Calculation method of electric field of transmission line based on sparrow search algorithm and simulated charge method

Abstract

The present invention discloses a method for calculating the electric field of a transmission line based on a sparrow search algorithm and a simulated charge method, and belongs to the technical field of high-voltage transmission lines. The present invention improves the traditional SSA algorithm, introduces a Sine-Cosine chaotic map to generate an initialization population with high traversal uniformity, introduces a dynamic proportional factor to adjust the proportion of discoverers in the population, and introduces a similarity function to determine whether it has fallen into a local optimum. This effectively solves the problems of reduced population diversity and easy to fall into a local optimum in the later stage of the traditional SSA algorithm; and obtains the optimal simulated line charge through the improved SSA algorithm, effectively avoiding the human error introduced by the traditional simulated charge method that relies on the user's intuitive experience to set the simulated charge; the present invention can be used to accurately calculate the three-dimensional space electric field generated around high-voltage transmission lines, which is of great benefit to research in environmental protection, optical cable line construction, human safety, etc.

Description

Technical Field

[0001] The invention relates to a method for calculating the electric field of a transmission line based on a sparrow search algorithm and a simulated charge method, and belongs to the technical field of high-voltage transmission lines. Background Art

[0002] Smart grid power special optical cables (hereinafter referred to as optical cables) are closely integrated with the power grid architecture for construction. They are economical, reliable, fast, and safe. Optical cables are installed on the same towers as high-voltage transmission lines for power communication and data transmission, and are an important part of power grid operation. Optical cables are often installed on various power towers with different voltage levels. Their electrical, mechanical, and optical fiber properties are subject to special requirements. In particular, to prevent the corrosion of optical cables such as ADSS cables due to high voltage fields, it is necessary to calculate the electric field generated by the surrounding transmission lines and select a suitable installation location to minimize the impact of the electric field strength during operation, thereby preventing ADSS cable breakage accidents caused by electrical corrosion.

[0003] The Charge Simulation Method (CSM) is a commonly used numerical calculation method for electrostatic fields. Its core concept is based on the uniqueness theorem of electrostatic fields and combines the characteristics of the mirror method. It uses a set of fictitious discrete simulated charges to replace the originally continuously distributed free charges on the conductor surface, transforming the non-uniform medium space into a uniform medium. Finally, using the superposition principle, these discrete charges can be used to calculate the potential or electric field intensity at any point in the field space. These simulated charges need to be set according to the actual boundary conditions. Therefore, the key to the entire CSM lies in the proper setting of the simulated charges.

[0004] Existing research has widely applied CSM to the study of the electric field in the space of power transmission lines. By calculating the electric field generated by high-voltage transmission lines, the distribution of the electric field can be analyzed. Based on the results of this analysis, high-voltage transmission lines can be properly installed to reduce the environmental and human safety hazards posed by high-voltage electric fields. It also provides guidance for the construction of overhead optical cables (such as ADSS cables). However, traditional CSM methods have always had problems with setting up the simulated charge. Existing methods mostly rely on the user's intuitive experience and lack a scientific theoretical basis. If the settings are not reasonable, large calculation errors will occur.

[0005] Patent CN202110561997.8 makes some improvements to the traditional CSM, constructing a complete tower-tower-transmission-line three-dimensional electric field calculation model. This model utilizes non-uniform discretization and spatial transformation to reduce the computational complexity of the traditional CSM and has been successfully applied to the selection of hanging points for ADSS optical cables. However, the problem of setting the simulated charge still exists, as it still relies on the user's intuitive experience, which can introduce human error and significantly affect subsequent calculation results.

[0006] Nowadays, artificial intelligence algorithms are developing rapidly and have been widely used in various practical engineering problems, achieving good results. Therefore, combining CSM with artificial intelligence algorithms to optimize the settings of simulated charges has become a research direction.

[0007] The Sparrow Search Algorithm (SSA) is a swarm intelligence optimization algorithm proposed by Xue Jiankai et al. in 2020. Its biomimetic principles are inspired by the foraging and anti-predator behaviors of sparrows. The entire algorithm can be abstracted as a finder-follower model and incorporates an early warning mechanism. Compared to other algorithms, it has advantages such as fewer parameters, faster convergence, better stability, and stronger robustness. However, like other algorithms, it also suffers from issues such as reduced population diversity in the later stages and a tendency to fall into local optima.

