An optimization design method of transmission line tower grounding resistance based on particle swarm optimization algorithm

Abstract

The application relates to an optimization design method of a grounding resistance of a transmission line tower based on a particle swarm optimization algorithm, which comprises the following steps: based on a control index of a transmission line back strike trip-out rate and an engineering implementation efficiency requirement of tower grounding design, constraint conditions and a target function in the optimization design of the grounding resistance of the transmission line tower are refined; particle swarm optimization algorithm parameters are set, a random initial particle swarm is generated by using the particle swarm optimization algorithm, in an iteration process, the position and speed of the particle swarm are updated according to the historical optimal value of the particle and the historical optimal position of the particle swarm, so that a global optimal solution is searched; and the global optimal solution is converted into soil resistivity grading and a grounding resistance value, which is used as an optimization design scheme of the grounding resistance. The application can guarantee that the grounding resistance value does not need to be designed too small, the cost of the grounding resistance is reduced, the proportion of a local total back strike trip-out rate exceeding the back strike trip-out rate control index is minimum, and the trip-out rate of the transmission line can meet the demand.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of grounding of power system, in particular to a kind of optimization design method of transmission line tower grounding resistance based on particle swarm optimization algorithm, for the design of transmission line soil resistivity classification and tower grounding resistance. BACKGROUND

[0002] With the rapid development of social economy, the demand for electricity is increasing, and the construction scale and density of high-voltage transmission lines are also increasing. When erecting transmission lines, it is inevitable to pass through different regions such as mountains, plains, farmland and towns, and the problem of safe and reliable transmission is paid more and more attention. With the rapid development of power grid, the number of lightning fault events of transmission lines increases. According to statistics, about 40% to 70% of high-voltage transmission line faults are caused by lightning, especially in areas with more lightning, the proportion of lightning faults exceeds 80%, which brings huge economic losses to power grid operation. As an important channel for lightning discharge current, grounding system is a basic measure to maintain the safe and stable operation of power grid, and good lightning protection grounding is an important prerequisite for ensuring the safe and stable operation of power grid.

[0003] The grounding resistance of tower in transmission line directly determines the tower top potential, and then affects the withstand voltage of line insulator string and back flashover probability, which will directly affect the lightning protection effect of line. Therefore, grounding resistance is the main control index of lightning protection design of transmission line. In the current power industry standard, the design limit of tower grounding impedance of high-voltage and above grade transmission line with lightning rod is simply determined according to soil resistivity, which divides soil resistivity into several grades, and the limit value of tower power frequency grounding impedance of each grade is specified. The allowable grounding impedance in areas with high soil resistivity is relatively high. However, the insulation level of line is quite different when the voltage grade, type and terrain of line tower are different. Even at the same grounding impedance level, the lightning withstand level of line is also quite different. Therefore, only according to the same soil resistivity, that is, according to the unified grounding impedance control, the lightning protection level of line will also be quite different, and the lightning protection level of some lines may be low, which will affect the operation reliability, while the lightning protection of some lines is too strict, which will affect the economy. Especially in some areas with relatively high soil resistivity, in order to meet the requirements of grounding impedance specified in the regulations, extensive grounding measures such as blindly increasing grounding body or laying auxiliary resistance reduction are adopted, which greatly increases the overall investment of the project. SUMMARY

[0004] The purpose of this application is to provide a method for optimizing the design of transmission line tower grounding resistance based on particle swarm optimization (PSO) algorithm. Combining the engineering implementation requirements of tower grounding resistance design, the PSO algorithm is used to design an optimal soil resistivity classification and tower grounding resistance scheme for transmission lines. Simultaneously, the economic efficiency of engineering construction and the lightning protection of transmission lines are comprehensively considered, thereby ensuring the safe and stable operation of the power grid.

[0005] To achieve the above objectives, this application provides the following technical solution:

[0006] This application provides an optimization design method for the grounding resistance of transmission line towers based on particle swarm optimization algorithm, including the following steps:

[0007] Based on the control index of transmission line backflashover tripping rate and the engineering implementation efficiency requirements of tower grounding design, the constraints and objective functions in the optimized design of transmission line tower grounding resistance are extracted.

[0008] Set the parameters of the particle swarm optimization algorithm, use the particle swarm optimization algorithm to generate a random initial particle swarm, and update the position and velocity of the particle swarm according to the historical best value of the particles and the historical best position of the particle swarm during the iteration process, so as to find the global optimal solution;

[0009] The global optimal solution is transformed into soil resistivity classification and grounding resistance value, which serve as the optimized design scheme for grounding resistance.

[0010] The constraints in the optimized design of the grounding resistance of the transmission line towers include:

[0011] The first constraint is: the total backflashover trip rate N of the transmission line. all The backflashover tripping rate control index N for transmission lines is less than max N all ≤N max ,

[0012] Where N all The fitting function N(R) between the backflashover tripping rate N and the grounding resistance R, and the proportional function L(ρ) of the soil resistivity are used to determine the relationship between the backflashover tripping rate N and the grounding resistance R. x The weighted sum is obtained.

