Method and system for positioning multiple partial discharge sources of large transformer substation

Abstract

The invention relates to the field of partial discharge source positioning, in particular to a large-scale substation multi-partial discharge source positioning method and system, and the method comprises the steps: forming a sensor array through a movable ultrasonic sensor and an ultrahigh frequency sensor, and collecting ultrasonic signals and ultrahigh frequency signals generated by partial discharge sources respectively; denoising the collected discharge signals, and calculating the distance between the partial discharge source and the sensor array; denoised ultrasonic array signals are divided into a plurality of narrowband signals by discrete Fourier transform, weighted covariance matrixes of all narrowband signals after focusing transformation are subjected to mean value calculation to obtain weighted covariance matrixes of focusing frequency points, and the weighted covariance matrixes of the focusing frequency points are subjected to unitary transformation to obtain a weighted covariance matrix of a plurality of narrowband signals. Estimating the number of partial discharge sources according to a signal source number estimation equation; and carrying out spectrum peak search in a specified space parameter range according to a preset step length to obtain a space parameter corresponding to each partial discharge source, and obtaining a partial discharge source coordinate in a three-dimensional space.

Description

Technical Field

[0001] The invention relates to the field of local discharge source positioning, and in particular to a method and system for positioning multiple local discharge sources in a large-scale substation. Background Art

[0002] Partial discharge refers to the electrical discharge phenomenon caused by local defects or inhomogeneities in insulating materials. It usually occurs in high-voltage equipment and is an early indicator of insulation system aging and failure. Its monitoring and analysis are crucial to ensuring the safety and reliability of equipment.

[0003] At present, the commonly used local discharge positioning methods mainly include electrical positioning method and signal arrival time difference method. The electrical positioning method measures the pulse current generated by local discharge and analyzes it to obtain the size and position of the discharge. The principle of this method is simple and easy to implement, but since there is often large electromagnetic interference at the discharge site, the measured current information is easily affected, reducing the accuracy of the positioning result. The arrival time difference method is to locate by measuring the time difference between the discharge signal arriving at different sensors. By substituting the obtained time difference information into the local discharge three-dimensional spatial positioning model, the location of the local discharge source can be inversely solved. This method has high accuracy for locating a single discharge source.

[0004] However, in complex large-scale substations, the number of local discharge sources is usually more than one, which makes it impossible to accurately distinguish the pulse current measured by the electrical positioning method, and the traditional signal arrival time difference method cannot effectively distinguish the signals of each discharge source, especially when the signal propagation path is similar, the measurement error of the time difference will significantly affect the positioning result. Therefore, an effective method is urgently needed to locate multiple discharge sources. Summary of the invention

[0005] The purpose of the present application is to provide a method and system for locating multiple local discharge sources in a large substation, which can efficiently determine the signals of each local discharge source, especially when the signal propagation paths are similar, and can accurately identify the number of local discharge sources. Combined with the azimuth and elevation angle information obtained by spatial spectrum scanning, the positioning results can be converted into three-dimensional coordinates, making the positioning results more intuitive and clear.

[0006] The present application provides a method and system for locating multiple local discharge sources in a large substation, which is implemented as follows: A method for locating multiple local discharge sources in a large substation, specifically comprising: S1. A sensor array is formed by a movable ultrasonic sensor and a UHF sensor to respectively collect ultrasonic signals and UHF signals generated by a local discharge source, and the ultrasonic signals and UHF signals are collectively referred to as discharge signals; The distance between the sensor array and the electrical equipment in the substation is less than 10 meters. Eight ultrasonic sensors and one UHF sensor are fixed by a bracket to form a sensor array. The sensor array collects discharge signals from the local discharge source. The UHF sensor is located at the center of the sensor array. Two ultrasonic sensors form a group and are distributed around the UHF sensor to form a cross-shaped sensor array with the UHF sensor. An array composed of eight ultrasonic sensors is called an ultrasonic array.

[0007] The UHF sensor antenna in the sensor array uses a half-wave dipole antenna with a length of 150mm, and the detection frequency band is between 300MHz and 1500MHz. The ultrasonic sensor antenna uses a spherical antenna with a diameter of 10mm, and the detection frequency band is 20KHz-200KHz of ultrasonic signals; the UHF sensor and the ultrasonic sensor are electrically connected to the oscilloscope through a feeder.

