Transmission line parameter inversion method, device and electronic equipment

Abstract

The present invention belongs to the field of data processing technology and relates to a method, device, and electronic device for inverting transmission line parameters using a spatial electric field Gaussian integral combined with a genetic algorithm. The method includes binary encoding the coordinate information of each integral node to generate initial coordinate points; calculating the coefficients in the Gaussian integral; inverting the voltage value; determining the objective function; judging whether the objective function meets the constraints; judging whether the objective function meets the set range; if the objective function does not meet the set range, performing genetic algorithm processing on the inverted voltage value to obtain a new population for the next position point calculation, until all initial coordinate points are calculated. The present invention has higher accuracy in inversion of complex electromagnetic environments; solves the multicollinearity problem that may occur in the Gaussian integral solution process; and incorporates sensitivity analysis into the constraints of the genetic algorithm for solution, thereby solving the sensitivity problem in the inversion process.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and in particular relates to a method, device and electronic equipment for inverting transmission line parameters using a spatial electric field Gaussian integral combined with a genetic algorithm. Background Art

[0002] Renewable energy sources have volatility and randomness in their power generation time, which will bring new challenges to the stability and security of the power system. Therefore, it is necessary to rely on highly reliable sensors and measurement technologies in the power system. Voltage measurement is the most important and basic measurement content in power grid sensing measurement.

[0003] Currently, power status monitoring on the transmission and distribution side is primarily focused on substations. In contrast, transmission lines are widely distributed, making monitoring more challenging. However, transmission lines are currently under-monitored. Monitoring transmission line voltage is crucial for power quality analysis, online overvoltage detection during grid operation, and relay protection. Furthermore, the accuracy and precision of voltage measurement is directly related to the dispatching, safety, and control of power systems.

[0004] Currently, the methods used for grid voltage measurement can be divided into contact and non-contact methods based on the physical contact. Among them, contact voltage measurement technologies such as electromagnetic and capacitive transformers are relatively mature and are currently the most widely used technical means in the field of grid measurement.

[0005] However, electromagnetic voltage transformers contain a large amount of iron core. When measuring large voltages, their transformers tend to operate in a nonlinear region, affecting the measurement accuracy of the entire transformer. They are also prone to causing ferromagnetic resonance between the transformer and the power grid. Since the primary side of the transformer is directly connected to the power grid, high insulation requirements are imposed under high voltage measurement conditions, and the corresponding volume and cost are also high. Capacitive transformers have voltage-dividing capacitors, which reduce the original voltage during measurement. However, capacitor transformers contain a large number of inertial components such as capacitors, which can easily lead to problems such as voltage measurement phase lag. At the same time, when overvoltage occurs in the transmission line, the capacitor voltage transformer will also experience transformer core saturation, thereby causing ferromagnetic resonance problems.

[0006] Spatial electric field data obtained using non-contact measurement technology requires correct inverse problem calculations to accurately determine the voltage parameters of transmission conductors. However, obtaining electric field signals around complex electromagnetic fields and calculating the field source parameters inversely involves solving an overdetermined set of equations, making the inverse problem of conductor voltage extremely difficult. Furthermore, the inverse problem requires solving a capacitance matrix, which lacks universality, further complicating the solution. Fixed integral algorithms cannot be adjusted to suit different electromagnetic environments, resulting in good applicability in simple electromagnetic environments but low accuracy in complex ones. These issues limit the application of inverse problem-solving methods in complex electric field measurement environments. Summary of the Invention

[0007] In order to solve the above technical problems, the present invention provides a transmission line parameter inversion method, device and electronic equipment.

[0008] In a first aspect, the present disclosure provides a method for inverting transmission line parameters, comprising:

[0009] The coordinate information of each integration node of the electromagnetic field model is binary-encoded, and a number of initial coordinate points are randomly generated;

[0010] Calculate the coefficients of the Gaussian integral according to the initial coordinate points and the Gaussian integral equation;

[0011] Obtaining an inversion voltage value according to the coefficient;

[0012] Determining an objective function according to the inverted voltage value and the actual voltage value;

[0013] Determining whether the objective function satisfies the constraint conditions, and if the objective function does not satisfy the constraint conditions, adding a penalty function to the objective function;

[0014] Determining whether the inversion voltage value satisfies a set range, and if the inversion voltage value satisfies the set range, outputting inversion coordinate point position data;

[0015] If the inversion voltage value does not meet the set range, the inversion voltage value is processed by a genetic algorithm to obtain a new population for the next position point calculation until all the initial coordinate points are calculated.

