Partial discharge positioning method and system based on array type photoelectric converter

Abstract

The invention relates to the technical field of partial discharge, in particular to a partial discharge positioning method and system based on an array photoelectric converter, and the positioning process of the method comprises the steps: data acquisition and preprocessing, azimuth model construction, PD source number estimation and PD azimuth estimation. In order to reduce the influence on the interior of equipment to the greatest extent, the invention provides a partial discharge positioning device based on an array type photoelectric converter, aiming at partial discharge caused by distortion or defects of a partial electric field in long-term operation in a power system and the need of partial discharge positioning by using the mentioned array and acquiring a partial discharge time domain signal. An optical signal is captured through a plurality of SiPM sensor arrays, and a partial discharge approximate position is calculated, so that preliminary partial discharge positioning is realized.

Description

Technical Field

[0001] This invention relates to the field of partial discharge technology, specifically to a partial discharge localization method and system based on an array-type photoelectric converter. Background Technology

[0002] Partial discharge (PD) localization is a technique that determines the specific location of a partial discharge source within equipment or cable lines by detecting and analyzing various signals generated by partial discharge. PD detection technology based on optical sensing has attracted widespread attention due to its advantages such as strong anti-electromagnetic interference capability and high spatial resolution.

[0003] There are few patents currently available for PD (partial discharge) location using optical signals. A representative example is CN110174597A, which describes a device that calculates the location of partial discharges by analyzing signals generated by partial discharges within electrical equipment using fluorescent optical fibers. However, certain problems remain. For instance, the accuracy of acoustic methods decreases in noisy environments. Ultrasonic waves attenuate significantly when propagating through a medium, especially over long distances, potentially resulting in weak and undetectable signals. Ultrasonic waves may also reflect and refract at the sensor along different paths, generating multiple signals that interfere with location. Electrical methods, relying solely on electrical signal characteristics, struggle to accurately pinpoint the location of partial discharges, typically only locating a relatively large area. Combining multiple methods increases system complexity, requiring more sophisticated signal processing and data fusion algorithms, leading to higher development and maintenance costs.

[0004] Silicon photomultiplier tubes (SiPMs), a novel solid-state photon counter, operate based on the avalanche effect of avalanche photodiodes. Each SiPM unit consists of multiple APDs connected in parallel. Each time a photon is captured by the photodetector, a weak electronic signal is generated. SiPMs feature high gain, low driving voltage, and fast response, effectively capturing the weak light signals associated with photodiodes (PDs). Through multispectral band design, SiPM sensor multispectral detection systems can further distinguish the spectral signal characteristics of different PD types, providing rich information for multi-source discharge diagnosis. Its advantages are as follows: High gain: SiPM gains are typically between 10⁵ and 10⁶, amplifying weak light signals; Low driving voltage: Compared to the hundreds of volts of traditional photomultiplier tubes, SiPMs typically only require around 30V; Response efficiency: Nanosecond-level response speed, suitable for real-time response to PD signals; High spatial resolution: Precisely locates the discharge signal position, suitable for high-precision spatial resolution detection.

[0005] SIPM array partial discharge localization is primarily used in fields such as radiation imaging to determine the spatial location of the interaction between radiation and detectors. It also has applications in partial discharge detection in power equipment, enabling precise identification of weak signals and differentiation of different defect types. A SIPM array consists of multiple silicon photomultiplier tube (SMT) units. When radiation or light signals generated by a partial discharge act on the SIPM array, the light signal generates a photoelectric effect at the photocathode of each SIPM unit, exciting electrons. These electrons are accelerated and multiplied under the influence of an electric field, ultimately generating a detectable electrical signal. The location of the partial discharge source is determined using specific localization algorithms based on information such as the amplitude and position of the output signal from each SIPM unit. Common algorithms include the centroid method and response function-based localization algorithms. The advantages are as follows: 1. High sensitivity and specificity; 2. Strong anti-interference capability, which can effectively reduce ambient light interference and improve detection stability; 3. Rich information acquisition, providing spectral information of the measured object in multiple bands, providing a basis for accurate analysis and judgment; 4. Non-contact detection, suitable for harsh environments, safe and efficient; 5. Real-time monitoring capability, which can quickly acquire and analyze spectral data and detect faults in a timely manner; 6. Strong adaptability, which can flexibly select filters and sensor parameters according to different needs and adapt to various application scenarios.

[0006] The invention patent with publication number CN118091343A discloses a partial discharge test platform for metal particles of GIS basin insulator based on SIPM array detection. It uses optical means to detect partial discharge signals, avoiding noisy electromagnetic interference in the environment and improving signal accuracy. However, it still does not solve the problem of partial discharge caused by local electric field distortion or defects in the power system during long-term operation and the problem of partial discharge location. Summary of the Invention

[0007] Purpose of the invention: In view of the problems existing in the prior art, the present invention proposes a partial discharge localization method and system based on an array-type photoelectric converter.