[0008] Yin Dexin et al. (Yin Dexin, Zhang Damin, Cai Pengchen, Qin Weina. Improved sparrow search optimization algorithm and its application [J / OL]. Computer Engineering and Science: 1-8.) proposed an improved sparrow search optimization algorithm, which improved the traditional sparrow search algorithm, used the reverse learning strategy to initialize the population, dynamically adjusted the step size factor, and introduced the Levy flight strategy into the sparrow position update formula for reconnaissance and early warning, solving the problem of poor solution accuracy of the traditional algorithm. However, there is still the problem of being prone to falling into local optimal solutions. Summary of the Invention

[0009] In order to solve the above problems, the present invention provides a method for calculating the electric field of a transmission line based on a sparrow search algorithm and a simulated charge method, characterized in that the method comprises:

[0010] The distance from the simulated line charge to the axis of the transmission line is used as the variable to be optimized, and the average potential error function of the potentials of several calibration points distributed on the surface of the transmission line is used as the fitness function. The improved sparrow search algorithm is used for optimization.

[0011] The potential or electric field intensity value of any point in the field is calculated based on the optimal simulated line charge obtained by optimization.

[0012] Optionally, the method includes:

[0013] S1: Construct a three-dimensional transmission line model and set matching points and check points, where the matching points and check points are evenly distributed on the surface of the transmission line and their potentials are known;

[0014] S2: Initialization parameters, including: the number of sparrow populations N, the dimension of the optimization variable D, which is equal to the number of simulated linear charges, the upper and lower bounds of the search space l b and u b , the initial proportion of discoverers in the population ω start and the termination ratio ω end ;

[0015] S3: using the distance from the simulated linear charge to the axis as an optimization variable and using a sequence generated by an improved Sine chaotic map to initialize the sparrow population;

[0016] S4: Calculating a simulated line charge value using the potential of the matching point;

[0017] S5: Calculate the potential of the check point using the calculated simulated line charge value, calculate the relative error with the known potential of the check point, average the error of each point, and obtain the fitness function;

[0018] S6: Adopting a dynamic scaling factor to adaptively adjust the ratio of finders to followers in the sparrow population;

[0019] S7: Update the positions of the discoverers and followers in the sparrow population according to the sparrow search algorithm;

[0020] S8: Update the position of the scout in the sparrow population according to the adaptive step size adjustment strategy;

[0021] S9: The Tanimoto similarity function is used to determine whether the algorithm has fallen into a local optimum. At the same time, an adaptive T-distribution mutation strategy and a Levy flight perturbation strategy are introduced based on the similarity to adjust the current global optimal position, prompting the algorithm to escape the local optimal solution.

[0022] S10: Update the optimal position and optimal fitness value according to the greedy rule;

[0023] S11: Determine whether the maximum number of iterations has been reached. If not, return to S4; if satisfied, output the optimal simulated line charge of the current optimal position and optimal fitness value.

[0024] Optionally, the improved Sine chaotic map in S3 introduces a cosine factor, which is expressed as follows:

[0025]

[0026] Among them, a represents a constant coefficient, rand represents a random number (0,1), mod represents a modulo function, and after multiple iterations, a chaotic sequence is generated to generate an initialized sparrow population. p Represents the value produced by the p-th iteration.

[0027] Optionally, the fitness function in S5 is:

[0028]

[0029] Where U represents the number of check points, represents the calculated potential at the u-th check point, represents the known potential value at the u-th check point;

[0030] The search space for optimizing variables is:

[0031] subjectto:r u (u=1,2,…,U)∈(0,R eq )

[0032] Where R eq is the equivalent radius of the transmission line;

[0033] The equivalent radius R of the transmission line eq for:

[0034]

[0035] Among them, R represents the splitting distance of the transmission line, r represents the radius of the split sub-conductor of the transmission line, and m represents the number of sub-conductors.

