Parameter determination method and device of current sensor and nonvolatile storage medium

Abstract

The invention discloses a parameter determination method and device of a current sensor and a nonvolatile storage medium. The method comprises the steps that the influence degree of hardware parameters in a current sensor on the sensitivity and the frequency band range is obtained, and the current sensor is used for collecting partial discharge signals of a target cable connector; determining an influence factor set based on the influence degree; based on the influence factor set, a target planning model is established, and the target planning model comprises a plurality of target functions; and calculating the target planning model by adopting a particle swarm algorithm, and determining a target value corresponding to a hardware parameter in the current sensor. According to the invention, the technical problem that the compatibility of high sensitivity and broadband is difficult to realize at the same time when a current sensor is applied to cable joint partial discharge detection in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of power electronics technology, and more specifically, to a method, apparatus, and non-volatile storage medium for determining the parameters of a current sensor. Background Technology

[0002] Partial discharge detection and location are crucial for timely detection of insulation defects in cable joints and ensuring the safe and reliable operation of cables. For cables in operation, online monitoring of the joints is necessary to avoid losses caused by power outages. However, partial discharge signals have relatively weak energy, typically in the milliampere or microampere range, and a relatively wide frequency band with high-frequency components. Currently, current sensors used for partial discharge detection suffer from the incompatibility between high sensitivity and wide detection bandwidth, resulting in poor detection performance.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method, apparatus, and non-volatile storage medium for determining the parameters of a current sensor, thereby at least solving the technical problem in the related art that current sensors are difficult to simultaneously achieve high sensitivity and wide bandwidth compatibility in the application of partial discharge detection of cable joints.

[0005] According to one aspect of the present invention, a method for determining parameters of a current sensor is provided, comprising: obtaining the degree of influence of hardware parameters in the current sensor on sensitivity and frequency band range, wherein the current sensor is used to collect partial discharge signals of a target cable joint; determining a set of influencing factors based on the degree of influence; establishing a target planning model based on the set of influencing factors, wherein the target planning model includes multiple objective functions; and calculating the target planning model using a particle swarm optimization algorithm to determine the target values ​​corresponding to the hardware parameters in the current sensor.

[0006] Optionally, the influence of hardware parameters in the current sensor on sensitivity and bandwidth is obtained, including: establishing a circuit model and the transfer function corresponding to the current sensor; determining the relationship expression between hardware parameters and frequency domain response based on the circuit model and transfer function; adjusting the values ​​of hardware parameters sequentially based on the relationship expression to determine the bandwidth response corresponding to each of the multiple values ​​of the hardware parameters; and determining the influence of hardware parameters on sensitivity and bandwidth based on the bandwidth response corresponding to each of the multiple values ​​of the hardware parameters.

[0007] Optionally, based on the set of influencing factors, a target programming model is established, including: setting decision variables, wherein the decision variables represent the number of hardware parameters in the current sensor; establishing a first objective function based on the number of hardware parameters and the set of factors affecting sensitivity in the set of influencing factors, wherein the first objective function is a function that aims to satisfy a first condition; establishing a second objective function based on the number of hardware parameters and the set of factors affecting frequency band range in the set of influencing factors, wherein the second objective function is a function that aims to satisfy a second condition in the frequency band range; and determining the target programming model based on the first objective function and the second objective function.

[0008] Optionally, a particle swarm optimization algorithm is used to calculate the target programming model and determine the target values ​​corresponding to the hardware parameters in the current sensor. This includes: establishing constraints based on the self-integration working mode, the cable size of the target cable, and the preset frequency band range; setting the maximum number of iterations; and using the particle swarm optimization algorithm to calculate the target programming model based on the maximum number of iterations and the constraints to determine the target values ​​corresponding to the hardware parameters in the current sensor.

[0009] Optionally, a particle swarm optimization algorithm is used to calculate the target programming model and determine the target values ​​corresponding to the hardware parameters in the current sensor. This includes: randomly generating an initial population, where the initial population represents a set of values ​​corresponding to the hardware parameters; calculating the objective function value corresponding to the initial population based on the target programming model; selecting a value in the initial population that reaches a preset threshold as the first value based on the objective function value; determining a new population based on the first value; calculating the objective function value corresponding to the new population based on the target programming model; repeating the above operations until the objective function values ​​corresponding to the hardware parameters in the new population all reach the preset conditions, and then determining the target values ​​corresponding to the hardware parameters in the current sensor.

[0010] Optionally, determining a new population based on a first value includes: determining a first population based on the first value; performing a mutation operation on the first value based on a preset coefficient of variation to determine a second population; calculating the objective function values ​​corresponding to the first and second populations based on a goal programming model; and selecting a new population from the first and second populations based on the objective function values ​​corresponding to the first and second populations.

[0011] According to another aspect of the present invention, a parameter determination device for a current sensor is also provided, comprising: an acquisition module for acquiring the degree of influence of hardware parameters in the current sensor on sensitivity and frequency band range, wherein the current sensor is used to collect partial discharge signals of a target cable joint; a first determination module for determining a set of influencing factors based on the degree of influence; an establishment module for establishing a target planning model based on the set of influencing factors, wherein the target planning model includes multiple objective functions; and a second determination module for calculating the target planning model using a particle swarm optimization algorithm to determine the target values ​​corresponding to the hardware parameters in the current sensor.

