Parameter optimization method and system based on ultra / extra-high voltage alternating current transmission line

Abstract

The invention relates to a parameter optimization method and system based on ultra / extra-high voltage AC power transmission lines. The method comprises the following specific steps: obtaining wire parameters related to a spatial electric field intensity value in each ultra / extra-high voltage AC power transmission line; the genetic evolution optimization method based on bionic characteristics performs binary unified coding on wire parameters related to spatial electric field intensity values in all wires to form a population sequence required by the genetic evolution method, and performs unified serialization on all the wire parameters subjected to binary coding to obtain an initial population; based on the initial population, executing the following genetic evolution method: fitness selection: obtaining a space electric field intensity value of each ultra / extra-high voltage alternating current transmission line, calculating the fitness of each lead parameter in the initial population according to the space electric field intensity value, and then setting a selection probability according to the size of the fitness value of each lead parameter in the same proportion, and conducting wire parameter selection is carried out by using a roulette method.

Description

Technical Field

[0001] The present invention relates to the technical field of electromagnetic environment of power transmission and transformation engineering, and in particular to a parameter optimization method and system based on ultra / ultra-high voltage alternating current transmission lines. Background Art

[0002] The electromagnetic environment of ultra-high / ultra-high voltage AC transmission lines has attracted widespread attention from the industry and the public. The state has issued relevant standards for the electromagnetic environment of AC transmission lines and electromagnetic environment parameter limits to standardize the design of transmission projects. As one of the electromagnetic environment parameters, the electric field strength in space has clearly specified the voltage level limit in the electromagnetic environment standard for transmission lines. Therefore, it is necessary to repeatedly calculate this limit for unconstructed AC transmission lines and find the optimal transmission line-related parameters so that the electromagnetic environment parameters they generate meet the standard limits and standardize the design of AC transmission lines.

[0003] The engineering parameters of ultra-high / ultra-high voltage AC transmission lines are not single, and there is a relationship of mutual influence and restriction between multiple parameters. At present, the optimization of conductors is in the manual stage, which is prone to local optimization and ignores the global optimal solution. As a result, the hidden optimal solution cannot be obtained. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention proposes a parameter optimization method and system based on ultra / ultra-high voltage AC transmission lines.

[0005] The technical solution of the present invention is as follows:

[0006] On the one hand, the present invention proposes a parameter optimization method based on an ultra-high / ultra-high voltage AC transmission line, and the specific steps include:

[0007] Obtaining conductor parameters related to the numerical value of the spatial electric field strength in each EHV / UHV AC transmission line;

[0008] The genetic evolution optimization method based on bionic characteristics performs binary uniform coding on the wire parameters related to the numerical value of the spatial electric field intensity in all wires to form the population sequence required by the genetic evolution method, and uniformly sequences all the wire parameters that have completed the binary coding to obtain the initial population;

[0009] Based on the initial population, the following genetic evolution methods are performed:

[0010] Fitness selection: obtain the spatial electric field strength values ​​of each EHV / UHV AC transmission line, calculate the fitness of each conductor parameter in the initial population according to the spatial electric field strength values, then set the selection probability in proportion to the fitness values ​​of each conductor parameter, and use the roulette method to select the conductor parameters;

[0011] Random crossover: Randomly select two wire parameters from the wire parameters selected by the roulette method and perform binary-coded random crossover to generate a new wire parameter population;

[0012] Mutation: In the newly generated parameter population, the binary code value of each parameter is randomly changed to generate mutation, and after the mutation, a mutant wire parameter population is generated;

[0013] The variant conductor parameter population is subjected to the above fitness selection, random crossover and mutation steps again, and the iteration termination condition is preset until the new variant conductor parameter population meets the iteration termination condition and is output as the final conductor parameter population. According to the conductor parameters in the final conductor parameter population, the qualified line material and circuit planning are selected to complete the optimization process.

[0014] As a preferred implementation, the conductor parameters related to the numerical value of the spatial electric field strength in the ultra-high / ultra-high voltage AC transmission line include the conductor X-coordinate, the conductor Y-coordinate, the maximum sag of the conductor, the conductor voltage, the conductor phase sequence, the number of splits of the split conductor, the split spacing of the split conductor, and the sub-conductor radius of the split conductor.

