Power distribution network fault location method and device based on adaptive weighted average algorithm

Abstract

The application is suitable for the technical field of power distribution network fault positioning, and a power distribution network fault positioning method based on an adaptive weighted average algorithm is provided in the application. First, line operation state information of a power distribution network to be detected is acquired; fitness of each section is determined according to the line operation state information; then a population is initialized based on the adaptive weighted average algorithm; individuals meeting a preset rule in a current population are selected according to the fitness; a population updating rule is determined according to the selected individuals and the population is updated; finally, when the population is updated to a maximum iteration number, a power distribution network fault point position is determined according to a population optimal solution. In the application, the fitness of each section is calculated, the fault positioning algorithm is optimized, and the accuracy and fault tolerance of complex power distribution network fault positioning are improved. In addition, related devices are also provided in the application.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power distribution network fault location, and particularly relates to a power distribution network fault location method and device based on an adaptive weighted average algorithm. BACKGROUND

[0002] With the transformation of economic development mode and the optimization and upgrading of energy structure, large-scale distributed power is connected to the power distribution network, making the structure of the power distribution system and the power flow more complex, and the traditional fault location method for single radial power distribution network has been difficult to meet the development needs of complex new power distribution networks.

[0003] For the problem of fault location of complex power distribution networks containing multiple distributed power sources, a direct positioning algorithm based on matrix theory and an indirect positioning method based on artificial intelligence technology have been formed. The matrix algorithm is based on the alarm information uploaded by the FTU to construct a fault information matrix, and combines the correlation matrix between switches and line sections to establish a logical discrimination relationship, and then realizes fault location. This method has the advantages of simple principle and fast fault location speed, but has poor fault tolerance and low universality. The fault location method based on artificial intelligence technology is a new method that uses optimization theory for fault modeling, combines the state approximation idea and the principle of minimum fault diagnosis set to realize fault location. This method has the advantages of high fault tolerance, strong universality and high positioning accuracy. Among them, the swarm intelligence algorithm has outstanding processing effect on discrete variables in the fault location process, and can theoretically obtain the global optimal decision, becoming a research hotspot in this field. Therefore, various intelligent swarm optimization algorithms such as particle swarm algorithm, harmony search algorithm, ant colony algorithm and electromagnetic simulation algorithm are gradually applied in power distribution network fault location, and have improved the accuracy and efficiency of fault location to different degrees.

[0004] However, for complex power distribution networks, intelligent optimization algorithms have the problem of "curse of dimensionality" and are prone to fall into local optimal solutions, which seriously affects the speed and accuracy of fault location. SUMMARY

[0005] The embodiments of the application provide a power distribution network fault location method and device based on an adaptive weighted average algorithm, which can solve the technical problems of slow speed and low accuracy of traditional power distribution network fault location.

[0006] In a first aspect, the embodiments of the application provide a power distribution network fault location method based on an adaptive weighted average algorithm, which comprises:

[0007] Obtaining line operation state information of a power distribution network to be detected;

[0008] Determining the fitness of each section according to the line operation state information;

[0009] Initialize the population based on an adaptive weighted average algorithm;

[0010] Select individuals in the current population that meet preset rules according to fitness;

[0011] Determine a population update rule according to the selected individuals and update the population;

[0012] When the population is updated to the maximum number of iterations, determine the fault point position of the power distribution network according to the optimal solution of the population.

[0013] In combination with the first aspect, in an implementation provided by the embodiments of the present application, the line operation state information of the power distribution network to be detected is acquired, including:

[0014] Acquire node current information uploaded by a feeder terminal unit (FTU);

[0015] Determine the state S of the node j according to a first formula j ;

[0016] Determine the state S of the section B according to a second formula B ;

[0017] Determine the feeder operation state and the node overcurrent information according to a third formula

[0018] The first formula is:

[0019]

[0020] The second formula is:

[0021]

[0022] The third formula is:

[0023]

[0024] Wherein, S j (X) represents a switch function of the node j, X represents a feeder operation state quantity, N d represents the number of downstream nodes of the node j, x j (n d ) represents the operation state of the downstream section of the node j.