[0008] Summary of the Invention: This invention provides a partial discharge localization method based on an array-type photoelectric converter, the method comprising: The original partial discharge time domain signal is acquired by a SiPM array, which consists of multiple SiPM units. The anodes of each SiPM unit are connected together and the cathode is grounded, which is used to convert the optical signal emitted by the discharge power source into an electrical signal. The original partial discharge time-domain signal is amplified by a preamplifier module to obtain an enhanced signal; The orientation function of the array is determined based on the arrangement of the SiPM units in the SiPM array. The orientation function includes the azimuth and elevation angles of the enhanced signal in the spatial coordinate system. Based on the enhanced signal, a signal covariance matrix of several discharge sources is constructed, which is then used to express the likelihood discriminant. The number of signal sources is iterated from zero. When the likelihood discriminant shows a sudden change in value, the actual number of discharge sources K is determined. Based on the defined direction function, a signal direction model matrix is ​​determined, and the relationship between the signal corresponding to each discharge source and the coordinate axes is determined. x shaft and y The angle between the axes is used to deduce the azimuth and elevation angles. By constructing direction finding lines, the intersection of each direction finding line is taken as the position of the target.

[0009] Furthermore, including: The step of determining the direction function of the array based on the arrangement of SiPM cells in the SiPM array includes: Fix 4×4 SiPM elements in the spatial coordinate system y plane and z Planar, based on SiPM elements in y The projected length on the axis and in z The projected length on the axis represents the spatial phase difference between the current SiPM element and the origin; The direction function of the array is determined based on the amplitude weighting coefficients of the current SiPM cell and the phase shift provided by the relative reference SiPM cell.

[0010] Furthermore, including: The construction of signal covariance matrices for several discharge sources based on the enhanced signal, and the subsequent expression of the likelihood discriminant, includes: An observation data model X is established based on the SiPM array. The observation data model is obtained through the first... k The array manifold matrix A corresponding to the power supply k The result is obtained by multiplying the enhanced signal vector with the noise and then adding the enhanced signal vector. The sample covariance matrix R is obtained based on the observed data model. z This leads to the likelihood discriminant, expressed as: , where R S,k For the first k The signal covariance matrix of the power supply, A k H For A k The conjugate transpose of , where L is the number of snapshots of the received signal, which is equal to the number of columns in the matrix corresponding to the observation data model. The sample covariance matrix R z The estimate is given by `det()`, which is a determinant function.

[0011] Furthermore, including: The first kThe signal covariance matrix of the power source is represented by a diagonal matrix, and this diagonal matrix satisfies the following two conditions: ; ;in, It is the first k The signal covariance matrix of the power supply R S,k diagonal matrix ( i , i The value at position ) and L is the number of data on the diagonal.

[0012] Furthermore, including: The signal direction model matrix is ​​defined based on the direction function to determine the relationship between the signal corresponding to each discharge source and the coordinate axes. x shaft and y The included angle of the axis includes: The signal direction model matrix is ​​represented by the direction function of the array, and then the sample covariance matrix R is represented. The covariance matrix R is decomposed into eigenvalues, which are divided into a signal subspace and a noise subspace. The azimuth and elevation angles corresponding to the enhanced signal in the spatial coordinate system are converted into... x shaft and y The included angles α and β of the axes are used to decompose the two-dimensional vector of the signal direction model matrix into two one-dimensional vectors including the included angles α and β. The new signal direction model is represented by two one-dimensional vectors. Based on the new signal direction model and the MUSIC algorithm, the first... k The power supply corresponds to and x shaft and y The included angle α of the axis k、 β k .

[0013] Furthermore, including: The two-dimensional vector decomposition of the signal direction model matrix into two one-dimensional vectors including angles α and β is expressed as follows: Where λ is the wavelength of the enhanced signal, and d is the distance between two adjacent SiPM units.

[0014] Furthermore, including: The calculation based on the new signal direction model and the MUSIC algorithm yields the first... k The power supply corresponds to x shaft and y The included angle α of the axis k、 β k ,include: The new direction model of the signal is represented as follows: , among which, Sk (t) is the enhanced signal vector corresponding to the k-th discharge source; N(t) is the noise; ⊗ is the Kronecker product; A new determination formula is obtained based on the MUSIC spatial spectrum combined with the new signal direction model, and the identity matrix I is used. m Replace a m The final determination method is shown in the following formula: ;in, The noise subspace matrix, is the conjugate transpose of the noise subspace matrix.