[0036] Optionally, the expression of the dynamic scaling factor in S6 is:

[0037]

[0038] Among them, ω represents the proportion of discoverers in the population, ω start represents the starting ratio, ω end Indicates the termination ratio, iter indicates the current number of iterations, iter max Indicates the maximum number of iterations.

[0039] Optionally, updating the positions of the discoverer and the follower in the sparrow population according to the sparrow search algorithm in S7 includes:

[0040] Finder location update:

[0041]

[0042] Where t represents the current number of iterations, T maxrepresents the maximum number of iterations, α represents a uniform random number, Q is a random number that obeys the standard normal distribution, L represents an all-one matrix with dimension 1×d, R2 and ST represent the warning value and safety value respectively;

[0043] Follower location update:

[0044]

[0045] Where, represents the worst position of the sparrow in the dth dimension in the tth iteration, represents the optimal position of the sparrow in the dth dimension in the t+1th iteration, h represents the hth sparrow in the population, A represents a 1×d matrix, each element is randomly assigned a value of ±1, and A + =A T (AA T ) -1 .

[0046] Optionally, updating the position of the scout in the sparrow population according to the adaptive step size adjustment strategy in S8 includes:

[0047]

[0048]

[0049]

[0050] Among them, β represents the step length control parameter, K represents the direction of the sparrow's movement, and also controls the step length, e represents a very small number to avoid the denominator being 0, f h represents the fitness value of the h-th sparrow, f g and f w are the optimal and worst fitness values ​​of the current sparrow population respectively;

[0051] Optionally, the S9 includes:

[0052] By calculating the global optimal position X before and after the update best The Tanimoto similarity coefficient is used to select different mutation perturbation strategies according to the relationship with the similarity threshold.

[0053] When the similarity is less than the similarity threshold, it indicates that diffusion occurs and the Levy flight strategy is used for perturbation;

[0054] When the similarity is greater than the similarity threshold, it indicates that clustering has occurred, and adaptive T distribution variation is used for intervention;

[0055] The calculation expression of the Tanimoto similarity coefficient is:

[0056]

[0057] in, represents the global optimal position after the tth iteration, represents the global optimal position before iteration;

[0058] The overall mutation perturbation strategy is:

[0059]

[0060]

[0061] Among them, X best represents the global optimal position before the disturbance, Represents the global optimal position after disturbance, Tan represents the similarity value between the two, Tan ST represents the similarity threshold, randn(D) represents a D-dimensional random vector that obeys the standard normal distribution, Levy(D) represents a D-dimensional Levy random vector, X e Represents the position of a random individual, T(D, t) represents a D-dimensional T-distributed random vector with t degrees of freedom, where t is the current iteration number.

[0062] Optionally, the process of calculating the simulated line charge value using the potential of the matching point includes:

[0063] Use Q j Represents the jth simulated charge, j∈[1,n]; use M i Represents the i-th matching point, i∈[1, k], and the potential of the matching point is

[0064] According to the superposition principle, the potential of each matching point is generated by the superposition of all simulated charges, so a potential equation group consisting of n simulated charges is constructed:

[0065]

[0066] Construct the equivalent matrix equation:

[0067]

[0068] Among them, the matrix P is the potential coefficient matrix, and its element P ij represents the potential coefficient generated by the j-th segment of simulated charge at the i-th matching point; P ij It only depends on the location of the simulated charge and the matching point, and has nothing to do with the size of the charge; is the potential matrix of the matching point;

[0069] The Gaussian pivot elimination method is used to solve the simulated charge value matrix Q.

[0070] The present invention also provides a method for determining the location of an overhead optical cable hanging point. The method uses the above-mentioned transmission line electric field calculation method based on the sparrow search algorithm and the simulated charge method to calculate the electric field values ​​at various points on the tower where the overhead optical cable can be hung, and then selects the point with the smallest electric field value as the overhead optical cable hanging point.