[0013] The soil resistivity along the transmission line is classified, and a grounding resistance value is assigned to each soil resistivity class. The higher the soil resistivity class, the larger the grounding resistance value is set. The upper limit value of the soil resistivity class k is ρ. k The upper limit of the soil resistivity at level k-1 is ρ. k-1 The grounding resistance value of the kth level is R. k ,but:

[0014] N all=∑(N(R k )(L(ρ k )-L(ρ k-1 ))),2≤k≤n k

[0015] where n k is the total number of soil resistivity grades;

[0016] The second constraint condition is that in order to improve the implementation efficiency of the engineering grounding design, the difference between the two adjacent grades of soil resistivity should be greater than or equal to 500Ω·m, i.e., ρ k -ρ k-1 ≥ 500, 2≤k≤n k ;

[0017] The third constraint condition is that the difference between the two adjacent grades of grounding resistance should be greater than or equal to 5Ω, i.e., R k -R k-1 ≥ 5, 2≤k≤n k ;

[0018] The fourth constraint condition is that in order to meet the feasibility of the implementation of the engineering grounding design, when the soil resistivity grade ρ k ≥ ρ down , 2≤k≤n k , R k ≥ R down , 2≤k≤n k should be met, where ρ down is the lower limit of the soil resistivity range in the minimum grounding resistance requirement data set, and R down is the minimum grounding resistance corresponding to the soil resistivity range in the minimum grounding resistance requirement data set.

[0019] The objective function in the optimization design of the transmission line tower includes:

[0020] The multi-objective problem is converted into a single-objective optimization problem by the weighted average method, and the objective function is established as follows:

[0021]

[0022] where θ represents the weight coefficient, 0≤θ≤1,

[0023] The first objective is the expected total back strike trip-out rate N all as close as possible to the control index N max of the transmission line back strike trip-out rate, i.e., N max -N all as close as possible to 0,

[0024] The second objective is that the expected local back strike trip-out rate N k of each grade is greater than Nmax The smaller the proportion, the better. As close as possible to 0, where L k This indicates the proportion of the k-th grade in the soil resistivity classification.

[0025] θ is an adjustable parameter that can be adjusted according to different engineering needs. The larger θ is, the more important the first objective is, and the smaller θ is, the more important the second objective is.

[0026] The setting of particle swarm optimization algorithm parameters includes:

[0027] The parameter settings for the particle swarm optimization algorithm require determining the particle swarm size m, the number of iterations GEN, the inertia factor ω, the acceleration factors c1 and c2, and the maximum velocity V. max Minimum speed V min And the range of values ​​for soil resistivity and grounding resistance.

[0028] The generation of a random initial particle swarm using the particle swarm optimization algorithm includes:

[0029] Within the range of soil resistivity and grounding resistance, a particle swarm position matrix X = [x1, x2, ..., x...] is randomly generated. m The initial velocity matrix V = [v1, v2, ..., v] corresponding to the particles. m ]; where x m and v m All are of size 2n k A column vector of size 1, n k This indicates the total number of levels in the soil resistivity classification.

[0030] The particle position matrix, where each column vector represents a particle position vector, wherein the first n... k The term represents the soil resistivity classification, followed by n k The item represents the grounding resistance value at each level.

[0031] Updating the position and velocity of the particle swarm includes:

[0032] During the iteration process, conditional statements can be used to select particles that simultaneously satisfy constraints one, two, three, and four. The objective function values ​​of the particles that satisfy the conditions are calculated, and the minimum objective function value of the particles throughout the iterations is recorded as P. best The minimum value of the objective function of the particle swarm throughout the generations is denoted as G. best According to P best and G best Update particle velocity and position according to the following rules:

[0033]

[0034]

[0035] wherein is the velocity of the i-th particle in the T+1 generation; is the velocity of the i-th particle in the T generation; is the position of the i-th particle in the T+1 generation; is the position of the i-th particle in the T generation, X P is P best is the corresponding particle historical optimal position, X G is G best is the corresponding particle swarm historical optimal position.

[0036] The particle velocity updating rule adjusts the velocity of the particle according to the current velocity, the particle historical optimal position and the particle swarm historical optimal position, and the position of the particle is updated according to the current particle position and the particle velocity.

[0037] The converting the global optimal solution into the soil resistivity classification and the grounding resistance value comprises:

[0038] According to the global objective function optimal solution calculated by the particle swarm optimization algorithm, the position of the corresponding particle is found out, and the particle position vector is converted into the soil resistivity classification and the grounding resistance value as the optimal solution, that is, as the optimal design scheme of the grounding resistance.

