Position optimization method, device and equipment for non-contact magnetic sensing array of single-circuit overhead line and medium

Abstract

The invention discloses a position optimization method and device for a non-contact magnetic sensing array of a single-circuit overhead line, equipment and a medium, and relates to the technical field of non-contact measurement. The optimization method comprises the following steps: establishing a magnetic induction intensity model of a sensor based on spatial position parameters of a single-circuit three-phase aerial cable and a to-be-arranged magnetic sensing array; establishing an error model of a measured value and an actual value based on the measured value of the magnetic sensing array obtained by the magnetic induction intensity model; converting the problem of solving the error minimization of the error model into the problem of solving the matrix condition number minimization of the sensing coefficient matrix; and taking the matrix condition number as a fitness function, and solving the optimal layout position of the magnetic sensor array through a particle swarm optimization algorithm. According to the invention, a position optimization scheme for arranging the magnetic sensing array on the tower is provided, the problems of measurement and installation of sensors arranged on a non-tower target are solved, and the inverse solution capability of the sensing array to a space electromagnetic field is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of non-contact measurement technologies, and particularly to a method, device, equipment and medium for optimizing the position of a non-contact magnetic sensing array for a single-circuit overhead line. Background Art

[0002] Through remote sensing and data analysis, non-contact measurement technologies can collect power information and spatial data in real time and accurately without affecting the normal operation of transmission lines, which is a new concept compared with traditional contact sensing methods.

[0003] In recent years, with the rise of image recognition technologies, methods for monitoring the operating state of non-contact lines using image recognition technologies have been widely used. However, their application effects are poor in some special scenarios such as at night, in fog, rain or snow. Magnetoelectric measurement technologies can monitor the current, voltage and spatial state of transmission towers through magnetoelectric methods without being affected by factors such as night and weather. However, most measurement sensors are arranged along the transmission line, which leads to difficult installation and high maintenance costs. There are also some methods that arrange sensors on iron towers, solving the problems brought by the measurement and installation methods, and having the advantages of convenient installation and easy maintenance. However, the space for arranging sensors on towers is limited, which affects the measurement results.

[0004] Therefore, there is an urgent need for a method for optimizing the position of a non-contact magnetic sensing array on an overhead tower to enhance the ability of the sensing array to inversely solve the spatial electromagnetic field. Summary of the Invention

[0005] The present invention provides a method, device, equipment and medium for optimizing the position of a non-contact magnetic sensing array for a single-circuit overhead line to solve the problem that due to the limited space for arranging sensors on towers and the fact that the position of arranging sensors on towers is not considered in the prior art, the best measurement results cannot be obtained.

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

[0007] In a first aspect of the present invention, there is provided a method for optimizing the position of a non-contact magnetic sensing array for a single-circuit overhead line, including:

[0008] Establish a magnetic induction intensity model of a sensor based on the spatial position parameters of a single-circuit three-phase overhead cable and a magnetic sensing array;

[0009] Based on the measured values of the magnetic sensing array obtained from the magnetic induction intensity model, establish an error model between the measured values and the actual values;

[0010] Convert the problem of minimizing the error of solving the error model into the problem of minimizing the matrix condition number of a sensing coefficient matrix;

[0011] Taking the matrix condition number as the fitness function, the optimal layout position of the magnetic sensor array is solved by the particle swarm optimization algorithm.

[0012] Based on the non-contact current measurement technology of magnetic sensors, the present invention arranges the sensors on the iron tower, solving the problem of inconvenient layout caused by the measurement and installation methods of the sensors. However, due to the limited layout space of overhead poles and towers, and the presence of currents in multiple conductors simultaneously, it is necessary to consider the safety of the installation environment and distance in the actual environment. Unreasonable layout will cause the spatial electromagnetic noise in the actual environment to have a greater interference on the actual measurement. Therefore, from the perspective of magnetic field strength modeling, based on the inverse problem solving model for reconstructing current from magnetic field strength, the solution process of this inverse problem is further optimized. In the solution of the inverse problem, the role of the matrix condition number is very crucial. It measures the sensitivity of matrix calculation to errors. Generally speaking, if the condition number is large, even a very small data error may be amplified, resulting in a large error in the solution. If the condition number is small, the error of the solution is relatively controllable. Therefore, in the present invention, the measurement error optimization problem is converted into the optimization problem of the condition number of the sensing coefficient matrix, and the particle swarm optimization algorithm is used to optimize the parameters in the field source analysis method to enhance the inverse solution ability of the sensor array to the spatial electromagnetic field. By optimizing the array position, the interference of environmental noise is reduced, and the accuracy and reliability of the data of the non-contact measurement technology are improved.

[0013] Further, the spatial position parameters of the single-circuit three-phase overhead cable and the magnetic sensor array to be arranged include: the span between poles, the phase spacing between cables, the sag and wind deflection angle of the cable, and the relative position between the cable and the magnetic sensor.

