specifically:

[0098] After a VHF sensor receives a partial discharge signal, in the three-dimensional simulation model, the signal U(Tt) after time reversal transformation is added to the sensor antenna as an excitation source;

[0099] Since two or more sensors are used, the signals after each time reversal transformation are sequentially added from the corresponding sensor positions to obtain the inversion electric field results when each sensor is used.

[0100] Further, in S3, the analysis method based on finite element or finite difference is used to obtain the distribution of electric field intensity varying with time, specifically:

[0101] Import the established power equipment model into the finite element or finite difference analysis software (or directly establish a simulation model in the finite element or finite difference analysis software), and the model is divided into many units. The added electrical signal excitation radiates into the model of the equipment, causing the electromagnetic field in the model to change. Use the finite element or finite difference analysis method to solve the partial differential equations related to the electromagnetic field, that is, solve the three-dimensional Maxwell equations, and obtain the numerical approximate solution of the electromagnetic and magnetic field signals of each unit. The electric field signals of each unit are summarized to obtain the distribution of the electric field intensity in the model that changes with time; both finite elements and finite differences are methods for solving differential equations. The finite element method is used in smaller devices, and the finite difference method is used in larger devices.

[0102] Furthermore, the three-dimensional Maxwell equations are specifically:

[0103]

[0104] Where E is the electric field intensity, H is the magnetic field intensity, μ is the magnetic permeability of the medium, ε is the dielectric constant of the medium, j is the current density, and t is the time; is the sign for calculating partial derivatives, is the differential operator.

[0105] Example 4

[0106] This embodiment mainly introduces S4.

[0107] In S4, the inversion electric field is processed by performing Fourier transform on the inversion electric field. After the Fourier transform, a frequency domain expression is obtained, which is specifically:

[0108] After obtaining the distribution of electric field intensity that changes with time in the three-dimensional simulation model, taking a certain point r in the model as an example, after reverse injection using the i-th sensor, the electric field intensity signal at this point is E TR Represents the electric field strength after the sensor signal is reversely injected.

[0109] right Perform Fourier transform to obtain its frequency domain expression Where ω represents the frequency.

[0110] According to the frequency domain expression, the amplitude spectrum of the electric field strength signal is obtained respectively: And the phase spectrum

[0111]

[0112] Further, in S4, the frequency band is selected by setting a threshold value for the amplitude spectrum, and the frequency band is added to the phase spectrum as a window to obtain a windowed phase spectrum, and the cross-correlation between the windowed phase spectra of the inverted electric field of a pair of sensors is calculated, specifically:

[0113] Amplitude spectrum Set threshold A TH , the frequency subband whose amplitude exceeds the threshold is obtained as the window. The threshold is determined by the median absolute difference (MAD) method. When multiple sensors are used, the window is obtained by taking the intersection of all sensors.

[0114] In the phase spectrum Add a window in the middle, retain only the phase of the frequency subband whose amplitude exceeds the threshold, and obtain the windowed phase spectrum

[0115] Take a pair of sensors numbered 1 and 2 as an example, and find the windowed phase spectrum using sensor 1. and the windowed phase spectrum using sensor 2 The cross-correlation between the two is used to obtain the cross-correlation distribution of the windowed phase spectrum. The cross-correlation uses the Pearson correlation coefficient.

[0116] The principle of locating the local discharge source based on phase spectrum cross-correlation is as follows:

[0117] Assume that there is a local discharge source at point p in space, generating a pulse signal x(t), and there are n sensors in space, receiving signals y1(t), y2(t), …, y n (t). According to the Lorentz reciprocity theorem, the transfer function h between the PD source point p and the i-th sensor pi (t) and the transfer function h from the i-th sensor to the PD source point p ip (t) are equal, let h pi (t) = h ip (t) = h i (t). Then a sensor receives the signal y i (t) = x i (t)*h i (t), the symbol * represents convolution. TR transformation is performed on the signals received by each sensor, y i (t) becomes y i (-t), and are injected into the corresponding sensors in turn, then the electric field strength signal generated at a certain point r in space is:

[0118]

[0119] where h ir (t) is the transfer function from the i-th sensor to point r. When point r is the original PD point p, h ir (t) = h ip (t) = h i (t), then:

[0120]

[0121] Its frequency domain expression, amplitude spectrum, and phase spectrum are:

[0122]

[0123] X(ω) and H(ω) are the frequency domain forms of x(t) and h(t), respectively, and * indicates frequency domain conjugation. At the local discharge location, the frequency domain expression of the inverted electric field obtained by different sensors, its phase spectrum They are all frequency domain conjugates of the original partial discharge signals, X*(ω), and have a high correlation. Therefore, by obtaining the frequency domain correlation of the inversion signals of each sensor in the region, the partial discharge position is determined by the maximum cross-correlation.