[0012] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, the device where the non-volatile storage medium is located is controlled to execute any of the above-described current sensor parameter determination methods.

[0013] According to another aspect of the present invention, a computer device is also provided, the computer device including a processor, the processor being configured to run a program, wherein the program executes any of the above-described methods for determining the parameters of a current sensor.

[0014] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the parameter determination method for any of the above-described current sensors.

[0015] In this embodiment of the invention, a parameter determination method for a current sensor is adopted. This method acquires the degree of influence of the hardware parameters of the current sensor on the sensitivity and bandwidth. The current sensor is used to collect partial discharge signals from the target cable joint. Based on the degree of influence, a set of influencing factors is determined. Based on the set of influencing factors, a target planning model is established, which includes multiple objective functions. A particle swarm optimization algorithm is used to calculate the target planning model and determine the target values ​​corresponding to the hardware parameters of the current sensor. This achieves the goal of determining the hardware parameters of a current sensor that simultaneously considers high sensitivity and wide bandwidth, thereby improving the technical effect of partial discharge detection. Furthermore, it solves the technical problem in related technologies where current sensors are difficult to simultaneously achieve high sensitivity and wide bandwidth compatibility in cable joint partial discharge detection applications. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0017] Figure 1A hardware block diagram of a computer terminal for implementing a parameter determination method for a current sensor is shown.

[0018] Figure 2 This is a flowchart illustrating a method for determining the parameters of a current sensor according to an embodiment of the present invention.

[0019] Figure 3 This is a flowchart illustrating a method for determining the parameters of a current sensor according to an optional embodiment of the present invention.

[0020] Figure 4 This is a structural block diagram of a current sensor parameter determination device provided according to an embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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 scope of protection of the present invention.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] According to an embodiment of the present invention, a method embodiment for determining parameters of a current sensor is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0024] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a parameter determination method for a current sensor is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0025] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be implemented wholly or partially as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be wholly or partially integrated into any other element in the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as processor control (e.g., selection of a variable resistor termination path connected to an interface).

[0026] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the parameter determination method of the current sensor in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby implementing the parameter determination method of the current sensor in the aforementioned application program. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0027] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0028] Figure 2 This is a flowchart illustrating a method for determining the parameters of a current sensor according to an embodiment of the present invention, as shown below. Figure 2As shown, the method includes the following steps:

[0029] Step S202: Obtain the degree of influence of the hardware parameters of the current sensor on the sensitivity and frequency band range, wherein the current sensor is used to collect the partial discharge signal of the target cable joint.

[0030] In this step, the influence of the hardware parameters of the current sensor on its sensitivity and bandwidth can be determined by establishing circuit and mathematical models of the current sensor based on the principles of electromagnetic induction and Rogowski coil theory. These models will consider all hardware parameters that may affect sensor performance, such as the number of coil turns, inner and outer radii of the coil, wire diameter, coil thickness, sampling resistance, and core material properties. Virtual experiments can be conducted using simulation software (such as MATLAB, ANSYS, CST Studio, etc.) to observe the impact of changing each hardware parameter on sensor performance (sensitivity and bandwidth). This analysis typically employs the controlled variable method, where only one parameter is changed at a time while the others remain constant, to clearly observe the effect of individual parameter changes.

[0031] Step S204: Determine the set of influencing factors based on the degree of influence.

[0032] In this step, the influence of each quantified parameter can be organized to form an influence factor set. For example, statistical analysis can be performed on the data in the original sample set to quantify the influence of each hardware parameter on sensor sensitivity and bandwidth. By comparing performance data under different parameter variations, it can be determined which parameters have a significant impact on sensor performance, thus forming the influence factor set. This set helps to understand which parameters are most critical for improving sensor performance and how they interact to achieve optimal performance. The influence factor set can be applied to multi-objective optimization algorithms, such as particle swarm optimization (PSO).

[0033] Step S206: Based on the set of influencing factors, establish a goal programming model, wherein the goal programming model includes multiple objective functions.

[0034] In this step, the set of influencing factors is applied to a multi-objective optimization algorithm, such as Particle Swarm Optimization (PSO). In this algorithm, high bandwidth and high sensitivity are set as objective functions, while the optimal values ​​for the sensor hardware parameters are constrained by limitations. Through algorithmic iteration, a set of hardware parameter values ​​can be found that maximizes the sensor's sensitivity while meeting bandwidth requirements. For example, high sensitivity and wide bandwidth can be used as objective functions, with decision variables x (representing the sensor hardware parameters) and objective functions Bandwidth(x) and Sensitivity(x). Then, based on the specific dimensions of the cable connector, operating conditions, and the characteristics of the partial discharge signal, the range of sensor hardware parameter values ​​is set, forming constraints to ensure that the designed sensor is suitable for the target environment.

[0035] Step S208: The particle swarm optimization algorithm is used to calculate the target planning model and determine the target values ​​corresponding to the hardware parameters in the current sensor.