[0015] a is obtained according to the final determination method. m (β) about α k The function, construct α k Maximize the objective function, and set the intermediate variable z based on maximizing the objective function. k α is obtained by inverse solving based on the optimal solution of the intermediate variable. k ; Establish a fitting vector d k , used to fit β k And the intermediate value d is obtained by using the least squares method. k (2), and then solve for β. k .

[0016] Furthermore, including: The a is obtained according to the final determination method. m (β) about α k The functions include: α is constructed according to the final determination method. k a m The relevant quadratic optimization formula is expressed as: , where e1 is a unit vector; For the Lagrange cost function Find the partial derivative and set it equal to 0 to finally obtain a. m (β) about α k function , where μ is a Lagrange multiplier.

[0017] Furthermore, including: The structure α k Maximize the objective function, and set the intermediate variable z based on maximizing the objective function. k α is obtained by inverse solving based on the optimal solution of the intermediate variable. k ,include: The objective function to be maximized is determined based on the constructed constraints, representing the intermediate variable z. k , represented as: ; Make |z k | is 1, thus obtaining the intermediate variable z k The optimal solution, and then based on z k With α k The relational formula yields α k .

[0018] Furthermore, including: The establishment of a fitting vector d k , used to fit β k And the intermediate value d is obtained by using the least squares method. k (2), and then solve for β. k This includes: the one-dimensional vector a m (β) Take the phase angle to obtain the vector l k , represented as: ; According to vector l k Establish a fitting vector d_k, represented as: The purpose is to approximate or fit the target vector β_k; Solve the least squares problem, find the optimal coefficients x_k, and use the obtained x_k to calculate... Take the second element d from this fitted vector. k (2); according to Solving for β k .

[0019] On the other hand, the present invention also provides a partial discharge localization system based on an array-type photoelectric converter, the system comprising: The signal acquisition module is used to acquire the raw partial discharge time domain signal through the SiPM array. The SiPM array is composed of multiple SiPM units, with the anodes of each SiPM unit connected together and the cathode grounded. It is used to convert the optical signal emitted by the discharge power source into an electrical signal. The signal amplification module is used to amplify the original partial discharge time-domain signal through the preamplifier module to obtain an enhanced signal; The direction function determination module is used to determine the direction function of the array based on the arrangement of SiPM units in the SiPM array. The direction function includes the azimuth and elevation angles of the enhancement signal in the spatial coordinate system. The discharge source quantity determination module is used to construct a signal covariance matrix of several discharge sources based on the enhanced signal, and then express the likelihood discriminant. Iterates the number of signal sources starting from zero. When the likelihood discriminant shows a sudden change in value, the actual number of discharge sources K is determined. The positioning module is used to define a signal direction model matrix based on the direction function, and to determine the relationship between the signal corresponding to each discharge source and the coordinate axes. x shaft and y The angle between the axes is used to deduce the azimuth and elevation angles. By constructing direction finding lines, the intersection of each direction finding line is taken as the position of the target.

[0020] Beneficial effects: Compared with the prior art, the present invention has the following advantages: This invention addresses the need for partial discharge localization in power systems caused by local electric field distortion or defects during long-term operation. It proposes a partial discharge localization method based on an array-type photoelectric converter (SiPM). This method captures optical signals using a SiPM sensor array and calculates the approximate location of the partial discharge, achieving preliminary localization. Firstly, the use of an optical method minimizes noise interference. Secondly, the localization method eliminates some noise interference to improve accuracy. Finally, this invention performs dimensionality reduction processing on the data, improving the operating speed. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of the partial discharge localization method based on an array-type photoelectric converter according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the partial discharge positioning device based on an array-type photoelectric converter according to an embodiment of the present invention; Figure 3 This is a diagram of the SiPM array structure described in an embodiment of the present invention; Figure 4 This is a circuit diagram of the sensor described in an embodiment of the present invention; Figure 5 This is a schematic diagram of the planar array and its spatial signal according to an embodiment of the present invention. Detailed Implementation

[0023] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0024] Example 1: Refer to Figure 1 One embodiment of the present invention provides a partial discharge localization method based on an array-type photoelectric converter, the method comprising the following steps: Step 1: Acquire the original partial discharge time domain signal through the SiPM array. The SiPM array consists of multiple SiPM units, with the anodes of each SiPM unit connected together and the cathode grounded, which is used to convert the optical signal emitted by the discharge power source into an electrical signal. Step 2: Amplify the original partial discharge time-domain signal using a preamplifier module to obtain an enhanced signal; Step 3: Determine the direction function of the array based on the arrangement of the SiPM units in the SiPM array. The direction function includes the azimuth and elevation angles of the enhanced signal in the spatial coordinate system. Step 4: Construct signal covariance matrices for several discharge sources based on the enhanced signal, and then express the likelihood discriminant. Iterate the number of signal sources starting from zero. When the likelihood discriminant shows a sudden change in value, determine the actual number of discharge sources K. Step 5: Define the signal direction model matrix according to the direction function, and determine the relationship between the signal corresponding to each discharge source and the coordinate axis. x shaft and y The angle between the axes is used to deduce the azimuth and elevation angles. By constructing direction finding lines, the intersection of each direction finding line is taken as the position of the target.

