Distribution network fault section location method and device based on dynamic weight update strategy

Abstract

The present invention discloses a method and device for locating a fault section of a distribution network based on a dynamic weight update strategy, and relates to the field of distribution networks or other related fields. The method comprises: responding to a detection instruction, using a chaotic mapping strategy to generate a population, receiving a segment actual vector, calculating an expected vector, using an improved fitness function to obtain segment fitness, forward sorting the fitness set, selecting the top three individuals in the sorting as wolves of the gray wolf algorithm, using a dynamic weight update strategy to update the position of the wolf, using a mutation and crossover strategy for the individuals of the population to obtain a mutated crossover individual, when the fitness of the mutated crossover individual is less than that of the individuals of the population, using the mutated crossover individual to replace the individuals of the population to obtain a new population, and obtaining a target population by iterating the new population, and locating the fault section in the segment vector represented by the optimal individual in the target population. The present invention solves the technical problems in the related art that the algorithm converges slowly and is prone to fall into the dilemma of local optimal solutions.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network or other related fields, and in particular to a method and device for locating a fault section of a distribution network based on a dynamic weight updating strategy. Background Art

[0002] In the field of distribution network technology, a large number of distributed power sources represented by wind power and solar energy will gradually replace traditional energy access to distribution network construction, the scale of distribution network will be further expanded, and the power supply mode of distribution network will be changed from a single radial power supply network to a multi-power supply network. This leads to that when a section of the distribution network fails, its fault characteristics are greatly changed compared with the traditional distribution network. Using the existing fault section location method to locate the fault area of ​​the multi-power distribution network is prone to misjudgment. Therefore, it is necessary to improve the existing fault section location method to adapt to the multi-power distribution network.

[0003] In the related art, when a section of the distribution network fails, the feeder terminal unit will collect information such as the voltage and current of the distribution network and upload it to the system, and then use the fault section locating method to locate the fault section. At present, the fault section location methods based on feeder terminal units are mainly divided into two categories: one is the direct method represented by the matrix method and the overheat arc search algorithm, and the other is the indirect method represented by artificial intelligence technologies such as optimization algorithms and neural networks. Among them, the indirect method has the most obvious advantages. The method of using intelligent algorithms to realize distribution network section fault location is simpler and has a faster calculation speed. However, intelligent algorithms also have disadvantages, as follows: (1) Some intelligent algorithms do not consider the situation of distributed power access, which makes these algorithms unsuitable for multi-power distribution networks; (2) The convergence speed of some intelligent algorithms is insufficient. For example, the iteration speed of the gray wolf algorithm depends on the initial generated population. If the quality of the initial randomly generated population is poor, it will lead to a high number of algorithm iterations and low efficiency, and may even lead to the occurrence of erroneous solutions; (3) Intelligent algorithms generally have the problem of premature convergence, especially the gray wolf algorithm. Due to the lack of a mechanism to jump out of the local optimal solution, the gray wolf algorithm is prone to fall into the dilemma of local optimal solution when dealing with the distribution network fault location problem, and may thus obtain erroneous results.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiment of the present invention provides a method and device for locating a fault section of a distribution network based on a dynamic weight update strategy, so as to at least solve the technical problems in the related art that the algorithm converges slowly and is prone to falling into the dilemma of local optimal solution.

[0006] According to one aspect of an embodiment of the present invention, a method for locating a fault section of a distribution network based on a dynamic weight update strategy is provided, comprising: responding to a fault detection instruction, generating a fault population using a chaotic mapping strategy, wherein the fault population includes N+1 fault population individuals, and N is a positive integer greater than or equal to 3; receiving an actual segment vector of a line segment uploaded by a feeder terminal unit in the distribution network, obtaining an expected segment vector according to the actual segment vector, obtaining a fitness value of the line segment according to the actual segment vector and the expected segment vector using an improved fitness function containing a misjudgment term, and forward sorting a fitness value set formed by all fitness values; selecting the top three fault population individuals in the fitness value set after forward sorting, recording them as α wolf, β wolf and δ wolf of the grey wolf algorithm, and recording the remaining The fault population individual is recorded as ω wolf, and the dynamic weight update strategy is used to update the position of ω wolf; the mutation strategy is used to mutate each fault population individual in the fault population to obtain a mutant individual, and the crossover strategy is used for the fault population individual and the corresponding mutant individual to obtain a mutant crossover individual; the fitness values ​​of the fault population individual and the corresponding mutant crossover individual are calculated respectively. When the fitness value of the mutant crossover individual is less than the fitness value of the fault population individual, the mutant crossover individual is used to replace the fault population individual in the fault population to obtain a new fault population. The target fault population is obtained by iterating the new fault population. For the optimal target fault population individual in the target fault population, the fault segment determined by the specified value in the segment vector represented by the target fault population individual is located.

[0007] Optionally, in response to a fault detection instruction, the step of generating a fault population using a chaotic mapping strategy includes: randomly generating a first fault population individual, wherein the first fault population individual is an integer vector with a dimension of 1×n, the value of any element in the integer vector is a first value or a second value, different elements represent different line sections in the distribution network, if the line section has not failed, the value of the element corresponding to the line section is the first value, if the line section has failed, the value of the element corresponding to the line section is the second value; generating the remaining N fault population individuals using a chaotic mapping strategy, inputting the segment vector represented by the previous fault population individual into the first formula, and calculating the segment vector represented by the next fault population individual.

[0008] Optionally, the step of obtaining a section expected vector according to the section actual vector, and obtaining the fitness value of the line section by using an improved fitness function containing a misjudgment term according to the section actual vector and the section expected vector includes: inputting the section actual vector into the switching function to obtain the section expected vector; inputting the section actual vector and the section expected vector into the second formula to calculate the fitness value of the line section, wherein the improved fitness function containing the misjudgment term is represented by the second formula.

[0009] Optionally, the step of updating the position of the ω wolf using a dynamic weight update strategy includes: inputting the current number of iterations and the maximum number of iterations of the gray wolf algorithm into a third formula, updating the nonlinear convergence factor of the gray wolf algorithm, and adjusting the convergence degree of the gray wolf algorithm using the updated nonlinear convergence factor.

[0010] Optionally, the step of updating the position of ω wolf using a dynamic weight update strategy includes: calculating the distances between ω wolf and α wolf, β wolf and δ wolf respectively according to the relative positions of ω wolf and α wolf, β wolf and δ wolf; calculating the position direction vector of ω wolf relative to α wolf, β wolf and δ wolf respectively according to the distances between ω wolf and α wolf, β wolf and δ wolf; inputting the weight coefficient, the current number of iterations, and the maximum number of iterations into a fourth formula to calculate the dynamic weight of ω wolf; inputting the dynamic weight and the position direction vector into a fifth formula to update the position of ω wolf.

