A distribution network fault location method based on improved adaptive genetic algorithm

Abstract

The present invention discloses a distribution network fault interval positioning method based on an improved adaptive genetic algorithm, which relates to distribution network fault interval positioning, and comprises the following steps: encoding a fault current; initializing a population to obtain an initialized population; establishing and calculating a fitness function according to the fault current encoding; selecting some individuals in the initialized population to obtain a selected population; establishing an adjustment coefficient according to the diversity and individual fitness of the selected population; obtaining an adjusted adaptive crossover probability and an adaptive mutation probability according to the individual fitness value and the adjustment coefficient; performing crossover and mutation on the individuals in the selected population according to the adaptive crossover probability and the adaptive mutation probability respectively; selecting excellent mutant individuals from the selected population that has undergone the mutation operation to obtain a new population; judging whether the new population has converged, and if so, decoding to obtain a positioning interval; if not, recalculating the fitness value of the new population, and executing the above steps in sequence.

Description

Technical Field

[0001] The present invention relates to distribution network fault interval positioning, and in particular to a distribution network fault interval positioning method based on an improved adaptive genetic algorithm. Background Art

[0002] In the research on distribution network fault location, intelligent algorithms based on genetic algorithms are the research trend. Genetic algorithms evolved based on the genetic processes of organisms in nature and have natural global optimization and anti-interference capabilities. Since the method was proposed in the 1970s, it has been continuously developed and improved for decades and is now widely used in various fields.

[0003] Despite this, the current traditional genetic algorithm-based fault location distribution network is prone to local optimality, and it cannot achieve a win-win situation between optimization speed and optimization accuracy. When a distribution network failure occurs, it is difficult to quickly and accurately locate the fault location to eliminate the fault and restore power supply, which to some extent affects the service quality of power supply companies. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to improve the optimization speed and accuracy, and the purpose is to provide a distribution network fault interval positioning method based on an improved adaptive genetic algorithm, which solves the problem that the optimization speed and accuracy cannot be achieved simultaneously.

[0005] The present invention is achieved through the following technical solutions:

[0006] A method for locating a distribution network fault interval based on an improved adaptive genetic algorithm comprises the following steps:

[0007] Obtaining a fault current in the distribution network and performing binary coding on the fault current; initializing a population in a genetic algorithm to obtain an initialized population;

[0008] According to the above fault current coding, a fitness function reflecting the accuracy of the measured fault point is established;

[0009] Select some individuals in the initialization population to obtain the selected population;

[0010] Calculate the individual fitness values of the above selected population and select crossover and mutation individuals;

[0011] Establish an adjustment coefficient based on the diversity of the selected population and the individual fitness values;

[0012] According to the above individual fitness value and adjustment coefficient, the adaptive algorithm is used to obtain the adjusted adaptive crossover probability and adaptive mutation probability;

[0013] Crossover the individuals in the above selected population according to the adjusted adaptive crossover probability;

[0014] The individuals in the selected population after the above crossover operation are mutated according to the adjusted adaptive mutation probability;

[0015] Select excellent mutant individuals from the selected population that has undergone the above mutation operation to obtain a new population;

[0016] Determine whether the new population has converged. If so, perform binary decoding to obtain the positioning interval.

[0017] If not, recalculate the fitness value of the new population and execute the above steps in sequence.

[0018] During the evolution of a group, the global population may lack diversity, leading to a local optimum. The above adjustment coefficient takes into account both the diversity of the global population and the fitness of the individual.

[0019] The crossover probability and mutation probability mentioned above adopt an adaptive algorithm. Compared with the constant crossover probability and mutation probability, the number of generations is reduced, and the excellent individuals are retained and the optimization speed is improved.

[0020] The above adjustment coefficients adjust the adaptive crossover probability and adaptive mutation probability, which improves the diversity of the population while ensuring high convergence and optimization capabilities. At the same time, it also greatly reduces the occurrence of local optimal situations and improves the optimization accuracy.

[0021] Furthermore, before establishing the selective adjustment coefficient, it is also necessary to establish the diversity level index of the selected population. Establishing the diversity level index of the selected population includes the following steps:

[0022] The diversity level index of the above selected population is calculated using the maximum individual fitness and the average individual fitness in the above selected population. The formula is as follows:

[0023]

[0024] Among them, Q1 represents the diversity level index of the selected population, f max Indicates the maximum fitness of individuals in the selected population, f avg represents the average fitness of individuals in the selected population.