[0025] In this embodiment, determining the direction function of the array based on the arrangement of SiPM cells in the SiPM array includes: like Figure 5 As shown, 4×4 SiPM elements are fixed in the spatial coordinate system. y plane and z Planar, based on SiPM elements in y The projected length on the axis and in z The projected length on the axis represents the spatial phase difference between the current SiPM element and the origin; the array uses 4×4 elements (p=4, q=4), fixed at... yz The plane; azimuth angle θ (-90°~90°), elevation angle φ (0°~90°), the azimuth information of the discharge source can be calculated using θ and φ. The specific azimuth angle of the discharge source will be obtained by MUSIC spatial spectrum calculation. These two angles together determine the spatial propagation direction of the optical signal from the discharge source to the array, and are also the core parameters that need to be estimated in subsequent positioning.

[0026] The direction function of the array is determined based on the amplitude weighting coefficients of the current SiPM cell and the phase shift provided by the relative reference SiPM cell.

[0027] In this embodiment, the step of constructing signal covariance matrices of several discharge sources based on the enhanced signal, and then expressing the likelihood discriminant, includes: An observation data model X is established based on the SiPM array. The observation data model is obtained through the first... k The array manifold matrix A corresponding to the power supply k The result is obtained by multiplying the enhanced signal vector with the noise and then adding the enhanced signal vector. The sample covariance matrix R is obtained based on the observed data model. z This leads to the likelihood discriminant, expressed as: , where R S,k For the first k The signal covariance matrix of the power supply, A k H For A k The conjugate transpose of , where L is the number of snapshots of the received signal, which is equal to the number of columns in the matrix corresponding to the observation data model. The sample covariance matrix R z The estimate is given by `det()`, which is a determinant function.

[0028] In this embodiment, the first k The signal covariance matrix of the power source is represented by a diagonal matrix, and this diagonal matrix satisfies the following two conditions: ; ;in, It is the first k The signal covariance matrix of the power supply R S,k diagonal matrix ( i , i The value at position ) and L is the number of data on the diagonal.

[0029] In this embodiment, the signal direction model matrix is ​​defined according to the direction function, and the relationship between the signal corresponding to each discharge source and the coordinate axis is determined. x shaft and y The included angle of the axis includes: The signal direction model matrix is ​​represented by the direction function of the array, and then the sample covariance matrix R is represented. The covariance matrix R is decomposed into eigenvalues, which are divided into a signal subspace and a noise subspace. The azimuth and elevation angles corresponding to the enhanced signal in the spatial coordinate system are converted into... x shaft and y The included angles α and β of the axes are used to decompose the two-dimensional vector of the signal direction model matrix into two one-dimensional vectors including the included angles α and β. The new signal direction model is represented by two one-dimensional vectors. Based on the new signal direction model and the MUSIC algorithm, the first... k The power supply corresponds to x shaft and y The included angle α of the axis k、 β k .

[0030] In this embodiment, the two-dimensional vector of the signal direction model matrix is ​​decomposed into two one-dimensional vectors including an included angle α and β, as follows: Where λ is the wavelength of the enhanced signal, and d is the distance between two adjacent SiPM units.

[0031] In this embodiment, the first signal direction model calculated based on the MUSIC algorithm is obtained. k The power supply corresponds to x shaft and y The included angle α of the axis k、 β k ,include: The new direction model of the signal is represented as follows: , among which, S k (t) is the enhanced signal vector corresponding to the k-th discharge source; N(t) is the noise; ⊗ is the Kronecker product; A new determination formula is obtained based on the MUSIC spatial spectrum combined with the new signal direction model, and the identity matrix I is used. m Replace a m The final determination method is shown in the following formula: ;in, The noise subspace matrix, is the conjugate transpose of the noise subspace matrix.

[0032] a is obtained according to the final determination method. m (β) about α k The function, construct α k Maximize the objective function, and set the intermediate variable z based on maximizing the objective function. k α is obtained by inverse solving based on the optimal solution of the intermediate variable. k ; Establish a fitting vector d k , used to fit β k And the intermediate value d is obtained by using the least squares method. k (2), and then solve for β. k .