[0016] In a second aspect, the present disclosure provides a transmission line parameter inversion device, comprising a coding unit, a first calculation processing unit, a second calculation processing unit, an objective function determination unit, a first judgment unit, a second judgment unit, an output unit, and a genetic algorithm processing unit:

[0017] The encoding unit is used to perform binary encoding on the coordinate information of each integration node of the electromagnetic field model and randomly generate a number of initial coordinate points;

[0018] The first calculation processing unit is used to calculate the coefficients of the Gaussian integral based on the initial coordinate point and the Gaussian integral equation;

[0019] The second calculation processing unit obtains an inversion voltage value according to the coefficient;

[0020] The objective function determining unit is configured to determine an objective function based on the inverted voltage value and the actual voltage value;

[0021] The first judgment unit is used to judge whether the objective function satisfies the constraint condition, and if the objective function does not satisfy the constraint condition, add a penalty function to the objective function;

[0022] The second judgment unit is used to judge whether the inversion voltage value meets the set range, and output the inversion coordinate point position data if the inversion voltage value meets the set range;

[0023] The output unit is used to output the inversion coordinate point position data;

[0024] The genetic algorithm processing unit is used to perform genetic algorithm processing on the inversion voltage value if the inversion voltage value does not meet the set range, to obtain a new population for the next position point calculation until all the initial coordinate points are calculated.

[0025] In a third aspect, the present disclosure provides an electronic device, including:

[0026] processor and memory;

[0027] The memory is used to store computer operating instructions;

[0028] The processor is configured to execute the method according to any one of claims 1 to 5 by calling the computer operation instructions.

[0029] The beneficial effects of the present invention are:

[0030] (1) The present invention can adjust the weights and integration nodes according to the actual conditions of different complex electromagnetic field models. Compared with the traditional fixed-point integration inversion scheme, it has higher accuracy in the inversion of complex electromagnetic environments.

[0031] (2) The present invention can calculate the sum of the electric field values ​​of multiple nodes by multiplying them by weights, with a fast calculation speed. At the same time, the ridge regression method is added to solve the multicollinearity problem that may occur in the Gaussian integral solution process;

[0032] (3) In order to solve the problem of reduced inversion accuracy due to position deviation in the actual electric field sensor process, the present invention adds sensitivity analysis to the constraints of the genetic algorithm to solve the sensitivity problem in the inversion process.

[0033] On the basis of the above technical solution, the present invention can also be improved as follows.

[0034] Furthermore, the genetic algorithm processing process includes:

[0035] Two chromosomes were randomly selected using the roulette wheel method;

[0036] Randomly select a binary site for the two selected chromosomes and exchange the binary sites of the two chromosomes;

[0037] Randomly select a binary site, transform the binary site, and generate a new population for the next position point calculation.

[0038] Furthermore, determining the objective function according to the inversion voltage and the actual voltage value includes: taking the absolute value of the difference between the inversion voltage and the actual voltage as the objective function.

[0039] Further, judging whether the objective function satisfies the constraint condition includes: solving the inverted voltage value after any integral node moves a set distance, and judging whether the absolute value of the difference between the inverted voltage value and the actual voltage value is within the set range; if the absolute value of the difference between the inverted voltage value and the actual voltage value is within the set range, then the objective function satisfies the constraint condition; otherwise, if the absolute value of the difference between the inverted voltage value and the actual voltage value is not within the set range, then adding the penalty function to the objective function.

[0040] Furthermore, the penalty function is the product of the portion of the inversion voltage value that exceeds the set range and the coefficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Flowchart of the transmission line parameter inversion method provided in Example 1 of the present invention;

[0042] Figure 2 Flowchart of the genetic algorithm processing process provided in Example 1 of the present invention;

[0043] Figure 3 A single-conductor potential simulation diagram provided in Example 1 of the present invention;

[0044] Figure 4 A single-conductor potential simulation diagram improved by the mirror image method provided in Example 1 of the present invention;

[0045] Figure 5The simulation diagram of the single-conductor simulation, fitting, and theoretical electric field provided in Example 1 of the present invention;