[0124] Using phase instead of amplitude as the judgment criterion reduces the impact of signal attenuation. When noise exists, windowing can select the main frequency band related to partial discharge in the noisy signal to reduce the impact of noise.

[0125] Furthermore, the absolute median difference method is specifically as follows:

[0126] For a discrete sequence, first find the median of the sequence, then find the absolute value of each point in the discrete sequence minus the median to get a new sequence, and finally find the median of the new sequence, which is MAD. The calculation formula of MAD is:

[0127] MAD=median(x i -x m |);

[0128] Among them, median means to find the median operation, x i is the sample, here is the amplitude of the i-th frequency point x m is the median of the sample, here is the amplitude at each frequency point If the amplitude x at a certain frequency point i Greater than x m +α·MAD (α is any multiple, such as 2), then the frequency point is considered to be prominent, and the multiple α can be adjusted according to the specific application scenario and data distribution.

[0129] Further, the Pearson correlation coefficient is specifically:

[0130] For two signal sequences x(n), y(n), the Pearson correlation coefficient rho(x,y) is calculated as:

[0131]

[0132] Where n is the sequence length, Represents the mean of the sequence. The value of the correlation coefficient ranges from –1 to +1. –1 indicates a perfect negative correlation, +1 indicates a perfect positive correlation, and 0 indicates no correlation between the sequences. When a pair (two) of sensors are used, the windowed phase spectrum of the electric field is inverted using the two sensors. and The calculation is performed as a signal sequence x(n), y(n). When more than two sensors are used, the results of each sensor are calculated two by two and then multiplied and added.

[0133] Specifically, when calculating the cross-correlation of the windowed phase spectrum, n is the number of frequency points in the phase spectrum, i = 1, 2, ..., n; j = 1, 2, ..., n; x(n) and y(n) are the windowed phase spectra of the inverted electric field of the two sensors, respectively. and x(i) and y(i) are the phases of the windowed phase spectra of the inverted electric fields of the two sensors at the i-th frequency point; are the mean values ​​of the phases of all frequency points of the windowed phase spectrum of the inverted electric field of the two sensors respectively; after calculating the cross-correlation for each point, the cross-correlation of the inverted electric field is summarized.

[0134] In S5, the location of the maximum cross-correlation is determined, specifically:

[0135] First determine the position of the maximum cross-correlation and the size of the maximum cross-correlation, the maximum cross-correlation position is a maximum cross-correlation position;

[0136] Find all positions other than the maximum cross-correlation and nearby positions where the cross-correlation is greater than or equal to the adjacent points, that is, all the maximum value points;

[0137] If the cross-correlation of a certain maximum point is greater than or equal to 80% of the maximum cross-correlation, it is also considered to be a maximum cross-correlation position.

[0138] Example 5

[0139] The present invention also discloses a multi-source partial discharge positioning system for electric power equipment, comprising:

[0140] A very high frequency acquisition module is used to collect partial discharge pulse signals of the power equipment to be tested through a very high frequency sensor;

[0141] A model building module is used to build a three-dimensional simulation model of the power equipment to be tested;

[0142] The time reversal module performs time reversal transformation on the collected partial discharge pulse signal, and injects the time reversal transformed signal back into the three-dimensional simulation model to obtain the distribution of the inverted electric field;

[0143] The cross-correlation module is used to process the inverted electric field to obtain a windowed phase spectrum, calculate the cross-correlation between the windowed phase spectra of the inverted electric field of a pair of sensors, and obtain the distribution of the cross-correlation of the windowed phase spectra;

[0144] The multi-source partial discharge positioning module is used to determine the location of a single or multiple maximum cross-correlations based on the distribution of the cross-correlations of the windowed phase spectrum, which is the location of the partial discharge.

[0145] The features and performances of the present invention are further described in detail below in conjunction with specific verification examples.