[0015] As a preferred implementation, the specific calculation formula for the step of calculating the fitness of each wire parameter in the initial population is as follows:

[0016]

[0017] Where, Fitness is the fitness value of the conductor parameter population, X is the difference vector between the electric field strength solution value vector of each conductor space region and the electric field strength control value vector of the space region; ∥∥ 2 is the Euclidean norm; n is the total number of wire optimization parameters; x i It is an optimization parameter of the wire.

[0018] As a preferred implementation, the probability of selection is set in proportion to the fitness value of each conductor parameter, and the probability calculation model of the roulette method in the conductor parameter selection step using the roulette method is specifically:

[0019]

[0020] Where P i is the probability of the i-th parameter being selected; Fitness(x i ) is the fitness of the i-th parameter; n is the total number of wire optimization parameters; x j is an optimization parameter of the wire.

[0021] As a preferred implementation, two wire parameters are randomly selected from the wire parameters selected by the roulette method for random crossover in binary coding, wherein the random crossover methods include single-point crossover, multi-point crossover and uniform crossover.

[0022] On the other hand, the present invention proposes a parameter optimization system based on an ultra-high / ultra-high voltage AC transmission line, comprising:

[0023] A conductor parameter selection module is used to obtain conductor parameters related to the numerical value of the spatial electric field strength in each ultra-high / ultra-high voltage AC transmission line;

[0024] The wire parameter coding module uses a genetic evolution optimization method based on bionic characteristics to perform binary uniform coding on the wire parameters related to the numerical value of the spatial electric field intensity in all wires to form the population sequence required by the genetic evolution method, and uniformly serializes all the wire parameters that have completed binary coding to obtain the initial population;

[0025] Based on the initial population, the genetic evolution method is performed using the following modules:

[0026] Conductor parameter fitness selection module, fitness selection: obtain the spatial electric field strength values ​​of each EHV / UHV AC transmission line, calculate the fitness of each conductor parameter in the initial population according to the spatial electric field strength values, then set the selection probability in the same proportion according to the fitness values ​​of each conductor parameter, and use the roulette method to select the conductor parameters;

[0027] Wire parameter gene crossover module, random crossover: randomly select two wire parameters from the wire parameters selected by the roulette method for binary-coded random crossover to generate a new wire parameter population;

[0028] Wire parameter gene mutation module, mutation: in the newly generated parameter population, the binary code value of each parameter is randomly changed to generate mutation, and after mutation, a mutant wire parameter population is generated;

[0029] The optimal conductor parameter setting module re-performs the above fitness selection, random crossover and mutation steps on the variant conductor parameter population, and presets the iteration termination condition until the new variant conductor parameter population meets the iteration termination condition and outputs it as the final conductor parameter population. According to the conductor parameters in the final conductor parameter population, the qualified line material and circuit planning are selected to complete the optimization process.

[0030] As a preferred implementation, the conductor parameters related to the numerical value of the spatial electric field strength in the ultra-high / ultra-high voltage AC transmission line include the conductor X-coordinate, the conductor Y-coordinate, the maximum sag of the conductor, the conductor voltage, the conductor phase sequence, the number of splits of the split conductor, the split spacing of the split conductor, and the sub-conductor radius of the split conductor.

[0031] As a preferred implementation, the specific calculation formula for the step of calculating the fitness of each wire parameter in the initial population is as follows:

[0032]

[0033] Where, Fitness is the fitness value of the conductor parameter population, X is the difference vector between the electric field strength solution value vector of each conductor space region and the electric field strength control value vector of the space region; ∥∥ 2 is the Euclidean norm; n is the total number of wire optimization parameters; x i It is an optimization parameter of the wire.

[0034] As a preferred implementation, the probability of selection is set in proportion to the fitness value of each conductor parameter, and the probability calculation model of the roulette method in the conductor parameter selection step using the roulette method is specifically:

[0035]

[0036] Where P i is the probability of the i-th parameter being selected; Fitness(x i ) is the fitness of the i-th parameter; n is the total number of wire optimization parameters; x j is an optimization parameter of the wire.

[0037] As a preferred implementation, two wire parameters are randomly selected from the wire parameters selected by the roulette method for random crossover in binary coding, wherein the random crossover methods include single-point crossover, multi-point crossover and uniform crossover.