[0025] The above diversity level indicators are considered from the perspective of the diversity of the global population. The larger the value of Q1, the better the diversity of the selected population.

[0026] Furthermore, before establishing the selective adjustment coefficient, it is also necessary to establish the individual fitness level index of the selected population. Establishing the individual fitness level index of the selected population includes the following steps:

[0027] The individual fitness level index of the selected population is calculated using the maximum individual fitness, the average individual fitness and the individual fitness value of the mutation operation in the selected population. The formula is as follows:

[0028]

[0029] Among them, Q2 represents the individual fitness level index of the selected population, f represents the individual fitness value of the mutation operation in the selected population, and f max Indicates the maximum fitness of individuals in the selected population, f avg represents the average fitness of individuals in the selected population.

[0030] The above individual fitness level indicators are considered from the perspective of the individual itself. The larger the value of Q2, the better the individual fitness in the selected population.

[0031] Furthermore, an adjustment coefficient is established based on the diversity and individual fitness of the selected population, including the following steps:

[0032] The above diversity level indicators are divided into three levels: high, medium and low. The above value ranges are

[0033] The individual fitness level indicators are divided into three levels: high, medium and low, and the value ranges are (0,1], 0, [-1,0] respectively;

[0034] Calculate the diversity level index and individual fitness level index of the above selected population;

[0035] Select the above adjustment coefficient according to the corresponding grades of the above calculated diversity level index and individual fitness level index;

[0036] If the diversity level index or individual fitness level index of the selected population is lower, the adjustment coefficient will be greater;

[0037] If the diversity level index or individual fitness level index of the above-mentioned selected population is higher, the above-mentioned adjustment coefficient will be smaller.

[0038] The adjustment coefficient is selected according to the above-mentioned diversity level indicators and individual fitness level indicators. When the diversity of the selected population is good and the individual fitness is high, a smaller adjustment coefficient is used to ensure higher convergence and optimization capabilities; when the diversity of the selected population is poor and the individual fitness is low, a larger adjustment coefficient is used to improve the diversity of the population, greatly reduce the occurrence of local optimal situations, and improve the optimization accuracy.

[0039] Furthermore, selecting excellent mutant individuals from the selected population after the mutation operation to obtain a new population includes the following steps:

[0040] Step 1: Divide the selected individuals into several branches based on the main branch point, and randomly select points in each branch for mutation;

[0041] Step 2: Compare the individual fitness values after each mutation and select the corresponding branch with the higher individual fitness value after mutation;

[0042] Step 3: Determine whether the selected branch has any secondary branches;

[0043] If there is a secondary branch, determine whether the secondary number of the secondary branch is less than 2.

[0044] If the secondary number of the secondary branch is less than 2, the secondary branch is regarded as the selected individual, and the process returns to step 1, with the secondary number increased by 1.

[0045] The point with the highest individual fitness value after the secondary branch mutation is selected as the mutation point;

[0046] If there is no secondary branch, the point with the highest individual fitness value after mutation is the mutation point.

[0047] When the above-mentioned individuals mutate, by comparing the effects of mutations at different points on individual fitness, the points with better mutation effects are selected to perform mutation operations, which can effectively prevent low-fitness individuals from entering the next generation, thereby improving the convergence speed and the optimization speed.

[0048] Furthermore, according to the above individual fitness value and adjustment coefficient, the adjusted adaptive crossover probability is obtained, and the calculation formula is as follows:

[0049]

[0050] P c 1 =θP c

[0051] Among them, P c represents the adaptive crossover probability; P c1 and P c2 is the crossover factor, which represents the maximum and minimum values of the crossover probability range; f1 represents the larger individual fitness value of the two individuals in the selected population that perform the crossover operation; f avg represents the average fitness of individuals in the selected population; f max Indicates the maximum individual fitness in the selected population; θ represents the adjustment coefficient; P c 1 represents the adjusted adaptive crossover probability.

[0052] The above-mentioned introduction of the adjustment coefficient enables the adjusted adaptive crossover probability to take into account the diversity of the selection population and individual fitness from a global perspective. On the basis of ensuring high convergence and optimization ability, it improves the diversity of the selection population, greatly reduces the occurrence of local optimal situations, and improves the optimization accuracy.