[0146] like Figure 4 As shown in a, a tubular structure of a gas insulated switch has defects inside the structure, generating a partial discharge signal. A pair of very high frequency sensors are installed on the tubular structure, and the sensors at both ends of the tubular structure detect the partial discharge signal.

[0147] According to the material and size information of the gas-insulated switch and the sensor, a three-dimensional simulation model is established in a commercial three-dimensional modeling software, and then imported into a commercial finite element or finite difference analysis software (or directly in the commercial finite element or finite difference analysis software) to establish the following Figure 4 b shows the three-dimensional simulation model, and sets parameters such as conductivity, magnetic permeability, and dielectric constant.

[0148] The signal received by the sensor is time-reversed and injected into the corresponding sensor in the model as an excitation signal to obtain the distribution of the inverted electric field.

[0149] The inverted electric field of the two sensors is transformed into a frequency domain expression using Fourier transform, including amplitude spectrum and phase spectrum. A threshold is set for the amplitude spectrum to select a frequency band, which is then added to the phase spectrum as a window to obtain a windowed phase spectrum. The cross-correlation between the windowed phase spectra of the inverted electric field of a pair of sensors is calculated.

[0150] Specifically, Figure 3 As shown in FIG. 1 , a schematic diagram of a phase spectrum windowing method provided by the present invention and its effect on a noisy signal is shown in FIG. A threshold is set for the amplitude spectrum to select a frequency band, and the frequency band is added to the phase spectrum as a window to obtain a windowed phase spectrum, and the cross-correlation between a pair of sensor inversion electric field windowed phase spectra is calculated, specifically: Set threshold A TH , and obtain the frequency subband whose amplitude exceeds the threshold as the window. The threshold is determined by the median absolute difference (MAD) method. Add a window in the middle, retain only the phase of the frequency subband whose amplitude exceeds the threshold, and obtain the windowed phase spectrum

[0151] Figure 3 The effect of windowing the phase spectrum of a noisy discharge signal is compared with the windowing effect of the noise-free original signal and its phase spectrum corresponding to the noisy signal. Among them, Figure a is the amplitude spectrum of the noisy discharge signal, and the entire frequency band in the amplitude spectrum contains noise components; Figure b is the amplitude spectrum of the noise-free original signal corresponding to the noisy signal, and the original local discharge amplitude spectrum is concentrated in some frequency bands; Figure c is the phase spectrum of the noisy discharge signal; Figure d is the phase spectrum of the noise-free original signal corresponding to the noisy signal; Figure e is the windowed phase spectrum of the noisy discharge signal; Figure f is the windowed phase spectrum of the noise-free original signal corresponding to the noisy signal; it can be seen that the phase spectrum windowing method provided by the present invention screens out the main frequency bands related to local discharge in the noisy signal, and retains the part of the phase spectrum of the noisy signal close to the original signal.

[0152] Take a pair of sensors numbered 1 and 2 as an example, and find the windowed phase spectrum using sensor 1. and the windowed phase spectrum using sensor 2 The Pearson cross-correlation between them is used to obtain the windowed phase spectrum cross-correlation distribution.

[0153] like Figure 5 As shown, the embodiment of the present invention uses the windowed phase spectrum cross-correlation criterion to locate the result of a single partial discharge source. Based on the distribution of the windowed phase spectrum cross-correlation, the position of the maximum cross-correlation is determined. There is only one maximum cross-correlation position with a large value, which is the position of the single partial discharge source.

[0154] like Figure 6 As shown in FIG. 1 , the results of locating multiple local discharge sources using the windowed phase spectrum cross-correlation criterion in an embodiment of the present invention. Based on the distribution of the windowed phase spectrum cross-correlation, there are multiple maximum cross-correlations with large values, so there are multiple local discharge sources, and there are 2 in this embodiment. The positions of these maximum cross-correlations with large values ​​are the positions of the multiple local discharge sources.