[0033] In this embodiment, the step of obtaining a according to the final determination method m (β) about α k The functions include: α is constructed according to the final determination method. k a m The relevant quadratic optimization formula is expressed as: , where e1 is a unit vector; For the Lagrange cost function Find the partial derivative and set it equal to 0 to finally obtain a. m (β) about α k function , where μ is a Lagrange multiplier.

[0034] In this embodiment, the construction α k Maximize the objective function, and set the intermediate variable z based on maximizing the objective function. k α is obtained by inverse solving based on the optimal solution of the intermediate variable. k ,include: The objective function to be maximized is determined based on the constructed constraints, representing the intermediate variable z. k , represented as: ; makes |z k | is 1, thus obtaining the intermediate variable z k The optimal solution, and then based on z k With α k The relational formula yields α k .

[0035] Furthermore, including: The establishment of a fitting vector d k , used to fit β k And the intermediate value d is obtained by using the least squares method. k (2), and then solve for β. k ,include: For the one-dimensional vector a m (β) Take the phase angle to obtain the vector l k , represented as: ; According to vector l k Establish a fitting vector d_k, represented as: The purpose is to approximate or fit the target vector β_k; Solve the least squares problem, find the optimal coefficients x_k, and use the obtained x_k to calculate... Take the second element d from this fitted vector. k (2); according to Solving for β k .

[0036] In a preferred embodiment, the positioning process includes data acquisition and preprocessing, constructing an azimuth model, PD source number estimation, and PD azimuth estimation. To minimize the impact on the internal components of the equipment, two arrays are used to acquire partial discharge time-domain signals.

[0037] First, we construct the planar orientation matrix using formulas 1 and 2, as shown in formula 2.

[0038] Where, Φ (i-1)(j-1) z is the spatial phase difference between the (i,j)th element and the (0,0)th element; λ is the wavelength of the incident signal; i Let y be the projection length of the (i,j) array element on the z-axis; i Let be the projection length of the (i,j) array element on the y-axis. ij φ is the amplitude weighting coefficient for the (i,j) array element; B F is relative to the phase shift provided by the reference array element. (θ,φ) is the direction function of the array. d is the distance between two adjacent SiPM cells.

[0039] Next, the PD source is estimated. 1. Establishing "X=A" based on SiPM array k S+N signal model (X represents observed data, A...) k For the (M*k) array manifold matrix corresponding to k sources, S is the incident signal vector, and N is the noise). 2. Calculate the sample covariance matrix R using the received data. z =E(XX H ); 3. Calculate the maximum likelihood discriminant Λ(k), as shown in Formula 3.

[0040] Among them, R S,k Let A be the covariance matrix of signals from k sources; k H For A k The conjugate transpose of X; L is the number of snapshots of the received signal, which is equal to the number of columns of the X matrix.

[0041] To improve computational efficiency, R was simplified. S,k The calculation, under the assumption that "the source signals are uncorrelated", R S,k Since it is a diagonal matrix, the calculation is simplified according to formulas 4 and 5:

[0042]

[0043] in, It is R S,kThe value at position (i,i) in the diagonal matrix.

[0044] Iterate over the value of k, starting from k=0. When k increases to the actual number of partial discharge sources K, the maximum likelihood discriminant will show a significant jump (such as a sudden change in value). At this time, K is the actual number of source signals.

[0045] Based on the determination of the number of partial discharge sources, the PD orientation is then estimated.

[0046] Define the signal direction model matrix X(t) = A(θ,φ)S(t) + N(t), where A(θ,φ) is the direction function matrix of the array; The sample covariance matrix R = E[X(t)X(t)] is calculated by receiving the data. H ]; Eigenvalue decomposition is performed on the covariance matrix R. The subspace is separated by utilizing the property that "signal eigenvalues ​​are much larger than noise eigenvalues" (the average eigenvalues ​​are calculated, and a coefficient is set; when an eigenvalue is greater than the average eigenvalue multiplied by the coefficient, it is a signal eigenvalue; otherwise, it is a noise eigenvalue. Accuracy is improved by adjusting the coefficient). U S Corresponding signal subspace, U N Corresponding noise subspace; To reduce computational load, the original two-dimensional calculations were changed to one-dimensional calculations. The incident light azimuth angles θ and φ were converted into the angles α and β between the incident light and the x-axis and y-axis, respectively, according to the conversion rules shown in Formula 6.