[0011] Optionally, a mutation strategy is used to mutate each fault population individual in the fault population to obtain a mutant individual, and a crossover strategy is used on the fault population individual and the corresponding mutant individual to obtain a mutant crossover individual, including: a mutation strategy, for any fault population individual in the fault population, three different fault population individuals are randomly selected in the fault population, and the segment vectors represented by the three fault population individuals are input into the sixth formula to calculate the mutant individual of the fault population individual; a crossover strategy, the segment vector represented by the fault population individual and the segment vector represented by the corresponding mutant individual are input into the seventh formula, and each element in the segment vector represented by the fault population individual and the segment vector represented by the corresponding mutant individual are crossed to obtain a mutant crossover individual according to the seventh formula; a mutation strategy and a crossover strategy are used on all fault population individuals in the fault population to obtain a mutant crossover individual population.

[0012] Optionally, when the fitness value of the mutated crossover individual is less than the fitness value of the fault population individual, in the fault population, the mutated crossover individual is used to replace the fault population individual to obtain a new fault population step, including: inputting the fitness values ​​of the fault population individual and its corresponding mutated crossover individual into the eighth formula, and performing a selection operation using the eighth formula; performing a selection operation, when the fitness value of the mutated crossover individual is less than the fitness value of the fault population individual, in the fault population, the mutated crossover individual is used to replace the fault population individual; or, performing a selection operation, when the fitness value of the mutated crossover individual is greater than or equal to the fitness value of the fault population individual, retaining the fault population individual in the fault population.

[0013] According to another aspect of an embodiment of the present invention, a distribution network fault section locating device based on a dynamic weight update strategy is also provided, comprising: a response unit, used to respond to a fault detection instruction, and to generate a fault population using a chaotic mapping strategy, wherein the fault population includes N+1 fault population individuals, and N is a positive integer greater than or equal to 3; a processing unit, used to receive a section actual vector of a line section uploaded by a feeder terminal unit in the distribution network, obtain a section expected vector according to the section actual vector, obtain a fitness value of the line section according to the section actual vector and the section expected vector using an improved fitness function containing a misjudgment term, and forward sort the fitness value set formed by all fitness values; an updating unit, used to select the top three fault population individuals in the fitness value set after forward sorting, and record them as α wolf, β wolf and δ wolf of the grey wolf algorithm, The remaining fault population individuals are recorded as ω wolves, and the position of ω wolves is updated by adopting a dynamic weight update strategy; a mutation crossover unit is used to adopt a mutation strategy to mutate each fault population individual in the fault population to obtain a mutant individual, and a crossover strategy is used on the fault population individual and the corresponding mutant individual to obtain a mutant crossover individual; a positioning unit is used to calculate the fitness values ​​of the fault population individual and its corresponding mutant crossover individual respectively, and when the fitness value of the mutant crossover individual is less than the fitness value of the fault population individual, the fault population individual is replaced by the mutant crossover individual in the fault population to obtain a new fault population, and the target fault population is obtained by iterating the new fault population, and for the optimal target fault population individual in the target fault population, the fault segment determined by the specified value in the segment vector represented by the target fault population individual is located.

[0014] Optionally, the response unit includes: an individual generation module, used to randomly generate a first fault population individual, wherein the first fault population individual is an integer vector with a dimension of 1×n, the value of any element in the integer vector is a first value or a second value, different elements represent different line sections in the distribution network, if the line section has not failed, the value of the element corresponding to the line section is the first value, if the line section has failed, the value of the element corresponding to the line section is the second value; a population generation module, used to generate the remaining N fault population individuals using a chaotic mapping strategy, input the segment vector represented by the previous fault population individual into the first formula, and calculate the segment vector represented by the next fault population individual.

[0015] Optionally, the processing unit includes: an expected vector calculation module, used to input the actual vector of the section into the switching function to obtain the expected vector of the section; a fitness value calculation module, used to input the actual vector of the section and the expected vector of the section into the second formula to calculate the fitness value of the line section, wherein the improved fitness function containing the misjudgment term is represented by the second formula.

[0016] Optionally, the updating unit includes: a convergence factor calculation module, which is used to input the current number of iterations and the maximum number of iterations of the gray wolf algorithm into a third formula, update the nonlinear convergence factor of the gray wolf algorithm, and use the updated nonlinear convergence factor to adjust the convergence degree of the gray wolf algorithm.

[0017] Optionally, the update unit also includes: a distance calculation module, used to calculate the distances between ω wolf and α wolf, β wolf and δ wolf respectively according to the relative positions of ω wolf and α wolf, β wolf and δ wolf; a direction vector calculation module, used to calculate the position direction vector of ω wolf relative to α wolf, β wolf and δ wolf respectively according to the distances between ω wolf and α wolf, β wolf and δ wolf; a dynamic weight calculation module, used to input the weight coefficient, the current number of iterations, and the maximum number of iterations into the fourth formula to calculate the dynamic weight of ω wolf; a position update module, used to input the dynamic weight and the position direction vector into the fifth formula to update the position of ω wolf.

[0018] Optionally, the mutation crossover unit includes: a mutation module, which is used for mutation strategy. For any fault population individual in the fault population, three different fault population individuals are randomly selected in the fault population, and the segment vectors representing the three fault population individuals are input into the sixth formula to calculate the mutation individual of the fault population individual; a crossover module, which is used for crossover strategy. The segment vector representing the fault population individual and the segment vector representing the corresponding mutation individual are input into the seventh formula, and each element in the segment vector representing the fault population individual and the segment vector representing the corresponding mutation individual are crossed to obtain the mutation crossover individual according to the seventh formula; a population mutation crossover module, which is used to use the mutation strategy and the crossover strategy for all fault population individuals in the fault population to obtain a mutation crossover individual population.

[0019] Optionally, the positioning unit includes: a selection module, used to input the fitness values ​​of the faulty population individuals and their corresponding variant crossover individuals into the eighth formula, and use the eighth formula to perform a selection operation; a first execution module, used to perform a selection operation, when the fitness value of the variant crossover individual is less than the fitness value of the faulty population individual, in the faulty population, the variant crossover individual is used to replace the faulty population individual; a second execution module, used to perform a selection operation, when the fitness value of the variant crossover individual is greater than or equal to the fitness value of the faulty population individual, the faulty population individual in the faulty population is retained.

[0020] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for locating a distribution network fault section based on a dynamic weight update strategy.

[0021] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any one of the above-mentioned methods for locating a fault section in a distribution network based on a dynamic weight update strategy.

[0022] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps of any one of the above-mentioned methods for locating a fault section in a distribution network based on a dynamic weight update strategy.