[0155] The above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Any modification or equivalent substitution based on the technical solutions made in accordance with the technical ideas proposed by the present invention without departing from the spirit and scope of the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for locating multi-source partial discharge of power equipment, characterized in that: The following steps are involved: S1. Collect the partial discharge pulse signal of the power equipment to be tested through a very high frequency sensor; S2. Establish a three-dimensional simulation model of the power equipment to be tested; S3, performing a time reversal transformation on the collected partial discharge pulse signal, and reversely injecting the time reversal transformed signal into the three-dimensional simulation model to obtain the distribution of the inversion electric field; S4, processing the inverted electric field to obtain a windowed phase spectrum, calculating the cross-correlation between the windowed phase spectra of the inverted electric field of a pair of sensors, and obtaining the distribution of the cross-correlation of the windowed phase spectra; S5. Based on the distribution of the cross-correlation of the windowed phase spectrum, determine the location of a single or multiple maximum cross-correlations, which is the location of the partial discharge.

2. The method for locating multi-source partial discharge of power equipment according to claim 1, characterized in that: S1 specifically comprises: installing at least one pair of very high frequency sensors on the power equipment to be tested, and collecting the partial discharge pulse signal generated by the partial discharge through the very high frequency sensors; In S2, the three-dimensional simulation model of the power equipment to be tested is established as follows: According to the type and specific model of the power equipment to be tested, obtain the material and size information of the power equipment to be tested, establish a three-dimensional simulation model, and set the electromagnetic characteristic parameters of the solid, liquid and gas materials in the three-dimensional simulation model; The electromagnetic boundary condition of the three-dimensional simulation model space is set as an absorption boundary, and the electromagnetic signal is completely refracted from the boundary to the outside; the metal edge of the three-dimensional simulation model is set as an electrical boundary to save calculation amount.

3. The method for locating multi-source partial discharge of electric power equipment according to claim 1, characterized in that: In S3, the collected partial discharge pulse signal is subjected to time reversal transformation, and the expression of time reversal transformation is: U TR (t)=U(T-t); Where t is time, U is the voltage signal sensed by the sensor antenna, and U TR is the signal after time reversal transformation, T is the time length of the voltage signal U; Tt means that after the original voltage signal U is flipped on the time axis, it is shifted in the positive direction of the time axis by a time length T of the voltage signal, so as to ensure that the analysis process time remains positive.

4. The method for locating multi-source partial discharge of electric power equipment according to claim 1, characterized in that: In S3, the signal after the time reversal transformation is reversely injected into the three-dimensional simulation model to obtain the distribution of the inversion electric field, specifically: After receiving the partial discharge signal through a certain sensor, in the three-dimensional simulation model, the signal after time reversal transformation is added to the sensor antenna as an excitation source; The signals after each time reversal transformation are sequentially added from the corresponding sensor antenna positions to obtain the electromagnetic time inversion results when each sensor is used; Based on the finite element or finite difference analysis method, the time-varying electric field intensity distribution is obtained, and then the inversion electric field distribution is obtained.

5. The method for locating multi-source partial discharge of electric power equipment according to claim 4, characterized in that: The analysis method based on finite element or finite difference is used to obtain the distribution of electric field intensity varying with time, specifically: In finite element or finite difference analysis software, the established three-dimensional simulation model is divided into many units; the added electrical signal excitation is radiated into the model of the device, causing the electromagnetic field of each unit in the three-dimensional simulation model to change; Use finite element or finite difference analysis methods to solve partial differential equations related to electromagnetic fields, that is, solve the three-dimensional Maxwell equations to obtain numerical approximate solutions of the electric field signal and magnetic field signal of each unit in the process of electromagnetic field changing with time; The numerical approximate solutions of the electric field signals of each unit in the process of the electromagnetic field changing with time are summarized to obtain the distribution of the electric field intensity changing with time in the three-dimensional simulation model.

6. The method for locating multi-source partial discharge of electric power equipment according to claim 1, characterized in that: In S4, the inverted electric field is processed to obtain a windowed phase spectrum, and the cross-correlation between the windowed phase spectra of the inverted electric field of a pair of sensors is calculated, specifically: Perform Fourier transform on the inverted electric field to obtain the frequency domain expression; Based on the frequency domain expression, the amplitude spectrum and phase spectrum of the electric field intensity signal are obtained; A threshold is set for the amplitude spectrum to select a frequency band, and the frequency band is added to the phase spectrum as a window to obtain a windowed phase spectrum; Based on the windowed phase spectrum, the cross-correlation between the windowed phase spectra of the inverted electric field of a pair of sensors is calculated.