[0046] Figure 6 for Figure 5 A local enlarged view of the electric field;

[0047] Figure 7 The dual-loop conductor simulation provided in Example 1 of the present invention;

[0048] Figure 8 Simulation diagrams of simplified theoretical electric field and simulated electric field provided in Example 1 of the present invention;

[0049] Figure 9 This is a simulation diagram of the electric field in the double-circuit neutral line under the improved theory provided in Example 1 of the present invention;

[0050] Figure 10 for Figure 9 A local enlarged view of the electric field;

[0051] Figure 11 This is a simulation diagram of the electric field below the double-loop conductor under the improved theory provided in Example 1 of the present invention;

[0052] Figure 12 The electric field logarithmic coordinate diagram provided in Example 1 of the present invention;

[0053] Figure 13 Schematic diagram of a transmission line parameter inversion device provided in Example 2 of the present invention;

[0054] Figure 14 This is a schematic diagram of an electronic device provided in Example 1 of the present invention.

[0055] Icon: 50 - electronic device; 510 - processor; 520 - bus; 530 - memory; 540 - transceiver. DETAILED DESCRIPTION

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0057] Example 1

[0058] As an example, Figure 1 As shown, to solve the above technical problems, this embodiment provides a transmission line parameter inversion method, including:

[0059] The coordinate information of each integration node of the electromagnetic field model is binary-encoded, and a number of initial coordinate points are randomly generated;

[0060] According to the initial coordinate points, the Gauss integral equation is combined to calculate the coefficients in the Gauss integral;

[0061] The inversion voltage value is obtained according to the coefficient;

[0062] Determine the objective function according to the inverted voltage value and the actual voltage value;

[0063] Determine whether the objective function meets the constraints. If the objective function does not meet the constraints, add a penalty function to the objective function.

[0064] Determine whether the inversion voltage value meets the set range. If the inversion voltage value meets the set range, output the inversion coordinate point position data;

[0065] If the inverted voltage value does not meet the set range, the inverted voltage value is processed by genetic algorithm to obtain a new population for the next position point calculation until all initial coordinate points are calculated.

[0066] Optionally, the genetic algorithm processing includes:

[0067] Two chromosomes were randomly selected using the roulette wheel method;

[0068] Randomly select a binary site for the two selected chromosomes and exchange the binary sites of the two chromosomes;

[0069] Randomly select a binary site, transform the binary site, and generate a new population for the next position point calculation.

[0070] Optionally, determining the objective function according to the inverted voltage and the actual voltage value includes: taking the absolute value of the difference between the inverted voltage and the actual voltage as the objective function.

[0071] Optionally, determining whether the objective function satisfies the constraint conditions includes: after any integral node moves a set distance, solving the inverted voltage value, and determining whether the absolute value of the difference between the inverted voltage value and the actual voltage value is within a set range; if the absolute value of the difference between the inverted voltage value and the actual voltage value is within the set range, the objective function satisfies the constraint conditions; otherwise, if the absolute value of the difference between the inverted voltage value and the actual voltage value is not within the set range, adding a penalty function to the objective function.

[0072] Optionally, the penalty function is the product of the portion of the inversion voltage value that exceeds the set range and the coefficient.

[0073] Specifically, the transmission line parameter inversion method using the spatial electric field Gaussian integral combined with the genetic algorithm comprises the following steps:

[0074] The coordinate information of each integration node of the electromagnetic field model is binary-encoded, the coordinate information of each integration node is represented by binary, and 50 initial coordinate points are randomly generated;

[0075] Combine these 50 initial coordinate points with the Gaussian integral equation to solve the coefficients in the Gaussian integral to obtain the inversion voltage value. The absolute value of the difference between the inversion voltage and the actual voltage is used as the objective function.

[0076] Solve the inversion voltage value after any integration node moves the set distance, and determine whether the inversion voltage value is still within the set range after any integration node moves the set distance. If the inversion voltage value is not within the set range, the product of the part exceeding the set range and the coefficient in the Gaussian integral is added to the objective function as a penalty function;

[0077] Determine whether the objective function meets the constraints. After any integral node moves a set distance, solve the inverted voltage value and determine whether the absolute value of the difference between the inverted voltage value and the actual voltage value is within the set range. If the absolute value of the difference between the inverted voltage value and the actual voltage value is within the set range, the objective function meets the constraints. Otherwise, if the absolute value of the difference between the inverted voltage value and the actual voltage value is not within the set range, the part of the inverted voltage value that exceeds the set range is multiplied by the coefficient as a penalty function and added to the objective function. Afterwards, if the objective function meets the set range, the inverted coordinate point position at this time is output, and the inverted voltage value at this time is the measured voltage value of the transmission line. Otherwise, if the objective function does not meet the set range, the genetic algorithm processing flow is performed.