[0038] The present invention has the following beneficial effects:

[0039] 1. The present invention is based on the genetic evolution method with bionic characteristics, and uses fitness selection, random crossover and mutation means to continuously optimize the obtained parameters until the optimal parameters are obtained for output. The optimization process is fast and the optimal parameters obtained are accurate.

[0040] 2. Through a continuous iterative process, the present invention can accurately select the optimal line parameters according to the changes between each parameter, providing a basis for the relevant design and evaluation of AC transmission lines. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 for

[0042] Figure 2 The conductor parameters, algorithm iteration parameters, and optimization control area parameter diagram for Case 1;

[0043] Figure 3This is the statistical chart of the optimal solution data after the algorithm optimization iteration of Case 1 is completed;

[0044] Figure 4 This is the electric field distribution in the line segment area and the relationship diagram of the conductor position after the algorithm optimization iteration in Case 1 is completed;

[0045] Figure 5 This is the electric field distribution in the rectangular area and the relationship diagram of the wire position after the algorithm optimization iteration in Case 1 is completed;

[0046] Figure 6 The detailed parameter statistics of the wire and the fitness parameter statistics chart after the algorithm optimization iteration in Case 1 are completed;

[0047] Figure 7 This is the conductor parameter data diagram for Case 2;

[0048] Figure 8 This is the statistical chart of the optimal solution data after the algorithm optimization iteration of Case 2 is completed;

[0049] Fig. 9 This is the electric field distribution in the line segment area and the relationship diagram of the wire position after the algorithm optimization iteration in Case 2 is completed;

[0050] Fig.10 This is the electric field distribution in the rectangular area and the relationship diagram of the wire position after the algorithm optimization iteration in Case 2 is completed;

[0051] Fig.11 The detailed parameter statistics of the wire and the fitness parameter statistics chart after the algorithm optimization iteration in Case 2 is completed;

[0052] Fig.12 This is the conductor parameter data diagram for Case 3;

[0053] Fig.13 This is the statistical chart of the optimal solution data after the algorithm optimization iteration of Case 3 is completed;

[0054] Fig.14 This is the electric field distribution in the line segment area and the relationship diagram of the wire position after the algorithm optimization iteration in Case 3 is completed;

[0055] Fig.15 This is the electric field distribution in the rectangular area and the relationship diagram of the wire position after the algorithm optimization iteration in Case 3;

[0056] Fig.16 This is the detailed parameter statistics of the wire and the fitness parameter statistics chart after the algorithm optimization iteration in Case 3. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0059] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0060] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0061] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0062] Embodiment 1:

[0063] See also Figure 1 , a parameter optimization method based on ultra / ultra-high voltage AC transmission lines, the specific steps include:

[0064] Obtain parameters related to the numerical value of the spatial electric field intensity in each ultra-high / ultra-high voltage AC transmission line;

[0065] In this embodiment, the physical model of the ultra-high voltage / UHV AC transmission line of the actual project is summarized to extract a series of parameters related to the spatial electric field strength in the line. After summarizing and refining, it can be found that the parameters related to the ultra-high voltage / UHV AC transmission line and the numerical value of the spatial electric field strength are: conductor X coordinate, conductor Y coordinate, conductor maximum sag, conductor voltage, conductor phase sequence, number of splits of the split conductor, split spacing of the split conductor, and sub-conductor radius of the split conductor, a total of eight parameters.

[0066] The genetic evolution optimization method based on bionic characteristics performs binary uniform coding on the wire parameters related to the numerical value of the spatial electric field intensity in all wires to form the population sequence required by the genetic evolution method, and uniformly sequences all the wire parameters that have completed the binary coding to obtain the initial population;

[0067] In this embodiment, all the parameters of the conductors that need to be adjusted by optimization methods are uniformly encoded in binary to form the population sequence required by the genetic evolution method. The genetic evolution algorithm is not suitable for directly processing variables in real number space and character space. Among the conductor parameters, such as the conductor voltage, the conductor X coordinate, the conductor Y coordinate, etc., are all variables in the real number space, while the conductor phase sequence is a variable in the character space. Although the number of split conductors is a variable in the real number space, it can only take several valid discontinuous integers. In view of the particularity of the conductor parameters, all parameters are binary encoded here to form a unified binary coding sequence.

