Non-contact electric field detection method and device, medium and electronic equipment

Abstract

The invention discloses a non-contact electric field detection method and device, a medium and electronic equipment, and relates to the field of non-contact voltage sensing. The detection accuracy can be improved. The non-contact electric field detection method comprises the following steps: determining the number and position of simulated charges of a conductor to be detected in the current iteration; based on the number and the position, a charge simulation method is adopted to obtain a linear relation between the electric quantity of the simulation charge and the potential of the matching point; correcting a coefficient matrix in the linear relation through regularization, and solving a corrected relational expression to obtain the electric quantity of the simulated charge; performing inversion calculation on the potential at the detection point of the conductor to be detected based on the electric quantity of the simulated charge obtained by solving, and determining the residual quantity between the potential obtained by the inversion calculation and the actual potential of the detection point; under the condition that the residual quantity does not meet the preset condition, correcting the quantity or the position of the simulated charge, performing the next round of iteration, and re-solving the electric quantity of the simulated charge; and if the residual quantity meets the preset condition, determining the electric field of the conductor to be measured according to the electric quantity of the simulated charge.

Description

Technical Field

[0001] This application relates to the field of non-contact voltage sensing, and more particularly to a non-contact electric field detection method, device, medium, and electronic equipment. Background Technology

[0002] Non-contact voltage sensing technology, as a core means of modern power system monitoring and fault diagnosis, enables remote capture and analysis of voltage signals in a non-invasive manner. This effectively avoids the risks of insulation breakdown, equipment damage, or personal safety associated with traditional contact measurements, and has thus found widespread application in smart grids, high-voltage transmission line monitoring, and new energy integration. The Charge Simulation Method (CSM), a key modeling tool in this technology, is based on the principle of charge superposition in electromagnetic field theory. It approximates complex electric field structures by discretizing charge distribution, thereby efficiently retrieving the target voltage value. This method can flexibly handle electric field problems in varying geometric environments. For example, in high-voltage transmission towers, substations, or distributed energy systems, the CSM model can provide continuous, real-time voltage data, supporting dynamic assessment and early warning of system status. Furthermore, its computational simplicity and adaptability make it an ideal choice for efficient monitoring, especially in microgrids and renewable energy scenarios, where CSM helps optimize energy distribution and improve system reliability.

[0003] Although the simulated charge method (CSM) exhibits high accuracy under theoretical modeling and ideal conditions, its inherent numerical defects severely limit its performance and reliability in real-world noisy environments. The coefficient matrix constructed by the CSM model often possesses a high condition number, exhibiting strong ill-conditioned characteristics. This is particularly pronounced when using singular value decomposition (SVD) for pseudo-inverse solving: small singular values ​​drastically amplify measurement noise and modeling errors, causing the numerical solution to be overly sensitive to input disturbances, leading to instability and bias in the inversion results. This problem is especially significant in voltage inversion sensors. For example, in transmission line monitoring or smart grid deployment, factors such as environmental electromagnetic interference, sensor inherent errors, soil resistivity variations, or frequency fluctuations can propagate through the CSM model, causing significant deviations in the inversion results, with error rates exceeding 20%, thus severely weakening the sensor's accuracy and practicality. Summary of the Invention

[0004] This application provides a non-contact electric field detection method, device, medium, and electronic device. By regularization optimization, it suppresses the negative impact of small singular values ​​of the analog charge method on the solution, reduces errors, and improves the accuracy and reliability of voltage detection.

[0005] In a first aspect, this application provides a non-contact electric field detection method, comprising: Determine the quantity and location of the simulated charges on the conductor under test in the current iteration; Based on the quantity and location, the linear relationship between the amount of simulated charge and the potential of the matching point is obtained using the simulated charge method. The coefficient matrix in the linear relationship is corrected by regularization, and the modified relationship is solved to obtain the amount of simulated charge. The potential at the matching point of the conductor under test is calculated based on the electric charge of the simulated charge obtained by the solution, and the residual between the calculated potential and the actual potential at the detection point is determined. If the residual does not meet the preset conditions, the quantity or position of the simulated charge is corrected, and the next iteration is performed to resolve the charge of the simulated charge. If the residual quantity satisfies the preset condition, the electric field of the conductor under test is determined based on the amount of the simulated charge.

[0006] In the non-contact electric field detection method provided in this embodiment, the coefficient matrix in the simulated charge method is corrected by regularization. The regularization parameter can be dynamically optimized according to the real-time noise level, thereby significantly reducing the noise amplification effect, reducing the inversion error, and improving the accuracy and reliability of the sensor.