[0053] Furthermore, based on the above individual fitness value and adjustment coefficient, the adaptive mutation probability is obtained, and the calculation formula is as follows:

[0054]

[0055]

[0056] Among them, P m represents the adaptive mutation probability; P m1 and P m2 is the mutation factor, which represents the maximum and minimum values of the possible range of mutation probability; f represents the fitness value of the individual that performs the mutation operation in the selected population; f avg represents the average fitness of individuals in the selected population; f max Indicates the maximum individual fitness in the selected population; θ represents the adjustment coefficient; Represents the adjusted adaptive mutation probability.

[0057] The above-mentioned introduction of the adjustment coefficient enables the adjusted adaptive mutation probability to take into account the diversity of the selection population and individual fitness from a global perspective. While ensuring high convergence and optimization capabilities, it improves the diversity of the selection population, greatly reduces the occurrence of local optimal situations, and improves the optimization accuracy.

[0058] Furthermore, the above fault current is binary-coded, and the direction from the power source to the user is the positive direction of the feeder. When the direction of the fault current is consistent with the positive direction, the fault current I j is 1; when the fault current direction is inconsistent with the positive direction, the fault current I j = -1; if there is no fault current, the fault current I j is 0.

[0059] Furthermore, before establishing the fitness function, it is necessary to solve the expected situation of each switch through the fault information of each device transmitted back by the distribution network line, that is, to establish a switching function. The formula of the switching function is as follows:

[0060]

[0061] in, represents the expected situation of the fault current flowing through the jth switch; X i Indicates the fault condition of the i-th device on the line; π represents the logical OR operation.

[0062] Furthermore, some individuals in the initialized population are selected to obtain the selected population. The above selection operation selects individuals by combining direct selection based on the maximum individual fitness and roulette wheel selection method, which can not only ensure that the best individuals are not discarded, but also reflect the natural selection advantage of the genetic algorithm.

[0063] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0064] During the evolution of a group, the global population may lack diversity, leading to a local optimum. The above adjustment coefficient takes into account both the diversity of the global population and the fitness of the individual.

[0065] The crossover probability and mutation probability adopt an adaptive algorithm. Compared with the constant crossover probability and mutation probability, the number of generations is reduced, the excellent individuals are retained, and the optimization speed is improved.

[0066] The above adjustment coefficients adjust the adaptive crossover probability and adaptive mutation probability, which improves the diversity of the population while ensuring high convergence and optimization capabilities. At the same time, it also greatly reduces the occurrence of local optimal situations and improves the optimization accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the examples. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be considered as limiting the scope. A person of ordinary skill in the art can also derive other relevant drawings based on these drawings without inventive effort. In the drawings:

[0068] Figure 1 A flowchart of Example 1 is provided;

[0069] Figure 2 A diagram of a single radiation power grid structure provided in Example 2;

[0070] Figure 3 A 20-node network structure diagram provided for Example 6;

[0071] Figure 4 The optimization curve for a single-point fault without signal distortion provided in Example 6;

[0072] Figure 5 The optimization curve for single-point failure with signal distortion provided in Example 6;

[0073] Figure 6 The optimization curve for multi-point faults without signal distortion provided in Example 6;

[0074] Figure 7 This is the optimization curve provided in Example 6 when there are multiple faults and signal distortion. DETAILED DESCRIPTION

[0075] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0076] Example 1

[0077] This embodiment 1 provides a method for locating a distribution network fault interval based on an improved adaptive genetic algorithm. Figure 1 As shown, the following steps are included:

[0078] S1. Obtain the fault current in the distribution network and perform binary coding on the fault current; initialize the population in the genetic algorithm to obtain an initialized population;

[0079] S2. Establishing a fitness function reflecting the accuracy of the measured fault point based on the above-mentioned fault current coding;

[0080] S3, select some individuals from the initialization population to obtain a selected population;

[0081] S31, calculating the individual fitness values of the selected population;

[0082] S32, select crossover and mutation individuals;

[0083] S4. Establish an adjustment coefficient based on the diversity of the selected population and the individual fitness value;

[0084] S5. Based on the individual fitness value and the adjustment coefficient, an adaptive algorithm is used to obtain an adjusted adaptive crossover probability and an adaptive mutation probability.