[0039] The particle position vector conversion, the position vector is a 2n k dimensional column vector, the former n k dimensional column vector, the former n k dimensional column vector, the former n

[0040] The fitting function N(R) between the back strike trip-out rate N and the grounding resistance R is established, comprising:

[0041] Before the fitting function N(R) between the back strike trip-out rate N and the grounding resistance R is established, the back strike trip-out rate N values of the typical transmission line tower under different grounding resistance R values are calculated based on the typical tower structure size and insulation parameters along the line, the lightning parameters and the terrain parameters along the line, and a data set of the relationship between the grounding resistance R and the back strike trip-out rate N is established.

[0042] The fitting function N(R) between the back strike trip-out rate N and the grounding resistance R is established, comprising:

[0043] According to the data set of the grounding resistance R and the back strike trip-out rate N, the fitting function between the back strike trip-out rate N and the grounding resistance R is established as follows:

[0044]

[0045] In the formula, the fitting anti-closing trip rate N(R) is a function of the grounding resistance R, and a2, a1 and a0 are constant coefficients of the fitting function model.

[0046] The proportional function L(p x ) of the soil resistivity is established, and the proportional function L(p x ) of the soil resistivity is established.

[0047] According to the soil resistivity p data set along the power transmission line, the soil resistivity proportional function is established as follows:

[0048]

[0049] In the formula, L(p x ) represents the proportion of the soil resistivity data set less than p x .

[0050] The grounding resistance minimum value requirement data set is established, and the grounding resistance minimum value requirement data set is established.

[0051] According to the user's desired grounding resistance minimum value in different soil resistivity ranges, the grounding resistance minimum value requirement data set is established, and the lower limit of each soil resistivity range is marked as p down , and the grounding resistance minimum value of each soil resistivity range is R down .

[0052] Compared with the prior art, the beneficial effects of the present application are: the total anti-closing trip rate can be as close as possible to the anti-closing trip rate control index of the power transmission line, and the proportion of the local anti-closing trip rate greater than the anti-closing trip rate control index is minimized. The grounding resistance value does not need to be designed too small, and the cost of the grounding resistance is reduced, and the proportion of the local total anti-closing trip rate exceeding the anti-closing trip rate control index is minimized, which can ensure that the trip rate of the power transmission line meets the demand. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0054] Figure 1 is a specific operation step schematic diagram of a particle swarm optimization algorithm-based optimization design method for a power transmission line tower grounding resistance shown in the present application;

[0055] Figure 2 is a program implementation flowchart of a particle swarm optimization algorithm-based optimization design method for a power transmission line tower grounding resistance shown in the present application. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. It should be noted that similar reference numerals and letters represent similar items in the drawings below, and therefore, once an item is defined in one drawing, it need not be further defined and explained in subsequent drawings.

[0057] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises a", "comprising", or "comprises" does not, without more constraints, exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0058] As shown in Figure 1 , the method specifically implements the following steps:

[0059] (1) providing a preliminary data set and performing data processing, including the following steps:

[0060] ①collecting preliminary data, including: the back strike trip rate control index N max of the local power transmission line, the soil resistivity p data set near each tower along the power transmission line, the typical tower structure size and insulation parameters of the power transmission line, the lightning parameters and terrain parameters along the line, and the minimum value of the grounding resistance in the range of different soil resistivities expected by the user.

[0061] ②establishing a relationship data set between the grounding resistance R and the back strike trip rate N: based on the typical tower structure size and insulation parameters of the power transmission line in the preliminary data, the lightning parameters and terrain parameters along the line, the back strike trip rate N value of the typical power transmission line tower under different grounding resistance R values is calculated, and the relationship data set between the grounding resistance R and the back strike trip rate N is established;

[0062] ③establishing a fitting function between the back strike trip rate N and the grounding resistance R: according to the data set of the grounding resistance R and the back strike trip rate N, the fitting function between the back strike trip rate N and the grounding resistance R is established as follows:

[0063]

[0064] In the formula, the fitting back strike trip rate N(R) is a function of the grounding resistance R, and a2, a1, and a0 are constant coefficients of the fitting function model.

[0065] ④establishing a proportional function of soil resistivity: according to the soil resistivity p data set in the preliminary data, the soil resistivity proportional function is established as follows:

[0066]

[0067] In the formula, L(p x ) represents the proportion of soil resistivity data less than p x .

[0068] ⑤Establishing the minimum grounding resistance requirement dataset: establishing the minimum grounding resistance requirement dataset according to the minimum grounding resistance of different soil resistivity ranges expected by the user, marking the lower limit of each soil resistivity range as p down , and the minimum grounding resistance of each soil resistivity range as R down .