[0025] In combination with the first aspect, in an implementation provided by the embodiments of the present application, the fitness of each section is determined according to the line operation state information, including:

[0026] Determine the fitness of each section according to the line operation state information through a fourth formula;

[0027] The fourth formula is:

[0028]

[0029] wherein F it (S B ) is the fitness of segment i at the tth iteration. Wherein F B is the segment operating state, S j is the on-off function uploaded on the node, is the expected operating function of each segment in the failure state, is the XOR operation, ω is the weight coefficient, ω = 0.5, A is a random number between [0, 1], S B [j, i] is the consistency factor of the downstream segment i and the upstream node j of the branch.

[0030] In combination with the first aspect, in an implementation provided by the embodiments of the present application, the population update rule is determined according to the selected individual, comprising:

[0031] The population update rule is determined by the fifth formula according to the selected individual;

[0032] The fifth formula is:

[0033]

[0034]

[0035]

[0036] wherein r is a random number between 0 and 0.5, H P is the adaptive acceleration factor, and ε is the reinforcement learning factor, x n is the average value of the vector position, and λ is the inflation constant. a , x b , x c are three solution vectors randomly selected in the solution space, x bs , x ws , x bt are the optimal, the worst, and the better individuals determined by the fitness of the current population, respectively, and ξ is a constant, and rand is a random number.

[0037] In combination with the first aspect, in an implementation provided by the embodiments of the present application, the reinforcement learning factor is calculated by the following formula:

[0038]

[0039] wherein r i is the sorting result of the vector weight, x n is the average value of the vector position, and λ is the inflation constant.

[0040] In combination with the first aspect, in an implementation provided by the embodiments of the present application, the adaptive acceleration factor is calculated by the following formula:

[0041] f(x)=f(x ws →x bs )

[0042] wherein x bs , x ws are the best and worst individuals in the current population determined by the fitness.

[0043] In combination with the first aspect, in an implementation provided by the embodiments of the present application, the population updating is specifically as follows:

[0044] X g+1 =X g +MeanRule+ξrand

[0045] wherein X g+1 is the updated population individual, X g is the population individual before updating, MeanRule is the updating rule, ξ is a constant, and rand is a random number.

[0046] In the second aspect, the embodiments of the present application provide a power distribution network fault locating device based on the adaptive weighted average algorithm, which comprises:

[0047] An acquisition module is configured to acquire line operation state information of a power distribution network to be detected.

[0048] A processing module is configured to determine the fitness of each section according to the line operation state information.

[0049] The processing module is further configured to initialize a population based on the adaptive weighted average algorithm.

[0050] The processing module is further configured to select individuals in the current population that meet a preset rule according to the fitness.

[0051] The processing module is further configured to determine a population updating rule according to the selected individuals and to update the population.

[0052] The processing module is further configured to determine the location of a fault point of the power distribution network according to the optimal solution of the population when the population is updated to a maximum iteration number.

[0053] In the third aspect, the embodiments of the present application provide a terminal device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method of the first aspect when executing the computer program.

[0054] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the method in the first aspect.

[0055] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on a terminal device, the terminal device executes the method in any one of the first aspect.

[0056] It can be understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description in the first aspect, and will not be repeated here.

[0057] Compared with the prior art, the beneficial effects of the embodiments of the present application are as follows:

[0058] The power distribution network fault positioning method based on the adaptive weighted average algorithm provided by the embodiments of the present application first acquires line operation state information of a to-be-detected power distribution network; determines the fitness of each section according to the line operation state information; then initializes a population based on the adaptive weighted average algorithm; selects individuals in a current population that meet a preset rule according to the fitness; determines a population updating rule according to the selected individuals and performs population updating; finally, when the population is updated to a maximum iteration number, determines a power distribution network fault point position according to an optimal solution of the population. The embodiments of the present application optimize the fault positioning algorithm by calculating the fitness of each section, and improve the accuracy and fault tolerance of complex power distribution network fault positioning. BRIEF DESCRIPTION OF DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0060] Figure 1 The power distribution network simulation model schematic diagram for illustrating the application scenario of the embodiments of the present application;

[0061] Figure 2 The system architecture diagram provided by the embodiments of the present application;

[0062] Figure 3 The schematic diagram of the terminal device provided by the embodiments of the present application;

[0063] Figure 4 The schematic diagram of the power distribution network fault positioning method based on the adaptive weighted average algorithm provided by the embodiments of the present application;

[0064] Figure 5A power distribution network simulation model diagram for explaining a switching function of an embodiment of the present application;

[0065] Figure 6 An application example flowchart of the power distribution network fault locating method based on the adaptive weighted average algorithm provided by the embodiment of the present application;

[0066] Figure 7 A detailed flowchart of the application example of the power distribution network fault locating method based on the adaptive weighted average algorithm provided by the embodiment of the present application;

[0067] Figure 8 A schematic diagram of the power distribution network fault locating device based on the adaptive weighted average algorithm provided by the embodiment of the present application. DETAILED DESCRIPTION

[0068] In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

[0069] It should be understood that the term "comprises" when used in this specification and the appended claims indicates the presence of the described features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0070] It should also be understood that the term "and / or" when used in this specification and the appended claims, means any one of the associated listed items can be present or can be absent, and includes the possibility of combinations of one or more of the associated listed items.