[0023] In the present disclosure, by responding to the fault detection instruction, a chaotic mapping strategy is adopted to generate a fault population, and by receiving the actual segment vector of the line segment uploaded by the feeder terminal unit in the distribution network, the segment expected vector is obtained, and then according to the segment actual vector and the segment expected vector, an improved fitness function containing a misjudgment term is adopted to obtain the fitness value of the line segment, and the fitness value set formed by all fitness values ​​is forward sorted, and then the top three fault population individuals in the fitness value set after forward sorting are selected and recorded as α wolf, β wolf and δ wolf of the grey wolf algorithm, and the remaining fault population individuals are recorded as ω wolf, and the position of the ω wolf is updated by adopting a dynamic weight update strategy, and then .... Each fault population individual in the fault population is mutated by adopting a mutation strategy to obtain a mutant individual, a crossover strategy is used on the fault population individual and the corresponding mutant individual to obtain a mutant crossover individual, and the fitness values ​​of the fault population individual and its corresponding mutant crossover individual are calculated respectively. When the fitness value of the mutant crossover individual is less than the fitness value of the fault population individual, the mutant crossover individual is used to replace the fault population individual to obtain a new fault population. The new fault population is iterated to obtain a target fault population. For the optimal target fault population individual in the target fault population, the fault segment determined by the specified value in the segment vector represented by the target fault population individual is finally located.

[0024] In the present disclosure, a chaotic mapping strategy is adopted to initialize the fault population, thereby avoiding the problem that the original grey wolf algorithm is prone to fall into the local optimal solution due to the random generation of the initialization population. By adopting a dynamic weight update strategy, the weight of the α wolf in the algorithm during position update is increased, thereby improving the convergence speed of the algorithm. At the same time, the present disclosure selects the better population individuals in the original population and the mutant population through a mutation crossover strategy to participate in the calculation of fitness, thereby obtaining a population covering more fault conditions, so that the population can jump out of the dilemma of the local optimal solution during iteration, thereby solving the technical problem of slow algorithm convergence speed and easy to fall into the dilemma of the local optimal solution in the related technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0026] Figure 1 is a flow chart of an optional method for locating a fault section of a distribution network based on a dynamic weight update strategy according to an embodiment of the present invention;

[0027] Figure 2 is a schematic diagram of an optional distribution network fault section locating device based on a dynamic weight updating strategy according to an embodiment of the present invention;

[0028] Figure 3 The present invention is a hardware structure block diagram of an electronic device (or mobile device) for locating a distribution network fault section based on a dynamic weight update strategy according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] To facilitate those skilled in the art to understand the present invention, some terms or nouns involved in the embodiments of the present invention are explained below:

[0032] Fault section location: used to determine whether a fault has occurred in each line section in the distribution network and which line sections have failed.

[0033] Population individual: a set of vectors used to characterize whether each section of the distribution network is faulty, and output the solution of the fault location result. Multiple solutions need to be generated during the fault location process. The optimal solution found by the fault location method proposed in the present invention is the optimal population individual.

[0034] Particle Swarm Optimization: Particle Swarm Optimization is an optimization algorithm based on swarm intelligence. It is inspired by the foraging behavior of bird flocks. It finds the optimal solution by simulating the dynamic behavior of bird flocks or fish schools when searching for food. The particle swarm algorithm iteratively updates the position and velocity properties of particles in the search space, so that the entire particle swarm moves towards the optimal solution.

[0035] Gray Wolf Algorithm: The Gray Wolf Algorithm is an optimization algorithm that simulates the social behavior of gray wolves. It is inspired by the hunting strategies and social behaviors of gray wolves. It searches for the optimal solution of the population by simulating the leadership hierarchy and collaboration strategies of gray wolves in the hunting process. The Gray Wolf Algorithm imitates the hunting behavior and social structure of gray wolves and has good convergence speed and search capabilities.

[0036] It should be noted that the distribution network fault section location method and device based on the dynamic weight update strategy in the present disclosure can be used in the field of distribution network technology. When the distribution network fault section location is realized based on the dynamic weight update strategy, it can also be used in any field outside the field of distribution network technology. When the distribution network fault section location is realized based on the dynamic weight update strategy, the present disclosure does not limit the application field of the distribution network fault section location method and device based on the dynamic weight update strategy.

[0037] It should be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) collected by this disclosure are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data are in compliance with the relevant laws, regulations and standards of the relevant regions, necessary confidentiality measures are taken, and public order and good customs are not violated, and corresponding operation portals are provided for users to choose to authorize or refuse. For example, an interface is set up between this system and relevant users or organizations. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or organization.

[0038] It should be noted that in the present disclosure, when collecting and analyzing customer information, corresponding operation entrances are provided for users to choose to agree or reject the automated decision results; if the user chooses to reject, the expert decision-making process is entered.

[0039] The following embodiments of the present invention can be applied to various systems / applications / devices for locating fault sections of distribution networks based on dynamic weight update strategies. The present invention uses a chaotic mapping strategy to initialize the fault population, thereby avoiding the problem that the original grey wolf algorithm is prone to fall into a local optimal solution due to the random generation of the initialization population. By adopting a dynamic weight update strategy, the weight of the α wolf in the algorithm during position update is increased, thereby improving the convergence speed of the algorithm.

[0040] At the same time, the present invention selects better individuals from the original population and the variant population to participate in the calculation of fitness through a mutation crossover strategy, thereby obtaining a population covering more fault conditions, so that the population can jump out of the dilemma of local optimal solution during iteration.

[0041] The present invention is described in detail below in conjunction with various embodiments.

[0042] Embodiment 1

[0043] According to an embodiment of the present invention, an embodiment of a method for locating a fault section in a distribution network based on a dynamic weight update strategy is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0044] Figure 1 is a flow chart of an optional method for locating a distribution network fault section based on a dynamic weight update strategy according to an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0045] Step S101, responding to a fault detection instruction, generating a fault population by adopting a chaotic mapping strategy, wherein the fault population includes N+1 fault population individuals, and N is a positive integer greater than or equal to 3.

[0046] Step S102, receiving the actual segment vector of the line segment uploaded by the feeder terminal unit in the distribution network, obtaining the expected segment vector according to the actual segment vector, obtaining the fitness value of the line segment according to the actual segment vector and the expected segment vector using an improved fitness function containing a misjudgment term, and performing forward sorting on the fitness value set formed by all fitness values.

[0047] Step S103, select the top three faulty population individuals in the fitness value set after forward sorting, record them as α wolf, β wolf and δ wolf of the grey wolf algorithm, record the remaining faulty population individuals as ω wolf, and use the dynamic weight update strategy to update the position of ω wolf.

[0048] Step S104, using a mutation strategy to mutate each fault population individual in the fault population to obtain a mutated individual, and using a crossover strategy on the fault population individual and the corresponding mutated individual to obtain a mutated crossover individual.