[0014] Further, the magnetic induction intensity model of the magnetic sensor is expressed as:

[0015]

[0016] where B xij represents the magnetic induction intensity generated by the i-th phase cable at the j-th magnetic sensor, I pi represents the current of the i-th phase cable, S i represents the sag coefficient of the i-th phase cable, μ 0 is the vacuum permeability, L represents the span between poles, represents the projection result of the formula in the X-axis direction at this time, (x sj , y sj , z sj ) represents the position of the j-th magnetic sensor in the three-dimensional coordinate system O-XYZ, (x pi , y pi , z pi) represents the position of the i-th phase cable in the three-dimensional coordinate system O-XYZ, where the origin of the three-dimensional coordinate system is the projection center of the pole tower on the ground, the X-axis is perpendicular to the cable direction, the Y-axis is parallel to the pole tower direction, and the Z-axis is parallel to the cable direction.

[0017] Furthermore, the measured values of the magnetic induction intensity of the three-phase cable by a single magnetic sensor obtained based on the magnetic induction intensity model are expressed as:

[0018]

[0019] where B xj represents the magnetic induction intensity generated by the three-phase cable at the j-th magnetic sensor, and A x1j , A x2j , A x3j respectively represent the space coefficients between the three-phase cable and the j-th magnetic sensor, and I p1 , I p2 , I p3 respectively represent the currents of the three-phase cable;

[0020] The measured values of the magnetic sensor array obtained based on the magnetic induction intensity model are expressed as:

[0021]

[0022] where B x1 , B x1 , B x3 respectively represent the measured values of the magnetic sensors in the magnetic sensor array, I P1 , I p2 , I p3 are respectively the currents of the three-phase cable, and the matrix is the sensing coefficient matrix, and the matrix elements represent the space coefficients between the three-phase cable and the magnetic sensors in the magnetic sensor array.

[0023] Furthermore, the error model between the measured value and the actual value is expressed as:

[0024]

[0025] where A represents the sensing coefficient matrix, I represents the actual current value of the cable, represents the magnetic induction intensity actually generated by the three-phase cable at the magnetic sensor, and there is △I represents the error generated in the current information, represents the measurement error of the magnetic sensor array.

[0026] Furthermore, converting the problem of minimizing the error of solving the error model into the problem of minimizing the matrix condition number of the sensing coefficient matrix includes the following conversion steps:

[0027] When the sensing coefficient matrix is a non - singular matrix, it is derived from the error model that:

[0028] According to the matrix property, there is: Then, represents the measurement error of the magnetic sensing array;

[0029] It is expressed by the matrix condition number as: cond(A) represents the matrix condition number of the sensing coefficient matrix A. When cond(A) is smaller, the reconstruction error is smaller.

[0030] Furthermore, the method for solving the optimal layout position of the magnetic sensing array by the particle swarm optimization algorithm specifically includes:

[0031] S101, Initialize the particle swarm, randomly generate the initial positions of the particles in the optimization space, and randomly assign an initial velocity, velocity interval, weight coefficients c 1 、c 2 and the inertia coefficient ω, where the velocity interval is used to limit the maximum velocity of the particles, and each particle represents a magnetic sensor in the magnetic sensing array to be solved;

[0032] S102, Take the condition number cond(A) of the sensing coefficient matrix as the optimization function, calculate the fitness of each particle at the current solution, obtain the individual optimal solution, compare the individual optimal solution with the global optimal solution, and update the global optimal solution;

[0033] S103, Update the positions and velocities of the particle swarm according to the updated global optimal solution and weight coefficients:

[0034] The velocity is updated as:

[0035] v i =ω×v i +c 1 ×rand(0,1)×(pbest i -x i )+c 2 ×rand(0,1)×(gbest i -x i );

[0036] The position is updated as:

[0037] x i =x i +v i

[0038] where i = 1, 2,....N, N is the total number of particles in the particle swarm, v iis the velocity of particle i; rand(0,1) is a random number between (0,1), and x i is the current position of the particle; ω is the inertia coefficient, c 1 and c 2 are the weight coefficients, pbest i is the individual optimal solution of the particle, and gbest i is the global optimal solution of the particle swarm;

[0039] S104. Repeat steps S102 - S103 until the stop condition is reached, and output the particle swarm optimization result.

[0040] In the second aspect of the present invention, a position optimization device for a non - contact magnetic sensing array of a single - circuit overhead line is provided, including:

[0041] A first modeling module, configured to establish a magnetic induction intensity model of the sensor based on the spatial position parameters of the single - circuit three - phase overhead cable and the magnetic sensing array;

[0042] A second modeling module, configured to establish an error model between the measured value and the actual value based on the measured values of the magnetic sensing array obtained from the magnetic induction intensity model;

[0043] A solving module, configured to convert the problem of minimizing the error of solving the error model into the problem of minimizing the matrix condition number of the sensing coefficient matrix; and,

[0044] Taking the matrix condition number as the fitness function, and solving the optimal layout position of the magnetic sensing array through the particle swarm optimization algorithm.

[0045] In the third aspect of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the position optimization method of the non - contact magnetic sensing array of the single - circuit overhead line according to any one of the first aspects of the present invention is implemented.