[0085] S6, crossover the selected crossover individuals according to the adjusted adaptive crossover probability;

[0086] S7, mutating the mutant individuals selected from the selected population after the crossover operation according to the adjusted adaptive mutation probability;

[0087] S8. Select excellent mutant individuals from the selected population that has undergone the above mutation operation to obtain a new population;

[0088] S9, judging whether the new population has converged;

[0089] S10, if yes, perform binary decoding to obtain the positioning interval; if no, recalculate the fitness value of the new population and execute from S31 in sequence.

[0090] During the evolution of a group, the global population may lack diversity, leading to a local optimum. The above adjustment coefficient takes into account both the diversity of the global population and the fitness of the individual.

[0091] The crossover probability and mutation probability mentioned above adopt an adaptive algorithm. Compared with the constant crossover probability and mutation probability, the number of generations is reduced, and the excellent individuals are retained and the optimization speed is improved.

[0092] The above adjustment coefficients adjust the adaptive crossover probability and adaptive mutation probability, which improves the diversity of the population while ensuring high convergence and optimization capabilities. At the same time, it also greatly reduces the occurrence of local optimal situations and improves the optimization accuracy.

[0093] Example 2

[0094] In a specific embodiment, the fault current is binary-coded, and the direction from the power source to the user is the positive direction of the feeder. When the direction of the fault current is consistent with the positive direction, the fault current I j is 1; when the fault current direction is inconsistent with the positive direction, the fault current I j = -1; if there is no fault current, the fault current I j is 0.

[0095] In a specific embodiment, before establishing the aforementioned fitness function, it is necessary to determine the expected state of each switch using the fault information transmitted from each device on the distribution network. This is known as establishing a switching function. In distribution network fault location, the optimal solution is to ensure that the expected switch state closely matches the information uploaded by the FTU, thereby properly interpreting the fault information and locating the fault zone.

[0096] The formula for the above switching function is as follows:

[0097]

[0098] in, represents the expected situation of the fault current flowing through the jth switch, where the current without fault is 0 and the current with fault is 1; X i Indicates the fault condition of the i-th device on the line, 0 if there is no fault and 1 if there is a fault; π represents a logical OR operation.

[0099] Specific embodiments, such as Figure 2As shown in the figure, CB1 represents the incoming circuit breaker, K1, K2, and K3 represent section switches, and S1, S2, S3, and S4 represent the distribution network equipment between the section switches. Each switch is installed with an FTU terminal to obtain overcurrent information. When a fault occurs in the equipment in the S4 section of the line, a fault current will appear in CB1, K1, K2, and K3; when a fault occurs in the equipment in the S3 section of the line, a fault current will appear in CB1, K1, and K2; and so on.

[0100] Based on the “minimum set” principle, the fitness function is established according to the above fault current encoding and switching function, and the following fitness function is obtained:

[0101]

[0102] In practical applications, in order to meet the algorithm operation requirements, the problem of finding the minimum fitness value is transformed into finding the maximum fitness value through subtraction operation, and the above fitness function is rewritten as:

[0103]

[0104] Among them, I j represents the fault current flowing through the jth switch; F(S B ) represents the fitness in the population; S B Indicates the status of each device in the distribution network. When S B When it is 0, it means the device is normal, and when it is 1, it means the device is faulty; N represents the number of switches on the line; represents the expected situation of the fault current flowing through the jth switch; X i Indicates the fault condition of the i-th device on the line; It represents the sum of the fault conditions of all devices on the line, M represents an integer, usually the number is 2N, to ensure that F(S B ) is always a positive number.

[0105] Example 3

[0106] Select some individuals from the initialization population to obtain the selected population. The above selection operation selects individuals by combining direct selection based on the maximum individual fitness and roulette wheel selection method. This can not only ensure that the best individuals are not discarded, but also reflect the natural selection advantage of the genetic algorithm.

[0107] In a specific embodiment, before establishing the selective adjustment coefficient, it is also necessary to establish the diversity level index and individual fitness level index of the selected population. The diversity level index of the selected population is calculated using the maximum individual fitness and the average individual fitness in the selected population. The formula is as follows:

[0108]

[0109] Among them, Q1 represents the diversity level index of the selected population, f max Indicates the maximum fitness of individuals in the selected population, f avg represents the average fitness of individuals in the selected population.