[0008] The spacing between each array element increases linearly with the distance between the sensor array and the high-voltage electrical equipment. The specific calculation formula is: ; in, represents the spacing between the sensor array elements, D represents the distance between the sensor array and the high-voltage electrical equipment, and the distance D satisfies , unit is meter.

[0009] S2, performing wavelet decomposition on the collected discharge signal, quantizing the wavelet coefficients obtained by the wavelet decomposition using a dynamic threshold function, and reconstructing the quantized wavelet coefficients by inverse wavelet transform to obtain a denoised discharge signal; The distance between the partial discharge source and the sensor array is calculated based on the time difference between the denoised ultrasonic signal reaching the reference ultrasonic sensor and the denoised UHF signal reaching the UHF sensor.

[0010] The reference ultrasonic sensor in S2 is specifically: During the process of collecting discharge signals, the first ultrasonic sensor in the sensor array that receives the discharge signal is the reference ultrasonic sensor.

[0011] S3. Use the discrete Fourier transform method to divide the denoised ultrasonic array signal into multiple narrowband signals, wherein the narrowband signal at the center frequency of the denoised ultrasonic array signal is the central narrow frequency band, calculate the focusing matrix, signal model and covariance matrix of each narrowband signal, perform focusing transformation, assign different inertia weights to the covariance matrix obtained after the focusing transformation of each narrowband signal, and obtain the weighted covariance matrix of each narrowband signal after the focusing transformation, and perform mean calculation on the weighted covariance matrices of all narrowband signals after the focusing transformation to obtain the weighted covariance matrix of the focusing frequency point.

[0012] In S3, different inertia weights are assigned to the covariance matrix obtained after focusing transformation of each narrowband signal. The specific operation is as follows: Assume that the center frequency of the denoised ultrasonic array signal is F0, define the narrowband signal where F0 is located as the central narrow frequency band, set the position of the central narrow frequency band to m, that is, the mth narrowband signal, and assign different inertia weights to the covariance matrix obtained after focusing transformation of each narrowband signal. The specific expression is: ; in, is the inertia weight, j is the location of the narrowband signal, represents the lower limit of weight, J is the location of the last narrowband signal.

[0013] In S3, the focus transformation is used to calculate the The weighted covariance matrix corresponding to the narrowband signal after focusing transformation is The specific method is: , in, is the inertia weight, Indicates the frequency The covariance matrix at , represents the focusing matrix and T represents the conjugate transpose.

[0014] S4. Perform unitary transformation on the weighted covariance matrix of the focused frequency points to obtain a unitary matrix. Based on the Gale circle criterion, perform similarity transformation on the unitary matrix using the center and radius of the Gale circle in the unitary matrix, and estimate the number of local discharge sources according to the signal source number estimation equation.

[0015] In S4, the weighted covariance matrix of the focused frequency points is unitarily transformed, and the transformation matrix for: ; in, for The unitary matrix composed of the eigenvectors of is the sub-matrix of the weighted covariance matrix of the focused frequency point after removing the last row and the last column, and N is the dimension of the weighted covariance matrix of the focused frequency point.

[0016] S5. Perform eigendecomposition on the weighted covariance matrix of the focused frequency points, construct a spatial spectrum function using a multiple signal classification algorithm, and perform a spectrum peak search with a preset step size within a specified spatial parameter range to obtain the spatial parameters corresponding to each local discharge source.

[0017] The specified spatial parameters in S5 include azimuth and elevation.

[0018] The specified spatial parameter range in S5 is 0-90° for elevation and 0-180° for azimuth.

[0019] S6. Perform coordinate transformation based on the distance between the local discharge source and the sensor array and the spatial parameters of the local discharge source to obtain the coordinates of the local discharge source in three-dimensional space.