[0046] In the fourth aspect of the present invention, a computer - readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the position optimization method of the non - contact magnetic sensing array of the single - circuit overhead line according to any one of the first aspects of the present invention is implemented.

[0047] Compared with the prior art, the present invention has the following advantages and beneficial effects: By constructing a spatial magnetic field intensity model excited by current, it is proved that there is a significant correlation between the spatial magnetic field and the conductor current. Further, it is obtained that the spatial coefficient matrix is an important factor leading to measurement errors, and the position parameters in the optimized field source analysis method are optimized through the particle swarm optimization algorithm. The optimization region is searched with the reduction of the current inverse - solution error as the optimization goal, and the optimal layout position is solved. Brief Description of the Drawings

[0048] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other relevant drawings can also be obtained based on these drawings. In the drawings:

[0049] Figure 1 is a flowchart of a method for optimizing the position of a non-contact magnetic sensing array for a single-circuit overhead line according to an embodiment of the present invention;

[0050] Figure 2 is a schematic diagram of an optimized area of a wine glass-shaped tower model according to an embodiment of the present invention;

[0051] Figure 3 is a flowchart of optimizing by a particle swarm algorithm according to an embodiment of the present invention;

[0052] Figure 4 is a convergence effect diagram of the condition number of a sensing coefficient matrix according to an embodiment of the present invention;

[0053] Figure 5 is the arrangement result of the magnetic sensor array after position optimization by the method of the present invention;

[0054] Fig. 6(a) is an effect diagram of current inverse solution under random position arrangement;

[0055] Fig. 6(b) is an effect diagram of current inverse solution after position optimization by the method of the present invention. Detailed Embodiments

[0056] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in combination with the embodiments and the drawings. The illustrative embodiments of the present invention and their descriptions are only used to explain the present invention and are not used to limit the present invention.

[0057] It should be noted that the terms "including" and "having" in the specification and claims of the present invention and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily limit to other steps or units inherent to the device.

[0058] The terms used in various embodiments of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the various embodiments of the present invention. As used herein, the singular forms are intended to include the plural forms as well, unless the context clearly indicates otherwise. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of the present invention belong. The terms (such as those defined in a commonly used dictionary) will be interpreted as having the same meaning as the contextual meaning in the relevant technical field and will not be interpreted as having an idealized meaning or an overly formal meaning, unless clearly defined in the various embodiments of the present invention.

[0059] Embodiments of the present invention provide a method for optimizing the position of a non-contact magnetic sensing array for a single-circuit overhead line. By establishing a magnetic induction intensity model of the magnetic sensing array based on physical dimensions such as the phase spacing, span, and sag of the overhead transmission tower, the problem of optimizing the measurement error is converted into optimizing the condition number of the spatial coefficient matrix of the target tower. Finally, the particle swarm optimization algorithm is used to search for the optimal solution in the area to be optimized for actual installation, and a position optimization scheme for arranging the non-contact magnetic sensing array on the overhead tower is obtained. This solves the problems of difficult installation and high maintenance costs caused by arranging sensors on non-tower targets, and improves the measurement accuracy and reliability of the tower-based observation method.

[0060] As Figure 1 shown is a flowchart of a method for optimizing the position of a non-contact magnetic sensing array for a single-circuit overhead line according to an embodiment of the present invention, including the following steps.

[0061] S1. Establish a magnetic induction intensity model of the sensor based on the spatial position parameters of the single-circuit three-phase overhead cable and the magnetic sensing array.

[0062] S2. Based on the measured values of the magnetic sensing array obtained from the magnetic induction intensity model, establish an error model between the measured values and the actual values.

[0063] S3. Convert the problem of minimizing the error of solving the error model into the problem of minimizing the condition number of the sensing coefficient matrix.

[0064] S4. Use the condition number of the matrix as the fitness function and solve the optimal layout position of the magnetic sensing array through the particle swarm optimization algorithm.

[0065] First, obtain the physical dimensions of the target tower, including physical parameters such as the span between towers, the phase spacing between cables, the sag of the cables, and the wind deflection angle. Establish a three-dimensional coordinate system O-XYZ with the tower as the center, and mark each physical dimension in the three-dimensional coordinates. As Figure 2For the shown wine glass-shaped tower model, the sensor array is arranged in the area to be optimized of the tower shown in the shaded area. Based on the Biot-Savart law, the magnetic field generated by the current at the sensor can be modeled according to the spatial position parameters of the sensor and the cable. Among them, the spatial position parameters include the span between target towers, the phase spacing between cables, the sag and wind deflection angle of the cable, and the relative position between the cable and the magnetic sensor in the same three-dimensional coordinate system, etc.

[0066] In an ideal long straight wire, based on the Biot-Savart law, the magnetic field generated by the current I at point P is expressed as:

[0067]

[0068] Among them, μ 0 is the vacuum magnetic permeability, is the wire element, is the distance vector from the wire element to point P.