7. The method for locating multi-source partial discharge of electric power equipment according to claim 6, characterized in that: The inverted electric field is subjected to Fourier transform to obtain a frequency domain expression, which is specifically: After obtaining the distribution of the electric field intensity that changes with time in the three-dimensional simulation model, taking a certain point r in the three-dimensional simulation model as an example, after reverse injection using the i-th sensor, the electric field intensity signal of the point is E TR Represents the electric field strength after the sensor signal is reversely injected; right Perform Fourier transform to obtain its frequency domain expression Where ω represents the frequency; Based on the frequency domain expression, the amplitude spectrum and phase spectrum of the electric field intensity signal are obtained, specifically: According to the frequency domain expression, the amplitude spectrum of the electric field strength signal is obtained respectively: And the phase spectrum The frequency band is selected by setting a threshold value for the amplitude spectrum, and the frequency band is added to the phase spectrum as a window to obtain the windowed phase spectrum, specifically: Amplitude spectrum Set threshold A TH , get the frequency subband whose amplitude exceeds the threshold as the window; In the phase spectrum Add a window in the middle, retain only the phase of the frequency subband whose amplitude exceeds the threshold, and obtain the windowed phase spectrum Based on the windowed phase spectrum, the cross-correlation between the windowed phase spectra of the inverted electric field of a pair of sensors is calculated, specifically: Find the windowed phase spectrum of one sensor in a pair of sensors and the windowed phase spectrum of another sensor The cross-correlation between them is used to obtain the cross-correlation distribution of the windowed phase spectrum; the cross-correlation is calculated using the Pearson correlation coefficient.

8. The method for locating multi-source partial discharge of electric power equipment according to claim 7, characterized in that: Threshold A TH Determined by the absolute median difference method; the absolute median difference method is specifically: For the amplitude spectrum of the inverted electric field signal, the calculation formula of the absolute median difference MAD is: MAD=median(x i -x m |); Among them, median means to find the median operation, x i is the amplitude of the ith frequency point x m is the median of the amplitudes of all frequency points; If the amplitude x at a certain frequency point i Greater than x m +α·MAD, then the frequency point is considered to be prominent; Among them, α is an arbitrary multiple, and the multiple α is adjusted according to the specific application scenario and data distribution; The cross-correlation is calculated using the Pearson correlation coefficient, specifically: Windowed phase spectrum of the inverted electric field of two sensors and Considered as two discrete signal sequences x(n) and y(n), the Pearson correlation coefficient rho(x,y) is calculated as: Where n is the number of frequency points in the phase spectrum, i = 1, 2, ..., n; j = 1, 2, ..., n; x(n) and y(n) are the windowed phase spectra of the inverted electric field of the two sensors, respectively. and x(i) and y(i) are the phases of the windowed phase spectra of the inverted electric fields of the two sensors at the i-th frequency point; are the mean values ​​of the phases of all frequency points of the windowed phase spectrum of the inverted electric field of the two sensors respectively; after calculating the cross-correlation for each point, the cross-correlation of the inverted electric field is summarized.

9. The method for locating multi-source partial discharge of electric power equipment according to claim 1, characterized in that: In S5, the location of the maximum cross-correlation is determined, specifically: First determine the position of the maximum cross-correlation and the size of the maximum cross-correlation, the maximum cross-correlation position is a maximum cross-correlation position; Find all positions other than the maximum cross-correlation and nearby positions where the cross-correlation is greater than or equal to the adjacent points, that is, all the maximum value points; If the cross-correlation of a certain maximum point is greater than or equal to 80% of the maximum cross-correlation, it is also considered to be a maximum cross-correlation position.

10. A multi-source partial discharge location system for power equipment, characterized in that: include: A very high frequency acquisition module is used to collect partial discharge pulse signals of the power equipment to be tested through a very high frequency sensor; A model building module is used to build a three-dimensional simulation model of the power equipment to be tested; The time reversal module performs time reversal transformation on the collected partial discharge pulse signal, and injects the time reversal transformed signal back into the three-dimensional simulation model to obtain the distribution of the inverted electric field; The cross-correlation module is used to process the inverted electric field to obtain a windowed phase spectrum, calculate the cross-correlation between the windowed phase spectra of the inverted electric field of a pair of sensors, and obtain the distribution of the cross-correlation of the windowed phase spectra; The multi-source partial discharge positioning module is used to determine the location of a single or multiple maximum cross-correlations based on the distribution of the cross-correlations of the windowed phase spectrum, which is the location of the partial discharge.

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