[0036] In this step, a particle swarm optimization (PSO) algorithm is used. The number of iterations, Tmax, can be set, and a population is randomly generated, with each particle representing a solution for a combination of hardware parameters. The objective function value for each particle (parameter combination) is calculated, representing the sensor's bandwidth and sensitivity. Through the iterative process of the PSO algorithm, the particle's position and velocity are continuously updated, while the coefficient of variation M is corrected. x The algorithm optimizes the objective function value. In each iteration, a better solution is selected for the next iteration until the termination condition is met (upper limit of iterations or convergence of performance metrics). After termination, the algorithm outputs the optimal solution set Optimal, which contains combinations of hardware parameters that simultaneously maximize the sensor's bandwidth and sensitivity.

[0037] Based on the optimal hardware parameters determined by a multi-objective programming algorithm, a prototype current sensor can be designed and fabricated. A simulated partial discharge signal is injected at the target cable connector, and the coupled signal is recorded using an oscilloscope or other testing equipment to verify whether the sensor performance meets the design goals of high sensitivity and wide bandwidth.

[0038] Through the steps described above, we can systematically understand the impact of current sensor hardware parameters on performance, thereby designing a better sensor, particularly suitable for partial discharge detection in cable joints. This optimization method based on multi-objective programming, through intelligent search using particle swarm optimization, overcomes the challenge of balancing high sensitivity and wide bandwidth in traditional optimization design, finding the optimal balance between performance indicators.

[0039] Through the above steps, the hardware parameters of a current sensor that simultaneously achieves high sensitivity and wide bandwidth can be determined, thereby improving the technical effect of partial discharge detection. This solves the technical problem in related technologies where current sensors are difficult to simultaneously achieve high sensitivity and wide bandwidth compatibility in the application of partial discharge detection in cable joints.

[0040] As an optional embodiment, obtaining the influence of hardware parameters in a current sensor on sensitivity and bandwidth includes: establishing a circuit model and a transfer function corresponding to the current sensor; determining the relationship expression between hardware parameters and frequency domain response based on the circuit model and transfer function; adjusting the values ​​of hardware parameters sequentially based on the relationship expression to determine the bandwidth response corresponding to each of the multiple values ​​of the hardware parameters; and determining the influence of hardware parameters on sensitivity and bandwidth based on the bandwidth response corresponding to each of the multiple values ​​of the hardware parameters.

[0041] Optionally, a circuit model can be established based on electromagnetic induction and the Rogowski coil principle. Based on the principle of electromagnetic induction, when the current flowing through the cable changes, a changing magnetic field is generated around the current sensor, which in turn induces an electromotive force through the sensor's Rogowski coil. Then, based on the law of electromagnetic induction, circuit theory, and the characteristics of the Rogowski coil, the transfer function H(s) of the current sensor is derived, where s is a complex frequency variable. The transfer function describes the relationship between the sensor's output signal and input signal, particularly its performance in the frequency domain. By analyzing the transfer function H(s), the sensor's hardware parameters can be linked to its frequency response, i.e., the relationship between hardware parameters (such as the number of coil turns N, wire diameter d, inner and outer radii of the coil, coil thickness h, sampling resistor R, etc.) and the sensor's gain and phase difference can be obtained. Using this relationship, the influence of hardware parameters on the sensor's output signal gain (in dB) and phase can be analyzed at different frequencies. A controlled variable method can be used, keeping other hardware parameters fixed and adjusting the value of each parameter sequentially, for example, testing the number of coil turns N from 4 to 20 one by one, observing its impact on the sensor's frequency domain response. For each parameter's different values, record the sensor's upper cutoff frequency FH ​​and lower cutoff frequency FL, as well as the sensitivity K at these frequencies. By comparing the bandwidth response under different parameter values, the influence of each parameter on the sensor's sensitivity and bandwidth can be assessed. For example, if a small change in a parameter can cause a significant change in bandwidth, then that parameter has a large impact on the bandwidth. Expressing the influence of each parameter on sensitivity and bandwidth numerically can be done by calculating the percentage change in gain or bandwidth with parameter variation, calculating the rate of change (derivative), or constructing a parameter influence factor matrix Influence, where each row represents a parameter, each column represents the type of influence (e.g., bandwidth change, sensitivity change), and the matrix elements represent the degree of influence.

[0042] Through the above process, we can gain a deeper understanding of the importance of hardware parameters in current sensor design and guide further optimization to ensure that the sensor can exhibit excellent performance in partial discharge detection, especially in applications such as partial discharge detection of cable joints, achieving a balance between high sensitivity and wide bandwidth.

[0043] As an optional embodiment, a target programming model is established based on the set of influencing factors, including: setting decision variables, wherein the decision variables characterize the number of hardware parameters in the current sensor; establishing a first objective function based on the number of hardware parameters and the set of factors affecting sensitivity in the set of influencing factors, wherein the first objective function is a function that aims to satisfy a first condition for sensitivity; establishing a second objective function based on the number of hardware parameters and the set of factors affecting frequency band range in the set of influencing factors, wherein the second objective function is a function that aims to satisfy a second condition for frequency band range; and determining the target programming model based on the first objective function and the second objective function.