[0049] Step S105, respectively calculate the fitness values ​​of the fault population individuals and their corresponding variant crossover individuals. When the fitness value of the variant crossover individual is less than the fitness value of the fault population individual, replace the fault population individual with the variant crossover individual in the fault population to obtain a new fault population. By iterating the new fault population, a target fault population is obtained. For the best target fault population individual in the target fault population, locate the fault segment determined by the specified value in the segment vector represented by the target fault population individual.

[0050] The above-mentioned distribution network fault section location method steps based on the dynamic weight update strategy can use the chaotic mapping strategy to initialize the fault population, avoiding the problem that the original grey wolf algorithm is prone to fall into the local optimal solution due to the random generation of the initialization population. By adopting the dynamic weight update strategy, the weight of the α wolf in the algorithm is increased during position update, thereby improving the convergence speed of the algorithm. At the same time, the better population individuals in the original population and the mutant population are selected through the mutation crossover strategy to participate in the calculation of fitness, and a population covering more fault conditions is obtained, so that the population can jump out of the dilemma of local optimal solution during iteration, thereby solving the technical problem of slow algorithm convergence and easy to fall into the dilemma of local optimal solution in related technologies.

[0051] The embodiment of the present invention is described in detail below in combination with the above steps.

[0052] Step S101, responding to a fault detection instruction, generating a fault population by adopting a chaotic mapping strategy, wherein the fault population includes N+1 fault population individuals, and N is a positive integer greater than or equal to 3.

[0053] Since the initial population of the original grey wolf algorithm is randomly generated, it is easy to cause the algorithm to fall into the dilemma of local optimal solution during the optimization iteration process. Therefore, in the embodiment of the present invention, a chaotic mapping strategy is introduced to initialize the population. The chaotic mapping strategy is a nonlinear mapping strategy with a high degree of randomness and uncertainty, which helps the population to obtain better diversity in the initialization stage, thereby avoiding the algorithm from falling into the dilemma of local optimal solution and improving the global search ability of the algorithm.

[0054] Optionally, step S101 includes: randomly generating a first fault population individual, wherein the first fault population individual is an integer vector with a dimension of 1×n, the value of any element in the integer vector is a first value or a second value, different elements represent different line sections in the distribution network, if the line section has not failed, the value of the element corresponding to the line section is the first value, if the line section has failed, the value of the element corresponding to the line section is the second value; using a chaotic mapping strategy to generate the remaining N fault population individuals, inputting the segment vector represented by the previous fault population individual into the first formula, and calculating the segment vector represented by the next fault population individual.

[0055] In an embodiment of the present invention, the size of the integer vector dimension n depends on the number of elements in the line section of the distribution network, wherein the first value and the second value can be set by oneself, preferably integers, such as -1, 0, 1, 2, 3, etc. For example, the first value can be determined to be 0, and the second value can be determined to be 1. When a fault occurs in a line section, the value of the element corresponding to the line section is 1, and when a fault does not occur in the line section, the value of the element corresponding to the line section is 0. After obtaining the first randomly generated fault population individual, this embodiment needs to use a chaotic mapping strategy to generate the remaining N fault population individuals. The segment vector represented by the previous fault population individual is input into the first formula to calculate the segment vector represented by the next fault population individual. The first formula is represented as:

[0056]

[0057] In the first formula, x n is the segment vector representing the individual of the previous fault population, x n+1 is a segment vector representing the next fault population individual. In this embodiment, the value of α is set by itself, for example, α is set to 0.55. By continuously iterating the first formula, the target number of fault population individuals is obtained, and finally all fault population individuals form the fault population of this embodiment.

[0058] Step S102, receiving the actual segment vector of the line segment uploaded by the feeder terminal unit in the distribution network, obtaining the expected segment vector according to the actual segment vector, obtaining the fitness value of the line segment according to the actual segment vector and the expected segment vector using an improved fitness function containing a misjudgment term, and performing forward sorting on the fitness value set formed by all fitness values.

[0059] In an embodiment of the present invention, the feeder terminal unit is an important component of the distribution network. When a fault occurs in the distribution network, the feeder terminal unit will upload the collected voltage and current information to the system. This part of the voltage and current information can be represented as a section actual vector. The section actual vector is an integer vector with a dimension of 1×n. The section expected vector can be obtained based on the section actual vector. The section actual vector and the section expected vector will be used in the fault location algorithm of this embodiment.

[0060] Optionally, the step of obtaining a section expected vector according to the section actual vector, and obtaining the fitness value of the line section by using an improved fitness function containing a misjudgment term according to the section actual vector and the section expected vector includes: inputting the section actual vector into the switching function to obtain the section expected vector; inputting the section actual vector and the section expected vector into the second formula to calculate the fitness value of the line section, wherein the improved fitness function containing the misjudgment term is represented by the second formula.

[0061] The switch function is a special function, and its output value usually has only two states: a first value (for example, a value of 0) and a second value (for example, a value of 1). In this embodiment, the actual vector of the section uploaded by the feeder terminal unit is input into the switch function to obtain the expected vector of the section, and then the actual vector of the section and the expected vector of the section are input into the second formula together to calculate the fitness value of the line section. The second formula is represented as follows:

[0062]

[0063] The second formula is an improved fitness function containing misjudgment terms, where I j The actual vector of the section uploaded by the feeder terminal unit, is the expected vector of the section, n is the number of switch devices in the switch function, m is the number of elements in the line section, ω is the coefficient of the misjudgment term, in this embodiment, the value of ω is 0.6, x j is the state of the elements in each line section. When a line section fails, x j The value of x is 1. When there is no fault in the line section, j The value of is 0.

[0064] This embodiment uses an improved fitness function containing a misjudgment term to evaluate the quality of individuals in the fault population, ensuring that the calculation result of each distribution network fault has only one fitness function value output, and the same fitness function value will not appear in multiple fault situations, thereby avoiding the problem of misjudgment of the algorithm due to the same fitness function value in different fault situations.

[0065] Step S103, select the top three faulty population individuals in the fitness value set after forward sorting, record them as α wolf, β wolf and δ wolf of the grey wolf algorithm, record the remaining faulty population individuals as ω wolf, and use the dynamic weight update strategy to update the position of ω wolf.

[0066] Gray wolf algorithm is an optimization algorithm inspired by the hunting behavior of gray wolves, and its main purpose is to find the optimal solution in the global optimization problem. The gray wolf group is divided into 4 populations. The gray wolves in the first level are represented by α wolves, the gray wolves in the second level are represented by β wolves, the gray wolves in the third level are represented by δ wolves, and the gray wolves in the fourth level are represented by ω wolves. According to the level division, α wolves have dominance over β wolves, δ wolves and ω wolves, β wolves have dominance over δ wolves and ω wolves, and δ wolves have dominance over ω wolves. In the embodiment of the present invention, the fault population is regarded as a gray wolf population, and each individual in the fault population is a gray wolf. In the iterative update of the algorithm, α wolf has an absolute dominant position, so α wolf has a higher fitness. In order to improve the convergence speed of the algorithm and avoid the algorithm being shared more weights by β wolf and δ wolf in the later stage of the algorithm, which leads to a slower convergence of the algorithm, this embodiment adopts a dynamic weight update strategy to complete the iteration of the algorithm.