[0020] The present invention also provides a large-scale substation multi-local discharge source positioning system, comprising: Sensor array: a sensor array is formed by a movable ultrasonic sensor and a UHF sensor to respectively collect ultrasonic signals and UHF signals generated by a local discharge source, and the ultrasonic signals and UHF signals are collectively referred to as discharge signals; Discharge signal denoising module: performing wavelet decomposition on the collected discharge signal, quantizing the wavelet coefficients obtained by wavelet decomposition using a dynamic threshold function, and reconstructing the quantized wavelet coefficients by inverse wavelet transform to obtain a denoised discharge signal; A local discharge source distance acquisition module; based on the time difference between the denoised ultrasonic signal reaching the reference ultrasonic sensor and the time difference between the denoised UHF signal reaching the UHF sensor, the distance between the local discharge source and the sensor array is calculated; Broadband signal focusing module: using discrete Fourier transform method to divide the denoised ultrasonic array signal into multiple narrowband signals, wherein the narrowband signal at the center frequency of the denoised ultrasonic array signal is the central narrow frequency band, calculating the focusing matrix, signal model and covariance matrix of each narrowband signal, performing focusing transformation, assigning different inertia weights to the covariance matrix obtained after the focusing transformation of each narrowband signal, obtaining the weighted covariance matrix after the focusing transformation of each narrowband signal, and performing mean calculation on the weighted covariance matrices after the focusing transformation of all narrowband signals to obtain the weighted covariance matrix of the focusing frequency point; Unitary transformation module: Perform unitary transformation on the weighted covariance matrix of the focus frequency point to obtain a unitary matrix, perform similarity transformation on the unitary matrix using the center and radius of the Gale circle in the unitary matrix based on the Gale circle criterion, and estimate the number of local discharge sources according to the signal source number estimation equation; Spatial parameter acquisition module: perform eigendecomposition on the weighted covariance matrix of the focus frequency point, construct the spatial spectrum function using the multiple signal classification algorithm, and perform spectrum peak search with a preset step size within the specified spatial parameter range to obtain the spatial parameters corresponding to each local discharge source; A coordinate acquisition module performs coordinate conversion based on the distance between the local discharge source and the sensor array and the spatial parameters of the local discharge source to obtain the coordinates of the local discharge source in three-dimensional space.

[0021] The invention provides a method for locating multiple local discharge sources in a large substation, which has the following beneficial effects: In complex environments, by using multiple ultrasonic sensors, signals can be collected from more angles and directions, increasing the comprehensiveness of signal capture; the combination of wavelet decomposition and dynamic threshold function can improve the signal-to-noise ratio of the signal in the case of large noise, ensuring that the positioning signal is clearer; through focusing transformation, the array manifold matrix at different frequencies is transformed to the same specific frequency, so that the narrowband signal processing method can also perform corresponding processing on the broadband signal; through inertia weight distribution, the covariance matrix obtained after focusing the narrowband signal near the center frequency point can obtain a higher weight, ensuring that these covariance matrices occupy a dominant position in the subsequent analysis results and better reflect the true characteristics of the signal; the Gale circle criterion is used to reasonably estimate the number of signal sources, thereby realizing the judgment of the number of signal sources; through the joint use of UHF sensors and ultrasonic sensors, the distance between the local discharge source and the sensor array can be calculated; combined with the azimuth and pitch angle information obtained by spatial spectrum scanning, the positioning result can be three-dimensionally coordinated, making the positioning result more intuitive and clear. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] By reading the detailed description of the preferred embodiment below, the scheme and advantages of the present application will become clear to those skilled in the art. The accompanying drawings are only for the purpose of illustrating the preferred embodiment and are not to be considered as limiting the present invention.

[0023] In the attached picture: Figure 1 A schematic diagram of the position of the sensor array in the embodiment; Figure 2 This is the time difference estimation diagram between UHF signal and ultrasonic signal; Figure 3 is the ultrasonic signal image before denoising; Figure 4 This is the ultrasonic signal image after denoising; Figure 5 This is the spatial spectrum scanning result diagram. DETAILED DESCRIPTION

[0024] Example 1 The purpose of the present application is to provide a method and system for locating multiple local discharge sources in a large substation, which can efficiently determine the signals of each local discharge source, especially when the signal propagation paths are similar, and can accurately identify the number of local discharge sources. Combined with the azimuth and elevation angle information obtained by spatial spectrum scanning, the positioning results can be converted into three-dimensional coordinates, making the positioning results more intuitive and clear.