[0069] Considering that the actual line will sag under the action of gravity and there are three-phase currents in the actual single-circuit transmission line at the same time, the magnetic field at point P is expressed as:

[0070]

[0071] Among them, B xij represents the magnetic induction intensity generated by the i-th phase cable at the j-th magnetic sensor, I pi represents the current of the i-th phase cable, s i represents the sag coefficient of the i-th phase cable, μ 0 is the vacuum magnetic permeability, L represents the span between towers, represents the projection result of the formula in the x-axis direction at this time, (x sj ,y sj ,z sj ) represents the position of the j-th magnetic sensor in the three-dimensional coordinate system O-XYZ, (x pi ,y pi ,z pi ) represents the position of the i-th phase cable in the three-dimensional coordinate system O-XYZ.

[0072] The three-dimensional coordinate system can be established with any point as the origin. In the present invention, it is preferably to use the projection center of the tower on the ground as the origin, or the ground directly below the connection of the B-phase cable and the tower as the origin. The X-axis is perpendicular to the cable direction, the Y-axis is parallel to the tower direction, and the Z-axis is parallel to the cable direction to establish the three-dimensional coordinate system.

[0073] Further, in step S2, since there are three-phase cables in the actual single-circuit transmission line, the magnetic fields generated by the three-phase cables are coupled at the sensor, which is manifested as a linear superposition at position P. Therefore, the magnetic induction intensity at the j-th sensor is expressed as:

[0074]

[0075] Among them, B xj represents the magnetic induction intensity generated by the three-phase cables at the j-th magnetic sensor, and A x1j 、A x2j 、A x3j respectively represent the space coefficients between the three-phase cables and the j-th magnetic sensor, and I p1 、I p2 、I p3 respectively represent the currents of the three-phase cables.

[0076] Then the measured value of the magnetic sensor array can be expressed as:

[0077]

[0078] Among them, N represents the number of magnetic sensors in the magnetic sensor array.

[0079] The matrix represents the sensing coefficient matrix of the magnetic sensor array. The element A Xij in A represents the space coefficient of the magnetic field generated by the i-th phase cable at the j-th sensor. Therefore, the elements in the sensing coefficient matrix A are only related to the spatial position parameters of the overhead cable and the to-be-deployed magnetic sensing array, such as the span between towers, the phase spacing between conductors, the relative position, the sag of the conductor, and the wind deflection angle, etc.

[0080] However, the more the number of sensor arrays, the more complex the inversion solution process. In this embodiment, a linear magnetic sensing array is taken as an example to optimize the position of the array composed of three magnetic sensors, and the three magnetic sensors are respectively located directly below the three cables. The magnetic induction intensity model of the three magnetic sensor arrays is expressed as:

[0081]

[0082] Among them, B x1 、B x1 、B x3 respectively represent the measured values of the three magnetic sensors in the magnetic sensing array, and the sensing coefficient matrix

[0083] Further, an error model between the measured value and the actual value is established, which is expressed as:

[0084]

[0085] There are inevitably errors in the acquisition of magnetic field signals. The deviation between the magnetic field information finally measured by the magnetic sensor array and the true solution is expressed as Furthermore, the error generated in the current information is expressed as ΔI.

[0086] Whether the true solution of the current can be obtained is mainly affected by the perturbation in the magnetic field information measured by the magnetic array. For the sensing coefficient matrix A, when A is non-singular, it means that A-1 exists and is bounded, then the system of equations has a solution, and the following derivation can be obtained:

[0087]

[0088] Then,

[0089]

[0090] Then,

[0091]

[0092] Among them, represents the measurement error of the magnetic sensing array.

[0093] It is expressed in terms of the matrix condition number as:

[0094]

[0095] Among them, cond(A) represents the matrix condition number of the sensing coefficient matrix A. When cond(A) is smaller, the reconstruction error is smaller, and the reconstructed value is closer to the true value. The influence of magnetic measurement interference on the electromagnetic relationship equation is reduced, so that the problem of minimizing the measurement error is transformed into the optimization problem of the matrix condition number cond(A).

[0096] Furthermore, referring to Figure 3 the flowchart of the particle swarm algorithm optimization shown, the optimal layout position of the magnetic sensing array is solved by the particle swarm optimization algorithm, which specifically includes the following steps.

[0097] S101, Initialize the particle swarm, randomly generate the initial positions of the particles in the optimization space, and randomly assign initial velocities, velocity intervals, weight coefficients, and inertia coefficients to each particle. Each particle represents a magnetic sensor in the magnetic sensing array to be solved, and the optimal value is obtained through each iteration.

[0098] The speed range is used to limit the maximum speed of the particles to prevent the particles from moving too fast, thus avoiding skipping potential optimal solutions during the search process; the inertia coefficient controls the weight of the particle speed and affects the global and local search capabilities. Generally speaking, a larger inertia coefficient is beneficial to global search, and a smaller inertia coefficient is beneficial to local search. There are usually two weight coefficients, denoted by c1 and c2 respectively, representing the weights of the moving speeds of the particles towards their own historical best positions and the global best position respectively. Generally speaking, the weight coefficients and the inertia coefficient in the example are constant values and vary according to the specific scenario.