[0007] Secondly, this application provides a non-contact electric field detection device, comprising: The simulated charge module is used to determine the quantity and location of simulated charges on the conductor under test in the current iteration; The CSM solver module is used to obtain the linear relationship between the charge of the simulated charge and the potential of the matching point based on the quantity and location using the simulated charge method. The regularization correction module is used to correct the coefficient matrix in the linear relationship through regularization, and solve the corrected relationship to obtain the charge of the simulated charge. The residual calculation module is used to calculate the potential at the detection point of the conductor under test based on the electric charge of the simulated charge obtained by solving, and to determine the residual between the calculated potential and the actual potential at the detection point. The adjustment module is used to correct the quantity or position of the simulated charge when the residual does not meet the preset conditions, and to perform the next iteration to resolve the charge of the simulated charge. The output module is used to determine the electric field of the conductor under test based on the amount of the simulated charge if the residual quantity meets the preset condition.

[0008] Thirdly, this application provides an electronic device including a memory and one or more processors. The memory stores one or more computer programs, each including instructions that, when executed by the processor, cause the electronic device to perform the non-contact electric field detection method as described in the first aspect.

[0009] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the non-contact electric field detection method as described in the first aspect.

[0010] Fifthly, this application provides a computer program product that, when run on an electronic device, causes the electronic device to perform the non-contact electric field detection method as described in the first aspect.

[0011] Understandably, the beneficial effects achieved by the non-contact electric field detection device, electronic device, computer-readable storage medium, and computer program product provided above can be referred to the beneficial effects in the first aspect, and will not be repeated here. Attached Figure Description

[0012] Figure 1 A schematic flowchart of the non-contact electric field detection method provided in the embodiments of this application; Figure 2 This is a schematic diagram of the structure of the non-contact electric field detection device provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0013] To facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. For example, "first chip" and "second chip" are only used to distinguish different chips and do not limit their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" do not necessarily imply that they are different. It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in this application should not be construed as being better or more advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner. In the embodiments of this application, "at least one" means one or more, and "more than one" means two or more.

[0014] It should be noted that "at the time of..." in the embodiments of this application can be either at the instant when a certain situation occurs, or within a period of time after the occurrence of a certain situation. The embodiments of this application do not specifically limit this.

[0015] Traditional analog charge methods typically rely on fixed-dimensional charge configurations and static solution processes. This leads to a sharp amplification of the high condition number problem of the coefficient matrix under noise interference, resulting in drastic fluctuations and large errors in the inversion results, severely limiting their practicality in power system monitoring. In contrast, this embodiment introduces regularization optimization, which can systematically suppress the negative impact of small singular values ​​on the solution. By dynamically adjusting the regularization parameters (e.g., using the L-curve method or generalized cross-validation), the algorithm can autonomously optimize the solution process based on the real-time noise level, thereby significantly reducing the noise amplification effect and minimizing errors.

[0016] The implementation of this embodiment will now be described in detail with reference to the accompanying drawings.

[0017] This embodiment provides a non-contact electric field detection method. For example, this non-contact electric field detection method can be applied to various electronic devices such as computers (PCs), tablets, virtual reality / augmented reality devices, wearable devices, industrial computers, and vehicle systems; it can also be applied to servers, cloud computing, server clusters, etc. This embodiment does not impose any special limitations on it.

[0018] Figure 1 A flowchart illustrating the non-contact electric field detection method provided in an embodiment of this application is shown.

[0019] like Figure 1 As shown, this non-contact electric field detection method may include the following steps: Step 101: Determine the number and location of the simulated charges on the conductor under test in the current iteration.

[0020] The initial quantity and location of the simulated charges can be preset and updated after each iteration.

[0021] Step 102: Based on the quantity and location, use the simulated charge method to obtain the linear relationship between the amount of simulated charge and the potential of the matching point.

[0022] The charge stimulation method (CSM) is a method for solving electrostatic field problems. It replaces the influence of boundaries with the influence of virtual charges. Several virtual charges placed inside a conductor or in different dielectric regions simulate the charge distribution on the electrode surface and the bound charges at the dielectric interface. By replacing the continuously distributed charges on the electrode surface or the continuously distributed bound charges on the dielectric surface with these virtual simulated charges, the electric field of the entire field can be calculated as long as the potentials generated by these simulated charges at the boundaries satisfy the given boundary conditions.