[0071] The beneficial effects of the present invention are:

[0072] (1) The traditional sparrow search algorithm is improved by introducing the Sine-Cosine chaotic map to initialize the population, which increases the diversity of the initial population and has higher ergodic uniformity, which helps to improve the convergence speed and find the optimal simulated charge position more quickly;

[0073] (2) A dynamic scaling factor is introduced to adaptively adjust the proportion of discoverers in the population according to the number of iterations, so that the algorithm can conduct a wider search in the search domain in the early stage and has a stronger local optimization ability in the later stage, effectively balancing the global and local search capabilities of the algorithm;

[0074] (3) To address the problem that traditional algorithms are prone to falling into local optimality, the Tanimoto similarity function is introduced to determine whether it has fallen into local optimality. At the same time, an adaptive T distribution mutation strategy and a Levy flight perturbation strategy are introduced according to the size of the similarity to adjust the current global optimal position, so as to enable it to jump out of the local optimal solution and facilitate the acquisition of the optimal simulated charge.

[0075] (4) The distance from each simulated line charge in the transmission line to the axis is used as the variable to be optimized, and the average potential error of the verification points on the surface of the transmission line is used as the fitness function. The improved SSA algorithm of the present invention is used for optimization, which effectively avoids the human error introduced by the traditional simulated charge method in setting the simulated charge based on the user's intuitive experience, thereby improving the calculation accuracy of the electric field. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0077] Figure 1 This is a chaotic sequence and its value distribution diagram generated by a Sine-Cosine chaotic map in an embodiment of the present invention.

[0078] Figure 2 This is a dynamic scale factor change trend diagram of an embodiment of the present invention.

[0079] Figure 3 Schematic diagram of the interior of a transmission line according to an embodiment of the present invention.

[0080] Figure 4 This is a flow chart of the improved sparrow search optimization simulated charge method of the present invention.

[0081] Figure 5 3D transmission line model diagram according to an embodiment of the present invention.

[0082] Figure 6 1 is an optimization iteration curve diagram of an embodiment of the present invention.

[0083] Figure 7 1 is a diagram of relative error of the check point potential according to an embodiment of the present invention, wherein (a) is a diagram of relative error of the check point potential according to the traditional method, and (b) is a diagram of relative error of the check point potential according to the method of the present invention. DETAILED DESCRIPTION

[0084] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0085] First, the basic theoretical knowledge involved in the present invention is introduced as follows:

[0086] 1. The Charge Simulation Method (CSM) is a commonly used numerical calculation method for electrostatic fields. Its core idea is based on the uniqueness theorem of electrostatic fields and combines the characteristics of the mirror method. It uses a set of fictitious discrete simulated charges to replace the originally continuously distributed free charges on the conductor surface, and transforms the non-uniform medium space into a uniform medium. Finally, using the superposition principle, these discrete charges can be used to calculate the potential or electric field strength at any point in the field space. These simulated charges need to be set according to the actual boundary conditions. Its application steps are as follows:

[0087] 1) Set n simulated charges Q inside the electrode j (j=1,2,…,n).

[0088] 2) Set a set of matching points M on the given field boundary (electrode surface) i (i=1,2,…,k), and the potential at each point Known, decided by actual problems

[0089] 3) According to the superposition principle, the potential of each matching point is generated by the superposition of all simulated charges, so a potential equation group consisting of n simulated charges can be constructed:

[0090]

[0091] An equivalent matrix equation can be formed:

[0092]

[0093] The matrix P is the potential coefficient matrix, and its element P ij P represents the potential coefficient generated by the j-th segment of simulated charge at the i-th matching point. ij It only depends on the location of the simulated charge and the matching point, and has nothing to do with the size of the charge.

[0094] 4) Use Gaussian pivot elimination to solve the simulated charge value Q.

[0095] 5) The calculated simulated charge can be used to calculate the potential and electric field strength at any point in the field.

[0096] 2. The Sparrow Search Algorithm (SSA) is a swarm intelligence optimization algorithm proposed by Xue Jiankai et al. in 2020. Its biomimetic principle is the foraging and anti-predation behavior of sparrows. The entire algorithm process can be abstracted as a finder-follower model, and an early warning mechanism is introduced. Its mathematical model is as follows:

[0097] 1) Population initialization: In a D-dimensional search space, there are N sparrows, and the position of the h-th sparrow is X h =[x h1 ,…,x hd ,…,x hD ], x hd Indicates the position of the h-th sparrow in the d-th dimension.