[0110] The individual fitness level index of the selected population is calculated using the maximum individual fitness, the average individual fitness and the individual fitness value of the mutation operation in the selected population. The formula is as follows:

[0111]

[0112] Among them, Q2 represents the individual fitness level index of the selected population, f represents the individual fitness value of the mutation operation in the selected population, and f max Indicates the maximum fitness of individuals in the selected population, f avg represents the average fitness of individuals in the selected population.

[0113] The above diversity level indicators are considered from the perspective of global population diversity. The larger the value of Q1, the better the diversity of the selected population;

[0114] The above individual fitness level indicators are considered from the perspective of the individual itself. The larger the value of Q2, the better the individual fitness in the selected population.

[0115] A specific embodiment, establishing an adjustment coefficient based on the diversity and individual fitness of the selected population, includes the following steps:

[0116] The above diversity level indicators are divided into three levels: high, medium and low. The above value ranges are

[0117] The individual fitness level indicators are divided into three levels: high, medium and low, and the value ranges are (0,1], 0, [-1,0] respectively;

[0118] Calculate the diversity level index and individual fitness level index of the above selected population;

[0119] Select the above adjustment coefficient according to the corresponding grades of the above calculated diversity level index and individual fitness level index;

[0120] If the diversity level index or individual fitness level index of the selected population is lower, the adjustment coefficient will be greater;

[0121] If the diversity level index or individual fitness level index of the above-mentioned selected population is higher, the above-mentioned adjustment coefficient will be smaller.

[0122] The corresponding relationship between the diversity level index or individual fitness level index of the selected population and the adjustment coefficient is shown in Table 1:

[0123]

[0124] The adjustment coefficient is selected according to the above-mentioned diversity level indicators and individual fitness level indicators. When the diversity of the selected population is good and the individual fitness is high, a smaller adjustment coefficient is used to ensure higher convergence and optimization capabilities; when the diversity of the selected population is poor and the individual fitness is low, a larger adjustment coefficient is used to improve the diversity of the population, greatly reduce the occurrence of local optimal situations, and improve the optimization accuracy.

[0125] Example 4

[0126] According to the above individual fitness values and adjustment coefficients, the adjusted adaptive crossover probability and adaptive mutation probability are obtained.

[0127] The calculation formula of the adaptive crossover probability after the above adjustment is as follows:

[0128]

[0129] P c 1 =θP c

[0130] Among them, P c represents the adaptive crossover probability; P c1 and P c2 is the crossover factor, which represents the maximum and minimum values of the crossover probability range; f1 represents the larger individual fitness value of the two individuals in the selected population that perform the crossover operation; f avg represents the average fitness of individuals in the selected population; f max Indicates the maximum individual fitness in the selected population; θ represents the adjustment coefficient; P c 1 Represents the adjusted adaptive crossover probability; according to previous experience, P in the above formula c1 =0.9, P c2 =0.6.

[0131] The calculation formula for the above-adjusted adaptive mutation probability is as follows:

[0132]

[0133]

[0134] Among them, P m represents the adaptive mutation probability; P m1 and P m2is the mutation factor, which represents the maximum and minimum values of the possible range of mutation probability; f represents the fitness value of the individual that performs the mutation operation in the selected population; f avg represents the average fitness of individuals in the selected population; f max Indicates the maximum individual fitness in the selected population; θ represents the adjustment coefficient; Represents the adjusted adaptive mutation probability; according to previous experience, P in the above formula m1 =0.1, P m2 =0.01.

[0135] The above adaptive crossover probability P c and adaptive mutation probability P m Although the size of can be adjusted in real time according to the individual situation of the population, it only considers the individual's own fitness factors, but does not consider the diversity factors of the global population. During the process of population evolution, especially in the early and late stages, the global population may be in a state of insufficient diversity, which will lead to a local optimal situation.

[0136] The above-mentioned introduction of the adjustment coefficient enables the adjusted adaptive crossover probability and adaptive mutation probability to take into account the diversity of the selection population and individual fitness from a global perspective. While ensuring high convergence and optimization capabilities, it improves the diversity of the selection population, greatly reduces the occurrence of local optimal situations, and improves the optimization accuracy.