[0044] Optionally, establishing a goal programming model based on the set of influencing factors involves converting hardware parameters and performance indicators (such as sensitivity and bandwidth) into mathematical expressions, thereby forming an objective function that can be identified and solved by optimization algorithms. Decision variables can be set first, where the decision variable (x) represents the sensor hardware parameters currently under consideration, such as the number of coil turns, inner and outer radii of the coil, coil thickness, wire diameter, and sampling resistance. Assuming the total number of sensor hardware parameters is (k), the decision variable (x) can be represented as a (k)-dimensional vector, where x... i Let represent the value of the (i)th parameter, where i = 1, 2, ..., k. Establish a first objective function, the goal of which is to improve the sensor's sensitivity to meet the first condition, i.e., reaching or exceeding a preset minimum sensitivity threshold. The first objective function can be designed as a function with the sensor's sensitivity (K) as the objective, and it needs to consider parameters in the Influence factor set that have a significant impact on sensitivity. The first objective function can be expressed as: Where M represents mutual inductance, L represents self-inductance, R2 is the sampling resistor, R is the resistance, and C is the capacitance. Then, a second objective function is established, aiming to broaden the sensor's bandwidth to satisfy the second condition, i.e., reaching or covering a preset bandwidth. The second objective function uses the sensor's bandwidth as its target, typically defined by the upper and lower cutoff frequencies L, with the goal of maximizing the bandwidth. If the set of influencing factors reveals that the coil's inner and outer radii, coil thickness, and number of turns significantly affect the bandwidth, then the second objective function can be expressed as follows: Where L represents self-inductance, R2 is the sampling resistor, R is the resistance, C is the capacitance, and B represents the bandwidth calculated based on these hardware parameters. The first and second objective functions are combined into a multi-objective programming model, i.e., finding a set of decision variables such that the solutions to both objective functions simultaneously reach their optimal values.

[0045] As an optional embodiment, a particle swarm optimization algorithm is used to calculate the target programming model and determine the hardware parameters of the sensor, including: establishing constraints based on the self-integration working mode, the cable size of the target cable, and the preset frequency band range; setting the maximum number of iterations; and using the particle swarm optimization algorithm to calculate the target programming model based on the maximum number of iterations and the constraints to determine the hardware parameters of the sensor.

[0046] Optionally, self-integrating mode means that when processing rapidly changing signals (such as pulse signals generated by partial discharge), the sensor can capture the transient characteristics of the signal without distortion. In high-frequency current sensor design, self-integrating mode typically involves specific circuit configurations and parameter settings to ensure that the sensor can accurately reflect the waveform of current changes. Cable size refers to the actual size and structural parameters of the cable connector (such as the diameter, length, and shape of the connector), which will limit the maximum size and shape of the sensor to ensure that the sensor can fit and will not interfere with or damage the cable connector. The preset frequency band range is set according to the frequency characteristics of the partial discharge signal at the cable connector, defining the frequency band range that the sensor should cover. This is usually based on the frequency distribution characteristics of the partial discharge, such as the center frequency and bandwidth, and the requirements of the detection system, such as signal fidelity and noise suppression. Setting the maximum number of iterations is one of the termination conditions for the particle swarm optimization algorithm. Too many iterations will increase computation time and resource consumption, but too few may not find a sufficiently good solution. The choice of Tmax should strike a balance between computational efficiency and solution quality.

[0047] Based on the maximum number of iterations and constraints, a particle swarm optimization (PSO) algorithm is used to compute the objective programming model and determine the sensor's hardware parameters. For example, a population can be randomly generated: a certain number of particles are randomly generated as the initial solution according to the range of constraints. The particle's position corresponds to the value of the sensor hardware parameter, and the particle's velocity represents the trend of the parameter value change. For each particle in the population, the influence of its corresponding sensor hardware parameter on high sensitivity and wide bandwidth is calculated. The basic rules of the PSO algorithm are used to update the position and velocity of each particle in order to find a better solution space.

[0048] Through the above steps, the particle swarm optimization algorithm can find the optimal combination of hardware parameters within a multi-objective programming framework to meet the high sensitivity and wide bandwidth requirements for partial discharge signal detection of cable joints, thereby effectively improving the accuracy and effectiveness of cable condition monitoring.

[0049] As an optional embodiment, a particle swarm optimization algorithm is used to calculate the target programming model and determine the target values ​​corresponding to the hardware parameters in the current sensor. This includes: randomly generating an initial population, where the initial population represents a set of values ​​corresponding to the hardware parameters; calculating the objective function value corresponding to the initial population based on the target programming model; selecting a value in the initial population that reaches a preset threshold as the first value based on the objective function value; determining a new population based on the first value; calculating the objective function value corresponding to the new population based on the target programming model; repeating the above operations until the objective function values ​​corresponding to the hardware parameters in the new population all reach the preset conditions, and then determining the target values ​​corresponding to the hardware parameters in the current sensor.