[0098] 2) Finder location update:

[0099]

[0100] Where t represents the current number of iterations, T max represents the maximum number of iterations, α represents a uniform random number, Q is a random number that obeys the standard normal distribution, L represents an all-one matrix with dimension 1×d, R2 and ST represent the warning value and safety value respectively.

[0101] 3) Follower location update;

[0102]

[0103] Where, represents the worst position of the sparrow in the dth dimension in the tth iteration, represents the optimal position of the sparrow in the dth dimension in the t+1th iteration, A represents a 1×d matrix, each element is randomly assigned a value of ±1, and A + =A T (AAT ) -1 .

[0104] 4) Scouts generally account for 10%-20% of the population, and their positions are updated as follows:

[0105]

[0106] In the formula, β represents the step length control parameter, which is a random number that follows the standard normal distribution. K is a random number between [-1, 1], which represents the direction of the sparrow's movement and also controls the step length. e represents a very small number to avoid the denominator being 0, and f h represents the fitness value of the h-th sparrow, f g and f w are the optimal and worst fitness values ​​of the current sparrow population respectively.

[0107] Example 1:

[0108] This embodiment provides a method for calculating the electric field of a transmission line based on a sparrow search algorithm and a simulated charge method.

[0109] First, the traditional SSA algorithm is improved as follows:

[0110] 1. Population initialization

[0111] In this embodiment, in order to increase the diversity of the initial population, a chaotic strategy is adopted to generate the initial population. The traditional Sine chaotic mapping is improved by introducing a cosine factor and proposing a Sine-Cosine chaotic mapping, which is expressed as follows:

[0112]

[0113] In the formula, a represents a constant coefficient, rand represents a random number (0,1), mod represents a modulo function. The chaotic sequence generated and its value distribution are as follows: Figure 1 As shown, from Figure 1 It can be found that the chaotic sequence generated after 5000 iterations has high ergodic uniformity and can be used to generate the initialization population.

[0114] 2. Dynamic Scale Factor

[0115] The entire sparrow population is composed of finders and followers. Finders are usually responsible for searching for areas with abundant food in the entire population and providing foraging areas and directions for all followers. That is, finders have strong global search capabilities, while followers can always find finders that provide the best food and forage around them. Therefore, followers have strong local search capabilities. Unlike traditional SSA, the present invention proposes a dynamic proportional factor to adaptively adjust the ratio of finders to followers. In the early stages of iteration, the proportion of finders is higher, and the entire population has stronger global search capabilities. In the later stages, the proportion of followers is higher, and they have stronger local optimization capabilities. The expression of the dynamic proportional factor is as follows:

[0116]

[0117] In the formula, ω represents the proportion of discoverers in the population, ω start represents the starting ratio, ω end Indicates the termination ratio, iter indicates the current number of iterations, iter max Indicates the maximum number of iterations. Its changing trend is as follows Figure 2 As shown in the figure, in the early stage of iteration, the proportion of discoverers is high, which can provide a wider range of global search capabilities. In the later stage of iteration, the proportion of discoverers decreases rapidly, and the proportion of followers increases, which strengthens the local search capabilities.

[0118] 3. Adaptive step size adjustment strategy

[0119] The step length control parameters β and K in equation (5) are optimized to balance the capabilities of global search and local development. The improvement of the step length control parameters is as follows:

[0120]

[0121]

[0122] Where, f g is the optimal fitness value, f w is the worst fitness value.