[0068] At the same time, based on the binary coding of all parameters to be optimized in the wire, all binary coding sequences are uniformly serialized to obtain an initial population, which is the basis of the algorithm. The population consists of a group of randomly generated individuals, each of which is the encoding of a potential solution to the problem. These individuals can be binary coded (such as a sequence of 0 and 1) or real number coded. In this scheme, a binary coding system is chosen. The diversity of the population is crucial to avoid premature convergence (falling into a local optimal solution too early). The initialization of the population usually involves the generation of uniformly distributed random numbers to ensure a wide coverage of the initial solutions. Population initialization involves uniform distribution in probability theory. Each gene of each individual is independently and randomly selected from a defined range, which ensures the initial diversity of the population.

[0069] Based on the initial population, the following genetic evolution methods are performed:

[0070] Fitness selection: obtain the spatial electric field strength values ​​of each EHV / UHV AC transmission line, calculate the fitness of each conductor parameter in the initial population according to the spatial electric field strength values, then set the selection probability in proportion to the fitness values ​​of each conductor parameter, and use the roulette method to select the conductor parameters;

[0071] In this embodiment, according to Appendix C of the standard HJ24-2020 "Technical Guidelines for Environmental Impact Assessment of Power Transmission and Transformation" issued by the Ministry of Ecology and Environment on December 14, 2020, a numerical calculation algorithm for solving the spatial electric field strength of ultra-high voltage AC transmission lines is compiled. An external call interface is provided, which can be provided to the optimization algorithm for iterative call.

[0072] We need to calculate the fitness value of each individual in the wire optimization parameter population. The fitness value is an indicator to measure the problem-solving ability of an individual, which is usually opposite to the objective function. In this optimization problem, considering that the known control target is the electric field intensity in the spatial region, the parameters to be optimized are the many parameters of the wire. Therefore, the optimization goal is to find the wire parameters that best suit the control target as much as possible as the global optimal solution to this optimization problem.

[0073] The merit selection operation is a step in the genetic evolution algorithm that simulates the natural selection process. In this step, we select individuals for the next generation of the population based on their fitness values. Individuals with high fitness have a higher probability of being selected and will be able to produce the next generation of the population. This is implemented algorithmically through the roulette wheel selection method. Roulette wheel selection is a probabilistic selection method in which the probability of each individual being selected is proportional to its fitness. You can also use the tournament selection method to randomly select a certain number of individuals from the population and then select the individual with the highest fitness from them.

[0074] Random crossover: Randomly select two wire parameters from the wire parameters selected by the roulette method and perform binary-coded random crossover to generate a new wire parameter population;

[0075] In the present embodiment, random crossover is a step in the genetic evolution algorithm to simulate the hybridization process in biological inheritance. In this step, we randomly select a pair of individuals from the selected individuals as parents, and cross over on their gene sequence to produce new offspring individuals. The crossover operation can be a single-point crossover, a multi-point crossover or a uniform crossover, etc. A single-point crossover is performed at a random position in the parent's gene sequence, and the gene after the position is exchanged into the offspring. Multi-point crossover is to cross over at multiple positions. Uniform crossover is to exchange genes uniformly on the parent's gene sequence.

[0076] Mutation: In the newly generated parameter population, the binary code value of each parameter is randomly changed to generate mutation, and after the mutation, a mutant wire parameter population is generated;

[0077] In this embodiment, the mutation operation is a key step in the genetic algorithm to increase population diversity. In this step, we mutate the newly generated offspring to introduce new genetic information. The mutation operation allows the algorithm to find the global optimal solution without being limited to the local optimal point. The mutation in this scheme is achieved by changing the value of the individual binary code, such as random reset (set to 0), random flip (binary 1 is set to 0, binary 0 is set to 1), etc.

[0078] The variant conductor parameter population is subjected to the above fitness selection, random crossover and mutation steps again, and the iteration termination condition is preset until the new variant conductor parameter population meets the iteration termination condition and is output as the final conductor parameter population. According to the conductor parameters in the final conductor parameter population, the qualified line material and circuit planning are selected to complete the optimization process.