[0137] Example 5

[0138] In actual distribution networks, there are many line nodes. If the traversal method is used for mutation, the calculation amount will increase and the optimization purpose will be lost. There is a branch structure in the distribution network structure. During gene mutation, nodes on the same branch structure often exhibit similar fault characteristics. Therefore, the present invention uses the interpolation mutation method to optimize the mutation operation when the branch nodes are numbered sequentially, so that the mutation always proceeds in the direction of producing excellent individuals.

[0139] In a specific embodiment, selecting excellent mutant individuals from the selected population after the mutation operation to obtain a new population includes the following steps:

[0140] Step 1: Divide the selected individuals into several branches based on the main branch point, and randomly select points in each branch for mutation;

[0141] Step 2: Compare the individual fitness values after each mutation and select the corresponding branch with the higher individual fitness value after mutation;

[0142] Step 3: Determine whether the selected branch has any secondary branches;

[0143] If there is a secondary branch, determine whether the secondary number of the secondary branch is less than 2.

[0144] If the secondary number of the secondary branch is less than 2, the secondary branch is regarded as the selected individual, and the process returns to step 1, with the secondary number increased by 1.

[0145] Select the point with the highest individual fitness value after mutation in the first-level branch and the second-level branch as the mutation point;

[0146] If there is no secondary branch, the point with the highest individual fitness value after mutation is the mutation point.

[0147] When the above-mentioned individuals mutate, by comparing the effects of mutations at different points on individual fitness, the points with better mutation effects are selected to perform mutation operations, which can effectively prevent low-fitness individuals from entering the next generation, thereby improving the convergence speed and the optimization speed.

[0148] The branch structure in the distribution network is generally no more than three levels, and the number of nodes decreases as the level decreases. Therefore, the present invention only interpolates to the second-level branch. During the interpolation process, if there are too many feasible solutions with redundant faults, the fitness values of the corresponding individuals before and after the mutation will be the same. At this time, any point can be selected for mutation.

[0149] Example 6

[0150] like Figure 3 As shown in the figure, a traditional single-power radial distribution network with 20 nodes is selected for simulation. According to the actual production situation and considering whether there is information distortion, single-point faults and multi-point faults are preset in the distribution network for simulation analysis.

[0151] In a specific embodiment, when a single point failure occurs in the network, the algorithm can accurately determine the fault interval regardless of whether the returned information is distorted, with good accuracy and fault tolerance. Figure 4 、 Figure 5 The fault interval optimization curve shown in the figure is compared with the optimization curve of the ordinary adaptive genetic algorithm. It can be seen that although both algorithms can obtain the optimal solution, the improved algorithm has better convergence.

[0152] In a specific embodiment, when a multi-point fault occurs in the network, considering that the probability of three or more faults occurring in actual production is very low, the algorithm can accurately determine the fault interval regardless of whether the return information is distorted. Figure 6 、 Figure 7 The fault interval optimization curve shown in the figure shows that the number of iterations of the improved algorithm is significantly less than that of the ordinary algorithm, and it can effectively avoid falling into the local optimal solution.

[0153] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of 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 distribution network fault interval location method based on an improved adaptive genetic algorithm, characterized in that: The following steps are involved: Obtaining a fault current in the distribution network, and performing binary coding on the fault current; Initialize the population in the genetic algorithm to obtain an initialized population; Establishing a fitness function reflecting the accuracy of the measured fault point according to the binary code of the fault current; Select some individuals in the initialization population to obtain the selected population; Calculating individual fitness values of the selected population, and selecting crossover and mutation individuals; Establishing a diversity level index and an individual fitness level index of the selected population; According to the diversity of the selected population and the individual fitness values, the adjustment coefficient is established, which includes the following steps: The diversity level index is divided into three levels: high, medium and low. The value ranges of the three levels of the diversity level index are ; The individual fitness level index is divided into three levels: high, medium and low. The value ranges of the three levels of the individual fitness level index are (0, 1], 0, and [-1, 0] respectively. Calculating the diversity level index and individual fitness level index of the selected population; Selecting the adjustment coefficient according to the calculated grades corresponding to the diversity level index and the individual fitness level index; If the diversity level index or individual fitness level index of the selected population is lower, the adjustment coefficient is larger; The higher the diversity level index or individual fitness level index of the selected population, the smaller the adjustment coefficient; according to the individual fitness value and the adjustment coefficient, an adaptive algorithm is used to obtain the adjusted adaptive crossover probability and adaptive mutation probability; Crossing over individuals in the selected population according to the adjusted adaptive crossover probability; Mutating the individuals in the selected population after the crossover operation according to the adjusted adaptive mutation probability; Selecting excellent mutant individuals from the selected population that has undergone the mutation operation to obtain a new population; Determine whether the new population has converged, and if so, perform binary decoding to obtain a positioning interval; If not, recalculate the individual fitness values of the new population and execute the steps in sequence.