[0078] The genetic algorithm processing process includes three processing steps: selection, crossover, and mutation. Figure 2 As shown, specifically including:

[0079] Two chromosomes were randomly selected using the roulette wheel method;

[0080] Randomly select a binary site for the two selected chromosomes and exchange the binary sites of the two chromosomes;

[0081] Randomly select a binary site and transform the binary site from 0 to 1 or from 1 to 0, and finally generate a new population for the next position point calculation.

[0082] The present invention has the following advantages:

[0083] (1) The present invention can adjust the weights and integration nodes according to the actual conditions of different complex electromagnetic field models. Compared with the traditional fixed-point integration inversion scheme, it has higher accuracy in the inversion of complex electromagnetic environments.

[0084] (2) The present invention can calculate the sum of the electric field values ​​of multiple nodes by multiplying them by weights, with a fast calculation speed. At the same time, ridge regression is added to solve the multicollinearity problem that may occur in the Gaussian integral solution process;

[0085] (3) In order to solve the problem of reduced inversion accuracy due to position deviation in the actual electric field sensor process, the present invention adds sensitivity analysis to the constraints of the genetic algorithm to solve the sensitivity problem in the inversion process.

[0086] Before inversion, the electric field distribution throughout the transmission line must be determined. This application example uses COMSOL Multiphysics for simulation. Because this inversion requires the electric field distribution outside the transmission line and within the infinite domain, boundary element methods (BEMs) provide a more accurate and rapid calculation of the electric field distribution.

[0087] Consider a single infinitely long conductor with an AC voltage of 110kV under operating conditions. The conductor is aluminum-clad steel stranded wire LGJ (10 / 3.6) and the height of the conductor from the ground is set to 13m. The corresponding model is established and solved using boundary elements in COMSOL Multiphysics software. The voltage distribution at the moment of phase 0 is obtained as the potential simulation diagram shown below. Figure 3 As shown. Figure 3 Since the ground is not infinite, there will be a certain error. In order to further reduce the error, according to the mirror method in the electromagnetic field, two transmission wires are set. The voltages of the two wires are opposite. At the same time, the positions of the two wires are symmetrical with the ground. Figure 4 shown.

[0088] The distribution of the electric field perpendicular to the ground below the conductor in the single conductor model is calculated using the mirror method as follows:

[0089] E=-U / (ln(2h-r) / r)×(1 / (h+y)+1 / (hy));

[0090] Where h is the height of the wire from the ground, r is the radius of the wire, U is the voltage on the wire, and y is the height of the electric field point from the ground.

[0091] Substituting the above formula into the actual single-conductor model, the electric field formula is:

[0092] E=-0.12718U×(1 / (13+y)+1 / (13-y))=-3.30668U / (169-y^2);

[0093] The simulated electric field is fitted according to the theoretical electric field to obtain the formula:

[0094] E=A / (By^2)=3.373827U / (169.138-y^2);

[0095] Comparing the simulated electric field A, the theoretical electric field B and the simulated fitting electric field, the simulation, fitting and theoretical electric fields of a single conductor are shown in the attached figure. Figure 5 And the local enlarged diagram of the electric field is as attached Figure 6 , Q1 is the simulated electric field curve, Q2 is the curve of the fitted electric field of the simulated electric field, and Q3 is the curve of the theoretically calculated electric field.

[0096] Further simulation of the electric field distribution of the double-circuit conductor is carried out. The voltage is also selected as 110kV AC, the type of conductor is LGJ (10 / 3.6), and the distance between the two conductors is set to 1m. The voltage simulation diagram is as follows Figure 7 As shown, the simplified theoretical electric field, fitting electric field and simulation electric field are obtained by using the simple double-circuit conductor electric field calculation formula (4). Figure 8 As shown, where d is the distance between the two wires, then:

[0097]

[0098] The formula for the electric field perpendicular to the ground in the electric field centerline of the double-loop conductor is calculated again using the mirror image method:

[0099]

[0100] The electric field distribution obtained from the above formula is plotted against the simulated electric field distribution to obtain the attached Figure 9 And the partial enlarged picture is as attached Figure 10 .