[0025] The present application provides a method and system for locating multiple local discharge sources in a large substation, which is implemented as follows: A method for locating multiple local discharge sources in a large substation, specifically comprising: S1. A sensor array is formed by a movable ultrasonic sensor and an ultra-high frequency sensor to respectively collect ultrasonic signals and ultra-high frequency signals generated by a local discharge source. The ultrasonic signals and ultra-high frequency signals are collectively referred to as discharge signals.

[0026] The distance between the sensor array and the electrical equipment in the substation is less than 10 meters. Eight ultrasonic sensors and one UHF sensor are fixed by a bracket to form a sensor array. The sensor array collects discharge signals from the local discharge source. The UHF sensor is located at the center of the sensor array. Two ultrasonic sensors form a group and are distributed around the UHF sensor to form a cross-shaped sensor array with the UHF sensor. Figure 1 As shown in FIG. 8 , an array composed of 8 ultrasonic sensors is called an ultrasonic array.

[0027] The UHF sensor antenna in the sensor array uses a half-wave dipole antenna with a length of 150mm, and the detection frequency band is between 300MHz and 1500MHz. The ultrasonic sensor antenna uses a spherical antenna with a diameter of 10mm, and the detection frequency band is 20KHz-200KHz of ultrasonic signals; the UHF sensor and the ultrasonic sensor are electrically connected to the oscilloscope through a feeder.

[0028] The spacing between each array element increases linearly with the distance between the sensor array and the high-voltage electrical equipment. The specific calculation formula is: ; in, represents the spacing between the sensor array elements, D represents the distance between the sensor array and the high-voltage electrical equipment, and the distance D satisfies , unit is meter.

[0029] Due to the range limitation of the sensor bandwidth, the signals collected here are all broadband signals.

[0030] S2. Decompose the collected discharge signal by wavelet, quantize the wavelet coefficients obtained by wavelet decomposition using a dynamic threshold function, and reconstruct the quantized wavelet coefficients by inverse wavelet transform to obtain the denoised discharge signal. The ultrasonic signal before denoising is shown in Figure 3 , the ultrasonic signal after denoising is shown in Figure 4 ; According to the time difference between the denoised ultrasonic signal reaching the reference ultrasonic sensor and the denoised UHF signal reaching the UHF sensor, the reference Figure 2 , calculate the distance between the local discharge source and the sensor array.

[0031] It should be noted that when performing distance calculation in S2, there may be local discharge sources that are at the same distance from the sensor array but at different positions and discharge simultaneously. When this happens, the specific number of local discharge sources cannot be accurately known.

[0032] The reference ultrasonic sensor in S2 is specifically: During the process of collecting discharge signals, the first ultrasonic sensor in the sensor array that receives the discharge signal is the reference ultrasonic sensor.

[0033] Since the propagation speed of UHF signals is extremely fast, usually at the speed of light, it is believed that the moment when the UHF sensor receives the signal is the moment when partial discharge occurs. The propagation speed of ultrasonic signals is relatively slow. Taking the first ultrasonic sensor that receives the discharge signal as a reference, the distance between the discharge source and the sensor array can be calculated by detecting the time difference between the signal reaching the UHF sensor and the reference ultrasonic sensor.

[0034] The collected discharge signal is decomposed using wavelet decomposition technology, and the decomposition formula is: , in, The decomposition results in u The scale of v wavelet coefficients, is the discharge signal, is the wavelet basis function, express The complex conjugate of t is the signal time variable, indicating the position of the signal in the time domain, and <·> represents the inner product operation.

[0035] In this embodiment, the wavelet basis function is the "dB8" wavelet basis, and the number of decomposition layers is selected to be three.

[0036] The approximate coefficients obtained by wavelet decomposition of each layer represent the low-frequency components of the discharge signal, which usually contain the main information of the discharge signal.

[0037] The detail coefficients obtained by wavelet decomposition of each layer represent the high-frequency components of the discharge signal, which usually contain the noise part of the discharge signal.

[0038] The decomposed wavelet coefficients are processed using a dynamic threshold function.

[0039] The expression of the dynamic threshold function is: , In the formula, is the regulating factor, is a fixed threshold, , is the variance of the noise, Q is the length of the discharge signal, is the wavelet coefficient after processing.

[0040] Perform inverse wavelet transform on the wavelet coefficients processed by the dynamic threshold function. The inverse transform formula is: ; in, Z Represents an integer, and the denoised discharge signal is reconstructed by inverse wavelet transform .