[0023] The matching point is a point on the conductor surface with a known potential, which is equivalent to a given boundary condition.

[0024] The location and quantity of the simulated charges, as well as the type of charges selected, can be preset. Once the location and type of the simulated charges are determined, the potential and field strength at any point in the field can be obtained by superimposing the field quantities generated by these simulated charges.

[0025] A linear relationship can be expressed as: (1) Where A is the coefficient matrix, x is the amount of the simulated charge, and b is the potential at the matching point.

[0026] Represented in matrix form as follows:

[0027] Where [A] is an n×n coefficient matrix, and [x] is an n-order column vector, representing the n simulated charges to be solved, [ ] is an n-order column vector, representing the potential values ​​corresponding to the n matching points.

[0028] Step 103: Correct the coefficient matrix in the linear relationship through regularization, and solve the corrected relationship to obtain the charge of the simulated charge.

[0029] Based on the linear relationship constructed using the Charge Simulation Method (CSM), observed electric field data is mapped to voltage source parameters, thereby enabling the estimation of unknown voltages. Specifically, the coefficient matrix in the linear relationship is first decomposed using Singular Value Decomposition (SVD), and then the decomposed coefficient matrix is ​​corrected using regularization to obtain the corrected solution. The Singular Value Decomposition (SVD) process decomposes the coefficient matrix A into a product of three matrices, as shown in the following equation: A = UΣV T Here, U and V are the left and right singular vector matrices, respectively, both of which are orthogonal matrices to ensure numerical stability during the transformation process; Σ is a diagonal matrix, whose diagonal elements are the singular values, arranged in descending order to characterize the spectral properties of matrix A. In electric field inversion, the coefficient matrix A is usually determined by the charge configuration and the location of the observation point, and its condition number is high, making it susceptible to noise interference.

[0030] The inverse operation of SVD decomposition can be expressed as: A -1 = VΣ -1 U T Where Σ -1The pseudo-inverse of Σ is achieved by taking the reciprocals of its diagonal elements. However, the presence of small singular values ​​amplifies observation errors, leading to drastic fluctuations in the inversion results. Therefore, the SVD process not only provides an intuitive analysis of the matrix structure but also lays the foundation for subsequent regularization optimization. By identifying and processing small singular values, noise propagation is suppressed, thereby improving inversion accuracy and robustness.

[0031] The pseudoinverse is regularized using Tikhonov. The formula for solving the pseudoinverse under the SVD framework is modified as follows: (2) in, Let r be the amount of the simulated charge after regularization correction, i.e., the charge quantity, and r be the number of simulated charges. For the i-th singular value, It is the i-th column of the left singular vector matrix U. λ is the i-th column of the right singular vector matrix, and λ is the regularization parameter. b is the external electric field potential.

[0032] The simulated charge is obtained using the modified solution formula. Furthermore, the regularization parameter can be dynamically adjusted using the L-curve method or generalized cross-validation, allowing the algorithm to autonomously optimize the solution process based on the real-time noise level, thereby reducing the noise amplification effect of the CSM algorithm.

[0033] Compared to the traditional pseudo-inverse, the regularized solution introduces a damping factor on the components corresponding to small singular values, effectively suppressing their contribution to the final solution and thus avoiding the infinite amplification of noise. It can be seen that the introduction of the regularization parameter essentially constructs a balance mechanism in the solution space, achieving a trade-off between "approaching the true solution" and "suppressing noise interference."

[0034] Step 104: Based on the obtained simulated charge, calculate the potential at the matching point of the conductor under test, and determine the residual between the calculated potential and the actual potential at the detection point.

[0035] n sensors are set on the surface of the conductor to be tested to detect the potential at the sensor locations. These n sensor locations are called detection points. The detection points are used to verify the accuracy of the simulated charge obtained by the solution. By measuring the potential at the detection points, the actual potential is obtained, and then the difference between the actual potential and the potential obtained by the inversion calculation is calculated, i.e., the residual.