[0050] Optionally, the initial weights W of each hardware parameter of the sensor with respect to the sensitivity and frequency band of the current sensor in the original sample set Soriginal can be set. x Let x = 1, 2, ..., k; k is the total number of samples in the original sample set Soriginal. Let the current particle swarm optimization iteration number be j, j∈[1, Tmax]. Based on the range of the decision variables, an initial population is randomly generated, and the objective function value of each population is calculated. The better solution is selected to enter the next generation based on the objective function value. When the objective function values ​​corresponding to the hardware parameters in the new population all meet the preset conditions, the target values ​​corresponding to the hardware parameters in the current sensor are determined.

[0051] As an optional embodiment, determining a new population based on a first value includes: determining a first population based on the first value; performing a mutation operation on the first value based on a preset coefficient of variation to determine a second population; calculating the objective function values ​​corresponding to the first population and the second population based on a goal programming model; and selecting a new population from the first population and the second population based on the objective function values ​​corresponding to the first population and the second population.

[0052] Optionally, in Particle Swarm Optimization (PSO), mutation is a common strategy for newly generated solutions, aiming to maintain or increase population diversity and prevent the algorithm from getting trapped in local optima. Mutation is typically applied during the iterative process of PSO, slightly modifying the positions of particles (i.e., potential solutions) to broaden and deepen the exploration of the solution space. Mutation can be performed in several ways:

[0053] Random mutation: A small random perturbation is added to a portion of the particle's dimensions (i.e., hardware parameter values). This perturbation typically follows a distribution, such as a Gaussian or uniform distribution, and its magnitude depends on the mutation probability and the mutation amplitude, both of which can be preset or dynamically adjusted.

[0054] Crossover mutation: Exchanging certain dimension values ​​of two or more particles in a population to generate new solutions. This method is similar to the crossover operation in genetic algorithms.

[0055] Boundary mutation: If some dimensional values ​​of a particle exceed the allowed range, these dimensional values ​​are "bounced" back into the boundary or reset to a random boundary value.

[0056] Elite mutation: Occasionally introduce a mutated version of the best-performing particle in the population to enhance the population's fitness.

[0057] By using mutation, the Particle Swarm Optimization (PSO) algorithm avoids particles becoming overly concentrated in certain regions, increasing population diversity and thus improving its ability to find the global optimum. Especially after the algorithm has been running for some time, particles may tend to cluster around some local optima. Mutation can help particles escape this limitation and explore a wider solution space.

[0058] For the mutated particles, i.e., the particles in the second population, their corresponding objective function values ​​are recalculated, i.e., the bandwidth and sensitivity of the sensor.

[0059] The objective function value of the mutated particle is compared with the objective function values ​​of the particles in the first swarm. If the new solution is superior to the current particle or other particles in the swarm in terms of objective function value, then the solution will replace the original particle or be added to the swarm, becoming part of the next generation of particles.

[0060] Figure 3 This is a flowchart illustrating a method for determining the parameters of a current sensor according to an optional embodiment of the present invention, as shown below. Figure 3 As shown, the specific steps are as follows:

[0061] A. Data preprocessing: Study the influence of sensor hardware parameters on the sensitivity and bandwidth of the current sensor to obtain the influence factor set Influence.

[0062] B. Based on the influence factor set of various hardware parameters, high bandwidth and high sensitivity are used as objective functions. Constraints are established in combination with cable size and working conditions. Particle swarm optimization algorithm is used to calculate and construct a multi-objective programming algorithm.

[0063] C. Use the method to design a current sensor to collect partial discharge signals from cable joints.

[0064] Step A specifically includes the following steps:

[0065] A1. Based on electromagnetic induction and the principle of Rogowski coil, a circuit model is established. The transfer function H(s) of the current sensor is calculated based on circuit theory, thereby obtaining the relationship expression between hardware parameters and sensor frequency domain response.

[0066] A2. Using the controlled variable method, the frequency domain response of the current sensor is analyzed by successively changing the number of coil turns, wire diameter, inner and outer radii of the coil, thickness, and sampling resistor.

[0067] A3. Based on the frequency domain response obtained in A2, calculate the original sample set Soriginal for the effect of each hardware parameter of the sensor on the sensitivity and bandwidth of the current sensor. This set is a matrix, where Soriginal(x,1) represents the effect of the x-th parameter on the bandwidth, and Soriginal(x,2) represents the effect of the x-th parameter on the sensitivity.

[0068] Step B specifically includes the following steps:

[0069] B1. Initialization of the multi-objective programming algorithm: Set decision variables x = 1, 2, ..., k; k is the total number of sensor hardware parameters, and the objective functions are to maximize bandwidth (x) and maximize sensitivity (x).

[0070] B2. Establish constraints based on the self-integration operating mode, cable size, and desired frequency band range.