[0071] As used in this specification and the appended claims, the term "if" can be interpreted as meaning "when" or "once" or "in response to a determination" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [a described condition or event] is detected" can be interpreted as meaning "once it is determined" or "in response to a determination" or "once [the described condition or event] is detected" or "in response to detecting [the described condition or event]," depending on the context.

[0072] In addition, in the description of the specification and the appended claims of the present application, the terms "first", "second", "third", etc. are only used for differentiation in description, and cannot be understood as indicating or implying relative importance.

[0073] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0074] The distribution network fault location method based on the adaptive weighted average algorithm provided in this application can be applied to distribution network fault location. Figure 1 This is a schematic diagram of a power distribution network simulation model used to illustrate the application scenario in an embodiment of this application. Figure 1 As shown, this model is specifically an IEEE 33 distribution network simulation model, where nodes 11 and 28 are set as distributed generation (DG) access points. Distribution switch monitoring terminals / feeder terminal units (FTUs) are installed at each node of the distribution network. The FTUs have remote control, telemetry, remote signaling, and fault detection functions, and communicate with the distribution automation master station to provide information on the distribution system's operating status and various parameters, as well as information required for monitoring and control, including switch status, power parameters, phase-to-phase faults, ground faults, and parameters during faults. They also execute commands issued by the distribution master station to adjust and control the distribution equipment, achieving functions such as fault location, fault isolation, and rapid restoration of power supply to non-faulty areas. Using the FTUs at the sectionalizing switches and tie switches of each line in the distribution network, the line operating status information of the distribution network can be obtained.

[0075] Figure 2 This is a system architecture diagram provided for an embodiment of this application. The system architecture includes: FTUs (Fault Transfer Units) at sectionalizing switches and tie switches of each line in the distribution network, as well as terminal equipment. The line operating status information of the distribution network obtained through the FTUs can be uploaded to the terminal equipment, enabling the terminal equipment to implement the distribution network fault location method based on the adaptive weighted average algorithm provided in this embodiment of the application.

[0076] It is understood that the terminal device in the embodiments of this application may be a computer, a server, etc., and the specific form of the terminal device is not limited in the embodiments of this application.

[0077] Figure 3A schematic diagram of a terminal device provided in an embodiment of the present application. The terminal device 300 comprises a memory 302, a processor 301, and a computer program 303 stored in the memory 302 and executable on the processor 301, and the processor 301 implements the power distribution network fault locating method based on an adaptive weighted average algorithm provided in the present application when executing the computer program 303.

[0078] Figure 4 A schematic diagram of the power distribution network fault locating method based on an adaptive weighted average algorithm provided in the present application, the method comprising:

[0079] 401. Obtain line operation state information of a power distribution network to be detected;

[0080] In the present application, the line operation state information comprises the state of a node, the state of a section, and a switch function. The present application considers the case of distributed power supply access, and uses 0, 1, -1 mode to encode the current information uploaded by the FTU and the section operation state for the monitoring node data, i.e., 0 indicates that the node has no fault current flowing through, 1 indicates that the node has a forward fault current flowing through, and -1 indicates that the node has a reverse fault current flowing through; in addition, the section operation state is 1 indicates that the section is faulty, and the section operation state is 0 indicates that the section is not faulty.

[0081] Specifically, the state S j of the node j is determined according to a first formula, the state S B of the section B is determined according to a second formula, and the feeder operation state and the node overcurrent information are determined according to a third formula.

[0082] The first formula is:

[0083]

[0084] The second formula is:

[0085]

[0086] The third formula is:

[0087]

[0088] wherein S j (X) represents the switch function of the node j, X represents the feeder operation state quantity, N d represents the number of downstream nodes of the node j, x j (n d ) represents the downstream section operation state of the node j.