[0069] (2) Refining the constraint conditions and objective function in the optimal design of the grounding resistance of the transmission line tower, including the following steps:

[0070] ① Constructing the first constraint condition: the total back strike trip-out rate N all of the transmission line is less than the back strike trip-out rate control index N max of the transmission line, that is, N all ≤ N max . Wherein N all is obtained by weighted summation of the fitting function N(R) between the back strike trip-out rate N and the grounding resistance R and the proportional function L(p x ) of soil resistivity. The specific calculation formula is as follows:

[0071] Classifying the soil resistivity along the transmission line, setting a grounding resistance value for each soil resistivity classification, and the higher the soil resistivity classification, the greater the grounding resistance setting value. Marking the upper limit value of the soil resistivity of the kth classification as p k , the upper limit value of the soil resistivity of the k-1th classification as p k-1 , and the grounding resistance value of the kth classification as R k , then:

[0072] N all =∑(N(R k )(L(p k )-L(p k-1 ))),2≤k≤n k

[0073] Wherein n k is the total number of soil resistivity classifications.

[0074] ② Constructing the second constraint condition: to improve the engineering implementation efficiency of the engineering grounding design, the classification of soil resistivity needs to satisfy that the difference between the front and rear classifications is greater than or equal to 500 Ω·m, that is, p k -p k-1 ≥ 500, 2≤k≤n k .

[0075] ③ Construct the third constraint condition: the difference between the front and rear two levels of grounding resistance value is greater than or equal to 5Ω, that is, R k -R k-1 ≥5, 2≤k≤n k .

[0076] ④ Construct the fourth constraint condition: in order to meet the feasibility of the implementation of the engineering grounding design, when the soil resistivity classification p k ≥p down , 2≤k≤n k , R k ≥R down , 2≤k≤n k .

[0077] ⑤ Construct the objective function: convert the multi-objective into a single objective optimization problem by the weighted average method, and establish the objective function as follows:

[0078]

[0079] In the formula, θ represents the weight coefficient, 0≤θ≤1.

[0080] As the above-mentioned objective function, characterized in that the first target is the expected total back strike trip-out rate N all as close as possible to the control index N max of the transmission line back strike trip-out rate, that is, N max -N all as close as possible to 0.

[0081] As the above-mentioned objective function, characterized in that the second target is that the proportion of the expected local back strike trip-out rate N k of each classification is greater than N max as small as possible, that is, as close as possible to 0, wherein L k represents the proportion of the kth level in the soil resistivity classification.

[0082] As the above-mentioned objective function, characterized in that θ is an adjustable parameter, which can be adjusted according to different engineering requirements. The greater θ is, the more important the first target is, and the smaller θ is, the more important the second target is.

[0083] (3) The particle swarm optimization algorithm is used to solve the minimum value of the objective function, including the following steps:

[0084] ① Set the parameters: first determine the size m of the particle swarm, the iteration number GEN, the inertia factor ω, the acceleration factors c1 and c2, the maximum speed V max , the minimum speed V min , and the value range of soil resistivity and grounding resistance.

[0085] As described above, the parameter settings are characterized in that the maximum number of iterations GEN and the particle swarm size m are related to the quality of the optimal solution. GEN should not be less than 200 and m should not be less than 100, and can be adjusted according to engineering needs.

[0086] As described above, the parameter settings are characterized in that the inertia factor ω has a significant impact on the global search capability and convergence speed of the particle swarm optimization algorithm. The inertia factor is adaptively and dynamically adjusted so that it decreases as the number of iterations increases. The adjustment rules are as follows:

[0087] ω T+1 =ω T -(1.2-0.9) / GEN

[0088] In the formula ω T+1 For the (T+1)th generation inertia factor, ω T Let GEN be the inertia factor for the Tth generation, and GEN be the maximum number of iterations.

[0089] The parameter settings described above are characterized by acceleration factors c1 and c2, and maximum speed V. max and minimum speed V min Regarding the convergence speed of the algorithm, the acceleration factors c1 and c2 range from [0,4], V max The value range of V is [0, 20]. min The value range is [-20, 0], and the parameters can be adjusted according to project needs.

[0090] The parameter settings described above are characterized in that the range of grounding resistance values ​​can be determined based on the maximum and minimum values ​​of the minimum grounding resistance requirement dataset; and the range of soil resistivity values ​​can be determined based on the maximum and minimum values ​​of the soil resistivity dataset along the line.

[0091] ② Initialize the particle swarm: Within the range of soil resistivity and grounding resistance, randomly generate the particle swarm position matrix X = [x1, x2, ..., x...]. m The initial velocity matrix V = [v1, v2, ..., v] corresponding to the particles. m ]; where x m and v m All are of size 2n k A column vector of size 1, n k This indicates the total number of levels in the soil resistivity classification.

[0092] The particle position matrix described above is characterized in that each column vector in the particle position matrix represents a particle position vector, where the first n... k The term represents the soil resistivity classification, followed by n k The item represents the grounding resistance value at each level.

[0093] ③ Calculate the objective function value: Determine whether the position vectors in the current particle swarm satisfy the constraints. Set the objective function value of particles that do not satisfy the constraints to a larger value, and directly calculate the objective function value of particles that satisfy the constraints. Also, record the minimum objective function value of each particle throughout the generations, denoted as P. best The minimum objective function of the statistical particle swarm optimization is denoted as G. best .