[0071] B3. Set the maximum number of iterations Tmax for the multi-objective programming algorithm, and select the particle swarm optimization algorithm to calculate the multi-objective programming;

[0072] B4. Initial Sample Weight Allocation: Set the initial weight W for each data sample in the original sample set Soriginal for the current sensor's sensitivity and frequency band, based on the sensor's various hardware parameters. x x = 1, 2, ..., k; k is the total number of samples in the original sample set Soriginal;

[0073] B5. Execute the particle swarm optimization algorithm: Let the current number of particle swarm optimization iterations be j, j∈[1,Tmax]. Based on the range of values ​​of the decision variables, randomly generate an initial population, calculate the objective function value of each population, and select the better solution to enter the next generation based on the objective function value.

[0074] B6. Perform mutation operations on the newly generated solutions to increase population diversity. Calculate the objective function value of the new solution, compare the new solution with the old solution, and update the population.

[0075] B7. Update the coefficient of variation M in the particle swarm optimization algorithm based on the calculation results. x x = 1, 2, ..., k; k is the total number of samples in the original sample set Soriginal;

[0076] B8. Update the coefficient of variation M of samples in the next generation population. x ;

[0077] B9. Termination Decision of Multi-Objective Programming Algorithm: The multi-objective programming algorithm stops iterating and proceeds to step B10 when one of the following conditions is met; otherwise, proceed to step B5. Condition 1: j ≥ Tmax; Condition 2: The objective function sets Bandwidthj+1 and Sensitivityj+1 are consistent with the objective function sets Bandwidthj and Sensitivityj, respectively, that is, the change error εj of the multi-objective programming algorithm no longer changes.

[0078] B10 outputs the final objective function set Bandwidth, Sensitivity, and the optimal solution set Optimal for the particle swarm, and inversely obtains the optimal hardware parameters of the sensor.

[0079] Step C specifically includes the following steps:

[0080] C1. Construct a partial discharge signal experimental platform.

[0081] C2. A simulated partial discharge signal is injected at the cable joint to test the sensor's ability to extract the actual signal.

[0082] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0083] Through the above description of the embodiments, those skilled in the art can clearly understand that the parameter determination method of the current sensor according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0084] According to embodiments of the present invention, a parameter determination apparatus for a current sensor for implementing the above-described parameter determination method for a current sensor is also provided. Figure 4 This is a structural block diagram of a current sensor parameter determination device provided according to an embodiment of the present invention, such as... Figure 4As shown, the parameter determination device for the current sensor includes: an acquisition module 402, a first determination module 404, an establishment module 406, and a second determination module 408. The parameter determination device for the current sensor will be described below.

[0085] The acquisition module 402 is used to acquire the degree of influence of the hardware parameters of the current sensor on the sensitivity and frequency band range, wherein the current sensor is used to collect the partial discharge signal of the target cable joint.

[0086] The first determining module 404, connected to the obtaining module 402, is used to determine the set of influencing factors based on the degree of influence.

[0087] Establishment module 406, connected to the first determination module 404, is used to establish a target programming model based on the set of influencing factors, wherein the target programming model includes multiple objective functions.

[0088] The second determining module 408, connected to the establishing module 406, is used to calculate the target planning model using the particle swarm optimization algorithm to determine the target values ​​corresponding to the hardware parameters in the current sensor.

[0089] It should be noted that the aforementioned acquisition module 402, first determination module 404, establishment module 406, and second determination module 408 correspond to steps S202 to S208 in the embodiments. Multiple modules and their corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiments. It should also be noted that the aforementioned modules, as part of the device, can run on the computer terminal 10 provided in the embodiments.

[0090] Embodiments of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0091] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the parameter determination method and device for the current sensor in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned parameter determination method for the current sensor. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0092] The processor can access the information and application program stored in the memory via the transmission device to perform the following steps: obtaining the degree of influence of the hardware parameters in the current sensor on the sensitivity and frequency band, wherein the current sensor is used to collect the partial discharge signal of the target cable joint; determining the set of influencing factors based on the degree of influence; establishing a target programming model based on the set of influencing factors, wherein the target programming model includes multiple objective functions; and calculating the target programming model using the particle swarm optimization algorithm to determine the target values ​​corresponding to the hardware parameters in the current sensor.

[0093] Optionally, the processor may also execute program code that performs the following steps: obtaining the degree of influence of hardware parameters in the current sensor on sensitivity and bandwidth, including: establishing a circuit model and the transfer function corresponding to the current sensor; determining the relationship expression between hardware parameters and frequency domain response in the current sensor based on the circuit model and the transfer function; adjusting the values ​​of hardware parameters sequentially based on the relationship expression to determine the bandwidth response corresponding to each of the multiple values ​​of the hardware parameters; and determining the degree of influence of hardware parameters on sensitivity and bandwidth based on the bandwidth response corresponding to each of the multiple values ​​of the hardware parameters.

[0094] Optionally, the processor may also execute program code for the following steps: establishing a target programming model based on the set of influencing factors, including: setting decision variables, wherein the decision variables characterize the number of hardware parameters in the current sensor; establishing a first objective function based on the number of hardware parameters and the set of factors affecting sensitivity in the set of influencing factors, wherein the first objective function is a function that aims to satisfy a first condition; establishing a second objective function based on the number of hardware parameters and the set of factors affecting frequency band range in the set of influencing factors, wherein the second objective function is a function that aims to satisfy a second condition in the frequency band range; and determining the target programming model based on the first objective function and the second objective function.