[0089] In the embodiments of the present application, the feeder operating state and the node overcurrent information are expressed by a switch function. In a complex distribution network, the node on the feeder path from the node to the main power supply of the system is defined as the upstream node of the node, and the node on the feeder path from the node to the end of the line or the node where the distributed power supply is connected is defined as the downstream node of the node.

[0090] Figure 5 A distribution network simulation model diagram for explaining the switch function in the embodiments of the present application is shown in FIG. 1. It is assumed that Figure 5 When the section (5) fails, the feeder operating state is [000010], and the calculated switch function is [110010].

[0091] When the section (1) fails, the expected operating function is [100000], and at this time,

[0092] When the section (2) fails, the expected operating function is [110000], and at this time,

[0093] When the section (5) fails, the expected operating function is [110010], and at this time,

[0094] As can be seen, the switch function can express the feeder operating state and the node overcurrent information.

[0095] 402. Determine the fitness of each section according to the line operating state information;

[0096] In the embodiments of the present application, the fitness of each section is determined according to the line operating state information by a fourth formula;

[0097] The fourth formula is:

[0098]

[0099] Wherein, F it (S B ) is the fitness of the section i in the t th iteration. Wherein, S B is the section operating state, S j is the switch function uploaded by the node, is the expected operating function of each section failure, is the exclusive or operation, ω is the weight coefficient, ω = 0.5, A is a random number between [0, 1], S B [j, i] is the consistency factor of the branch downstream section i and the upstream node j. N is the total number of nodes in the distribution network to be detected. Therefore, the fitness function has a maximum value at the fault section.

[0100] The exclusive-OR logical operation of the fourth formula in the embodiment of the application can effectively avoid the situation that the fault probability is accumulated at the end section of a line in the traditional fault positioning due to logical operation addition in a complex power distribution network, and solves the problem that the probability of misjudging the end node of a branch as a fault node is too high.

[0101] In the embodiment of the application, the consistency factor S B [j,i] realizes voting of fault information of an upstream node to a downstream feeder, that is, the state of the downstream feeder of the node is the cause of the fault current of the node, S B [j,i] that is, when the state of the node j is ±1, a vote is cast to the downstream feeder, and the voting information traverses all nodes. Therefore, the greater the algebraic sum of the voting information of the upstream node is, the greater the probability of fault of the section is. Moreover, when the decision information satisfies T i-1 <T i = T i+1 , the maximum node decision information is present in the section i, that is, i is determined as the fault section. Wherein, T i represents the voting information of the section i, that is, when the voting information of the section i is greater than that of any upstream section, and the fault voting information of the downstream section no longer increases, the section is determined as the fault section. The fitness function is corrected by the weighted consistency factor, which can effectively improve the fault tolerance of the algorithm, that is, when there is a false alarm, the consistency factor will play a role with a larger weight. j

[0102] 403、initialize the population based on the adaptive weighted average algorithm;

[0103] In the embodiment of the application, step 403 can be: initializing the population based on the adaptive INFO algorithm, setting the population size N, solving the dimension D, the maximum number of iterations D max , the current number of iterations t, introducing a Tent chaotic sequence to generate a random initial population, so as to ensure that the system output initial population traverses the entire solution space.

[0104] 404、select individuals in the current population that meet the preset rule according to the fitness;

[0105] In the embodiment of the application, the fitness can represent the fault probability of the section (the advantages and disadvantages of the population individuals), and when the fitness is maximum, the fault probability is maximum (the population individual is optimal). Therefore, according to the size value of the fitness, individuals that meet the preset rule can be selected, specifically, the individual with the maximum fitness can be selected as the optimal individual x bs in the current population, and the individual with the minimum fitness can be selected as the worst individual x ws in the current population. In particular, the better individual x bt is randomly selected from the top 5 individuals in the fitness ranking. ​

[0106] 405. determining population update rule and performing population update according to the selected individual;

[0107] In the embodiment of the present application, the population update rule MeanRule is determined by the fifth formula according to the selected individual;

[0108] The fifth formula is:

[0109]

[0110]

[0111]

[0112] wherein r is a random number between 0 and 0.5, H P is an adaptive acceleration factor, and ε is a reinforcement learning factor, x n is the average value of vector positions, and λ is an inflation constant, a x b x c are three solution vectors randomly selected in the solution space, bs x ws x bt are the best, worst and better individuals of the current population determined by fitness (i.e. the individuals determined in step 405), and ξ is a small constant, and rand is a random number.