[0099] S102. Take the condition number cond(A) of the sensing coefficient matrix as the optimization function, calculate the fitness of each particle at the current solution to obtain the individual optimal solution, compare the individual optimal solution with the global optimal solution, and update the global optimal solution; if the individual optimal solution is better than the global optimal solution, update the global optimal solution to the individual optimal solution, and if the global optimal solution is better than the individual optimal solution, retain the previous global optimal solution.

[0100] S103. Update the particle swarm position and speed according to the updated global optimal solution.

[0101] The speed update formula is as follows:

[0102] v i = ω × v i + c 1 × rand(0,1) × (pbest i - x i ) + c 2 × rand(0,1) × (gbest i - x i )

[0103] The position update formula is as follows:

[0104] x i = x i + v i

[0105] where i = 1, 2,....N, and N is the total number of particles in this group; v i is the speed of the particle; rand(0,1) is a random number between (0,1); x i is the current position of the particle; ω is the inertia coefficient; c 1 and c 2 are the weight coefficients. pbest i is the individual optimal value of the particle, and gbest iis the global optimal value of the particle swarm. In the velocity update formula, the first part is called the memory term, which represents the influence of the previous velocity magnitude and direction, and the influence magnitude is controlled by the inertia coefficient ω; the second part is called the self-cognition term, which is a vector pointing from the current position to the best position of the particle itself, indicating the part of the particle's action derived from its own experience; the third part is called the swarm-cognition term, which is a vector pointing from the current position to the best position of the population, reflecting the cooperation and knowledge sharing among particles.

[0106] S104. Repeat steps S102 - S103 until the preset number of iterations is reached, and output the particle swarm optimization result.

[0107] For the linear array model for solving the positions of three magnetic sensors, there are a total of six coordinate observation points on the two-dimensional plane, so the algorithm can be set as a sixth-order system. Taking the condition number cond(A) of the sensing coefficient matrix as the fitness function, the actually installed area to be optimized is set as the optimization space, and the optimization goal is to minimize the fitness function within the optimization space. In the stage of updating the optimal solution, the particles adjust their positions according to the individual and collective experience of the group. At the same time, they update their individual best positions and the global best position. Subsequently, the algorithm evaluates whether the maximum number of iterations is reached, or determines whether the solution converges, or other satisfied termination conditions, and outputs the solution result of the algorithm termination, which is the optimal layout position of the magnetic sensing array.

[0108] As Figure 4 shown is the convergence effect diagram of the condition number of the sensing coefficient matrix, and the algorithm converges within 15 iterations. Figure 5 is the optimized position result. Figures 6(a) and 6(b) show the layout positions of the sensor array before and after the optimization algorithm and the optimization effect of the current inverse solution at this position. The position before optimization is randomly selected. It can be seen that more accurate and stable inversion results are obtained through the optimized observation positions.

[0109] An embodiment of the present invention also provides a position optimization device for a non-contact magnetic sensing array of a single-circuit overhead line, which is used to execute the position optimization method of the non-contact magnetic sensing array of the single-circuit overhead line in the above embodiment of the present invention. The device includes a first modeling module, a second modeling module, and a solving module.

[0110] The first modeling module is used to establish a magnetic induction intensity model of the sensor based on the spatial position parameters of the single-circuit three-phase overhead cable and the magnetic sensing array.

[0111] The second modeling module is used to establish an error model between the measured value and the actual value based on the measured values of the magnetic sensing array obtained from the magnetic induction intensity model.

[0112] The solution module is used to convert the problem of minimizing the error of the solution error model into the problem of minimizing the matrix condition number of the sensing coefficient matrix; and use the matrix condition number as the fitness function to solve the optimal layout position of the magnetic sensor array through the particle swarm optimization algorithm.

[0113] Furthermore, establishing the magnetic induction intensity model of the sensor in the first modeling module specifically includes:

[0114] Establish the following magnetic induction intensity model:

[0115]

[0116] where B xij represents the magnetic induction intensity generated by the i-th phase cable at the j-th magnetic sensor, I pi represents the current of the i-th phase cable, S i represents the sag coefficient of the i-th phase cable, μ 0 is the magnetic permeability of vacuum, L represents the span between the poles, represents that the formula is the projection result in the X-axis direction at this time, (x sj , y sj , z sj ) represents the position of the j-th magnetic sensor in the three-dimensional coordinate system O-XYZ, (x pi , y pi , z pi ) represents the position of the i-th phase cable in the three-dimensional coordinate system O-XYZ. Among them, the origin of the three-dimensional coordinate system is the projection center of the pole on the ground, the X-axis is perpendicular to the cable direction, the Y-axis is parallel to the pole direction, and the Z-axis is parallel to the cable direction.

[0117] Furthermore, the measured value of the magnetic induction intensity of a single magnetic sensor for the three-phase cable obtained based on the magnetic induction intensity model is expressed as:

[0118]

[0119] where B xj represents the magnetic induction intensity generated by the three-phase cable at the j-th magnetic sensor, A x1j , A x2j , A x3j respectively represent the space coefficients between the three-phase cable and the j-th magnetic sensor, I p1 , I p2 , I p3 respectively represent the currents of the three-phase cable;

[0120] The measured value of the magnetic sensor array obtained based on the magnetic induction intensity model is expressed as:

[0121]

[0122] Among them, B x1 , B x1 , B x3 respectively represent the measured values of each magnetic sensor in the magnetic sensing array, I P1 , I p2 , I p3 are respectively the currents of the three-phase cables, and the matrix is the sensing coefficient matrix, and the matrix elements represent the spatial coefficients between the three-phase cables and each magnetic sensor in the magnetic sensing array.