[0095] Optionally, the processor may also execute program code for the following steps: using a particle swarm optimization algorithm to calculate the target programming model and determine the target values ​​corresponding to the hardware parameters in the current sensor, including: establishing constraints based on the self-integration working mode, the cable size of the target cable, and the preset frequency band range; setting the maximum number of iterations; and using a particle swarm optimization algorithm to calculate the target programming model based on the maximum number of iterations and the constraints to determine the target values ​​corresponding to the hardware parameters in the current sensor.

[0096] Optionally, the processor may also execute program code with the following steps: using a particle swarm optimization algorithm to calculate a target programming model and determine the target values ​​corresponding to the hardware parameters in the current sensor, including: randomly generating an initial population, wherein the initial population represents a set of values ​​corresponding to the hardware parameters; calculating the objective function value corresponding to the initial population based on the target programming model; selecting a value in the initial population that reaches a preset threshold as the first value based on the objective function value; determining a new population based on the first value; calculating the objective function value corresponding to the new population based on the target programming model; repeating the above operations until the objective function values ​​corresponding to the hardware parameters in the new population all reach the preset conditions, and then determining the target values ​​corresponding to the hardware parameters in the current sensor.

[0097] Optionally, the processor may also execute program code for the following steps: determining a new population based on a first value, including: determining a first population based on the first value; performing a mutation operation on the first value based on a preset coefficient of variation to determine a second population; calculating the objective function values ​​corresponding to the first and second populations based on a goal programming model; and selecting a new population from the first and second populations based on the objective function values ​​corresponding to the first and second populations.

[0098] This invention provides a method for determining the parameters of a current sensor. By acquiring the degree of influence of the hardware parameters of the current sensor on its sensitivity and bandwidth, the method determines a set of influencing factors based on the degree of influence. A target programming model, comprising multiple objective functions, is then established based on this set of influencing factors. A particle swarm optimization algorithm is used to calculate the target values ​​corresponding to the hardware parameters of the current sensor. This method achieves the goal of determining the hardware parameters of a current sensor that simultaneously achieves high sensitivity and wide bandwidth, thereby improving the effectiveness of partial discharge detection. This solves the technical problem in related technologies where current sensors in cable joint partial discharge detection applications struggle to simultaneously achieve high sensitivity and wide bandwidth compatibility.

[0099] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a non-volatile storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0100] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the aforementioned non-volatile storage medium can be used to store the program code executed by the parameter determination method of the current sensor provided in the above embodiments.

[0101] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0102] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining the degree of influence of hardware parameters in the current sensor on sensitivity and bandwidth, wherein the current sensor is used to collect partial discharge signals of the target cable joint; determining a set of influence factors based on the degree of influence; establishing a target planning model based on the set of influence factors, wherein the target planning model includes multiple objective functions; and using a particle swarm optimization algorithm to calculate the target planning model and determine the target values ​​corresponding to the hardware parameters in the current sensor.

[0103] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining the degree of influence of hardware parameters in the current sensor on sensitivity and bandwidth, including: establishing a circuit model and the transfer function corresponding to the current sensor; determining the relationship expression between hardware parameters and frequency domain response in the current sensor based on the circuit model and the transfer function; adjusting the values ​​of hardware parameters sequentially based on the relationship expression to determine the bandwidth response corresponding to each of the multiple values ​​of the hardware parameters; and determining the degree of influence of hardware parameters on sensitivity and bandwidth based on the bandwidth response corresponding to each of the multiple values ​​of the hardware parameters.

[0104] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: establishing a target programming model based on a set of influencing factors, including: setting decision variables, wherein the decision variables characterize the number of hardware parameters in the current sensor; establishing a first objective function based on the number of hardware parameters and a set of factors influencing sensitivity, wherein the first objective function is a function whose objective is to satisfy a first condition; establishing a second objective function based on the number of hardware parameters and a set of factors influencing frequency band range, wherein the second objective function is a function whose objective is to satisfy a second condition; and determining the target programming model based on the first objective function and the second objective function.

[0105] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: using a particle swarm optimization algorithm to calculate a target planning model and determine the target values ​​corresponding to the hardware parameters in the current sensor, including: establishing constraints based on the self-integration working mode, the cable size of the target cable, and the preset frequency band range; setting a maximum number of iterations; and using a particle swarm optimization algorithm to calculate a target planning model based on the maximum number of iterations and the constraints to determine the target values ​​corresponding to the hardware parameters in the current sensor.

[0106] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: using a particle swarm optimization algorithm to calculate a target programming model and determine the target values ​​corresponding to the hardware parameters in the current sensor, including: randomly generating an initial population, wherein the initial population represents a set of values ​​corresponding to the hardware parameters; calculating the objective function value corresponding to the initial population based on the target programming model; selecting a value in the initial population that reaches a preset threshold as a first value based on the objective function value; determining a new population based on the first value; calculating the objective function value corresponding to the new population based on the target programming model; repeating the above operations until the objective function values ​​corresponding to the hardware parameters in the new population all reach the preset conditions, and then determining the target values ​​corresponding to the hardware parameters in the current sensor.