[0041] Assume that the time difference between the denoised ultrasonic signal reaching the reference ultrasonic sensor and the denoised UHF signal reaching the UHF sensor is , calculate the distance between the discharge source and the sensor array d , the specific calculation formula is as follows: ; in, c is the propagation speed of ultrasound in air, which is .

[0042] S3. Use the discrete Fourier transform method to divide the denoised ultrasonic array signal into multiple narrowband signals, wherein the narrowband signal at the center frequency of the denoised ultrasonic array signal is the central narrow frequency band, calculate the focusing matrix, signal model and covariance matrix of each narrowband signal, perform focusing transformation, assign different inertia weights to the covariance matrix obtained after the focusing transformation of each narrowband signal, and obtain the weighted covariance matrix of each narrowband signal after the focusing transformation, and perform mean calculation on the weighted covariance matrices of all narrowband signals after the focusing transformation to obtain the weighted covariance matrix of the focusing frequency point.

[0043] The denoised ultrasound array signal is The broadband ultrasound array signal is divided into discrete Fourier transform of narrowband signals, each narrowband signal model can be written as: , in, , , Respectively expressed in f j Discrete Fourier transform of narrowband signals, signal sources and noise at frequencies, express f j The array manifold matrix in frequency, f j For the j The frequency of a narrowband signal, .

[0044] The focusing matrix expression of each narrowband signal is as follows: , in, represents the focusing matrix, and They are The matrix composed of the left singular value vector and the right singular value vector of , The array manifold matrix representing the focused frequency points, To focus on the frequency point, T represents the conjugate transpose operation.

[0045] In S3, different inertia weights are assigned to the covariance matrix after focusing transformation of each narrowband signal. The specific operation is as follows: Assume that the center frequency of the denoised ultrasonic array signal is F 0 ,Will F 0 The narrowband signal is defined as the central narrow frequency band, and the position of the central narrow frequency band is set as m , that is, m narrowband signals, and assign different inertia weights to the covariance matrix of each narrowband signal after focusing transformation. The specific expression is: , in, is the inertia weight, j is the location of the narrowband signal, represents the lower limit of weight, J is the location of the last narrowband signal.

[0046] By allocating weights through inertia weights, the covariance matrix obtained after focusing the narrowband signal near the center frequency point can obtain a higher weight, ensuring that these covariance matrices occupy a dominant position in the subsequent analysis results and better reflect the true characteristics of the signal.

[0047] Through focus transformation, The weighted covariance matrix corresponding to the narrowband signal after focusing transformation is for: , in, is the inertia weight, Indicates the frequency The covariance matrix at , represents the focusing matrix and T represents the conjugate transpose.

[0048] The weighted covariance matrix of the final focused frequency point yes The mean of the weighted covariance matrices of narrowband signals: .

[0049] S4. Perform unitary transformation on the weighted covariance matrix of the focused frequency points to obtain a unitary matrix. Based on the Gale circle criterion, perform similarity transformation on the unitary matrix using the center and radius of the Gale circle in the unitary matrix, and estimate the number of local discharge sources according to the signal source number estimation equation.

[0050] In S4, the weighted covariance matrix of the focused frequency points is unitarily transformed, and the transformation matrix for: ; in, for The unitary matrix composed of the eigenvectors of is the sub-matrix of the weighted covariance matrix of the focused frequency point after removing the last row and the last column, and N is the dimension of the weighted covariance matrix of the focused frequency point.

[0051] After unitary transformation, we can get: ; is a unitary transformation matrix. According to the Gale circle criterion, , denote the center and radius of the Gale circle, respectively. and is a constant, i Indicates i A Gaelic circle.

[0052] Construct a diagonal matrix P : , in, , obviously ,Right now .

[0053] right Perform similarity transformation, and we can get: , in, for Matrix after similarity transformation. Through similarity transformation, under the premise that the center of the Gael circle remains unchanged, the radius of the Gael circle is compressed according to the center of each Gael circle. Generally, the smaller the radius, the greater the compression ratio. This can make the radius of the noise Gael circle far away from the radius of the signal Gael circle. Combined with the minimum description length criterion, the obtained signal source number estimation equation is as follows: , in, is the radius of the new Gale circle, N is the dimension of Ry, k is an integer from small to large, and k is the number of local discharge sources when the above formula is minimized.