[0067] Optionally, the step of updating the position of the ω wolf using a dynamic weight update strategy includes: inputting the current number of iterations and the maximum number of iterations of the gray wolf algorithm into a third formula, updating the nonlinear convergence factor of the gray wolf algorithm, and adjusting the convergence degree of the gray wolf algorithm using the updated nonlinear convergence factor.

[0068] The convergence factor is an important parameter in the Gray Wolf Algorithm. The convergence factor of the original Gray Wolf Algorithm is a linear parameter. This embodiment improves the linear convergence factor into a nonlinear convergence factor. The nonlinear convergence factor is represented by the third formula:

[0069]

[0070] In the third formula, t is the current iteration number of the algorithm, T max is the maximum number of iterations of the algorithm.

[0071] This embodiment adopts a nonlinear convergence factor. In the early stage of the algorithm, the nonlinear convergence factor changes more slowly, so that the population can search for the optimal solution in a larger global range, which helps the algorithm to escape the dilemma of local optimal solution. At the same time, in the later stage of the algorithm, the change speed of the nonlinear convergence factor increases, so that the population can obtain the optimal solution that represents the correct fault point at a faster speed, thereby improving the convergence speed and search efficiency of the algorithm.

[0072] Optionally, the step of updating the position of ω wolf using a dynamic weight update strategy includes: calculating the distances between ω wolf and α wolf, β wolf and δ wolf respectively according to the relative positions of ω wolf and α wolf, β wolf and δ wolf; calculating the position direction vector of ω wolf relative to α wolf, β wolf and δ wolf respectively according to the distances between ω wolf and α wolf, β wolf and δ wolf; inputting the weight coefficient, the current number of iterations, and the maximum number of iterations into a fourth formula to calculate the dynamic weight of ω wolf; inputting the dynamic weight and the position direction vector into a fifth formula to update the position of ω wolf.

[0073] In the embodiment of the present invention, the α wolf has a higher fitness in the algorithm. In order to improve the convergence speed of the algorithm, this embodiment uses a dynamic weight method to update the position of the ω wolf. The dynamic weight is represented by the fourth formula:

[0074]

[0075] In the fourth formula, ω1, ω2, and ω3 are the direction weights obtained by the positions of ω wolf relative to α wolf, β wolf, and δ wolf, respectively. k1, k2, and k3 are weight coefficients. In this embodiment, k1 is The value of k2 is The value of k3 is t is the current iteration number of the algorithm, T max is the maximum number of iterations of the algorithm.

[0076] In this embodiment, the distances between ω wolf and α wolf, β wolf and δ wolf are calculated respectively according to the relative positions of ω wolf and α wolf, β wolf and δ wolf, and then the position direction vector of ω wolf relative to α wolf, β wolf and δ wolf is calculated respectively according to the distances between ω wolf and α wolf, β wolf and δ wolf, and then the position direction vector and the dynamic weight calculated by the fourth formula are simultaneously input into the fifth formula, and the position of ω wolf is updated by the fifth formula. The fifth formula is represented as:

[0077] X(t+1)=ω1X1+ω2X2+ω3X3

[0078] In the fifth formula, ω1, ω2, and ω3 are the direction weights obtained from the positions of ω wolf relative to α wolf, β wolf, and δ wolf, respectively; X1, X2, and X3 are the position direction vectors of ω wolf relative to α wolf, β wolf, and δ wolf, respectively; and t is the current iteration number of the algorithm.

[0079] Step S104, using a mutation strategy to mutate each fault population individual in the fault population to obtain a mutated individual, and using a crossover strategy on the fault population individual and the corresponding mutated individual to obtain a mutated crossover individual.

[0080] In an embodiment of the present invention, since the original grey wolf algorithm is prone to fall into the dilemma of local optimal solutions, resulting in erroneous solutions for fault location, this embodiment uses mutation strategy and crossover strategy to select better population individuals from the original population and the mutation population to participate in the calculation of fitness, so that the algorithm can traverse populations covering more fault conditions, which is conducive to the algorithm to escape the dilemma of local optimal solutions during the iteration process.

[0081] Optionally, a mutation strategy is used to mutate each fault population individual in the fault population to obtain a mutant individual, and a crossover strategy is used on the fault population individual and the corresponding mutant individual to obtain a mutant crossover individual, including: a mutation strategy, for any fault population individual in the fault population, three different fault population individuals are randomly selected in the fault population, and the segment vectors represented by the three fault population individuals are input into the sixth formula to calculate the mutant individual of the fault population individual; a crossover strategy, the segment vector represented by the fault population individual and the segment vector represented by the corresponding mutant individual are input into the seventh formula, and each element in the segment vector represented by the fault population individual and the segment vector represented by the corresponding mutant individual are crossed to obtain a mutant crossover individual according to the seventh formula; a mutation strategy and a crossover strategy are used on all fault population individuals in the fault population to obtain a mutant crossover individual population.

[0082] In the embodiment of the present invention, the mutation strategy randomly selects three different fault population individuals from any fault population individual in the fault population, inputs the segment vectors represented by the three fault population individuals into the sixth formula, and calculates the variant individual of the fault population individual. The sixth formula is represented as:

[0083] P i =X k1 +Z(X k2 -X k3 )

[0084] In the sixth formula, P i is the mutant individual of the i-th individual in the fault population, X k1 , X k2 and X k3 are three different population individuals randomly selected from the fault population, and Z is a variation factor, which is set and adjusted by the user. In this embodiment, the value of Z may be 0.6.

[0085] After obtaining the variant individuals of all fault population individuals in the fault population, this embodiment uses a crossover strategy to input the segment vector represented by the fault population individual and the segment vector represented by its corresponding variant individual into the seventh formula, which is represented as follows:

[0086]

[0087] The seventh formula crosses each element in the segment vector representing the individual of the fault population and the segment vector representing the corresponding variant individual. In the seventh formula, U i,j is the jth element in the individual segment vector of the i-th fault population, rand[0,1] is a random number in the closed interval [0,1], CR is a crossover factor, and the crossover factor is set and adjusted by the user. In this embodiment, the value of CR can be 0.6.

[0088] In this embodiment, it is necessary to use the mutation strategy and the crossover strategy on all fault population individuals in the fault population to obtain a mutation crossover individual population.

[0089] Step S105, respectively calculate the fitness values ​​of the fault population individuals and their corresponding variant crossover individuals. When the fitness value of the variant crossover individual is less than the fitness value of the fault population individual, replace the fault population individual with the variant crossover individual in the fault population to obtain a new fault population. By iterating the new fault population, a target fault population is obtained. For the best target fault population individual in the target fault population, locate the fault segment determined by the specified value in the segment vector represented by the target fault population individual.