[0123] 4. Mutation perturbation strategy based on similarity

[0124] Aiming at the problem of falling into local optimum in the late iteration, this paper proposes a mutation perturbation strategy based on similarity, which calculates the global optimal position X before and after the update. bestThe Tanimoto similarity coefficient is used to select different mutation perturbation strategies based on the relationship with the similarity threshold. When the similarity is less than the threshold, it indicates that diffusion occurs, and the Levy flight strategy is used for perturbation; when the similarity is greater than the threshold, it indicates that aggregation occurs, and the adaptive T distribution mutation is used for intervention. The calculation expression of the Tanimoto coefficient is as follows:

[0125]

[0126] Where, represents the global optimal position after the tth iteration, Represents the optimal position before iteration. The overall mutation perturbation strategy is as follows;

[0127]

[0128] Where, X best represents the global optimal position before the disturbance, Represents the global optimal position after disturbance, Tan represents the similarity value between the two, Tan ST represents the similarity threshold, randn(D) represents a D-dimensional random vector that obeys the standard normal distribution, Levy(D) represents a D-dimensional Levy random vector, X e Represents the position of a random individual, and T(D, t) represents a D-dimensional T-distributed random vector with t degrees of freedom (the current number of iterations).

[0129] A greedy rule is adopted to select the global best position with the best fitness value.

[0130] Then the improved SSA is used to find the optimal simulated charge, including:

[0131] 1. Determine the optimization variables

[0132] The result of the potential coefficient determines the result of the simulated charge, and the calculation of the potential coefficient is only related to the position of the simulated charge and the matching point. Inside the transmission line, the actual position of the simulated line charge is determined by its distance r from the axis of the transmission line.

[0133] In traditional CSM, the distance is manually specified, and the distance from all charges to the axis is the same, which obviously has a certain error. Therefore, in this embodiment, the distance from each simulated line charge to the axis is r j (j=1,2,…,n) is used as the optimization variable. The schematic diagram is as follows Figure 3 As shown, the number of simulated charges inside each transmission line is the same as the number of its separated sub-conductors, the distances to the axis are different, and the matching points and check points are evenly distributed on the surface of the transmission line.

[0134] 2. Constructing fitness function

[0135] After calculating the simulated line charge using the matching points, a set of checkpoints different from the matching points are set on the transmission line surface. The resulting charge is used to calculate the potential at the checkpoints, and the relative error is calculated with the known potential. Finally, the error at each point is averaged to obtain the final fitness function. The ultimate goal of the optimization is to minimize the average relative error of the checkpoints. The entire optimization function is as follows:

[0136]

[0137] Where U represents the number of check points, represents the calculated potential at the u-th check point, Represents the known potential value at the u-th check point.

[0138] The search space for optimizing variables is as follows:

[0139] subject to:r u (i=1,2,…,U)∈(0,R eq ) (13)

[0140] Where R eq It represents the equivalent radius of the transmission line, which is calculated as follows:

[0141]

[0142] Among them, R represents the splitting distance of the transmission line, r represents the radius of the split sub-conductor of the transmission line, and m represents the number of sub-conductors.

[0143] 3. Parameter initialization

[0144] Before the optimization, the parameters in the algorithm need to be initialized, including the number of sparrow populations N, the dimension of the optimization variable D, whose value is equal to the number of simulated linear charges, and the upper and lower bounds of the search space l b and u b , the initial proportion of discoverers in the population ω start and the termination ratio ω end .

[0145] The flow chart of the entire improved sparrow search optimization simulation charge method of this embodiment is as follows Figure 4 As shown, the specific process includes the following steps:

[0146] S1: Build a 3D transmission line model and set matching points and check points. The matching points and check points are evenly distributed on the surface of the transmission line and their potentials are known.

[0147] S2: Initialization parameters, including: the number of sparrow populations N, the dimension of the optimization variable D, which is equal to the number of simulated linear charges, the upper and lower bounds of the search space l b and u b , the initial proportion of discoverers in the population ω start and the termination ratio ω end ;

[0148] S3: using the distance from the simulated linear charge to the axis as an optimization variable and using a sequence generated by an improved Sine chaotic map to initialize the sparrow population;

[0149] S4: Calculating a simulated line charge value using the potential of the matching point;

[0150] S5: Calculate the potential of the check point using the calculated simulated line charge value, calculate the relative error with the known potential of the check point, average the error of each point, and obtain a fitness function;

[0151] S6: Adopting a dynamic scaling factor to adaptively adjust the ratio of finders to followers in the sparrow population;

[0152] S7: Update the positions of the discoverers and followers in the sparrow population according to the sparrow search algorithm;

[0153] S8: Update the position of the scout in the sparrow population according to the adaptive step size adjustment strategy;

[0154] S9: The Tanimoto similarity function is used to determine whether the algorithm has fallen into a local optimum. At the same time, an adaptive T-distribution mutation strategy and a Levy flight perturbation strategy are introduced based on the similarity to adjust the current global optimal position, prompting the algorithm to escape the local optimal solution.