[0094] ④ Update particle velocity and position: based on P best and G best Update particle velocity and position according to the following rules:

[0095]

[0096]

[0097] In the formula Let be the velocity of the i-th particle in the (T+1)-th generation; Let be the velocity of the i-th particle in generation T; Let i be the position of the i-th particle in the (T+1)-th generation; Let X be the position of the i-th particle in generation T. P For P best The corresponding optimal position of the particle throughout history, X G For G best The corresponding optimal position of the particle swarm throughout history.

[0098] The particle velocity update rule described above is characterized by ω being an inertia factor; c1 and c2 being acceleration factors; and the particle velocity being adjusted based on the current particle's historical best position and the historical best position of the particle swarm. The particle's position is updated based on the current particle position and particle velocity.

[0099] ⑤ Find the global optimal solution: Based on the optimal solution of the global objective function obtained by the particle swarm optimization algorithm, find the position of the corresponding particle, and convert the position vector of the particle into soil resistivity classification and grounding resistance value as the optimal solution, that is, as the optimal design scheme of grounding resistance.

[0100] The transformation of particle position information as described above is characterized by the fact that the position vector is a 2n k A column vector of size 1, the first n k The n-dimensional characterization of soil resistivity classification k The dimension represents the grounding resistance value at each level.

[0101] like Figure 2As shown, this embodiment utilizes the professional computational software MTATLAB to model and simulate the optimized design method for the grounding resistance of transmission line towers according to the present invention. The steps of the particle swarm optimization algorithm implemented in the MTATLAB main program are as follows:

[0102] ① Save the prepared data in the root directory of the MATLAB main program in the formats of soil resistivity dataset.xlsx, backflashover rate dataset.xlsx, and minimum grounding resistance requirement dataset.xlsx. You can use the xlsread command to directly import the data into the MATLAB workspace.

[0103] ② Establish the fitting function for the backflashover trip rate and the proportional function for soil resistivity: Based on the dataset of grounding resistance R and backflashover trip rate N in the prepared data, use the polyfit(R,log(N),2) command in MATLAB to determine the constant coefficients a2, a1, a0 of the fitting function model. Calculate the proportional function for soil resistivity based on the prepared soil resistivity ρ dataset.

[0104] ③ Parameter settings: Determine the maximum number of iterations (GEN), particle swarm size (m) and dimension, inertia factor (ω), acceleration factors (c1 and c2), and maximum velocity (V). max Minimum speed V min The values ​​of soil resistivity and grounding resistance are set to T = 0.

[0105] Initialize the particle swarm: Within the range of soil resistivity and grounding resistance, randomly generate the particle swarm position matrix X = [x1, x2, ..., x...]. m The initial velocity matrix V = [v1, v2, ..., v] corresponding to the particles. m ]; where x m and v m All are of size 2n k A column vector of size 1, n k This indicates the total number of levels in the soil resistivity classification.

[0106] The particle position matrix described above is characterized in that each column vector in the particle position matrix represents a particle position vector, where the first n... k The term represents the soil resistivity classification, followed by n k The item represents the grounding resistance value at each level.

[0107] ④ Determine if convergence conditions are met: The convergence conditions in this algorithm include two items: T reaches the maximum number of iterations GEN; and the optimal solution of the objective function remains unchanged after multiple iterations. If either convergence condition is met, output the optimal grounding resistance value, soil resistivity classification, and optimal objective function value, and end the program; otherwise, proceed to step ⑤.

[0108] (5) Update the velocity and position of the particle: according to P best and G best Update the velocity and position of the particle, the update rule is as follows:

[0109]

[0110]

[0111] wherein is the velocity of the i-th particle in the T+1-th generation; is the velocity of the i-th particle in the T-th generation; is the position of the i-th particle in the T+1-th generation; is the position of the i-th particle in the T-th generation, X P is P best the optimal position of the corresponding particle in the previous generation, X G is G best the optimal position of the corresponding particle group in the previous generation.

[0112] The particle velocity update rule is as described above, characterized in that ω is an inertia factor; c1 and c2 are acceleration factors, and the velocity of the particle is adjusted according to the current velocity, the optimal position of the particle in the previous generation, and the optimal position of the particle group in the previous generation. The position of the particle is updated according to the current particle position and the particle velocity.

[0113] (6) Adaptive mutation: randomly select a mutation node of the particle position, and randomly mutate the node to other values in the value range according to the value range of the soil resistivity and the grounding resistance.

[0114] (7) Judge four constraint conditions: judge whether the four constraint conditions are all satisfied, that is, judge whether N all ≤ N max , p k -p k-1 ≥ 500, R k -R k-1 ≥ 5, when p k ≥ p down , R k ≥ R down . If the four constraint conditions are all satisfied, proceed to step (8); otherwise, mark f = 10 10 and proceed to step (9).