[0036] The residuals include the standard deviation of the potential and the standard deviation of the residuals: (3) in, The standard deviation of the potential. The standard deviation of the residuals; (4) (5) in, The number of conductors to be tested. The number of sampling points, Let m be the inversion potential at the m-th conductor detection point at time i. For the current moment, the first m Instantaneous value of conductor potential inversion; The number of detection points; , For the first The residual components of each detection point in rectangular coordinates. The residual is the difference between the electric field value at the detection point calculated based on the inversion potential and the actual electric field value at the detection point, that is, assigning the calculated potential of the conductor to the potential matching point on the conductor, and letting... ,and ,but , It can be represented as: (6) Let x be the component of the measured electric field at the nth detection point in the x-direction. Let x be the component of the background electric field in the x-direction. Let be the component of the measured electric field in the y-direction at the nth detection point. This represents the component of the background electric field in the y-direction. The normalization coefficient for the nth detection point is... The number of time sampling points, Let be the component of the electric field in the x-direction generated by the i-th simulated charge at the n-th detection point. Let be the y-component of the electric field generated by the i-th simulated charge at the n-th detection point. Let be the magnitude of the i-th simulated charge, i.e. The i-th value.

[0037] Step 105: If the residual does not meet the preset conditions, correct the quantity or position of the simulated charge, proceed to the next iteration, and resolve the charge of the simulated charge.

[0038] The preset condition is that the residual value is less than a certain threshold. A smaller residual value means the simulated charge is close to the true value of the conductor under test. If the residual value is larger, the simulated charge can be adjusted in the direction of the error gradient based on the residual value. In this embodiment, the current model dimension is also adjusted using the residual value to obtain the adjusted dimension, and the number of simulated charges for the next iteration is determined based on the adjusted dimension.

[0039] The adjusted dimensions are: (7) (8) To adjust the dimensions of the model, For the current model dimension, For adjustment rate, For adjustment purposes; For the historical number of iterations, The attenuation coefficient is... Let P be the model dimension in the Pth iteration. for The residual of the dimension, Let P+1 be the model dimension. for The residual of the dimension.

[0040] Model dimension is the dimensional parameter of the CSM model. The convergence performance of the CSM model is closely related to its dimensional parameter. This implementation introduces a dimension adjustment mechanism based on historical steps: in each iteration step, the model approximates the gradient direction using the ratio of parameter change to objective function change, based on the dimension and stability indices from previous steps. This process is implemented through a multi-step weighted difference method, which helps to balance convergence and stability during dimension updates. Specifically, the CSM model dimensional parameter is defined based on the simulated charge quantity. ,and ,in For the historical number of iterations, For the model dimension of the next iteration, This represents the current model dimension, and the others follow the same pattern, representing historical model dimensions. (The model dimension is...) The stability index obtained by voltage inversion is written as follows The gradient direction is estimated based on historical data, and the gradient is approximated by the ratio of parameter changes to target value changes. A multi-step weighted difference solution is used. Then, the current model dimension is calculated based on the adjustment rate. Adjustment rate The calculation formula is as follows: (9) in, To prevent the quantity from being divided by zero, Based on the adjustment rate.

[0041] The number of simulated charges in the next iteration is determined based on the adjusted dimensions, and the calculation formula is as follows: (10) in, To update the number of simulated charges, To simulate the maximum value of the charge number, This represents the minimum value of the simulated charge number.

[0042] Simultaneously, the positions of the simulated charges are updated, and then the number and positions of the simulated charges from the next iteration are used for the next iteration. Steps 101 to 105 in this embodiment constitute a cyclical iterative process, through which the simulated charge closest to the true value is finally determined.

[0043] In this implementation, an adaptive adjustment of the CSM model is achieved by establishing a dynamic mapping between stability indices (such as condition number or residual norm) and charge configuration dimensions. In high-noise scenarios, the algorithm automatically reduces the number of charges to mitigate overfitting risk and enhance numerical stability; while under low-noise conditions, it increases the dimensions to capture more detailed electric field features, ensuring inversion accuracy. This intelligent balancing mechanism not only avoids the common problems of solution divergence or overfitting in traditional methods but also enables the algorithm to flexibly respond to environmental changes (such as soil resistivity fluctuations or frequency interference), maintaining high-performance output without manual intervention.

[0044] Step 106: If the residual quantity satisfies the preset condition, determine the electric field of the conductor under test based on the amount of the simulated charge.

[0045] By using the final determined simulated charge, the potential and electric field intensity at any point on the conductor under test can be calculated, thereby determining the electric field structure of the conductor. The conductor under test can include transmission lines, high-voltage transmission towers, substations, etc.