[0047]

[0048]

[0049] Decompose the two-dimensional vector A(θ,φ) into two one-dimensional vectors A(α)(a n (α)) and A(β)(a m (β)), and calculate them separately, and the converted a n (α), a m (β) As shown in Formula 7, α k、 β k Let k be the angle between the power supply signal and the x-axis and y-axis. The corresponding new signal direction model is Equation 8, S. k (t) is the incident signal vector k; N(t) is noise; ⊗ is the Kronecker product.

[0050]

[0051] First, find a. m and α kThe connection is established. Based on the MUSIC space spectrum of the traditional MUSIC algorithm, a new decision formula is obtained, and then the identity matrix I is used. m Replace a m The final determination method is shown in Formula 9.

[0052]

[0053] Based on only α k The relevant decision formula, while retaining a m With a n The connection between them constructed α k a m The relevant secondary optimization is shown in Equation 10, where e1 is a unit vector. Then, using the Lagrange cost function, as shown in Equation 11, where μ is a Lagrange multiplier, the partial derivative is calculated and set to 0 to finally obtain a. m (β) about α k The function is shown in Formula 12.

[0054]

[0055]

[0056]

[0057] Next, find α. k Construct α k Maximize the objective function. Set z k As an intermediate variable, z k With α k The relationship is shown in Equation 13. By observing z... k With α k From the relational formula, we can obtain |z k | is 1. Based on this characteristic, the optimal solution z of the objective function can be obtained. k Finally, according to Formula 13 in Figure 13, where d is the spacing between array elements and λ is the signal wavelength, α can be obtained by reverse calculation. k ;

[0058] Based on the a obtained above m (β), β can be calculated k The angle vector of a. m (β) Take the phase angle to obtain a new vector l k As shown in Equation 14, a fitting vector d is established. k , used to fit β k And obtain d using the least squares method. k (2). Obtain β according to Formula 15. k.

[0059]

[0060]

[0061] Based on the above, the number of corresponding partial discharge sources α is obtained. k and β k The values ​​can be used to deduce θ and φ.

[0062] Finally, based on θ and φ, knowing the pitch angle and the plane phase angle is enough to determine the azimuth. By constructing direction finding lines, the intersection of each direction finding line is taken as the position of the target.

[0063] The key points and protection points for partial discharge localization of SiPM arrays mainly focus on the following aspects: The positioning accuracy of weak partial discharges can be improved by using an array of photoelectric converters.

[0064] Because SiPM has excellent electromagnetic interference resistance, the device can operate stably in harsh environments with acoustic waves and electromagnetic interference, especially under low air pressure and high frequency voltage conditions.

[0065] The device features a simple structural design, making it easy to install and maintain, while ensuring high detection efficiency. The multi-layered fiber optic coil design results in a small, compact sensor that is easy to install inside power transformers.

[0066] The device enables real-time monitoring of partial discharge activity, which is crucial for the safe operation of power systems.

[0067] On the other hand, the present invention also provides a partial discharge localization system based on an array-type photoelectric converter, the system comprising: The signal acquisition module is used to acquire the raw partial discharge time domain signal through the SiPM array. The SiPM array is composed of multiple SiPM units, with the anodes of each SiPM unit connected together and the cathode grounded. It is used to convert the optical signal emitted by the discharge power source into an electrical signal. The signal amplification module is used to amplify the original partial discharge time-domain signal through the preamplifier module to obtain an enhanced signal; The direction function determination module is used to determine the direction function of the array based on the arrangement of SiPM units in the SiPM array. The direction function includes the azimuth and elevation angles of the enhancement signal in the spatial coordinate system. The discharge source quantity determination module is used to construct a signal covariance matrix of several discharge sources based on the enhanced signal, and then express the likelihood discriminant. Iterates the number of signal sources starting from zero. When the likelihood discriminant shows a sudden change in value, the actual number of discharge sources K is determined. The positioning module is used to define a signal direction model matrix based on the direction function, and to determine the relationship between the signal corresponding to each discharge source and the coordinate axes. x shaft and y The angle between the axes is used to deduce the azimuth and elevation angles. By constructing direction finding lines, the intersection of each direction finding line is taken as the position of the target.

[0068] Other technical features of the partial discharge location system based on an array-type photoelectric converter described in this embodiment are similar to those of the partial discharge location method based on an array-type photoelectric converter, and will not be repeated here.

[0069] Thirdly, the present invention also provides a partial discharge positioning device based on an array-type photoelectric converter, such as... Figure 2 As shown, the device includes a support unit, a SiPM optical sensor array unit, a partial discharge platform, a power supply, and a signal processing unit. The support unit is constructed entirely of epoxy material, with both upper and lower plates being cuboids 80mm in length and width and 5mm in thickness, spaced 40mm apart, supporting the SiPM array and the epoxy fiberglass PCB board, respectively. Figure 3 As shown, the SiPM array consists of N*N SiPM optical detectors, each SiPM outputting a signal independently, where N is 4. The signal processing unit incorporates the aforementioned positioning method.