[0115] (8) Calculate the objective function f: calculate the objective function value of the particle according to the formula .

[0116] (9) Update the minimum value of the objective function of the particle in the previous generation P best and the minimum value of the objective function of the particle group in the previous generation G best : P bestUpdate the particle generation target function minimum value P compared with the target function value of the current particle best ; update G best the particle swarm target function minimum value compared with the current particle swarm target function minimum value best .

[0117] The following is the specific application of the application in the design of grounding resistance of 5B1-ZB3 type tower on a certain 500kV transmission line.

[0118] Preparation of data and data processing

[0119] ①Collecting preparation data

[0120] Collect the local control requirement index N of the back strike trip rate of 500kV transmission line max 0.042 times / (100km(year).

[0121] Collect the soil resistivity p data set along the 500kV transmission line, as shown in Table 1.

[0122] Table 1 Soil resistivity p along the transmission line

[0123]

[0124]

[0125] ②Establish the relationship data set of grounding resistance R and back strike trip rate N

[0126] Based on the ATP program, the transient analysis of the wave process of lightning striking 500kV line is carried out, and the relationship data set of grounding resistance R and back strike trip rate N of tower 5B1-ZB3 is calculated and obtained, as shown in Table 2.

[0127] Table 2 ATP calculation of grounding resistance and back strike trip rate

[0128] R(Ω) 10 15 20 25 30 35 65 200 N(Next / (100km·year) 0.007 0.012 0.02 0.026 0.038 0.044 0.137 0.435

[0129] ③Establish the fitting function of back strike trip rate

[0130] Using the polyfit(R,log(N),2) function in MATLAB software, the constant coefficients of the quadratic function model can be obtained: a2=-1.0415×10 -5 ,a1=0.0307,a0=-2.3320. Therefore, the fitting function of back strike trip rate is:

[0131]

[0132] ④Establish the soil resistivity proportion function

[0133] According to the soil resistivity ρ along the 500 kV transmission line, a proportional function of soil resistivity can be constructed When the independent variable ρ x is taken at intervals of 100, the values of L(ρ x ) are shown in Table 3.

[0134] Table 3 Proportional function of soil resistivity

[0135]

[0136]

[0137] ⑤Establishing a data set of minimum grounding resistance requirement

[0138] Since the higher the soil resistivity, the greater the grounding resistance of the grounding body of the same size, that is, under the same grounding resistance design target, the higher the soil resistivity, the larger the size of the grounding body needs to be designed. Based on the geographical conditions along the line, in order to facilitate the design and construction of the grounding device, and not to make the size of the grounding body too large to be implemented, under different soil resistivity ranges, the minimum requirement of grounding resistance is proposed:

[0139] When the soil resistivity is 100 Ω·m-200 Ω·m, the expected grounding resistance is not less than 10 Ω; when the soil resistivity is 2000 Ω·m-5000 Ω·m, the expected grounding resistance is not less than 25 Ω; when the soil resistivity is 5000 Ω·m-10000 Ω·m, the expected grounding resistance is not less than 40 Ω; when the soil resistivity is more than 10000 Ω·m, the expected grounding resistance is not less than 60 Ω.

[0140] The maximum soil resistivity along the project is 10382.9 Ω·m, therefore, the data set of minimum grounding resistance requirement is shown in Table 4.

[0141] Table 4 Lower limit of soil resistivity range and minimum grounding resistance

[0142] lower boundary of the range of soil resistivity p down ]]> Minimum value of the ground resistance R down ]]> 100 10 2000 25 5000 40 10000 60 20000 200

[0143] (2) Refining the constraint conditions and objective function in the optimal design of transmission line tower grounding resistance, including the following steps:

[0144] ① Construct the first constraint condition: the total back strike trip-out rate N all of the transmission line is less than the back strike trip-out rate control index N max , that is, N all ≤ N max . Wherein N all is the fitting function N(R) between the back strike trip-out rate N and the grounding resistance R, and L(ρ x) weighted sum is obtained. The specific calculation formula is as follows:

[0145] The soil resistivity along the transmission line is graded, and a grounding resistance value is set for each soil resistivity grade. The upper limit value of the soil resistivity of the kth grade is denoted as p k , and the grounding resistance value of the kth grade is denoted as R k , then:

[0146] N all =∑(N(R k )(L(ρ k-1 )-L(ρ k ))),2≤k≤n k

[0147] where n k is the total number of grades.

[0148] ② Construct the second constraint condition: in order to improve the efficiency of engineering implementation, the difference between the soil resistivity of the two adjacent grades should be greater than or equal to 500, i.e. p k -p k-1 ≥500, 2≤k≤n k .

[0149] ③ Construct the third constraint condition: the difference between the grounding resistance values of the two adjacent grades should be greater than or equal to 5, i.e. R k -R k-1 ≥5, 2≤k≤n k .