[0079] In this embodiment, the iteration process of the genetic evolution algorithm will continue until a termination condition is met, which may be a preset maximum number of iterations or a fitness value reaching a certain threshold or no significant improvement in the optimal solution in consecutive generations.

[0080] As a preferred implementation manner of this embodiment, the conductor parameters related to the numerical value of the spatial electric field strength in the ultra-high / ultra-high voltage AC transmission line include the conductor X-coordinate, the conductor Y-coordinate, the maximum sag of the conductor, the conductor voltage, the conductor phase sequence, the number of splits of the split conductor, the split spacing of the split conductor, and the sub-conductor radius of the split conductor.

[0081] As a preferred implementation of this embodiment, the specific calculation formula for the step of calculating the fitness of each wire parameter in the initial population is as follows:

[0082]

[0083] Where, Fitness is the fitness value of the conductor parameter population, X is the difference vector between the electric field strength solution value vector of each conductor space region and the electric field strength control value vector of the space region; ∥∥ 2 is the Euclidean norm; n is the total number of wire optimization parameters; x i It is an optimization parameter of the wire.

[0084] As a preferred implementation of this embodiment, the probability of selection is set in proportion to the fitness value of each conductor parameter, and the probability calculation model of the roulette method in the conductor parameter selection step using the roulette method is specifically:

[0085]

[0086] Where P i is the probability of the i-th parameter being selected; Fitness(x i ) is the fitness of the i-th parameter; n is the total number of wire optimization parameters; x j is an optimization parameter of the wire.

[0087] As a preferred implementation manner of this embodiment, two wire parameters are randomly selected from the wire parameters selected by the roulette method for random crossover in binary coding, wherein the random crossover methods include single-point crossover, multi-point crossover and uniform crossover.

[0088] In this embodiment, the single-point crossover is performed at a random position in the gene sequence of the parent parameters, and the gene after the position is exchanged into the offspring; the multi-point crossover is to cross-exchange the gene sequence of the parent parameters at multiple positions; the uniform crossover is to uniformly exchange genes on the gene sequence of the parent parameters.

[0089] In this solution, the core code example of the intelligent optimization algorithm is:

[0090] 1. Calculate the fitness of the wire parameter population in this iteration

[0091] function fitness=fitnessFunction(X)

[0092] CalulatedEFieldArray = CalEField(X); % Calculate the electric field strength at the spatial position based on the wire parameters

[0093] fitness=norm(CalulatedEFieldArray-ControlledEFieldArray); % Solve the interpolation of the calculated value vector of the electric field strength at the spatial position and the control value vector, and solve the second-order norm of the interpolation, and return this norm value as the fitness

[0094] End

[0095] 2. Initialization parameters

[0096] N = 50; % population size

[0097] maxGen=100;% Maximum number of iterations

[0098] lb=[-10,-10];% individual lower bound

[0099] ub=[10,10];% individual upper bound

[0100] fitnessFcn=@(X)fitnessFunction(X); %Fitness function X is the parameter vector of the wire to be optimized

[0101] 3. Initialize the wire parameter population

[0102] pop=rand(N,2)*(ub-lb)+lb;

[0103] 4. Genetic evolutionary algorithm main loop

[0104] for gen=1:maxGen

[0105] 4.1 Evaluating the fitness of the wire parameter population

[0106] fitness=arrayfun(fitnessFcn,pop);

[0107] 4.2 Select Operation

[0108] [~,idx]=sort(fitness,'descend');

[0109] pop=pop(idx(1:floor(N / 2)),:);

[0110] 4.3 Crossover Operation

[0111]

[0112] 4.4 Mutation Operation

[0113]

[0114]

[0115] 4.5 Output the optimal solution of the current generation

[0116] disp(['Generation',num2str(gen),':Best Fitness=',num2str(fitness(1))]);

[0117] end

[0118] 5. Output the final result

[0119] disp(['Best Solution:',num2str(pop(1,:))]);

[0120] The pseudo code of the intelligent optimization algorithm is as follows:

[0121]

[0122]

[0123] Embodiment 2:

[0124] Experiment and simulation were performed according to the method of Example 1:

[0125] The experiments were conducted in three scenarios, and the experimental results are shown in Table 1 below.