[0046] In this embodiment, the inversion solution process is decomposed into two key steps: one is to dynamically suppress the noise amplification effect caused by the ill-conditioned nature of the coefficient matrix through regularization optimization (such as Tikhonov regularization or truncated singular value decomposition); the other is to adjust the charge configuration dimension in real time through a model dimension reconstruction strategy to balance numerical stability and inversion accuracy. This integrated approach can effectively compensate for errors caused by environmental interference without significantly increasing computational complexity, thereby achieving higher reliability output in the voltage inversion process. By adaptively adjusting the regularization parameters and charge dimension, this scheme can adapt to different noise levels, earth resistivity, and operating frequency conditions, significantly improving the robustness and practicality of non-contact voltage sensing.

[0047] In this exemplary manner, the aforementioned non-contact electric field detection method can be applied to a sensor platform to perform non-contact voltage detection on conductors in a power system. The overall process includes three steps: SVD electric field inversion inverse operation, regularization optimization of the inverse operation, and model dimension reconstruction. The specific process is as follows: 1. SVD electric field inversion inverse operation This step, based on a linear system constructed using the Charge Simulation Method (CSM), maps observed electric field data to voltage source parameters, thereby estimating the unknown voltage. The SVD process decomposes the coefficient matrix A into a product of three matrices. In electric field inversion, the coefficient matrix A is typically determined by charge configuration and observation point locations, resulting in a high condition number and susceptibility to noise interference. Through SVD decomposition, the inverse operation can be represented as A -1 = VΣ -1 U T Σ -1 The pseudo-inverse of Σ is achieved by taking the reciprocals of its diagonal elements. However, the presence of small singular values ​​amplifies observation errors, leading to drastic fluctuations in the inversion results. Therefore, the SVD process not only provides an intuitive analysis of the matrix structure but also lays the foundation for subsequent regularization optimization. By identifying and processing small singular values, it suppresses noise propagation, thereby improving inversion accuracy and robustness. This step ensures the mathematical rigor of the electric field modeling and provides core numerical support for the entire optimization method.

[0048] 2. Regularization optimization of inverse operation The pseudoinverse is regularized using Tikhonov. The solution formula for the pseudoinverse under the SVD framework is modified as follows:

[0049] in For regularization parameters, For singular values, compared to the traditional pseudo-inverse, the regularized solution introduces a damping factor on the components corresponding to small singular values, effectively suppressing their contribution to the final solution and thus avoiding the infinite amplification of noise. It can be seen that the introduction of the regularization parameter essentially constructs a balance mechanism in the solution space, allowing the solution to achieve a trade-off between "approaching the true solution" and "suppressing noise interference."

[0050] This method not only guarantees the stability of the solution but also enhances the algorithm's robustness to model uncertainties and external disturbances to a certain extent. At the same time, it maintains the interpretability and decomposition characteristics of the SVD framework. The choice of regularization parameter directly determines the balancing effect of the solution. If the parameter is too large, it will excessively suppress the components corresponding to small singular values, resulting in an overly smooth solution that deviates from the true physical quantity; if it is too small, the regularization effect will be insufficient, and noise may still be amplified. Regarding the selection of the regularization parameter, the L-curve method is used. By plotting the relationship between the norm of the solution and the norm of the residuals, the parameter corresponding to the "inflection point" of the curve is determined as the optimal parameter. This method can automatically find a balance between fitting accuracy and solution stability.

[0051] 3. Model Dimension Reconstruction First, construct a system based on the standard deviation of the matching point potential. With residual standard deviation The core comprehensive performance quantification index is defined as follows. Among them, the standard deviation of the matching point potential measures the consistency of the conductor potential inversion results at each matching point, while the standard deviation of the residual is used to characterize the deviation between the electric field at the detection point calculated from the inverted potential and the actual measured electric field. The comprehensive stability index is defined accordingly. This can be formally expressed as the sum of the standard deviation of the matching point potential and the standard deviation of the residuals, i.e.

[0052] lower A higher value implies better solution stability. (Standard deviation of the matching point potential) It can be represented as

[0053] in, The number of conductors to be tested. The number of sampling points, Let m be the inversion potential at the m-th conductor detection point at time i. For the current moment, the first m Instantaneous value of conductor potential inversion.