[0113] wherein the reinforcement learning factor is calculated by the following formula:

[0114]

[0115] wherein r i is the sorting result of vector weights, x n is the average value of vector positions, and λ is an inflation constant.

[0116] It can be understood that the formula for calculating the vector weight is:

[0117]

[0118] wherein x n is the average value of vector positions, and λ is an inflation constant.

[0119] The embodiment of the present application introduces the reinforcement learning factor ε to strengthen the effective learning ability of the individual. The role of the operator is to limit only the vectors with the top 5 weights to play a role in decision-making.

[0120] Wherein, the adaptive acceleration factor is calculated by the following formula:

[0121] f(x) = f(x ws → x bs )

[0122] Wherein, x bs , x ws are the best, worst individuals determined by fitness of the current population.

[0123] In the embodiment of the present application, the adaptive acceleration factor is constructed based on the hausdorff distance Wherein, f(x) = f(x ws → x bs ) represents the hausdorff distance between the position vector of the worst individual in the current population and the two sub-regions of the global optimal individual, and H P ≥ d(x ws → x bs ). First, the entire search space is divided into sub-regions of the same size and regularity, and the individual searches in the entire space. In the early stage of space search, the individual distribution space is large, the individual fitness is low and scattered; in the later stage of space search, the individual fitness is high and concentrated. Therefore, limiting the search range to the hausdorff distance of the two sub-regions when the current worst individual is updated to the optimal individual can adaptively adjust the search space range, avoid the algorithm falling into local optimum in the early stage of iteration, and effectively improve the convergence speed of the algorithm in the later stage of iteration.

[0124] In the embodiment of the present application, the population update is specifically:

[0125] X g+1 = X g + MeanRule + ξrand

[0126] Wherein, X g+1 is the updated population individual, X g is the population individual before updating, MeanRule is the update rule, ξ is a constant, and rand is a random number.

[0127] The embodiment of the present application updates the vector position as X g+1 = X g + MeanRule + ξrand. Wherein, if rand < 0.5, then new vectors X1 g+1 and X2 g+1 are respectively generated based on the randomly selected individual and the optimal individual, and if rand > 0.5, then new vectors X1 g+1 and X2 g+1.

[0128] Then, a random number rand is selected for the selection of the new vector, when rand<0.5, the new vector selection strategy is: X g+1 (1)=X1 g+1 +μ|X1 g+1 -X2 g+1 |, when rand>0.5, the new vector selection strategy is: X g+1 (2)=X2 g+1 +μ|X1 g+1 -X2 g+1 |, when rand=0.5, the new vector selection strategy is: X g+1 (3)=X g . Wherein, μ=0.05*rand.

[0129] 406、When the population is updated to the maximum number of iterations, the fault point position of the power distribution network is determined according to the optimal solution of the population.

[0130] In the embodiment of the application, after updating the population each time, it can be judged whether the maximum number of iterations is reached, if yes, the overall optimal solution is obtained, and the fault point position is determined. Specifically, in the embodiment of the application, the fitness function has a maximum value at the fault section, therefore, the optimal individual of the population and the corresponding fault point position can be determined according to the fitness values of each section.

[0131] The power distribution network fault positioning method based on the adaptive weighted average algorithm provided in the embodiment of the application has the following advantages:

[0132] (1) The fault positioning fitness function is improved in the application, the fault tolerance of the algorithm as a whole is improved, and the problem of misjudgment caused by the accumulation of fault probability at the end node of the line caused by traditional logical operation is solved.

[0133] (2) By introducing a reinforcement learning factor, the effective learning ability of the individual is strengthened. The convergence speed of the algorithm is accelerated and the accuracy of the algorithm is improved.

[0134] (3) An adaptive acceleration factor is introduced to improve the global search ability of the algorithm, and the size of the search space is adaptively adjusted. While effectively improving the global search ability of the algorithm and avoiding the algorithm falling into a local optimal solution, the convergence speed of the algorithm can be improved.

[0135] (4) The application has outstanding use value on both the traditional power distribution network and the complex power distribution network containing DG.

[0136] The application examples of the power distribution network fault positioning method based on the adaptive weighted average algorithm provided in the embodiment of the application are described in detail below.

[0137] Figure 6The application example flow chart of the power distribution network fault location method based on the adaptive weighted average algorithm provided by the embodiment of the present application is provided. The flow chart comprises the following steps:

[0138] Step 1, constructing a complex power distribution network model based on the adaptive INFO algorithm.