[0123] Furthermore, establishing the error model between the measured value and the actual value in the second modeling module specifically includes:

[0124] Establish the following error model:

[0125]

[0126] Among them, A represents the sensing coefficient matrix, I represents the actual current value of the cable, represents the actual magnetic induction intensity generated by the three-phase cables at the magnetic sensing position, and there is △I represents the error generated in the current information, represents the measurement error of the magnetic sensing array.

[0127] Furthermore, the solution module includes a solution target conversion module and a particle swarm optimization algorithm module. The solution target conversion module first converts the problem of minimizing the solution error into the problem of minimizing the matrix condition number of the sensing coefficient matrix, that is, converts the optimization function with the error model as the solution target into the optimization function with the matrix condition number as the solution target, and then solves the minimum matrix condition number through the particle swarm optimization algorithm module to obtain the optimal layout position of the magnetic sensing array.

[0128] Furthermore, converting the problem of minimizing the solution error into the problem of minimizing the matrix condition number of the sensing coefficient matrix in the solution target conversion module includes the following conversion steps:

[0129] When the sensing coefficient matrix is a non-singular matrix, it is derived from the error model:

[0130] According to the matrix properties, there is: Then, represents the measurement error of the magnetic sensing array;

[0131] It is expressed in terms of the matrix condition number as: cond(A) represents the matrix condition number of the sensing coefficient matrix A. When cond(A) is smaller, the reconstruction error is smaller.

[0132] Further, in the particle swarm optimization algorithm module, the optimal layout position of the magnetic sensor array is solved, specifically including:

[0133] S101, Initialize the particle swarm, randomly generate the initial positions of the particles in the optimization space, and randomly assign initial velocities, velocity intervals, weight coefficients c 1 , c 2 and the inertia coefficient ω, where the velocity interval is used to limit the maximum velocity of the particles, and each particle represents a magnetic sensor in the magnetic sensor array to be solved;

[0134] S102, Take the condition number cond(A) of the sensing coefficient matrix as the optimization function, calculate the fitness of each particle at the current solution, obtain the individual optimal solution, compare the individual optimal solution with the global optimal solution, and update the global optimal solution;

[0135] S103, Update the positions and velocities of the particle swarm according to the updated global optimal solution:

[0136] The velocity is updated as:

[0137] v i = ω × v i + c 1 × rand(0,1) × (pbest i - x i ) + c 2 × rand(0,1) × (gbest i - x i );

[0138] The position is updated as:

[0139] x i = x i + v i

[0140] where i = 1, 2,....N, N is the total number of particles in the particle swarm, v i is the velocity of particle i; rand(0,1) is a random number between (0,1), x i is the current position of the particle; ω is the inertia coefficient, c 1 and c 2 are weight coefficients, pbest i is the individual optimal solution of the particle, and gbest i is the global optimal solution of the particle swarm;

[0141] S104, Repeat steps S102 to S103 until the stop condition is reached, and output the particle swarm optimization result.

[0142] An embodiment of the present invention also provides an electronic device, which includes a processor and a memory. The number of processors can be one or more. The memory, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules. The processor executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory, so as to implement the position optimization method of the single-circuit overhead line non-contact magnetic sensing array according to any one of the above embodiments of the present invention.

[0143] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the terminal, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some instances, the memory may further include a memory remotely set relative to the processor, and these remote memories can be connected to the electronic device through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0144] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the position optimization method of the single-circuit overhead line non-contact magnetic sensing array according to any one of the embodiments of the present invention is implemented.

[0145] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device.

[0146] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0147] Embodiments of the present invention also provide a computer program product, which, when running on a computer, causes the computer to execute the position optimization method of the single-circuit overhead line non-contact magnetic sensing array according to any one of the foregoing embodiments of the present invention.

[0148] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing the position of a non-contact magnetic sensor array for a single overhead line, characterized in that: include: The magnetic induction intensity model of the sensor is established based on the spatial position parameters of the single-circuit three-phase overhead cable and the magnetic sensor array; Based on the measurement values ​​of the magnetic sensor array obtained by the magnetic induction intensity model, an error model between the measurement values ​​and the actual values ​​is established; Converting the error minimization problem of solving the error model into a matrix condition number minimization problem of solving the sensing coefficient matrix; The matrix condition number is used as a fitness function, and the optimal layout position of the magnetic sensor array is solved by a particle swarm optimization algorithm.

2. The method for optimizing the position of a non-contact magnetic sensor array for a single overhead line according to claim 1, characterized in that: The spatial position parameters of the single-circuit three-phase overhead cable and the magnetic sensor array to be deployed include: the spacing between poles and towers, the phase spacing between cables, the verticality and wind deflection angle of the cable, and the relative position of the cable and the magnetic sensor.