[0054] in For the current moment, the first m Instantaneous value of conductor potential inversion. Residual standard deviation. It can be represented as

[0055] The number of detection points; , For the first The residual components of each detection point in rectangular coordinates. The residual is the difference between the electric field value at the detection point calculated based on the inversion potential and the actual electric field value at the detection point, that is, assigning the calculated potential of the conductor to the potential matching point on the conductor, and letting... ,and ,but , It can be represented as

[0056] Let x be the component of the measured electric field at the nth detection point in the x-direction. Let x be the component of the background electric field in the x-direction. Let be the component of the measured electric field in the y-direction at the nth detection point. This represents the component of the background electric field in the y-direction. The normalization coefficient for the nth detection point is... The number of time sampling points, Let be the component of the electric field in the x-direction generated by the i-th simulated charge at the n-th detection point. Let be the y-component of the electric field generated by the i-th simulated charge at the n-th detection point. Let be the magnitude of the i-th simulated charge, i.e. The i-th value.

[0057] Furthermore, the convergence performance of the CSM model is closely related to its dimensional parameters. Therefore, a dimensionality adjustment mechanism based on historical steps is introduced: in each iteration, the model approximates the gradient direction using the ratio of parameter changes to objective function changes, based on the dimensionality and stability indices from previous steps. This process is implemented through a multi-step weighted difference method, which helps to balance convergence and stability during dimensionality updates. Specifically, the dimensional parameters of the CSM model are defined based on the simulated charge quantity. ,and ,in For historical steps, For the next iteration of the model dimension, This represents the current model dimension, and the others follow the same pattern, representing historical model dimensions. (The model dimension is...) The stability index obtained by voltage inversion is written as follows The gradient direction is estimated based on historical data, and the gradient is approximated by the ratio of parameter changes to target value changes. A multi-step weighted difference solution is used.

[0058]

[0059]

[0060] To adjust the dimensions of the model, For the current model dimension, For adjustment rate, For adjustment purposes; For the historical number of iterations, The attenuation coefficient is... Let P be the model dimension in the Pth iteration. for The residual of the dimension, Let P+1 be the model dimension. for The residual of the dimension. And

[0061] in To prevent small quantities from being divided by zero, Based on the adjustment rate.

[0062] After calculating the new model dimension, the model dimension is converted into the number of simulated charges of a single-phase conductor generated in the next iteration. The calculation formula is as follows:

[0063] in To simulate the maximum value of the charge number, This simulates the minimum charge number. After completing the new charge number calculation, the model is updated to reflect this value. Based on this foundation, the next round of model building and voltage inversion is carried out. This cyclical process forms a closed-loop iterative system of "dimensional update - charge number mapping - model reconstruction," enabling the model to gradually approach the optimal solution through continuous evolution. By introducing a decay factor and adaptive adjustment rate, the model maintains high flexibility while ensuring stability; through the dynamic mapping of dimension and charge number, the model can balance accuracy and computational efficiency.

[0064] In this embodiment, by introducing regularization optimization (such as Tikhonov regularization or truncated singular value decomposition) and dynamic parameter adjustment, the noise amplification effect caused by the ill-conditioned coefficient matrix is ​​effectively suppressed, reducing the voltage inversion error from more than 20% in the traditional method to less than 8%, and even further reducing it to less than 5% under stable conditions, ensuring that the sensor output more reliably approximates the true voltage value.

[0065] Furthermore, by combining the model dimension reconstruction strategy, stability indicators (such as condition number or residual norm) are evaluated in real time, and the charge configuration dimension is automatically adjusted according to the noise level. This improves numerical stability in high-noise scenarios and ensures inversion accuracy under low-noise conditions, achieving intelligent optimization without human intervention.

[0066] Meanwhile, while maintaining low computational complexity, it avoids the overfitting or solution divergence problems common in traditional methods. Through a dynamic balancing mechanism, it ensures that the algorithm can maintain stable output under sudden disturbances or environmental changes, significantly improving the practicality of non-contact voltage sensing in complex power systems.

[0067] This method can be directly applied to existing sensor platforms without large-scale hardware modifications, reducing deployment and maintenance costs. At the same time, it can quickly improve performance through software-level improvements, providing an efficient and economical solution for power system monitoring.