[0090] In the embodiment of the present invention, after the mutant crossover individual population is obtained, the fitness values ​​of the faulty population individual and its corresponding mutant crossover individual are calculated respectively. If the fitness value of the mutant crossover individual is less than the fitness value of the faulty population individual, the mutant crossover individual is used to replace the original faulty population individual.

[0091] Optionally, when the fitness value of the mutated crossover individual is less than the fitness value of the fault population individual, in the fault population, the mutated crossover individual is used to replace the fault population individual to obtain a new fault population step, including: inputting the fitness values ​​of the fault population individual and its corresponding mutated crossover individual into the eighth formula, and performing a selection operation using the eighth formula; performing a selection operation, when the fitness value of the mutated crossover individual is less than the fitness value of the fault population individual, in the fault population, the mutated crossover individual is used to replace the fault population individual; or, performing a selection operation, when the fitness value of the mutated crossover individual is greater than or equal to the fitness value of the fault population individual, retaining the fault population individual in the fault population.

[0092] In the embodiment of the present invention, the fitness values ​​of the fault population individuals and their corresponding variant crossover individuals are input into the eighth formula, and the eighth formula is used to perform the selection operation. The eighth formula is characterized as follows:

[0093]

[0094] In the eighth formula, Xi t , P i t They are the i-th fault population individual and its corresponding mutation crossover individual in the t-th iteration of the algorithm. When performing the selection operation, if the fitness value of the mutation crossover individual is less than the fitness value of the fault population individual, the fault population individual is replaced by the mutation crossover individual in the fault population, and if the fitness value of the mutation crossover individual is greater than or equal to the fitness value of the fault population individual, the fault population individual in the fault population is retained.

[0095] After the new fault population is obtained through the selection operation, this embodiment obtains the target fault population by iterating the new fault population multiple times, and finally locates the fault segment determined by the specified value in the segment vector represented by the target fault population individual for the best target fault population individual in the target fault population.

[0096] The following is a detailed description in conjunction with another embodiment.

[0097] Embodiment 2

[0098] A distribution network fault section locating device based on a dynamic weight updating strategy provided in this embodiment includes a plurality of implementation units, each of which corresponds to each implementation step in the above-mentioned embodiment 1.

[0099] Figure 2 is a schematic diagram of an optional distribution network fault section locating device based on a dynamic weight updating strategy according to an embodiment of the present invention, such as Figure 2 As shown, the distribution network fault section locating device based on the dynamic weight updating strategy may include: a response unit 20 , a processing unit 21 , an updating unit 22 , a variation crossover unit 23 , and a locating unit 24 .

[0100] The response unit 20 is used to respond to the fault detection instruction and generate a fault population by adopting a chaotic mapping strategy, wherein the fault population includes N+1 fault population individuals, and N is a positive integer greater than or equal to 3.

[0101] The processing unit 21 is used to receive the actual segment vector of the line segment uploaded by the feeder terminal unit in the distribution network, obtain the expected segment vector according to the actual segment vector, obtain the fitness value of the line segment according to the actual segment vector and the expected segment vector using an improved fitness function containing a misjudgment term, and perform forward sorting on the fitness value set formed by all the fitness values.

[0102] The updating unit 22 is used to select the top three fault population individuals in the fitness value set after forward sorting, record them as α wolf, β wolf and δ wolf of the grey wolf algorithm, record the remaining fault population individuals as ω wolf, and use the dynamic weight update strategy to update the position of ω wolf.

[0103] The mutation crossover unit 23 is used to mutate each fault population individual in the fault population by using a mutation strategy to obtain a mutant individual, and to use a crossover strategy on the fault population individual and the corresponding mutant individual to obtain a mutant crossover individual.

[0104] The positioning unit 24 is used to respectively calculate the fitness values ​​of the fault population individuals and their corresponding mutant crossover individuals. When the fitness value of the mutant crossover individual is less than the fitness value of the fault population individual, the mutant crossover individual is used to replace the fault population individual in the fault population to obtain a new fault population. The target fault population is obtained by iterating the new fault population. For the optimal target fault population individual in the target fault population, the fault segment determined by the specified value in the segment vector represented by the target fault population individual is located.

[0105] In the above-mentioned distribution network fault section positioning device based on the dynamic weight update strategy, the response unit 20 initializes the fault population by adopting the chaotic mapping strategy, thereby avoiding the problem that the original grey wolf algorithm is prone to fall into the local optimal solution due to the random generation of the initialization population. The update unit 22 increases the weight of the α wolf in the algorithm when updating the position by adopting the dynamic weight update strategy, thereby improving the convergence speed of the algorithm. At the same time, the mutation crossover unit 23 selects the better population individuals in the original population and the mutation population through the mutation crossover strategy to participate in the calculation of fitness, and obtains a population covering more fault conditions, so that the population can jump out of the dilemma of the local optimal solution during iteration, thereby solving the technical problem of slow algorithm convergence speed and easy to fall into the dilemma of the local optimal solution in the related technology.

[0106] Optionally, the response unit 20 includes: an individual generation module, used to randomly generate a first fault population individual, wherein the first fault population individual is an integer vector with a dimension of 1×n, the value of any element in the integer vector is a first value or a second value, different elements represent different line sections in the distribution network, if the line section has not failed, the value of the element corresponding to the line section is the first value, if the line section has failed, the value of the element corresponding to the line section is the second value; a population generation module, used to generate the remaining N fault population individuals using a chaotic mapping strategy, input the segment vector represented by the previous fault population individual into the first formula, and calculate the segment vector represented by the next fault population individual.

[0107] Optionally, the processing unit 21 includes: an expected vector calculation module, used to input the actual vector of the section into the switching function to obtain the expected vector of the section; a fitness value calculation module, used to input the actual vector of the section and the expected vector of the section into the second formula to calculate the fitness value of the line section, wherein the improved fitness function containing the misjudgment term is represented by the second formula.

[0108] Optionally, the updating unit 22 includes: a convergence factor calculation module, which is used to input the current number of iterations and the maximum number of iterations of the gray wolf algorithm into a third formula, update the nonlinear convergence factor of the gray wolf algorithm, and use the updated nonlinear convergence factor to adjust the convergence degree of the gray wolf algorithm.

[0109] Optionally, the updating unit 22 also includes: a distance calculation module, which is used to calculate the distances between ω wolf and α wolf, β wolf and δ wolf respectively according to the relative positions of ω wolf and α wolf, β wolf and δ wolf; a direction vector calculation module, which is used to calculate the position direction vector of ω wolf relative to α wolf, β wolf and δ wolf respectively according to the distances between ω wolf and α wolf, β wolf and δ wolf; a dynamic weight calculation module, which is used to input the weight coefficient, the current number of iterations, and the maximum number of iterations into the fourth formula to calculate the dynamic weight of ω wolf; and a position updating module, which is used to input the dynamic weight and the position direction vector into the fifth formula to update the position of ω wolf.