[0139] Step 2, constructing a complex power distribution network fault location mathematical model based on the adaptive INFO algorithm.

[0140] Step 3, realizing complex power distribution network fault location based on the adaptive INFO algorithm.

[0141] The specific process of the application example is shown in Figure 7 Figure 7 The detailed flow chart of the application example of the power distribution network fault location method based on the adaptive weighted average algorithm provided by the embodiment of the present application is provided.

[0142] As shown in Figure 7 , step 1 further comprises the following steps:

[0143] Step 1.1: building an IEEE33 power distribution network simulation model, as shown in Figure 1 . Among them, node 11 and node 28 are set as distributed generation (DG) access points.

[0144] Step 1.2: obtaining the node current information uploaded by the FTU at each line section switch, tie switch and the like of the power distribution network, the current information including the detection data of the line in the normal working state and the monitoring data of the line in the fault working state.

[0145] Step 1.3: considering the case of distributed power supply access, the uploaded current information and section operation state are encoded by using 0, 1 and -1 for the monitoring node data. That is, 0 represents that the node has no fault current flowing through, 1 represents that the node has positive fault current flowing through, and -1 represents that the node has reverse fault current flowing through; in addition, the section operation state is 1, which means that the section is faulty, and the section operation state is 0, which means that the section is not faulty.

[0146]

[0147] The section state between nodes

[0148]

[0149] Step 1.4, constructing a complex power distribution network fault location switch function based on the improved INFO algorithm.

[0150] ​Feeder operating status and node overcurrent information are expressed through switching functions. In complex distribution networks, nodes along the feeder path from a node to the system's main power source are defined as upstream nodes, while nodes along the feeder path from a node to the end of the line or a distributed generation access node are defined as downstream nodes. The switching function is defined as follows:

[0151]

[0152] Among them, S j (X) represents the switching function of node j, X represents the feeder operating state variable, and N d x represents the number of downstream nodes of node j. j (n d ) indicates the operating status of the downstream segment of node j.

[0153] In this embodiment of the application, step 2 further includes:

[0154] Step 2.1: Construct the fitness function:

[0155]

[0156] In the formula, F it (S B ) represents an improved fitness function for fault location in complex distribution networks based on the adaptive INFO algorithm. Where S B For the section's operating status, S j For the switch function uploaded by the node, The expected execution function for each section in case of a fault. For the XOR operation, ω is the weighting coefficient, ω = 0.5, A is a random number between [0,1], and S... B [j,i] represents the consistency factor between downstream segment i and upstream node j. Therefore, the fitness function has a maximum value at the faulty segment.

[0157] In complex power distribution networks, the XOR logic operation can effectively avoid the situation in traditional fault location where the probability of faults accumulates in the end section of the line due to the addition of logical operations, and solves the problem of the high probability of branch end nodes being misjudged as fault nodes.

[0158] Consistency factor S B [j,i] implements the voting of fault information from upstream nodes to downstream feeders, meaning the state of the downstream feeder is the cause of the fault current at the node. B [j,i] means that when node j's state is ±1, it casts a vote for the downstream feeder, and the voting information traverses all nodes. Therefore, the larger the algebraic sum of the upstream node's voting information, the greater the probability of a fault occurring in that segment. Furthermore, when the decision information satisfies T... i-1 <Ti = T i+1 , the maximum node decision information appears in section i, that is, i is determined as the fault section. Wherein, T i represents the voting information of section i, that is, when the voting information of section i is greater than that of any upstream section, and the fault voting information of the downstream section no longer increases, the section is determined as the fault section. The fitness function is corrected by the weighted consistency factor, which can effectively improve the fault tolerance of the algorithm, that is, when S j When there is a false alarm, the consistency factor will play a role with a larger weight.

[0159] Step 2.2: Construct reinforcement learning factor

[0160] By introducing the reinforcement learning factor ε, the effective learning ability of the individual is strengthened. The role of this operator is to limit only the top 5 vectors in weight order to be able to play a role in decision-making. Wherein, r i is the sorting result of the vector weight.

[0161] Step 2.3: Construct adaptive acceleration factor

[0162] By constructing an adaptive acceleration factor based on the hausdorff distance Wherein, f(x) = f(x ws → x bs ). First, the entire search space is divided into sub-regions of the same size and regularity. The individual searches in the entire space. In the early stage of space search, the individual distribution space is large, the individual fitness is low and scattered; in the later stage of space search, the individual fitness is high and concentrated. Therefore, limiting the search range to the hausdorff distance of the two sub-regions when the current worst individual updates to the optimal individual can achieve adaptive adjustment of the search space range, avoid the algorithm falling into local optimum in the early stage of iteration, and effectively improve the convergence speed of the algorithm in the later stage of algorithm iteration.