[0155] S10: Update the optimal position and optimal fitness value according to the greedy rule;

[0156] S11: Determine whether the maximum number of iterations has been reached. If not, return to S4; if so, output the optimal simulated charge of the current optimal position and optimal fitness value.

[0157] In order to prove the effectiveness of the method of this embodiment, a 220kV three-phase single-circuit transmission line is taken as an example to establish a three-dimensional transmission line model. Figure 5 As shown in the figure, the X axis represents the direction of the transmission line, the Y axis represents the direction perpendicular to the transmission line, and the Z axis represents the vertical direction upward. The figure takes into account the spans on both sides and ignores the existence of the tower. After discretization, the average potential error of the checkpoint is used as the optimization target. The process is as follows Figure 6 As shown in the figure, the method is compared with the traditional method, and the results are as follows Figure 7 shown.

[0158] The average potential error calculated by the traditional method is 2.4557×10 -3 , and using the method of the present invention, after 100 iterations, the average potential error of the checkpoints dropped to 1.2362×10 -3 Compared with the traditional method, the average error is reduced by 50%. Due to the end effect, the error of the calibration points at the ends of the transmission lines is relatively high, but the error of the calibration points in other parts has dropped from about 0.6% to the current 0.2%, a decrease of more than 60%. This proves the effectiveness of the present invention, which can be used to accurately calculate the three-dimensional electric field generated around high-voltage transmission lines and can be widely used in the selection of hanging points for overhead optical cables. It is of great benefit to research on environmental protection, optical cable line installation, human safety, etc.

[0159] Some steps in the embodiments of the present invention may be implemented using software, and the corresponding software program may be stored in a readable storage medium, such as a CD or a hard disk.

[0160] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for calculating the electric field of a transmission line based on a sparrow search algorithm and a simulated charge method, characterized in that: The method comprises: The distance from the simulated line charge to the axis of the transmission line is used as the variable to be optimized, and the average potential error function of the potentials of several calibration points distributed on the surface of the transmission line is used as the fitness function. The improved sparrow search algorithm is used for optimization. Calculate the potential or electric field intensity value at any point in the field based on the optimal simulated line charge obtained by optimization; The method comprises: S1: Construct a three-dimensional transmission line model and set matching points and check points, where the matching points and check points are evenly distributed on the surface of the transmission line and their potentials are known; S2: Initialization parameters, including: number of sparrow populations N , the dimension of the optimization variable D , the dimension D Equal to the number of simulated line charges, the upper and lower bounds of the search space l b and u b , the starting proportion of discoverers in the population and termination ratio ; S3: using the distance from the simulated linear charge to the axis as an optimization variable and using a sequence generated by an improved Sine chaotic map to initialize the sparrow population; The improved Sine chaotic map introduces a cosine factor, which is expressed as follows: in, a represents the constant coefficient, rand Represents a random number (0,1), mod represents the modulo function, and after multiple iterations, a chaotic sequence is generated to generate the initial sparrow population. y p Indicates the p The value produced by the iteration; S4: Calculating a simulated line charge value using the potential of the matching point; S5: Calculate the potential of the check point using the calculated simulated line charge value, calculate the relative error with the known potential of the check point, average the error of each point, and obtain the fitness function; S6: Adopting a dynamic scaling factor to adaptively adjust the ratio of finders to followers in the sparrow population; S7: Update the positions of the discoverers and followers in the sparrow population according to the sparrow search algorithm; S8: Update the position of the scout in the sparrow population according to the adaptive step size adjustment strategy; S9: The Tanimoto similarity function is used to determine whether the algorithm has fallen into a local optimum, and an adaptive T Distribution mutation strategy and Levy The flight perturbation strategy adjusts the current global optimal position, prompting the algorithm to jump out of the local optimal solution; S10: Update the optimal position and optimal fitness value according to the greedy rule; S11: Determine whether the maximum number of iterations has been reached. If not, return to S4; if satisfied, output the optimal simulated line charge of the current optimal position and optimal fitness value.