[0068] Furthermore, this embodiment also provides a non-contact electric field detection device, which can be used to perform the above-described non-contact electric field detection method. For example... Figure 2As shown, the non-contact electric field detection device 200 specifically includes: a simulated charge module 201, used to determine the quantity and location of simulated charges on the conductor under test in the current iteration; a CSM solving module 202, used to obtain a linear relationship between the charge of the simulated charges and the potential of the matching point based on the quantity and location using the simulated charge method; a regularization correction module 203, used to correct the coefficient matrix in the linear relationship through regularization, and solve the corrected relationship to obtain the charge of the simulated charges; a residual calculation module 204, used to invert the potential at the detection point of the conductor under test based on the obtained charge of the simulated charges, and determine the residual between the inverted calculated potential and the actual potential of the detection point; an adjustment module 205, used to correct the quantity or location of the simulated charges when the residual does not meet the preset conditions, and to perform the next iteration to re-solve the charge of the simulated charges; and an output module 206, used to determine the electric field of the conductor under test based on the charge of the simulated charges if the residual meets the preset conditions.

[0069] In one exemplary embodiment, the regularization correction module 203 is specifically used to decompose the coefficient matrix in the linear relationship through singular value decomposition, and to correct the decomposed coefficient matrix through regularization to obtain a corrected solution; the linear relationship is:

[0070] Where A is the coefficient matrix, x is the charge of the simulated charge, and b is the potential at the matching point. The singular value decomposition yields: A = UΣ

[0071] Where U and V are the left and right singular vector matrices, respectively, and Σ is a diagonal matrix with singular values ​​as its diagonal elements.

[0072] The corrected solution is expressed as follows:

[0073] in, Let r be the amount of the simulated charge after regularization correction, and r be the number of simulated charges. For the i-th singular value, It is the i-th column of the left singular vector matrix U. λ is the i-th column of the right singular vector matrix, and λ is the regularization parameter. b is the external electric field potential.

[0074] In one exemplary embodiment, the residual quantity includes the potential standard deviation and the residual standard deviation:

[0075] in, The standard deviation of the potential. The standard deviation of the residuals;

[0076]

[0077] in, The number of conductors to be tested. The number of sampling points, Let m be the inversion potential at the m-th conductor detection point at time i. For the current moment, the first m Instantaneous value of conductor potential inversion; The number of detection points; , For the first The residual components of each detection point in rectangular coordinates.

[0078] In one exemplary embodiment, the adjustment module 205 is specifically used to adjust the current model dimension using the residual amount to obtain the adjusted dimension, and to determine the number of simulated charges in the next iteration based on the adjusted dimension.

[0079] In one exemplary implementation, the adjusted dimension is:

[0080]

[0081] To adjust the dimensions of the model, For the current model dimension, For adjustment rate, For adjustment purposes; For the historical number of iterations, The attenuation coefficient is... Let P be the model dimension in the Pth iteration. for The residual of the dimension, Let P+1 be the model dimension. for The residual of the dimension.

[0082] In one exemplary embodiment, the adjustment module 205 is specifically used to calculate the number of simulated charges in the next iteration using the following formula:

[0083] in, To update the number of simulated charges, To simulate the maximum value of the charge number, This represents the minimum value of the simulated charge number.

[0084] In one exemplary implementation, the adjustment rate is:

[0085] in, To prevent the quantity from being divided by zero, Based on the adjustment rate.

[0086] The specific details of each module or unit in the above-mentioned non-contact electric field detection device have been described in detail in the corresponding non-contact electric field detection method, so they will not be repeated here.

[0087] This application also provides an electronic device. Figure 3 A schematic diagram of the structure of an electronic device suitable for implementing embodiments of the present disclosure is shown. Figure 3 The electronic device 600 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0088] like Figure 3 As shown, the electronic device 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 602 or a program loaded from a storage section 608 into a random access memory (RAM) 603. The RAM 603 also stores various programs and data required for system operation. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0089] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0090] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit (CPU) 601, it performs the functions defined in the embodiments of this application.

[0091] For example, when the computer program is executed by the central processing unit (CPU) 601, it can perform the following: determine the quantity and location of the simulated charge on the conductor under test in the current iteration; obtain the linear relationship between the charge of the simulated charge and the potential of the matching point using the simulated charge method based on the quantity and location; correct the coefficient matrix in the linear relationship through regularization, and solve the corrected relationship to obtain the charge of the simulated charge; calculate the potential at the detection point of the conductor under test based on the obtained charge of the simulated charge, and determine the residual between the calculated potential and the actual potential of the detection point; if the residual does not meet the preset conditions, correct the quantity or location of the simulated charge, proceed to the next iteration, and re-solve the charge of the simulated charge; if the residual meets the preset conditions, determine the electric field of the conductor under test based on the charge of the simulated charge.

[0092] It should be noted that the computer-readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0093] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0094] The units described in the embodiments of this disclosure can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the unit itself.