[0110] Optionally, the variation crossover unit 23 includes: a variation module, which is used for variation strategy. For any fault population individual in the fault population, three different fault population individuals are randomly selected in the fault population, and the segment vectors representing the three fault population individuals are input into the sixth formula to calculate the variation individual of the fault population individual; a crossover module, which is used for crossover strategy. The segment vector representing the fault population individual and the segment vector representing the corresponding variation individual are input into the seventh formula, and each element in the segment vector representing the fault population individual and the segment vector representing the corresponding variation individual are crossovered to obtain the variation crossover individual according to the seventh formula; a population variation crossover module, which is used to use the variation strategy and the crossover strategy for all fault population individuals in the fault population to obtain a variation crossover individual population.

[0111] Optionally, the positioning unit 24 includes: a selection module, which is used to input the fitness values ​​of the faulty population individuals and their corresponding variant crossover individuals into the eighth formula, and use the eighth formula to perform a selection operation; a first execution module, which is used to perform a selection operation, and when the fitness value of the variant crossover individual is less than the fitness value of the faulty population individual, in the faulty population, the variant crossover individual is used to replace the faulty population individual; and a second execution module, which is used to perform a selection operation, and when the fitness value of the variant crossover individual is greater than or equal to the fitness value of the faulty population individual, the faulty population individual in the faulty population is retained.

[0112] The above-mentioned distribution network fault section locating device based on the dynamic weight updating strategy may also include a processor and a memory. The above-mentioned response unit 20, processing unit 21, update unit 22, variation cross unit 23, locating unit 24, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions.

[0113] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the technical problems of insufficient algorithm convergence speed and easy to fall into the local optimal solution dilemma in the related technology can be solved by adjusting the kernel parameters.

[0114] The above-mentioned memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one storage chip.

[0115] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the distribution network fault section location method based on the dynamic weight update strategy according to any one of the above-mentioned embodiments one.

[0116] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors implement the distribution network fault section locating method based on a dynamic weight update strategy of any one of the above-mentioned embodiments one.

[0117] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the method for locating a distribution network fault section based on a dynamic weight update strategy described in each embodiment of the present application.

[0118] The present application also provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the distribution network fault section locating method based on the dynamic weight update strategy described in each embodiment of the present application are implemented.

[0119] Figure 3 1 is a hardware structure block diagram of an electronic device (or mobile device) for locating a distribution network fault section based on a dynamic weight update strategy according to an embodiment of the present invention. Figure 3 As shown, the electronic device may include one or more ( Figure 3302a, 302b, ..., 302n are used to illustrate) processor 302 (processor 302 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), memory 304 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a keyboard, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 3 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 3 More or fewer components as shown, or with Figure 3 Different configurations are shown.

[0120] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0121] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0123] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0124] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0125] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0126] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for locating fault sections in a distribution network based on a dynamic weight update strategy, characterized in that: include: In response to the fault detection instruction, a chaotic mapping strategy is used to generate a fault population, wherein the fault population includes N+1 fault population individuals, and N is a positive integer greater than or equal to 3; Receiving an actual segment vector of a line segment uploaded by a feeder terminal unit in a distribution network, obtaining an expected segment vector according to the actual segment vector, obtaining a fitness value of the line segment by using an improved fitness function containing a misjudgment term according to the actual segment vector and the expected segment vector, and performing forward sorting on a fitness value set formed by all the fitness values; Select the top three fault population individuals in the fitness value set after forward sorting from the fault population, record them as α wolf, β wolf and δ wolf of the gray wolf algorithm, record the remaining fault population individuals as ω wolf, and use a dynamic weight update strategy to update the position of the ω wolf, wherein the α wolf is the first-level gray wolf of the gray wolf algorithm, the β wolf is the second-level gray wolf of the gray wolf algorithm, the δ wolf is the third-level gray wolf of the gray wolf algorithm, and the ω wolf is the fourth-level gray wolf of the gray wolf algorithm; The step of updating the position of the ω wolf using a dynamic weight update strategy includes: inputting the current number of iterations and the maximum number of iterations of the gray wolf algorithm into a third formula, updating the nonlinear convergence factor of the gray wolf algorithm, and adjusting the convergence degree of the gray wolf algorithm using the updated nonlinear convergence factor. The third formula is: In the third formula, α is the nonlinear convergence factor of the gray wolf algorithm, t is the current number of iterations, T max is the maximum number of iterations; Using a mutation strategy to mutate the fault population individuals in the fault population to obtain mutant individuals, and using a crossover strategy on the fault population individuals in the fault population and the corresponding mutant individuals to obtain mutant crossover individuals; The fitness values ​​of the fault population individuals and their corresponding variant crossover individuals in the fault population are calculated respectively. When the fitness value of the variant crossover individual is less than the fitness value of the corresponding fault population individual, the variant crossover individual is used to replace the corresponding fault population individual in the fault population to obtain a new fault population. The target fault population is obtained by iterating the new fault population. For the optimal target fault population individual in the target fault population, the fault segment determined by the specified value in the segment vector represented by the target fault population individual is located.

2. The method according to claim 1, characterized in that In response to the fault detection instruction, the steps of generating a fault population using a chaotic mapping strategy include: Randomly generate a first fault population individual, wherein the first fault population individual is an integer vector with a dimension of 1×n, the value of any element in the integer vector is a first value or a second value, different elements represent different line sections in the distribution network, if the line section has no fault, the value of the element corresponding to the line section is the first value, if the line section has a fault, the value of the element corresponding to the line section is the second value, wherein n is a positive integer greater than or equal to 1; The chaotic mapping strategy is used to generate the remaining N fault population individuals, and the segment vector represented by the previous fault population individual is input into the first formula to calculate the segment vector represented by the next fault population individual. The first formula is: In the first formula, x n is the segment vector representing the individual characterization of the previous fault population, x n+1 is the segment vector representing the individual of the latter fault population, and the value of α is 0.

55.

3. The method according to claim 1, characterized in that The steps of obtaining a section expected vector according to the section actual vector, and obtaining a fitness value of the line section by using an improved fitness function containing a misjudgment term according to the section actual vector and the section expected vector, include: Inputting the actual vector of the segment into the switching function to obtain the expected vector of the segment; The actual vector of the section and the expected vector of the section are input into the second formula to calculate the fitness value of the line section, wherein the improved fitness function containing the misjudgment term is represented by the second formula, and the second formula is: In the second formula, fitness(x) is the improved fitness function containing the misjudgment term, I j is the actual vector of the segment, is the expected vector of the section, n is the number of switch devices in the switch function, m is the number of elements in the line section, ω is the coefficient of the misjudgment term, the value of ω is 0.6, x is the line section, x j is the state of the jth element in the line segment. When the jth segment of the line segment fails, x j The value of x is 1, when the jth section of the line segment has no fault, j The value of is 0.