2. A distribution network fault section location method based on an improved adaptive genetic algorithm according to claim 1, characterized in that: Establishing the diversity level index of the selected population comprises the following steps: The diversity level index of the selected population is calculated using the maximum individual fitness and the average individual fitness in the selected population. The formula is as follows: Among them, Q1 represents the diversity level index of the selected population; f max Indicates the maximum individual fitness in the selected population; f avg represents the average fitness of individuals in the selected population.

3. A distribution network fault section location method based on an improved adaptive genetic algorithm according to claim 2, characterized in that: Establishing the individual fitness level index of the selected population comprises the following steps: The individual fitness level index of the selected population is calculated using the maximum individual fitness value, the average individual fitness value and the individual fitness value of the mutation operation in the selected population. The formula is as follows: Among them, Q2 represents the individual fitness level index of the selected population; f represents the individual performing the mutation operation in the selected population Fitness value; f max Indicates the maximum individual fitness in the selected population; f avg represents the average fitness of individuals in the selected population.

4. The method for locating a distribution network fault interval based on an improved adaptive genetic algorithm according to claim 1, characterized in that: Selecting excellent mutant individuals from the selected population after the mutation operation to obtain a new population includes the following steps: Step 1: Divide the selected individuals into several branches based on the main branch point, and randomly select points in each branch for mutation; Step 2: Compare the individual fitness values after each mutation and select the corresponding branch with the higher individual fitness value after mutation; Step 3: Determine whether the selected branch has any secondary branches; If there is a secondary branch, determine whether the secondary number of the secondary branch is less than 2. If the secondary number of the secondary branch is less than 2, the secondary branch is selected as the individual, and return to step 1 to execute, and the secondary number is increased by 1; The point with the highest individual fitness value after the secondary branch mutation is selected as the mutation point; If there is no secondary branch, the point with the highest individual fitness value after mutation is the mutation point.

5. The method for locating a distribution network fault interval based on an improved adaptive genetic algorithm according to claim 1, characterized in that: According to the individual fitness value and the adjustment coefficient, the adjusted adaptive crossover probability is obtained, and the calculation formula is as follows: Among them, P c represents the adaptive crossover probability; P c1 and P c2 is the crossover factor, which represents the maximum and minimum values of the crossover probability range; f1 represents the larger individual fitness value of the two individuals in the selected population that perform the crossover operation; f avg represents the average fitness of individuals in the selected population; f max Indicates the maximum individual fitness in the selected population; θ represents the adjustment coefficient; represents the adjusted adaptive crossover probability.

6. A method for locating fault intervals in a distribution network based on an improved adaptive genetic algorithm according to claim 5, characterized in that: According to the individual fitness value and the adjustment coefficient, the adaptive mutation probability is obtained, and the calculation formula is as follows: Among them, P m represents the adaptive mutation probability; P m1 and P m2 is the mutation factor, which represents the maximum and minimum values of the possible range of mutation probability; f represents the fitness value of the individual that performs the mutation operation in the selected population; f avg represents the average fitness of individuals in the selected population; f max Indicates the maximum individual fitness in the selected population; θ represents the adjustment coefficient; Represents the adjusted adaptive mutation probability.

7. A distribution network fault section location method based on an improved adaptive genetic algorithm according to claim 1, characterized in that: The fault current is binary-coded, and the direction from the power source to the user is the positive direction of the feeder. When the direction of the fault current is consistent with the positive direction, the fault current I j is 1; When the fault current direction is inconsistent with the positive direction, the fault current I j = -1; if there is no fault current, the fault current I j is 0.

8. The method for locating a distribution network fault interval based on an improved adaptive genetic algorithm according to claim 1, characterized in that: A part of individuals in the initialization population is selected to obtain a selection population, wherein the selection operation is to select individuals by combining direct selection based on the maximum individual fitness value and roulette wheel selection method.

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