[0150] ④ Construct the fourth constraint condition: in order to meet the feasibility of construction implementation, when the soil resistivity grading p k ≥p down , 2≤k≤n k , it should meet R k ≥R down , 2≤k≤n k .

[0151] ⑤ Construct the objective function: convert the multi-objective into a single-objective optimization problem by the weighted average method, and establish the objective function as follows:

[0152]

[0153] In the formula, θ represents the weight coefficient, and 0≤θ≤1.

[0154] The objective function is as described above, characterized in that the first objective is to make the expected total back strike trip rate N all as close as possible to the back strike trip rate control index N max of the transmission line, i.e. N max -N all as close as possible to 0.

[0155] As the objective function described above, characterized in that the second target is to expect the local counterattack trip-out rate N of each classification k The smaller the proportion of N max is, the better, that is As close to 0 as possible, where L k represents the proportion of the kth classification in the soil resistivity classification.

[0156] As the objective function described above, characterized in that θ is an adjustable parameter, which can be adjusted according to different engineering requirements. The larger θ is, the more important the first target is, and the smaller θ is, the more important the second target is.

[0157] (3) Particle swarm optimization algorithm is used to solve the minimum value of the objective function

[0158] ① Parameter selection

[0159] In this example, the parameters are set as follows: the size of the particle swarm m is set to 500, the iteration number GEN is set to 500, the inertia factor ω is updated according to ω T+1 = ω T -(1.2-0.9) / GEN iteration, the acceleration factors c1=0.5 and c2=0.5, the maximum speed V max =5, the minimum speed V min =-5. From the soil resistivity data set and the minimum value of the grounding resistance data set, it can be known that in this case, the value range of the soil resistivity classification L k is [100,10383], and the value range of the grounding resistance R k is [10,200]. In addition, the weight coefficient θ of the objective function is related to the proportion of the two sub-targets. In this example, the weight coefficient θ is 0.9.

[0160] ② Particle swarm optimization algorithm simulation

[0161] According to the process in Figure 2 , the soil resistivity classification and the grounding resistance random value are generated by using the related program of MTATLAB, and are screened according to the constraint conditions and the objective function. The output of the grounding resistance optimization value recommendation is shown in Table 5.

[0162] Table 5 Soil resistivity classification and grounding resistance design value

[0163]

[0164] As can be seen from Table 5, the optimal grounding resistance design value solved in the example can achieve an average back strike trip rate of 0.042 times per 100 km per year, which is equal to the maximum value of the back strike trip rate design control. And the proportion of the classification exceeding the maximum back strike trip rate is 18.45%, which is very small, and the line is safe and reliable. The total time of the particle swarm optimization algorithm is 3.1945 seconds, which takes a shorter time, and the calculation result can meet the preset construction requirements.

[0165] The above only describes the embodiments of the present application and is not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An optimization design method for the grounding resistance of transmission line towers based on particle swarm optimization algorithm, characterized in that, Includes the following steps: Based on the control index of transmission line backflashover tripping rate and the engineering implementation efficiency requirements of tower grounding design, the constraints and objective functions in the optimized design of transmission line tower grounding resistance are extracted. Set the parameters of the particle swarm optimization algorithm, use the particle swarm optimization algorithm to generate a random initial particle swarm, and update the position and velocity of the particle swarm according to the historical best value of the particles and the historical best position of the particle swarm during the iteration process, so as to find the global optimal solution; The global optimal solution is transformed into soil resistivity classification and grounding resistance value, which serve as the optimized design scheme for grounding resistance. The constraints in the optimized design of the grounding resistance of the transmission line towers include: The first constraint is: the total backflashover trip rate of the transmission line. Less than the backflashover tripping rate control target for transmission lines ,Right now , in The fitting function between the backflashover trip rate N and the grounding resistance R is used. proportional function of soil resistivity obtained by weighted summation, The soil resistivity along the transmission line is classified, and a grounding resistance value is assigned to each soil resistivity class. The higher the soil resistivity class, the larger the grounding resistance value is set. The upper limit value for the soil resistivity of class k is... The upper limit of the soil resistivity at level k-1 is The grounding resistance value of the kth level is ,but: ， ， in The total number of levels for soil resistivity classification; The second constraint is: to improve the efficiency of engineering grounding design implementation, the soil resistivity classification must meet the requirement that the difference between the two classifications is greater than or equal to 500 Ω·m, i.e. ; The third constraint is: the difference between the grounding resistance values ​​of the two stages is greater than or equal to 5Ω, i.e. ; The fourth constraint is: to ensure the feasibility of the engineering grounding design and implementation, when the soil resistivity is classified... At that time, it is necessary to meet the following requirements. ,in This is the lower bound of the soil resistivity range in the minimum grounding resistance requirement dataset. The minimum grounding resistance is the minimum grounding resistance corresponding to the soil resistivity range in the dataset. The objective function in the optimized design of transmission line towers includes: The multi-objective problem is transformed into a single-objective optimization problem by using a weighted average method, and the objective function is established as follows: ， In the formula Indicates the weighting coefficients. , The primary objective is to achieve a high overall circuit breaker trip rate. As close as possible to the transmission line backflashover trip rate control target ,Right now As close to 0 as possible The second objective is to expect the local backlash trip rate at each level. Greater than The smaller the proportion, the better. As close to 0 as possible, where This indicates the proportion of the k-th grade in the soil resistivity classification. These are adjustable parameters, which can be adjusted according to different project requirements. The larger the value, the more important the primary goal. The smaller the value, the more important the second objective is.