[0054] The number of partial discharge sources, k, includes the case where partial discharge sources are at the same distance from the sensor array but at different positions and discharge simultaneously.

[0055] S5. Perform eigendecomposition on the weighted covariance matrix of the focused frequency points, construct a spatial spectrum function using a multiple signal classification algorithm, and perform a spectrum peak search with a preset step size within a specified spatial parameter range to obtain the spatial parameters corresponding to each local discharge source.

[0056] The weighted covariance matrix of the focused frequency points Performing feature decomposition, we have: , After eigendecomposition, the signal subspace is recorded as , the noise subspace is recorded as , represents the diagonal matrix consisting of the eigenvalues ​​corresponding to the eigenvectors constituting the signal subspace, represents the diagonal matrix consisting of the eigenvalues ​​corresponding to the eigenvectors that constitute the noise subspace.

[0057] The constructed spatial spectrum function expression is: ; G represents the spatial spectrum function, is the column vector (i.e., steering vector) of the array manifold matrix. The maximum value, i.e., the peak position, is found through spatial scanning. The position where the maximum value appears reflects the azimuth of the local discharge source. and pitch angle information.

[0058] Combined with the number of local discharge sources estimated by S4 and the maximum point obtained by spatial spectrum scanning, the spectrum peak is searched to locate multiple local discharge sources. Specifically, the spectrum peak is taken downward based on the maximum point, and the number of spectrum peaks obtained is k.

[0059] The specified spatial parameters in S5 include azimuth and elevation.

[0060] The specified spatial parameter range in S5 is 0-90° for elevation angle and 0-180° for azimuth angle. The spectrum peak search results are as follows: Figure 5 shown.

[0061] S6. Perform coordinate transformation based on the distance between the local discharge source and the sensor array and the spatial parameters of the local discharge source to obtain the coordinates of the local discharge source in three-dimensional space.

[0062] When the distance between the local discharge source and the sensor array obtained in S2 is combined with the specified spatial parameters obtained in S5 to perform three-dimensional coordinate transformation, the specific transformation formula is: , , , Wherein, x represents the abscissa of the local discharge source, y represents the ordinate of the local discharge source, and z represents the height of the local discharge source.

[0063] By converting spatial parameters into three-dimensional coordinates, the positioning results are more intuitive and clear.

[0064] The present invention also provides a large-scale substation multi-local discharge source positioning system, comprising: Sensor array: a sensor array is formed by a movable ultrasonic sensor and a UHF sensor to respectively collect ultrasonic signals and UHF signals generated by a local discharge source, and the ultrasonic signals and UHF signals are collectively referred to as discharge signals; Discharge signal denoising module: performing wavelet decomposition on the collected discharge signal, quantizing the wavelet coefficients obtained by wavelet decomposition using a dynamic threshold function, and reconstructing the quantized wavelet coefficients by inverse wavelet transform to obtain a denoised discharge signal; A local discharge source distance acquisition module; based on the time difference between the denoised ultrasonic signal reaching the reference ultrasonic sensor and the time difference between the denoised UHF signal reaching the UHF sensor, the distance between the local discharge source and the sensor array is calculated; Broadband signal focusing module: using discrete Fourier transform method to divide the denoised ultrasonic array signal into multiple narrowband signals, wherein the narrowband signal at the center frequency of the denoised ultrasonic array signal is the central narrow frequency band, calculating the focusing matrix, signal model and covariance matrix of each narrowband signal, performing focusing transformation, assigning different inertia weights to the covariance matrix obtained after the focusing transformation of each narrowband signal, obtaining the weighted covariance matrix after the focusing transformation of each narrowband signal, and performing mean calculation on the weighted covariance matrices after the focusing transformation of all narrowband signals to obtain the weighted covariance matrix of the focusing frequency point; Unitary transformation module: Perform unitary transformation on the weighted covariance matrix of the focus frequency point to obtain a unitary matrix, perform similarity transformation on the unitary matrix using the center and radius of the Gale circle in the unitary matrix based on the Gale circle criterion, and estimate the number of local discharge sources according to the signal source number estimation equation; Spatial parameter acquisition module: perform eigendecomposition on the weighted covariance matrix of the focus frequency point, construct the spatial spectrum function using the multiple signal classification algorithm, and perform spectrum peak search with a preset step size within the specified spatial parameter range to obtain the spatial parameters corresponding to each local discharge source; A coordinate acquisition module performs coordinate conversion based on the distance between the local discharge source and the sensor array and the spatial parameters of the local discharge source to obtain the coordinates of the local discharge source in three-dimensional space.