[0070] SiPM optical detector: Used for optical detection. When a photon is incident on a pixel of the SiPM, a pulse signal is generated within that pixel. The pulse signals from multiple pixels are superimposed and output from a common output terminal.

[0071] like Figure 4 As shown, the sensor circuit is mainly divided into three modules: a power supply module, a SiPM array, and a preamplifier. Filtering and impedance matching are achieved using RC components to facilitate signal processing in subsequent circuits.

[0072] 1. The power supply module consists of resistors R1, R2, R3 and capacitors C1, C2, C3, C4, which are used to provide bias voltage Vbias and perform voltage distribution and stabilization to power the entire circuit.

[0073] 2. A SiPM array consists of multiple SiPM units, with the anodes of each SiPM unit connected together and the cathode grounded, converting optical signals into electrical signals.

[0074] 3. The preamplifier module includes resistors R4, R5, and R6, capacitors C5, C6, and C7, as well as two operational amplifiers, which perform preliminary amplification and signal conditioning on the weak electrical signals output by the SiPM array.

[0075] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0076] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0077] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0078] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0079] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0080] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0081] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0082] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0083] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0084] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A partial discharge localization method based on an array-type photoelectric converter, characterized in that, The method includes: The original partial discharge time domain signal is acquired by a SiPM array, which consists of multiple SiPM units. The anodes of each SiPM unit are connected together and the cathode is grounded, which is used to convert the optical signal emitted by the discharge power source into an electrical signal. The original partial discharge time-domain signal is amplified by a preamplifier module to obtain an enhanced signal; The orientation function of the array is determined based on the arrangement of the SiPM units in the SiPM array. The orientation function includes the azimuth and elevation angles of the enhanced signal in the spatial coordinate system. Based on the enhanced signal, a signal covariance matrix of several discharge sources is constructed, which is then used to express the likelihood discriminant. The number of signal sources is iterated from zero. When the likelihood discriminant shows a sudden change in value, the actual number of discharge sources K is determined. Based on the defined direction function, a signal direction model matrix is ​​determined, and the relationship between the signal corresponding to each discharge source and the coordinate axes is determined. x shaft and y The angle between the axes is used to deduce the azimuth and elevation angles. By constructing direction finding lines, the intersection of each direction finding line is taken as the position of the target.

2. The partial discharge localization method based on an array-type photoelectric converter according to claim 1, characterized in that, The step of determining the direction function of the array based on the arrangement of SiPM cells in the SiPM array includes: Fix 4×4 SiPM elements in the spatial coordinate system y plane and z Planar, based on SiPM elements in y The projected length on the axis and in z The projected length on the axis represents the spatial phase difference between the current SiPM element and the origin; The direction function of the array is determined based on the amplitude weighting coefficients of the current SiPM cell and the phase shift provided by the relative reference SiPM cell.

3. The partial discharge localization method based on an array-type photoelectric converter according to claim 2, characterized in that, The construction of signal covariance matrices for several discharge sources based on the enhanced signal, and the subsequent expression of the likelihood discriminant, includes: An observation data model X is established based on the SiPM array. The observation data model is obtained through the first... k The array manifold matrix A corresponding to the power supply k The result is obtained by multiplying the enhanced signal vector with the noise and then adding the enhanced signal vector. The sample covariance matrix R is obtained based on the observed data model. z This leads to the likelihood discriminant, expressed as: , among which, among which, R S,k For the first k The signal covariance matrix of the power supply, A k H For A k The conjugate transpose of , where L is the number of snapshots of the received signal, which is equal to the number of columns in the matrix corresponding to the observation data model. The sample covariance matrix R z The estimate is given by `det()`, which is a determinant function.

4. The partial discharge localization method based on an array-type photoelectric converter according to claim 3, characterized in that, The first k The signal covariance matrix of the power source is represented by a diagonal matrix, and this diagonal matrix satisfies the following two conditions: ; ;in, It is the first k The signal covariance matrix of the power supply R S,k diagonal matrix ( i , i The value at position ) and L is the number of data on the diagonal.