[0101] Applying the above formula to the actual double-loop conductor model yields the corresponding electric field calculation formula:

[0102]

[0103] The corresponding theoretical electric field and simulated electric field are obtained from the above formula as shown in the attached figure. Figure 11 Its logarithmic coordinate diagram is shown in the attached Figure 12 .

[0104] It can be seen that the simulation value and the theoretical value coincide with each other, which proves the effectiveness of the invented method for the transmission line inversion method.

[0105] Example 2

[0106] Based on the same principle as the method shown in Example 1 of the present invention, as shown in the attached Figure 13As shown, an embodiment of the present invention further provides a transmission line parameter inversion device, including a coding unit, a first calculation processing unit, a second calculation processing unit, an objective function determination unit, a first judgment unit, a second judgment unit, an output unit and a genetic algorithm processing unit:

[0107] The encoding unit is used to perform binary encoding on the coordinate information of each integration node of the electromagnetic field model and randomly generate a number of initial coordinate points;

[0108] A first calculation processing unit is used to calculate the coefficients of the Gaussian integral according to the initial coordinate points and the Gaussian integral equation;

[0109] A second calculation processing unit obtains an inversion voltage value according to the coefficient;

[0110] An objective function determination unit, configured to determine an objective function based on an inverted voltage value and an actual voltage value;

[0111] A first judgment unit is used to judge whether the objective function satisfies the constraint conditions, and if the objective function does not satisfy the constraint conditions, a penalty function is added to the objective function;

[0112] A second judgment unit is used to judge whether the inversion voltage value meets the set range, and if the inversion voltage value meets the set range, output the inversion coordinate point position data;

[0113] Output unit, used for outputting inversion coordinate point position data;

[0114] The genetic algorithm processing unit is used to perform genetic algorithm processing on the inverted voltage value if the inverted voltage value does not meet the set range, obtain a new population for the next position point calculation, until all initial coordinate points are calculated.

[0115] Optionally, the genetic algorithm processing unit includes:

[0116] A selection unit for randomly selecting two chromosomes using a roulette wheel blocking method;

[0117] A crossover unit is used to randomly select a binary site from the two selected chromosomes and exchange the binary sites of the two chromosomes;

[0118] The mutation unit is used to randomly select a binary site, transform the binary site, and generate a new population for the next position point calculation.

[0119] Optionally, the objective function determination unit is configured to take the absolute value of the difference between the inverted voltage and the actual voltage as the objective function.

[0120] Optionally, the first judgment unit is used to solve the inverted voltage value after any integration node moves a set distance, and judge whether the absolute value of the difference between the inverted voltage value and the actual voltage value is within a set range; if the absolute value of the difference between the inverted voltage value and the actual voltage value is within the set range, the objective function satisfies the constraint condition; otherwise, if the absolute value of the difference between the inverted voltage value and the actual voltage value is not within the set range, a penalty function is added to the objective function.

[0121] Optionally, the penalty function is the product of the portion of the inversion voltage value that exceeds the set range and the coefficient.

[0122] Example 3

[0123] Based on the same principle as the method shown in the embodiment of the present invention, an electronic device is also provided in the embodiment of the present invention, as shown in the attached Figure 14 As shown, the electronic device may include but is not limited to: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the method shown in any embodiment of the present invention by calling the computer program.

[0124] In an alternative embodiment, an electronic device is provided, Figure 14 The electronic device 50 shown includes a processor 510 and a memory 550 , wherein the processor 510 and the memory 550 are connected, for example, via a bus 520 .

[0125] Optionally, the electronic device 50 may further include a transceiver 540. The transceiver 540 may be used for data exchange between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, there is not limited to one transceiver 540, and the structure of the electronic device 50 does not constitute a limitation on the embodiments of the present invention.

[0126] Processor 510 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, a hardware component, or any combination thereof. Processor 510 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, or a combination of a DSP and a microprocessor.