Claims

1. A method for locating multiple local discharge sources in a large substation, characterized in that: The specific steps are as follows: S1. A sensor array is formed by a movable ultrasonic sensor and a UHF sensor to respectively collect ultrasonic signals and UHF signals generated by a local discharge source, and the ultrasonic signals and UHF signals are collectively referred to as discharge signals; S2, performing wavelet decomposition on the collected discharge signal, quantizing the wavelet coefficients obtained by the wavelet decomposition using a dynamic threshold function, and reconstructing the quantized wavelet coefficients by inverse wavelet transform to obtain a denoised discharge signal; The distance between the partial discharge source and the sensor array is calculated based on the time difference between the denoised ultrasonic signal reaching the reference ultrasonic sensor and the denoised UHF signal reaching the UHF sensor; S3, using a discrete Fourier transform method to divide the denoised ultrasound array signal into multiple narrowband signals, and calculating a weighted covariance matrix of the focus frequency points based on the narrowband signals; S4, performing a unitary transformation on the weighted covariance matrix of the focus frequency point to obtain a unitary matrix, performing a similarity transformation on the unitary matrix using the center and radius of the Gale circle in the unitary matrix based on the Gale circle criterion, and estimating the number of local discharge sources according to the signal source number estimation equation; S5, performing eigendecomposition on the weighted covariance matrix of the focus frequency point, constructing a spatial spectrum function using a multiple signal classification algorithm, and performing a spectrum peak search with a preset step size within a specified spatial parameter range to obtain the spatial parameters corresponding to each local discharge source; S6. Perform coordinate transformation based on the distance between the local discharge source and the sensor array and the spatial parameters of the local discharge source to obtain the coordinates of the local discharge source in three-dimensional space.

2. A method for locating multiple local discharge sources in a large substation according to claim 1, characterized in that: The specific operation of allocating different inertia weights to the covariance matrix obtained after the focusing transformation of each narrowband signal in S3 is as follows: Assume that the center frequency of the denoised ultrasonic array signal is F 0 ,Will F 0 The narrowband signal is defined as the central narrow frequency band, and the position of the central narrow frequency band is set as m , that is, m narrowband signals, and assign different inertia weights to the covariance matrix after focusing transformation of each narrowband signal. The specific expression is: ； in, is the inertia weight, j is the location of the narrowband signal, represents the lower limit of weight, J is the location of the last narrowband signal.

3. A method for locating multiple local discharge sources in a large substation according to claim 1, characterized in that: In S4, the weighted covariance matrix of the focused frequency points is unitarily transformed, and the transformation matrix for: ； in, for The unitary matrix composed of the eigenvectors of is the submatrix obtained by removing the last row and the last column of the weighted covariance matrix of the focused frequency point, and N is the dimension of the obtained weighted covariance matrix of the focused frequency point.

4. A method for locating multiple local discharge sources in a large substation according to claim 1, characterized in that: In S3, the focusing transformation is used to calculate the The weighted covariance matrix corresponding to the narrowband signal after focusing transformation is The specific method is: ， in, is the inertia weight, Indicates the frequency The covariance matrix at , represents the focusing matrix and T represents the conjugate transpose.

5. A method for locating multiple local discharge sources in a large substation according to claim 1, characterized in that: The distance between the sensor array and the electrical equipment in the substation is less than 10 meters. Eight ultrasonic sensors and one ultra-high frequency sensor are fixed by a bracket to form a sensor array. The discharge signal of the local discharge source is collected by the sensor array. Among them, the ultra-high frequency sensor is located at the center of the sensor array. Two ultrasonic sensors form a group and are distributed around the ultra-high frequency sensor to form a cross-shaped sensor array with the ultra-high frequency sensor. The array composed of eight ultrasonic sensors alone is called an ultrasonic array.