[0126]

[0127]

[0128]

[0129] Table 1 Intelligent optimization table of conductor parameters in different scenarios

[0130] As shown in Table 1 above, the intelligent optimization results of the three scenarios are summarized and analyzed. In Case 1 and Case 2, a total of 6 parameters are involved in intelligent optimization, including the X and Y coordinates of the three wires; in Case 3, a total of 9 parameters are involved in intelligent optimization, including the X and Y coordinates of the three wires and the voltage. The data marked with * in the upper right corner of Table 1 are the data involved in intelligent optimization, and the wire data not marked with * are the data manually set by the user.

[0131] In the three cases, the electric field strength calculation area is the same, which is a 200m line segment with a ground height of 3m, a left endpoint of -100m, and a right endpoint of 100m; at the same time, as an auxiliary reference, a rectangular area with a width of -100m to 100m and a height of 100m is set.

[0132] For case 1 in different scenarios:

[0133] Case 1: The wire parameters, algorithm iteration parameters, and optimization control area parameters are as follows: Figure 2 As shown; Case 1 After the algorithm optimization iteration is completed, the optimal solution data statistics are as follows Figure 3 As shown in the figure; in case 1, after the algorithm optimization iteration is completed, the electric field distribution in the line segment area and the relationship between the wire positions are as follows Figure 4 As shown in the figure; after the algorithm optimization iteration in Case 1, the electric field distribution in the rectangular area and the relationship between the wire positions are as follows Figure 5 As shown in the figure; in case 1, after the algorithm optimization iteration is completed, the detailed parameter statistics of the wire and the fitness parameter statistics are as follows Figure 6 shown.

[0134] For case 2 in different scenarios:

[0135] Case 2: Wire parameters are as follows Figure 7 As shown; Case 2 After the algorithm optimization iteration is completed, the optimal solution data statistics are as follows Figure 8 As shown in the figure; in case 2, after the algorithm optimization iteration is completed, the electric field distribution in the line segment area and the relationship between the wire positions are as follows Fig. 9 As shown in the figure; after the algorithm optimization iteration in Case 2, the electric field distribution in the rectangular area and the relationship between the wire positions are as follows Fig.10 As shown in the figure; in case 2, after the algorithm optimization iteration is completed, the detailed parameter statistics of the wire and the fitness parameter statistics are as follows Fig.11 As shown;

[0136] For case three in different scenarios:

[0137] Case 3: Wire parameters are as follows Fig.12 As shown; Case 3 After the algorithm optimization iteration is completed, the optimal solution data statistics are as follows Fig.13 As shown in the figure; in case 3, after the algorithm optimization iteration is completed, the electric field distribution in the line segment area and the relationship between the wire positions are as follows Fig.14 As shown in the figure; after the algorithm optimization iteration in Case 3, the electric field distribution in the rectangular area and the relationship between the wire positions are as follows Fig.15 As shown in the figure; in case 3, after the algorithm optimization iteration is completed, the detailed parameter statistics of the wire and the fitness parameter statistics are as follows Fig.16 shown.

[0138] Embodiment three:

[0139] A line parameter optimization system based on an ultra / ultra-high voltage AC transmission line, comprising:

[0140] A parameter selection module is used to obtain parameters related to the numerical value of the spatial electric field intensity in each ultra-high / ultra-high voltage AC transmission line;

[0141] The parameter coding module uses a genetic evolution optimization method based on bionic characteristics to perform binary uniform coding on the parameters related to the numerical value of the spatial electric field intensity in all conductors to form the population sequence required by the genetic evolution method, and uniformly serializes all the parameters that have completed binary coding to obtain the initial population;

[0142] The parameter fitness selection module obtains the spatial electric field strength values ​​of each EHV / UHV AC transmission line, calculates the fitness of each parameter in the initial population, sets the selection probability in proportion to the fitness value of each parameter, and uses the roulette method to select parameters;

[0143] The parameter gene crossover module randomly selects two parameters from the selected parameters for random crossover of binary coding to generate a new parameter population;

[0144] The parameter gene mutation module randomly changes the binary code value of each parameter in the newly generated parameter population to generate mutations, and generates a mutant parameter population after mutation;

[0145] The optimal parameter generation module re-selects fitness, crosses and mutates the variant parameter population, presets the iteration termination condition, and outputs the final parameter population until the variant parameter population meets the iteration termination condition to complete the line parameter optimization.