[0095] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which include instructions that, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0096] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0097] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A non-contact electric field detection method, characterized in that, include: Determine the quantity and location of the simulated charges on the conductor under test in the current iteration; Based on the quantity and location, the linear relationship between the amount of simulated charge and the potential of the matching point is obtained using the simulated charge method. The coefficient matrix in the linear relationship is corrected by regularization, and the modified relationship is solved to obtain the amount of simulated charge. The potential at the detection point of the conductor under test is calculated by inverting the amount of the simulated charge obtained from the solution, and the residual between the calculated potential and the actual potential at the detection point is determined. If the residual does not meet the preset conditions, the quantity or position of the simulated charge is corrected, and the next iteration is performed to resolve the charge of the simulated charge. If the residual quantity satisfies the preset condition, the electric field of the conductor under test is determined based on the amount of the simulated charge.

2. The non-contact electric field detection method according to claim 1, characterized in that, The step of correcting the coefficient matrix in the linear relationship through regularization includes: The coefficient matrix in the linear relationship is decomposed by singular value decomposition, and the decomposed coefficient matrix is ​​corrected by regularization to obtain the corrected solution. The linear relationship is as follows: Where A is the coefficient matrix, x is the amount of the simulated charge, and b is the potential at the matching point; The singular value decomposition yields: A = UΣ Where U and V are the left singular vector matrix and the right singular vector matrix, respectively, and Σ is a diagonal matrix with singular values ​​on the diagonal; The corrected solution is expressed as follows: in, Let r be the amount of the simulated charge after regularization correction, and r be the number of simulated charges. For the i-th singular value, It is the i-th column of the left singular vector matrix U. λ is the i-th column of the right singular vector matrix, and λ is the regularization parameter. b is the external electric field potential.

3. The non-contact electric field detection method according to claim 1, characterized in that, The residual includes the standard deviation of the potential and the standard deviation of the residual: in, The standard deviation of the potential. The standard deviation of the residuals; in, The number of conductors to be tested. The number of sampling points, Let m be the inversion potential at the m-th conductor detection point at time i. For the current moment m Instantaneous value of conductor potential inversion; The number of detection points; , For the first The residual components of each detection point in rectangular coordinates.

4. The non-contact electric field detection method according to claim 1, characterized in that, The correction of the quantity or location of the simulated charges includes: The current model dimension is adjusted using the residual to obtain the adjusted dimension, and the number of simulated charges in the next iteration is determined based on the adjusted dimension.

5. The non-contact electric field detection method according to claim 4, characterized in that, The adjusted dimensions are: To adjust the dimensions of the model, For the current model dimension, For adjustment rate, For adjustment purposes; For the historical number of iterations, The attenuation coefficient is... Let P be the model dimension in the Pth iteration. for The residual of the dimension, Let P+1 be the model dimension. for The residual of the dimension.

6. The non-contact electric field detection method according to claim 4, characterized in that, The formula for determining the number of simulated charges in the next iteration based on the adjusted dimension is as follows: in, To update the number of simulated charges, To simulate the maximum value of the charge number, This represents the minimum value of the simulated charge number.

7. The non-contact electric field detection method according to claim 5, characterized in that, The adjustment rate is: in, To prevent division by zero, Based on the adjustment rate.

8. A non-contact electric field detection device, characterized in that, include: The simulated charge module is used to determine the quantity and location of simulated charges on the conductor under test in the current iteration; The CSM solver module is used to obtain the linear relationship between the charge of the simulated charge and the potential of the matching point based on the quantity and location using the simulated charge method. The regularization correction module is used to correct the coefficient matrix in the linear relationship through regularization, and solve the corrected relationship to obtain the charge of the simulated charge. The residual calculation module is used to calculate the potential at the detection point of the conductor under test based on the electric charge of the simulated charge obtained by solving, and to determine the residual between the calculated potential and the actual potential at the detection point. The adjustment module is used to correct the quantity or position of the simulated charge when the residual does not meet the preset conditions, and to perform the next iteration to resolve the charge of the simulated charge. The output module is used to determine the electric field of the conductor under test based on the amount of the simulated charge if the residual quantity meets the preset condition.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the non-contact electric field detection method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The device includes a processor and a memory, the memory storing one or more computer programs, the one or more computer programs including instructions that, when executed by the electronic device, cause the electronic device to perform the non-contact electric field detection method according to any one of claims 1 to 7.

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