5. The partial discharge localization method based on an array-type photoelectric converter according to claim 4, characterized in that, The signal direction model matrix is ​​defined based on the direction function to determine the relationship between the signal corresponding to each discharge source and the coordinate axes. x shaft and y The included angle of the axis includes: The signal direction model matrix is ​​represented by the direction function of the array, and then the sample covariance matrix R is represented. The covariance matrix R is decomposed into eigenvalues, which are divided into a signal subspace and a noise subspace. The azimuth and elevation angles corresponding to the enhanced signal in the spatial coordinate system are converted into... x shaft and y The included angles α and β of the axes are used to decompose the two-dimensional vector of the signal direction model matrix into two one-dimensional vectors including the included angles α and β. The new signal direction model is represented by two one-dimensional vectors. Based on the new signal direction model and the MUSIC algorithm, the first... k The power supply corresponds to x shaft and y The included angle α of the axis k、 β k .

6. The partial discharge localization method based on an array-type photoelectric converter according to claim 5, characterized in that, The two-dimensional vector decomposition of the signal direction model matrix into two one-dimensional vectors including angles α and β is expressed as follows: Where λ is the wavelength of the enhanced signal, and d is the distance between two adjacent SiPM units.

7. The partial discharge localization method based on an array-type photoelectric converter according to claim 6, characterized in that, The calculation based on the new signal direction model and the MUSIC algorithm yields the first... k The power supply corresponds to x shaft and y The included angle α of the axis k、 β k ,include: The new direction model of the signal is represented as follows: , among which, S k (t) is the enhanced signal vector corresponding to the k-th discharge source; N(t) is the noise; ⊗ is the Kronecker product; A new determination formula is obtained based on the MUSIC spatial spectrum combined with the new signal direction model, and the identity matrix I is used. m Replace a m The final determination method is shown in the following formula: ;in, The noise subspace matrix, is the conjugate transpose of the noise subspace matrix. a is obtained according to the final determination method described above. m (β) about α k The function, construct α k Maximize the objective function, and set the intermediate variable z based on maximizing the objective function. k α is obtained by inverse solving based on the optimal solution of the intermediate variable. k ; Establish a fitting vector d k , used to fit β k And the intermediate value d is obtained by using the least squares method. k (2), and then solve for β. k .

8. The partial discharge localization method based on an array-type photoelectric converter according to claim 7, characterized in that, The a is obtained according to the final determination method. m (β) about α k The functions include: α is constructed according to the final determination method. k a m The relevant quadratic optimization formula is expressed as: , where e1 is a unit vector; For the Lagrange cost function Find the partial derivative and set it equal to 0 to finally obtain a. m (β) about α k function , where μ is a Lagrange multiplier.

9. The partial discharge localization method based on an array-type photoelectric converter according to claim 8, characterized in that, The structure α k Maximize the objective function, and set the intermediate variable z based on maximizing the objective function. k α is obtained by inverse solving based on the optimal solution of the intermediate variable. k ,include: The objective function to be maximized is determined based on the constructed constraints, representing the intermediate variable z. k , represented as: ; Make |z k | is 1, thus obtaining the intermediate variable z k The optimal solution, and then based on z k With α k The relational formula yields α k .

10. The partial discharge localization method based on an array-type photoelectric converter according to claim 8, characterized in that, The establishment of a fitting vector d k , used to fit β k And the intermediate value d is obtained by using the least squares method. k (2), and then solve for β. k ,include: For the one-dimensional vector a m (β) Take the phase angle to obtain the vector l k , represented as: ; According to vector l k Establish a fitting vector d_k, represented as: The purpose is to approximate or fit the target vector β_k; Solve the least squares problem, find the optimal coefficients x_k, and use the obtained x_k to calculate... Take the second element d from this fitted vector. k (2); according to Solving for β k .

11. A partial discharge localization system based on an array-type photoelectric converter, characterized in that, The system includes: The signal acquisition module is used to acquire the raw partial discharge time domain signal through the SiPM array. The SiPM array is composed of multiple SiPM units, with the anodes of each SiPM unit connected together and the cathode grounded. It is used to convert the optical signal emitted by the discharge power source into an electrical signal. The signal amplification module is used to amplify the original partial discharge time-domain signal through the preamplifier module to obtain an enhanced signal; The direction function determination module is used to determine the direction function of the array based on the arrangement of SiPM units in the SiPM array. The direction function includes the azimuth and elevation angles of the enhancement signal in the spatial coordinate system. The discharge source quantity determination module is used to construct a signal covariance matrix of several discharge sources based on the enhanced signal, and then express the likelihood discriminant. Iterates the number of signal sources starting from zero. When the likelihood discriminant shows a sudden change in value, the actual number of discharge sources K is determined. The positioning module is used to define a signal direction model matrix based on the direction function, and to determine the relationship between the signal corresponding to each discharge source and the coordinate axes. x shaft and y The angle between the axes is used to deduce the azimuth and elevation angles. By constructing direction finding lines, the intersection of each direction finding line is taken as the position of the target.

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