[0163] In the embodiments of the application, step 3 further comprises:

[0164] Step 3.1: Initialize the population based on the adaptive INFO algorithm, set the population size N, the solution dimension D, the maximum number of iterations D max , the current iteration number t, and introduce a Tent chaotic sequence to generate a random initial population to ensure that the system output initial population traverses the entire solution space.

[0165] Step 3.2: Calculate the fitness F it (S B ) value and vector weight (WM) of each individual. The formula for calculating the vector weight is:

[0166]

[0167] where x n is the average value of the vector position, λ is the inflation constant.

[0168] Step 3.3: Randomly select three solution vectors x a , x b , x c in the solution space, and perform vector update operation based on reinforcement learning factor and adaptive acceleration factor. The update rule MeanRule is calculated by the following formula:

[0169]

[0170]

[0171]

[0172] where adaptive acceleration factor f(x) = f(x ws → x bs ) represents the Hausdorff distance from the position vector of the worst individual in the current population to the two sub-regions of the global optimal individual position, and H P ≥ d(x ws → x bs ). x bs , x ws , x bt are the best, worst, and better individuals in the current population, respectively. In particular, the better individual is randomly selected from the top 5 in the weight order, and ξ is a small constant.

[0173] Update the vector position with X g+1 = X g + MeanRule + ξrand. If rand < 0.5, then generate new vectors X1 g+1 and X2 g+1 based on the randomly selected individual and the optimal individual, respectively. If rand > 0.5, then generate new vectors X1 g+1 and X2 g+1 based on the randomly selected individual and the better individual, respectively.

[0174] Step 3.4: Select a random number rand for new vector selection. When rand < 0.5, the new vector selection strategy is: X g+1 (1) = X1 g+1 + μ |X1 g+1 - X2 g+1 |, and when rand > 0.5, the new vector selection strategy is: X g+1 (2) = X2g+1 + μ | X1 g+1 - X2 g+1 |, when rand = 0.5, the new vector selection strategy is: X g+1 (3) = X g . Wherein, μ = 0.05 * rand.

[0175] Step 3.5: Determine whether the maximum number of iterations is reached, obtain the overall optimal solution, and determine the fault point position.

[0176] The method provided by the embodiment of the application aims at the problems of poor fault positioning accuracy and slow speed of complex distribution network, provides a complex distribution network fault positioning intelligent algorithm based on adaptive INFO algorithm, and solves the following problems: (1) the probability of misjudgment of the end of the distribution branch as a fault section is high due to traditional logical operation; (2) the fault positioning algorithm has poor fault tolerance; and (3) the global search ability and algorithm convergence speed of the INFO algorithm are effectively improved.

[0177] The method provided by the embodiment of the application improves the accuracy and fault tolerance of complex distribution network fault positioning by improving the fitness function of the algorithm; the algorithm has better optimization precision and faster convergence speed by enhancing the effective learning ability of individuals in the INFO algorithm and introducing an adaptive acceleration factor. Moreover, the method can be applied to both traditional distribution networks and complex distribution networks to meet the demand for fast and accurate positioning of faults.

[0178] Figure 8 A schematic diagram of a distribution network fault positioning device based on an adaptive weighted average algorithm provided by the embodiment of the application, the distribution network fault positioning device 800 based on the adaptive weighted average algorithm comprises:

[0179] The acquisition module 801 is configured to perform or implement the steps 401 in the above Figure 4 corresponding embodiments;

[0180] The processing module 802 is configured to perform or implement the steps 402, 403, 404, 405 and 406 in the above Figure 4 corresponding embodiments.

[0181] It should be noted that the information interaction, execution process and the like between the above devices / units are based on the same concept as the method embodiments of the application, and the specific functions and the technical effects brought by the method embodiments can be referred to the method embodiments part, which will not be described here.

[0182] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or software functional unit. In addition, the specific name of each functional unit and module is only for easy distinction, and does not limit the protection scope of the application. The specific working process of the unit and module in the above system can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0183] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to realize the steps in the above-mentioned method embodiments.