3. The method for optimizing the position of a non-contact magnetic sensor array for a single overhead line according to claim 1, characterized in that: The magnetic induction intensity model of the magnetic sensor is expressed as: Among them, B xij It represents the magnetic induction intensity in the x-axis direction generated by the i-th phase cable at the j-th magnetic sensor, I pi represents the current of the i-th phase cable, s i represents the sag coefficient of the i-th phase cable, μ0 is the vacuum magnetic permeability, L represents the spacing between towers, Indicates that the formula is the projection result in the X-axis direction, (x sj ,y sj ,z sj ) represents the position of the jth magnetic sensor in the three-dimensional coordinate system O-XYZ, (x pi ,y pi ,z pi ) represents the position of the i-th phase cable in the three-dimensional coordinate system O-XYZ, where the origin of the three-dimensional coordinate system is the projection center of the tower on the ground, the X-axis is perpendicular to the cable direction, the Y-axis is parallel to the tower direction, and the Z-axis is parallel to the cable direction.

4. The method for optimizing the position of a non-contact magnetic sensor array for a single overhead line according to claim 3, characterized in that: The magnetic induction intensity measurement value of a single magnetic sensor on a three-phase cable obtained based on the magnetic induction intensity model is expressed as: Among them, B xj A represents the magnetic induction intensity generated by the three-phase cable at the jth magnetic sensor. x1j , A x2j , A x3j denote the space coefficient between the three-phase cable and the jth magnetic sensor, I p1 ,I p2 ,I p3 Respectively represent the current of the three-phase cable; The measured value of the magnetic sensor array obtained based on the magnetic induction intensity model is expressed as: Among them, B x1 , B x1 , B x3 Respectively represent the measurement value of each magnetic sensor in the magnetic sensor array, I P1 ,I p2 ,I p3 They are the current of the three-phase cable, matrix is the sensing coefficient matrix, and the matrix elements represent the spatial coefficients between the three-phase cables and each magnetic sensor in the magnetic sensing array.

5. The method for optimizing the position of a non-contact magnetic sensor array for a single overhead line according to claim 4, characterized in that: The error model between the measured value and the actual value is expressed as: Where A represents the sensing coefficient matrix, I represents the actual current value of the cable, It indicates the actual magnetic induction intensity generated by the three-phase cable at the magnetic sensor. △I represents the error generated in the current information, Represents the measurement error of the magnetic sensor array.

6. The method for optimizing the position of a non-contact magnetic sensor array for a single overhead line according to claim 5, characterized in that: The step of converting the error minimization problem of solving the error model into the matrix condition number minimization problem of solving the sensing coefficient matrix includes the following conversion steps: When the sensing coefficient matrix is ​​a non-singular matrix, it is derived from the error model: According to the matrix characteristics: but, represents the measurement error of the magnetic sensor array; It can be expressed as: cond(A) represents the matrix condition number of the sensing coefficient matrix A. The smaller cond(A) is, the smaller the reconstruction error is.

7. The method for optimizing the position of a non-contact magnetic sensor array for a single overhead line according to claim 6, characterized in that: The step of solving the optimal layout position of the magnetic sensor array by using a particle swarm optimization algorithm specifically includes: S101, initializing the particle swarm, randomly generating the initial positions of the particles in the optimization space, and randomly assigning an initial speed, speed range, weight coefficients c1, c2 and inertia coefficient ω to each particle, wherein the speed range is used to limit the maximum speed of the particle, and each particle represents a magnetic sensor in the magnetic sensor array to be solved; S102, using the condition number cond(A) of the sensing coefficient matrix as an optimization function, counting the fitness of each particle at the current solution, obtaining an individual optimal solution, comparing the individual optimal solution with the global optimal solution, and updating the global optimal solution; S103, update the particle swarm position and speed according to the updated global optimal solution: The speed is updated to: v i =ω×v i +c1×rand(0,1)×(pbest i -x i )+c2×rand(0,1)×(gbest i -x i ); The location is updated to: x i =x i +v i Where i = 1, 2, .... N, N is the total number of particles in the particle swarm, v i is the speed of particle i; rand(0,1) is a random number between (0,1), x i is the current position of the particle; ω is the inertia coefficient, c1 and c2 are weight coefficients, pbest i is the optimal solution for individual particles, gbest i It is the global optimal solution of the particle swarm; S104, repeating steps S102 to S103 until the stop condition is reached, and outputting the particle swarm optimization result.

8. A position optimization device for a non-contact magnetic sensor array of a single overhead line, characterized in that: include: The first modeling module is used to establish a magnetic induction intensity model of the sensor based on the spatial position parameters of the single-circuit three-phase overhead cable and the magnetic sensor array; A second modeling module is used to establish an error model between the measured value and the actual value based on the measured value of the magnetic sensor array obtained by the magnetic induction intensity model; A solution module, used for converting the error minimization problem of solving the error model into the matrix condition number minimization problem of solving the sensing coefficient matrix; and The matrix condition number is used as a fitness function, and the optimal layout position of the magnetic sensor array is solved by a particle swarm optimization algorithm.