[0127] The bus 520 may include a path for transmitting information between the above components. The bus 520 may be a PCI peripheral component interconnect standard bus or an EISA extended industry standard architecture bus. The bus 520 may be divided into a control bus, a data bus, an address bus, etc. For ease of representation, Figure 14 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0128] The memory 550 can be a ROM read-only memory or other type of static storage device that can store static information and instructions, a RAM random access memory or other type of dynamic storage device that can store information and instructions, or an EEPROM electrically erasable programmable read-only memory, a CD-ROM read-only optical disc or other optical disc storage, an optical disc storage (including optical disc, laser disc, compact disc, digital versatile disc, etc.), a magnetic disk storage medium, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0129] The memory 550 is used to store application code (computer program) for executing the solution of the present invention, and is controlled by the processor 510. The processor 510 is used to execute the application code stored in the memory 550 to implement the content shown in the above method embodiment.

[0130] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A transmission line parameter inversion method, characterized in that: include: The coordinate information of each integration node of the electromagnetic field model is binary-encoded, and a number of initial coordinate points are randomly generated; Calculate the coefficients of the Gaussian integral according to the initial coordinate points and the Gaussian integral equation; Obtaining an inversion voltage value according to the coefficient; determining an objective function according to the inverted voltage value and the actual voltage value; taking the absolute value of the difference between the inverted voltage and the actual voltage as the objective function; Determine whether the objective function satisfies the constraint conditions, solve the inverted voltage value after any integral node moves a set distance, and determine whether the absolute value of the difference between the inverted voltage value and the actual voltage value is within the set range; If the absolute value of the difference between the inverted voltage value and the actual voltage value is within the set range, the objective function satisfies the constraint condition; if the absolute value of the difference between the inverted voltage value and the actual voltage value is not within the set range, the objective function does not satisfy the constraint condition, and a penalty function is added to the objective function as a new objective function; Determine whether the new objective function satisfies the set range. If so, output the inversion coordinate point position data. If not, perform genetic algorithm processing on the inversion voltage value to obtain a new population for the next position point calculation, and continue until all the initial coordinate points are calculated.

2. The transmission line parameter inversion method according to claim 1, characterized in that: The genetic algorithm processing process includes: Two chromosomes were randomly selected using the roulette wheel method; Randomly select a binary site for the two selected chromosomes and exchange the binary sites of the two chromosomes; Randomly select a binary site, transform the binary site, and generate a new population for the next position point calculation.

3. The transmission line parameter inversion method according to claim 1, characterized in that: The penalty function is the product of the portion of the inversion voltage value that exceeds the set range and the coefficient.

4. A transmission line parameter inversion device, characterized in that: It includes an encoding unit, a first calculation processing unit, a second calculation processing unit, an objective function determination unit, a first judgment unit, a second judgment unit, an output unit and a genetic algorithm processing unit: The encoding unit is used to perform binary encoding on the coordinate information of each integration node of the electromagnetic field model and randomly generate a number of initial coordinate points; The first calculation processing unit is used to calculate the coefficients of the Gaussian integral based on the initial coordinate point and the Gaussian integral equation; The second calculation processing unit obtains an inversion voltage value according to the coefficient; The objective function determination unit is configured to determine an objective function based on the inverted voltage value and the actual voltage value; and take the absolute value of the difference between the inverted voltage and the actual voltage as the objective function; The first judgment unit is used to judge whether the objective function satisfies the constraint conditions, solve the inverted voltage value after any integral node moves a set distance, and judge whether the absolute value of the difference between the inverted voltage value and the actual voltage value is within a set range; If the absolute value of the difference between the inverted voltage value and the actual voltage value is within the set range, the objective function satisfies the constraint condition; if the absolute value of the difference between the inverted voltage value and the actual voltage value is not within the set range, the objective function does not satisfy the constraint condition, and a penalty function is added to the objective function as a new objective function; The second judgment unit is used to judge whether the new objective function meets the set range, and output the inversion coordinate point position data if the new objective function meets the set range; The output unit is used to output the inversion coordinate point position data; The genetic algorithm processing unit is used to perform genetic algorithm processing on the inversion voltage value when the new objective function does not meet the set range, to obtain a new population for the next position point calculation until all the initial coordinate points are calculated.

5. An electronic device, characterized in that: include: processor and memory; The memory is used to store computer operating instructions; The processor is configured to execute the transmission line parameter inversion method according to any one of claims 1 to 3 by calling the computer operation instructions.

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