[0107] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining a new population based on a first value, including: determining a first population based on the first value; performing a mutation operation on the first value based on a preset coefficient of variation to determine a second population; calculating the objective function values ​​corresponding to the first population and the second population based on a goal programming model; and selecting a new population from the first population and the second population based on the objective function values ​​corresponding to the first population and the second population.

[0108] Embodiments of the present invention also provide a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can: acquire the degree of influence of hardware parameters in a current sensor on sensitivity and frequency band range, wherein the current sensor is used to collect partial discharge signals of a target cable joint; determine a set of influencing factors based on the degree of influence; establish a target planning model based on the set of influencing factors, wherein the target planning model includes multiple objective functions; and use a particle swarm optimization algorithm to calculate the target planning model and determine the target values ​​corresponding to the hardware parameters in the current sensor.

[0109] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0110] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0111] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0112] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0113] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0114] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0115] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for determining the parameters of a current sensor, characterized in that, include: The degree of influence of hardware parameters in a current sensor on sensitivity and bandwidth is obtained, wherein the current sensor is used to acquire partial discharge signals of a target cable joint; Based on the degree of influence, a set of influencing factors is determined; Based on the set of influencing factors, a goal programming model is established, wherein the goal programming model includes multiple objective functions; The target planning model is calculated using the particle swarm optimization algorithm to determine the target values ​​corresponding to the hardware parameters in the current sensor.

2. The method according to claim 1, characterized in that, The degree of influence of the hardware parameters of the current sensor on the sensitivity and frequency band range includes: Establish the circuit model and the transfer function corresponding to the current sensor; Based on the circuit model and the transfer function, the relationship expression between the hardware parameters and the frequency domain response of the current sensor is determined. Based on the relational expression, the values ​​of the hardware parameters are adjusted sequentially to determine the frequency band response corresponding to each of the multiple values ​​of the hardware parameters. Based on the frequency band response corresponding to each of the multiple values ​​of the hardware parameters, the degree of influence of the hardware parameters on sensitivity and frequency band range is determined.

3. The method according to claim 1, characterized in that, The establishment of the target programming model based on the set of influencing factors includes: Set decision variables, wherein the decision variables represent the number of hardware parameters in the current sensor; Based on the number of hardware parameters and the set of factors influencing sensitivity in the set of influencing factors, a first objective function is established, wherein the first objective function is a function that aims to satisfy a first condition for sensitivity. Based on the number of hardware parameters and the set of factors affecting the frequency band range, a second objective function is established, wherein the second objective function is a function that aims to satisfy the second condition in the frequency band range; The objective programming model is determined based on the first objective function and the second objective function.

4. The method according to claim 1, characterized in that, The step of using particle swarm optimization to calculate the target planning model and determine the target values ​​corresponding to the hardware parameters in the current sensor includes: Constraints are established based on the self-integration working mode, the cable size of the target cable, and the preset frequency band range; Set the maximum number of iterations; Based on the maximum number of iterations and the constraints, the particle swarm optimization algorithm is used to calculate the target planning model and determine the target values ​​corresponding to the hardware parameters in the current sensor.

5. The method according to claim 1, characterized in that, The step of using particle swarm optimization to calculate the target planning model and determine the target values ​​corresponding to the hardware parameters in the current sensor includes: An initial population is randomly generated, wherein the initial population represents a set of values ​​corresponding to the hardware parameters; Based on the target programming model, calculate the objective function value corresponding to the initial population; Based on the objective function value, the value that reaches the preset threshold in the initial population is selected as the first value; Based on the first value, a new population is determined; Based on the target programming model, calculate the objective function value corresponding to the new population; Repeat the above operation until the objective function values ​​corresponding to the hardware parameters in the new population all meet the preset conditions, and then determine the target values ​​corresponding to the hardware parameters in the current sensor.

6. The method according to claim 5, characterized in that, The step of determining a new population based on the first value includes: Based on the first value, the first group is determined; Based on a preset coefficient of variation, a mutation operation is performed on the first value to determine the second population; Based on the objective programming model, calculate the objective function values ​​corresponding to the first population and the second population respectively; Based on the objective function values ​​corresponding to the first population and the second population, the new population is selected from the first population and the second population.

7. A parameter determination device for a current sensor, characterized in that, include: An acquisition module is used to acquire the degree of influence of the hardware parameters in the current sensor on the sensitivity and frequency band range, wherein the current sensor is used to collect the partial discharge signal of the target cable joint; The first determining module is used to determine the set of influencing factors based on the degree of influence. A module is established to build a target programming model based on the set of influencing factors, wherein the target programming model includes multiple objective functions; The second determining module is used to calculate the target planning model using the particle swarm optimization algorithm to determine the target values ​​corresponding to the hardware parameters in the current sensor.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the non-volatile storage medium to perform the parameter determination method of the current sensor according to any one of claims 1 to 6.

9. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the parameter determination method for the current sensor according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the parameter determination method of the current sensor according to any one of claims 1 to 6.

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