[0146] As a preferred implementation manner of this embodiment, the parameters related to the numerical value of the spatial electric field strength in the ultra-high / ultra-high voltage AC transmission line include the conductor X-coordinate, the conductor Y-coordinate, the maximum sag of the conductor, the conductor voltage, the conductor phase sequence, the number of splits of the split conductor, the split spacing of the split conductor, and the sub-conductor radius of the split conductor.

[0147] As a preferred implementation of this embodiment, the specific calculation formula for the step of calculating the fitness of each parameter in the initial population is as follows:

[0148]

[0149] Where, Fitness is the fitness value of the conductor parameter population, X is the difference vector between the electric field strength solution value vector of each conductor space region and the electric field strength control value vector of the space region; ∥∥ 2 is the Euclidean norm; n is the total number of wire optimization parameters; x i It is an optimization parameter of the wire.

[0150] As a preferred implementation of this embodiment, the probability of selection is set in proportion to the fitness value of each parameter, and the probability calculation model of the roulette method in the step of selecting parameters by the roulette method is specifically:

[0151]

[0152] Where P i is the probability of the i-th parameter being selected; Fitness(x i ) is the fitness of the i-th parameter; n is the total number of wire optimization parameters; x j It is an optimization parameter of the wire.

[0153] As a preferred implementation manner of this embodiment, the random crossover in the random crossover step of randomly selecting two parameters from the selected parameters for binary encoding includes single-point crossover, multi-point crossover and uniform crossover; the single-point crossover is performed at a random position in the gene sequence of the parent parameter, and the gene after the position is exchanged to the offspring; the multi-point crossover is to cross-exchange the gene sequence of the parent parameter at multiple positions; the uniform crossover is to uniformly exchange genes on the gene sequence of the parent parameter.

[0154] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A parameter optimization method based on ultra / ultra-high voltage AC transmission lines, characterized in that: The specific steps include: Obtaining conductor parameters related to the numerical value of the spatial electric field strength in each EHV / UHV AC transmission line; The genetic evolution optimization method based on bionic characteristics performs binary uniform coding on the wire parameters related to the numerical value of the spatial electric field intensity in all wires to form the population sequence required by the genetic evolution method, and uniformly sequences all the wire parameters that have completed the binary coding to obtain the initial population; Based on the initial population, the following genetic evolution methods are performed: Fitness selection: obtain the spatial electric field strength values ​​of each EHV / UHV AC transmission line, calculate the fitness of each conductor parameter in the initial population according to the spatial electric field strength values, then set the selection probability in proportion to the fitness values ​​of each conductor parameter, and use the roulette method to select the conductor parameters; Random crossover: Randomly select two wire parameters from the wire parameters selected by the roulette method and perform binary-coded random crossover to generate a new wire parameter population; Mutation: In the newly generated parameter population, the binary code value of each parameter is randomly changed to generate mutation, and after the mutation, a mutant wire parameter population is generated; The variant conductor parameter population is subjected to the above fitness selection, random crossover and mutation steps again, and the iteration termination condition is preset until the new variant conductor parameter population meets the iteration termination condition and is output as the final conductor parameter population. According to the conductor parameters in the final conductor parameter population, the qualified line material and circuit planning are selected to complete the optimization process.

2. The parameter optimization method based on ultra / ultra-high voltage AC transmission line according to claim 1, characterized in that: The conductor parameters related to the numerical value of the spatial electric field strength in the ultra-high / ultra-high voltage AC transmission line include the conductor X coordinate, the conductor Y coordinate, the maximum sag of the conductor, the conductor voltage, the conductor phase sequence, the number of splits of the split conductor, the split spacing of the split conductor, and the sub-conductor radius of the split conductor.

3. The parameter optimization method based on ultra / ultra-high voltage AC transmission line according to claim 1, characterized in that: The specific calculation formula for the step of calculating the fitness of each wire parameter in the initial population is as follows: Where, Fitness is the fitness value of the conductor parameter population, X is the difference vector between the electric field strength solution value vector of each conductor space region and the electric field strength control value vector of the space region; ∥∥ 2 is the Euclidean norm; n is the total number of wire optimization parameters; x i It is an optimization parameter of the wire.