2. The method according to claim 1, characterized in that The fitness function in S5 is: Where, U represents the number of the checkpoints, Indicates the calculated u The potential at the calibration point, Indicates the u The known potential value at each calibration point; The search space for optimizing variables is: Where, R eq is the equivalent radius of the transmission line; The equivalent radius of the transmission line R eq for: in, R Indicates the splitting distance of the transmission line, r represents the radius of the split conductor of the transmission line, m Indicates the number of sub-conductors.

3. The method according to claim 2, characterized in that The expression of the dynamic scale factor in S6 is: in, represents the proportion of discoverers in the population, represents the starting ratio, represents the termination ratio, iter Indicates the current iteration number, iter max Indicates the maximum number of iterations.

4. The method according to claim 3, characterized in that The updating of the positions of the discoverer and the follower in the sparrow population according to the sparrow search algorithm in S7 includes: Finder location update: Where, t Indicates the current iteration number, T max represents the maximum number of iterations, α represents a uniform random number, Q is a random number that follows a standard normal distribution, L Indicates the dimension The all-one matrix, R 2 and ST Represent the warning value and safety value respectively; Follower location update: Where, Indicates the t In the iteration, the sparrow d The worst position of the dimension, Indicates the t In the +1 iteration, the sparrow d The optimal position of the dimension, h Indicates the first h A sparrow, A Indicates a A matrix with each element randomly assigned ,and .

5. The method according to claim 4, characterized in that Updating the position of the scout in the sparrow population according to the adaptive step size adjustment strategy in S8 includes: in, β represents the step size control parameter, K Indicates the direction of the sparrow's movement and also controls the step length. e Represents a very small number, used to avoid the denominator being 0, Indicates the h The fitness value of a sparrow, and are the optimal and worst fitness values ​​of the current sparrow population respectively.

6. The method according to claim 5, characterized in that The S9 includes: By calculating the global optimal position before and after the update X best The Tanimoto similarity coefficient is used to select different mutation perturbation strategies according to the relationship with the similarity threshold. When the similarity is less than the similarity threshold, it means that diffusion occurs. Levy Flight strategy to perturb; When the similarity is greater than the similarity threshold, it indicates that aggregation occurs, and adaptive T Intervention to determine distribution variation; The calculation expression of the Tanimoto similarity coefficient is: in, Indicates the t The global optimal position after iterations is represents the global optimal position before iteration; The overall mutation perturbation strategy is: in, represents the global optimal position before the disturbance, represents the global optimal position after perturbation, Represents the similarity value between the two. represents the similarity threshold, randn ( D ) indicates that the D dimensional random vector, Levy ( D )express D Victoria Levy Random vectors, represents the position of a random individual, T( D, t ) means the degree of freedom is t of D dimension T Distributed random vector, the degrees of freedom t is the current iteration number.

7. The method according to claim 6, characterized in that The process of calculating the simulated line charge value using the potential of the matching point includes: use Indicates the j A simulated charge, ;use Indicates the i Matching points, i ∈[1, k ], the potential of the matching point is ; According to the superposition principle, the potential of each matching point is generated by the superposition of all simulated charges, so the potential of each matching point is generated by the superposition of all simulated charges. n The potential equations composed of simulated charges are: Construct the equivalent matrix equation: Among them, the matrix is the potential coefficient matrix, whose elements Indicates the The simulated charge in the first The potential coefficient generated at each matching point; It only depends on the location of the simulated charge and the matching point, and has nothing to do with the size of the charge; is the potential matrix of the matching point; Solve the simulated charge value matrix using Gaussian column pivot elimination .

8. A method for determining the location of an aerial optical cable hanging point, characterized in that: The method uses the transmission line electric field calculation method based on the sparrow search algorithm and the simulated charge method as described in any one of claims 1 to 7 to calculate the electric field values ​​at various points on the tower where the overhead optical cable can be hung, and then selects the point with the smallest electric field value as the overhead optical cable hanging point.

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