2. The optimization design method for the grounding resistance of transmission line towers based on particle swarm optimization algorithm according to claim 1, characterized in that, The parameters for the particle swarm optimization algorithm include: Setting the parameters for the particle swarm optimization algorithm requires first determining the particle swarm size m, the number of iterations GEN, and the inertia factor. Accelerator and Maximum speed Minimum speed And the range of values ​​for soil resistivity and grounding resistance.

3. The optimization design method for the grounding resistance of transmission line towers based on particle swarm optimization algorithm according to claim 2, characterized in that, The generation of a random initial particle swarm using the particle swarm optimization algorithm includes: Within the range of soil resistivity and grounding resistance values, the particle swarm location matrix is ​​randomly generated. The initial velocity matrix corresponding to the particle ;in and All are of size column vectors, This indicates the total number of levels in the soil resistivity classification. In the particle swarm position matrix, each column vector represents a particle position vector, where the first... The term represents the soil resistivity classification, followed by... The item represents the grounding resistance value at each level.

4. The optimization design method for the grounding resistance of transmission line towers based on particle swarm optimization algorithm according to claim 3, characterized in that, Updating the position and velocity of the particle swarm includes: During the iteration process, conditional statements can be used to select particles that simultaneously satisfy constraints one, two, three, and four. The objective function values ​​of the particles that satisfy the conditions are calculated, and the minimum objective function value of the particles throughout the iterations is recorded as follows: The minimum value of the objective function of the particle swarm throughout the generations is denoted as... ,according to and Update particle velocity and position according to the following rules: , , In the formula For the first The middle generation The velocity of each particle; For the first The middle generation The velocity of each particle; For the first The middle generation The position of each particle; For the first The middle generation The position of each particle. for The corresponding optimal positions of the particles throughout history. for The corresponding optimal positions of the particle swarm throughout history. The particle velocity is adjusted based on the current velocity, the particle's best position in previous generations, and the particle swarm's best position in previous generations. The particle's position is updated based on the current particle position and particle velocity.

5. The optimization design method for the grounding resistance of transmission line towers based on particle swarm optimization algorithm according to claim 4, characterized in that, The process of converting the global optimal solution into soil resistivity classification and grounding resistance values ​​includes: Based on the optimal solution of the global objective function obtained by the particle swarm optimization algorithm, the position of the corresponding particle is found, and the particle position vector is transformed into soil resistivity classification and grounding resistance value, which is taken as the optimal solution, i.e., the optimal design scheme for grounding resistance. A position vector is The column vector, first Dimensional characterization of soil resistivity classification, followed by The dimension represents the grounding resistance value at each level.

6. The method for optimizing the grounding resistance of transmission line towers based on particle swarm optimization algorithm according to claim 1, characterized in that, Establish the fitting function between the backflashover trip rate N and the grounding resistance R as described above. ,include: Establish a fitting function between the backflashover trip rate N and the grounding resistance R. Previously, it was necessary to calculate the backflashover trip rate N of typical transmission line towers under different grounding resistance R values, based on the typical tower structure dimensions and insulation parameters along the transmission line, as well as the lightning and terrain parameters along the line, and to establish a dataset relating grounding resistance R to backflashover trip rate N. Establish a fitting function between the backflashover tripping rate N and the grounding resistance R. ,include: Based on the dataset of grounding resistance R and backflashover tripping rate N, the following fitting function is established between the backflashover tripping rate N and grounding resistance R: ， In the formula, the fitted counter-attack trip rate It is a function of the grounding resistance R. These are the constant coefficients of the fitted function model.

7. The optimization design method for the grounding resistance of transmission line towers based on particle swarm optimization algorithm according to claim 6, characterized in that, Establish the proportional function of the soil resistivity ,include: Based on the soil resistivity along the transmission line The dataset is used to establish the soil resistivity proportional function as follows: ， In the formula This indicates that the soil resistivity data is less than The percentage.

8. The optimization design method for the grounding resistance of transmission line towers based on particle swarm optimization algorithm according to claim 6, characterized in that, Establish the minimum grounding resistance requirement dataset, including: A minimum grounding resistance requirement dataset is established based on the user's desired minimum grounding resistance within different soil resistivity ranges, and the lower bound of each soil resistivity range is marked as... The minimum grounding resistance for each soil resistivity range is .

Images