4. The method according to claim 1, characterized in that: The step of updating the position of the ω wolf using a dynamic weight update strategy comprises: According to the relative positions of the ω wolf and the α wolf, β wolf and δ wolf, the distances between the ω wolf and the α wolf, β wolf and δ wolf are calculated respectively; According to the distances between the ω wolf and the α wolf, β wolf and δ wolf, respectively calculate the position direction vector of the ω wolf relative to the α wolf, β wolf and δ wolf; The weight coefficient, the current number of iterations, and the maximum number of iterations are input into the fourth formula to calculate the dynamic weight of the ω wolf. The fourth formula is: In the fourth formula, ω1, ω2, and ω3 are the direction weights obtained by the positions of the ω wolf relative to the α wolf, β wolf, and δ wolf, respectively; k1, k2, and k3 are the weight coefficients, and k1 is The value of k2 is The value of k3 is t is the current iteration number, T max is the maximum number of iterations; The dynamic weight and the position direction vector are input into the fifth formula to update the position of the ω wolf. The fifth formula is: X(t+1)=ω1X1+ω2X2+ω3X3; In the fifth formula, X(t+1) is the updated position of the ω wolf, ω1, ω2, and ω3 are the direction weights obtained by the position of the ω wolf relative to the α wolf, β wolf, and δ wolf, respectively, X1, X2, and X3 are the position direction vectors of the ω wolf relative to the α wolf, β wolf, and δ wolf, respectively, and t is the current iteration number.

5. The method according to claim 1, characterized in that The steps of using a mutation strategy to mutate the fault population individuals in the fault population to obtain mutated individuals, and using a crossover strategy on the fault population individuals in the fault population and the corresponding mutated individuals to obtain mutated crossover individuals include: Mutation strategy: for the i-th individual in the fault population, three different fault population individuals are randomly selected from the fault population, and the segment vectors represented by the three different fault population individuals are input into the sixth formula to calculate the variant individual of the i-th individual. The sixth formula is: P i =X k1 +Z(X k2 -X k3 ); In the sixth formula, P i is the variant individual of the i-th individual in the fault population, X k1 , X k2 and X k3 are three different population individuals randomly selected from the fault population, Z is the variation factor, and the value of Z is 0.6; The crossover strategy is to input the segment vector of the i-th individual representation and the segment vector of the corresponding variant individual representation into the seventh formula, cross each element of the segment vector of the i-th individual representation and the segment vector of the corresponding variant individual representation, and obtain the variant crossover individual according to the seventh formula. The seventh formula is: In the seventh formula, U i，j is the jth element in the segment vector representing the i-th variant crossover individual, X i，j is the jth element in the segment vector representing the i-th individual, P i，j is the jth element in the segment vector representing the variant individual corresponding to the i-th individual, rand[0,1] is a random number in the closed interval [0,1], CR is the crossover factor, and the value of CR is 0.6; The mutation strategy and the crossover strategy are applied to all fault population individuals in the fault population to obtain a mutation crossover individual population.

6. The method according to claim 1, characterized in that In the case where the fitness value of the mutated crossover individual is less than the fitness value of the corresponding fault population individual, in the fault population, the step of replacing the corresponding fault population individual with the mutated crossover individual to obtain a new fault population includes: The fitness values ​​of the i-th individual in the fault population and its corresponding variant crossover individual are input into the eighth formula, and the eighth formula is used to perform the selection operation. The eighth formula is: In the eighth formula, X i t , P i t are the i-th individual and its corresponding mutation crossover individual in the t-th iteration of the grey wolf algorithm, is the fitness value of the i-th individual in the t-th iteration, f(P i t ) is the fitness value of the mutation crossover individual corresponding to the i-th individual in the t-th iteration; Performing a selection operation, when the fitness value of the mutated crossover individual is less than the fitness value of the corresponding fault population individual, in the fault population, the mutated crossover individual is used to replace the corresponding fault population individual; or, A selection operation is performed, and when the fitness value of the mutated crossover individual is greater than or equal to the fitness value of the corresponding fault population individual, the fault population individual in the fault population is retained.

7. A distribution network fault section location device based on a dynamic weight update strategy, characterized in that: include: A response unit, used to respond to the fault detection instruction and generate a fault population by adopting a chaotic mapping strategy, wherein the fault population includes N+1 fault population individuals, and N is a positive integer greater than or equal to 3; A processing unit, configured to receive an actual segment vector of a line segment uploaded by a feeder terminal unit in a distribution network, obtain an expected segment vector according to the actual segment vector, obtain a fitness value of the line segment by using an improved fitness function containing a misjudgment term according to the actual segment vector and the expected segment vector, and perform forward sorting on a fitness value set formed by all the fitness values; An updating unit is used to select the top three fault population individuals in the fitness value set after forward sorting from the fault population, record them as α wolf, β wolf and δ wolf of the gray wolf algorithm, record the remaining fault population individuals as ω wolf, and use a dynamic weight update strategy to update the position of the ω wolf, wherein the α wolf is the first-level gray wolf of the gray wolf algorithm, the β wolf is the second-level gray wolf of the gray wolf algorithm, the δ wolf is the third-level gray wolf of the gray wolf algorithm, and the ω wolf is the fourth-level gray wolf of the gray wolf algorithm; The updating unit includes: a convergence factor calculation module, which is used to input the current number of iterations and the maximum number of iterations of the gray wolf algorithm into a third formula, update the nonlinear convergence factor of the gray wolf algorithm, and use the updated nonlinear convergence factor to adjust the convergence degree of the gray wolf algorithm. The third formula is: In the third formula, α is the nonlinear convergence factor of the gray wolf algorithm, t is the current number of iterations, T max is the maximum number of iterations; A mutation crossover unit, used to mutate the fault population individuals in the fault population by using a mutation strategy to obtain mutant individuals, and to use a crossover strategy on the fault population individuals in the fault population and the corresponding mutant individuals to obtain mutant crossover individuals; A positioning unit is used to respectively calculate the fitness values ​​of the fault population individuals and their corresponding variant crossover individuals in the fault population. When the fitness value of the variant crossover individual is less than the fitness value of the corresponding fault population individual, the variant crossover individual is used to replace the corresponding fault population individual in the fault population to obtain a new fault population. The target fault population is obtained by iterating the new fault population. For the best target fault population individual in the target fault population, the fault segment determined by the specified value in the segment vector represented by the target fault population individual is located.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the distribution network fault section location method based on the dynamic weight update strategy as described in any one of claims 1 to 6.

9. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the distribution network fault section location method based on the dynamic weight update strategy as described in any one of claims 1 to 6.

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