6. A method for locating multiple local discharge sources in a large substation according to claim 5, characterized in that: The UHF sensor antenna in the sensor array uses a half-wave dipole antenna with a length of 150 mm, and the detection frequency band is between 300 MHz and 1500 MHz. The ultrasonic sensor antenna uses a spherical antenna with a diameter of 10 mm, and the detection frequency band is 20 KHz-200 KHz of ultrasonic signals; the UHF sensor and the ultrasonic sensor are electrically connected to the oscilloscope through a feeder; The spacing between each array element increases linearly with the distance between the sensor array and the high-voltage electrical equipment. The specific calculation formula is: ； in, represents the spacing between the sensor array elements, D represents the distance between the sensor array and the high-voltage electrical equipment, and the distance D satisfies , unit is meter.

7. A method for locating multiple local discharge sources in a large substation according to claim 1, characterized in that: The reference ultrasonic sensor in S2 is specifically: During the process of collecting discharge signals, the first ultrasonic sensor in the sensor array that receives the discharge signal is the reference ultrasonic sensor.

8. A method for locating multiple local discharge sources in a large substation according to claim 1, characterized in that: The denoised ultrasonic array signal is divided into multiple narrowband signals by using the discrete Fourier transform method, and the weighted covariance matrix of the focused frequency point is calculated based on the narrowband signal. The specific operation is as follows: the narrowband signal at the center frequency of the denoised ultrasonic array signal is the central narrow frequency band, and the focusing matrix, signal model and covariance matrix of each narrowband signal are calculated, and a focusing transformation is performed. Different inertia weights are assigned to the covariance matrix obtained after the focusing transformation of each narrowband signal to obtain the weighted covariance matrix of each narrowband signal after the focusing transformation, and the weighted covariance matrix of the focused frequency point is obtained by averaging the weighted covariance matrices of all narrowband signals after the focusing transformation.

9. A method for locating multiple local discharge sources in a large substation according to claim 8, characterized in that: The specified spatial parameter ranges described in S5 are 0-90° for elevation angle and 0-180° for azimuth angle.

10. A multi-local discharge source positioning system for a large substation, characterized in that: include: Sensor arrays; A sensor array is formed by a movable ultrasonic sensor and a UHF sensor to respectively collect ultrasonic signals and UHF signals generated by a local discharge source, and the ultrasonic signals and UHF signals are collectively referred to as discharge signals. Discharge signal denoising module: performing wavelet decomposition on the collected discharge signal, quantizing the wavelet coefficients obtained by wavelet decomposition using a dynamic threshold function, and reconstructing the quantized wavelet coefficients by inverse wavelet transform to obtain a denoised discharge signal; A local discharge source distance acquisition module; based on the time difference between the denoised ultrasonic signal reaching the reference ultrasonic sensor and the time difference between the denoised UHF signal reaching the UHF sensor, the distance between the local discharge source and the sensor array is calculated; Broadband signal focusing module: using discrete Fourier transform method to divide the denoised ultrasonic array signal into multiple narrowband signals, wherein the narrowband signal at the center frequency of the denoised ultrasonic array signal is the central narrow frequency band, calculating the focusing matrix, signal model and covariance matrix of each narrowband signal, performing focusing transformation, assigning different inertia weights to the covariance matrix obtained after the focusing transformation of each narrowband signal, obtaining the weighted covariance matrix after the focusing transformation of each narrowband signal, and performing mean calculation on the weighted covariance matrices after the focusing transformation of all narrowband signals to obtain the weighted covariance matrix of the focusing frequency point; Unitary transformation module: Perform unitary transformation on the weighted covariance matrix of the focus frequency point to obtain a unitary matrix, perform similarity transformation on the unitary matrix using the center and radius of the Gale circle in the unitary matrix based on the Gale circle criterion, and estimate the number of local discharge sources according to the signal source number estimation equation; Spatial parameter acquisition module: perform eigendecomposition on the weighted covariance matrix of the focus frequency point, construct the spatial spectrum function using the multiple signal classification algorithm, and perform spectrum peak search with a preset step size within the specified spatial parameter range to obtain the spatial parameters corresponding to each local discharge source; A coordinate acquisition module performs coordinate conversion based on the distance between the local discharge source and the sensor array and the spatial parameters of the local discharge source to obtain the coordinates of the local discharge source in three-dimensional space.

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