9. The position optimization device for a non-contact magnetic sensor array of a single overhead line according to claim 8, characterized in that: The spatial position parameters of the single-circuit three-phase overhead cable and the magnetic sensor array to be deployed include: the spacing between poles and towers, the phase spacing between cables, the verticality and wind deflection angle of the cable, and the relative position of the cable and the magnetic sensor.

10. The position optimization device for a non-contact magnetic sensor array of a single overhead line according to claim 8, characterized in that: In the first modeling module, the following magnetic induction intensity model of the sensor is established: Among them, B xij It represents the magnetic induction intensity generated by the i-th phase cable at the j-th magnetic sensor, I pi represents the current of the i-th phase cable, S i represents the sag coefficient of the i-th phase cable, μ0 is the vacuum magnetic permeability, L represents the spacing between towers, Indicates that the formula is the projection result in the X-axis direction, (x sj ,y sj ,z sj ) represents the position of the jth magnetic sensor in the three-dimensional coordinate system O-XYZ, (x pi ,y pi ,z pi ) represents the position of the i-th phase cable in the three-dimensional coordinate system O-XYZ, where the origin of the three-dimensional coordinate system is the projection center of the tower on the ground, the X-axis is perpendicular to the cable direction, the Y-axis is parallel to the tower direction, and the Z-axis is parallel to the cable direction.

11. The position optimization device for a non-contact magnetic sensor array of a single overhead line according to claim 10, characterized in that: In the second modeling module, the magnetic induction intensity measurement value of a single magnetic sensor on a three-phase cable obtained based on the magnetic induction intensity model is expressed as: Among them, B xj A represents the magnetic induction intensity generated by the three-phase cable at the jth magnetic sensor. x1j , A x2j , A x3j denote the space coefficient between the three-phase cable and the jth magnetic sensor, I p1 ,I p2 ,I p3 Respectively represent the current of the three-phase cable; The measured value of the magnetic sensor array obtained based on the magnetic induction intensity model is expressed as: Among them, B x1 , B x1 , B x3 Respectively represent the measurement value of each magnetic sensor in the magnetic sensor array, I P1 ,I p2 ,I p3 They are the current of the three-phase cable, matrix is the sensing coefficient matrix, and the matrix elements represent the spatial coefficients between the three-phase cables and each magnetic sensor in the magnetic sensing array.

12. The position optimization device for a non-contact magnetic sensor array of a single overhead line according to claim 11, characterized in that: In the second modeling module, the following error model between the measured value and the actual value is established: Where A represents the sensing coefficient matrix, I represents the actual current value of the cable, It indicates the actual magnetic induction intensity generated by the three-phase cable at the magnetic sensor. △I represents the error generated in the current information, Represents the measurement error of the magnetic sensor array.

13. The position optimization device for a non-contact magnetic sensor array of a single overhead line according to claim 12, characterized in that: In the solution module, the error minimization problem of solving the error model is converted into the matrix condition number minimization problem of solving the sensing coefficient matrix, including the following conversion steps: When the sensing coefficient matrix is ​​a non-singular matrix, it is derived from the error model: According to the matrix characteristics: but, represents the measurement error of the magnetic sensor array; It can be expressed as: cond(A) represents the matrix condition number of the sensing coefficient matrix A. The smaller cond(A) is, the smaller the reconstruction error is.

14. The position optimization device for a non-contact magnetic sensor array of a single overhead line according to claim 13, characterized in that: In the solution module, the optimal layout position of the magnetic sensor array is solved by a particle swarm optimization algorithm, which specifically includes: S101, initializing the particle swarm, randomly generating the initial positions of the particles in the optimization space, and randomly assigning an initial speed, speed range, weight coefficients c1, c2 and inertia coefficient ω to each particle, wherein the speed range is used to limit the maximum speed of the particle, and each particle represents a magnetic sensor in the magnetic sensor array to be solved; S102, using the condition number cond(A) of the sensing coefficient matrix as an optimization function, counting the fitness of each particle at the current solution, obtaining an individual optimal solution, comparing the individual optimal solution with the global optimal solution, and updating the global optimal solution; S103, update the particle swarm position and speed according to the updated global optimal solution: The speed is updated to: v i =ω×v i +c1×rand(0,1)×(pbest i -x i )+c2×rand(0,1)×(gbest i -x i ); The location is updated to: x i =x i +v i Where i = 1, 2, .... N, N is the total number of particles in the particle swarm, v i is the speed of particle i; rand(0,1) is a random number between (0,1), x i is the current position of the particle; ω is the inertia coefficient, c1 and c2 are weight coefficients, pbest i is the optimal solution for individual particles, gbest i It is the global optimal solution of the particle swarm; S104, repeating steps S102 to S103 until the stop condition is reached, and outputting the particle swarm optimization result.

15. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for optimizing the position of a non-contact magnetic sensor array for a single overhead line as described in any one of claims 1 to 7 is implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing the position of a non-contact magnetic sensor array for a single overhead line as described in any one of claims 1 to 7 is implemented.

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