4. The parameter optimization method based on ultra / ultra-high voltage AC transmission line according to claim 1, characterized in that: The probability of selection is set in proportion to the fitness value of each conductor parameter, and the probability calculation model of the roulette method in the conductor parameter selection step using the roulette method is specifically: Where P i is the probability of the i-th parameter being selected; Fitness(x i ) is the fitness of the i-th parameter; n is the total number of wire optimization parameters; x j is an optimization parameter of the wire.

5. The parameter optimization method based on ultra / ultra-high voltage AC transmission line according to claim 1, characterized in that: The method randomly selects two wire parameters from the wire parameters selected by the roulette wheel method to perform binary-coded random crossover, wherein the random crossover methods include single-point crossover, multi-point crossover and uniform crossover.

6. A parameter optimization system based on ultra / ultra-high voltage AC transmission lines, characterized in that: include: A conductor parameter selection module is used to obtain conductor parameters related to the numerical value of the spatial electric field strength in each ultra-high / ultra-high voltage AC transmission line; The wire parameter coding module uses a genetic evolution optimization method based on bionic characteristics to perform binary uniform coding on the wire parameters related to the numerical value of the spatial electric field intensity in all wires to form the population sequence required by the genetic evolution method, and uniformly serializes all the wire parameters that have completed binary coding to obtain the initial population; Based on the initial population, the genetic evolution method is performed using the following modules: Conductor parameter fitness selection module, fitness selection: obtain the spatial electric field strength values ​​of each EHV / UHV AC transmission line, calculate the fitness of each conductor parameter in the initial population according to the spatial electric field strength values, then set the selection probability in the same proportion according to the fitness values ​​of each conductor parameter, and use the roulette method to select the conductor parameters; Wire parameter gene crossover module, random crossover: randomly select two wire parameters from the wire parameters selected by the roulette method for binary-coded random crossover to generate a new wire parameter population; Wire parameter gene mutation module, mutation: in the newly generated parameter population, the binary code value of each parameter is randomly changed to generate mutation, and after mutation, a mutant wire parameter population is generated; The optimal conductor parameter setting module re-performs the above fitness selection, random crossover and mutation steps on the variant conductor parameter population, and presets the iteration termination condition until the new variant conductor parameter population meets the iteration termination condition and outputs it as the final conductor parameter population. According to the conductor parameters in the final conductor parameter population, the qualified line material and circuit planning are selected to complete the optimization process.

7. A parameter optimization system based on ultra / ultra-high voltage AC transmission lines according to claim 6, characterized in that: The conductor parameters related to the numerical value of the spatial electric field strength in the ultra-high / ultra-high voltage AC transmission line include the conductor X coordinate, the conductor Y coordinate, the maximum sag of the conductor, the conductor voltage, the conductor phase sequence, the number of splits of the split conductor, the split spacing of the split conductor, and the sub-conductor radius of the split conductor.

8. The parameter optimization system based on the ultra / ultra-high voltage AC transmission line according to claim 6, characterized in that: The specific calculation formula for the step of calculating the fitness of each wire parameter in the initial population is as follows: Where, Fitness is the fitness value of the conductor parameter population, X is the difference vector between the electric field strength solution value vector of each conductor space region and the electric field strength control value vector of the space region; ∥∥ 2 is the Euclidean norm; n is the total number of wire optimization parameters; x i It is an optimization parameter of the wire.

9. A parameter optimization system based on ultra / ultra-high voltage AC transmission lines according to claim 6, characterized in that: The probability of selection is set in proportion to the fitness value of each conductor parameter, and the probability calculation model of the roulette method in the conductor parameter selection step using the roulette method is specifically: Where P i is the probability of the i-th parameter being selected; Fitness(x i ) is the fitness of the i-th parameter; n is the total number of wire optimization parameters; x j is an optimization parameter of the wire.

10. A parameter optimization system based on ultra / ultra-high voltage AC transmission lines according to claim 6, characterized in that: The method randomly selects two wire parameters from the wire parameters selected by the roulette wheel method to perform binary-coded random crossover, wherein the random crossover methods include single-point crossover, multi-point crossover and uniform crossover.

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