[0184] The embodiment of the present application provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal is caused to execute the steps in the above-mentioned method embodiments.

[0185] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on this understanding, the embodiment of the present application realizes all or part of the processes in the above-mentioned method embodiments, which can be completed by a computer program instructing related hardware. The computer program can be stored in a computer readable storage medium. The computer program is executed by a processor to realize the steps in the above-mentioned method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable file or some intermediate form. The computer readable medium at least includes any entity or device capable of carrying the computer program code to the photographing device / terminal equipment, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium. For example, U disk, mobile hard disk, magnetic disk or optical disk, etc. In some jurisdictions, according to legislation and patent practice, the computer readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0186] In the above embodiments, the description of each embodiment focuses on different aspects, and the parts not described or recorded in a certain embodiment can be referred to the relevant description of other embodiments.

[0187] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0188] In the embodiments provided in the present application, it should be understood that the disclosed apparatus / network device and method can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely schematic, for example, the division of the modules or units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0189] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0190] The above described embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for fault location in distribution networks based on an adaptive weighted average algorithm, characterized in that, include: Obtain the line operation status information of the distribution network to be tested; The suitability of each section is determined based on the line operation status information; Population initialization is based on an adaptive weighted average algorithm; Individuals that meet the preset rules in the current population are selected based on the fitness. Determine the population update rules based on the selected individuals and perform population updates accordingly; When the population is updated to the maximum number of iterations, the location of the distribution network fault point is determined based on the optimal solution of the population. The acquisition of the line operating status information of the distribution network to be tested includes: Obtain node current information uploaded by the feeder terminal unit (FTU); The state S of node j is determined according to the first formula. j ; The state S of segment B is determined according to the second formula. B ; The feeder operating status and node overcurrent information are determined based on the third formula; The first formula is: The second formula is: The third formula is: Among them, S j (X) represents the switching function of node j, X represents the feeder operating state variable, and N d x represents the number of downstream nodes of node j. j (n d ) indicates the operating status of the downstream segment of node j; The step of determining the fitness of each segment based on the line operation status information includes: The fitness of each segment is determined using the fourth formula based on the line operation status information. The fourth formula is: Among them, F it (S B S is the fitness of segment i in the t-th iteration; where S B For the section's operating status, S j For the switch function uploaded by the node, The expected execution function for each section in case of a fault. For the XOR operation, ω is the weighting coefficient, ω = 0.5, A is a random number between [0,1], and S... B [j,i] is the consistency factor between downstream segment i and upstream node j of the branch.

2. The method as described in claim 1, characterized in that, The step of determining the population update rule based on the selected individuals includes: Based on the selected individuals, the population renewal rule is determined using the fifth formula; The fifth formula is: Where r is a random number between 0 and 0.5, H P ε is the adaptive acceleration factor, and ε is the reinforcement learning factor. x n Let x be the average value of the vector positions, λ be the dilation constant, and x be the average value of the vector positions. a x b x c To randomly select three solution vectors in the solution space, x bs x ws x bt These are the best, worst, and better individuals in the current population determined by the fitness, respectively, where ξ is a constant and rand is a random number.

3. The method as described in claim 2, characterized in that, The reinforcement learning factor is calculated using the following formula: Where, r i The sorting result is the vector weights. x n Let λ be the average value of the vector positions, and λ be the inflation constant.

4. The method as described in claim 2, characterized in that, The adaptive acceleration factor is calculated using the following formula: Where, x bs x ws These are the best and worst individuals in the current population, determined by the fitness scores, respectively.

5. The method as described in claim 2, characterized in that, The specific steps of population renewal are as follows: X g+1 =X g +MeanRule+ξrand Among them, X g+1 For the updated population individuals, X g ξ represents the individuals in the population before the update, MeanRule represents the update rule, ξ is a constant, and rand is a random number.

6. A distribution network fault location device based on an adaptive weighted average algorithm, characterized in that, The method described in any one of claims 1-5 includes: The acquisition module is used to acquire the line operating status information of the distribution network to be tested; The processing module is used to determine the fitness of each segment based on the line operation status information; The processing module is also used to initialize the population based on an adaptive weighted average algorithm; The processing module is also used to select individuals in the current population that meet preset rules based on the fitness. The processing module is also used to determine the population update rules based on the selected individuals and to perform population update; The processing module is also used to determine the location of the power distribution network fault point based on the optimal solution of the population when the population is updated to the maximum number of iterations.

7. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 5.

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