Fault positioning method and device for power distribution network containing distributed power supply

Abstract

The invention relates to the technical field of electric power, in particular to a fault positioning method and device for a power distribution network containing distributed power sources, and the method comprises the steps: obtaining a fault current information set; the fault current information set comprises fault current information respectively collected by a plurality of feeder terminal units in the distributed power supply-containing power distribution network; calculating each piece of fault current information in the fault current information set according to an adaptive particle swarm algorithm, and determining a fault position in the power distribution network containing the distributed power supply; the adaptive particle swarm algorithm is an algorithm for optimizing the particle swarm algorithm according to an adaptive inertia weight and a dynamic adjustment learning factor. According to the invention, the position of the fault occurring in the power distribution network containing the distributed power supply can be quickly and accurately positioned.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of electric power technology, and more particularly to a method and device for locating a fault in a distribution network including a distributed power source. Background Art

[0002] With the rapid development of distribution networks containing distributed generation, the fault current information can be uploaded to the data acquisition and monitoring control system through the feeder terminal unit, and then the feeder automation system uses the positioning algorithm to locate the fault in the distribution network containing distributed generation.

[0003] Although there are many fault location algorithms for distribution networks containing distributed generation, these algorithms still have shortcomings such as local convergence and slow location speed. Therefore, the fault location of distribution networks containing distributed generation using existing location algorithms is slow and inaccurate.

[0004] Therefore, a method for quickly and accurately locating the fault location in a distribution network containing distributed generation sources needs to be studied urgently. Summary of the invention

[0005] The present application provides a method and device for locating a fault in a distribution network containing distributed power sources, which can quickly and accurately locate the location of a fault in the distribution network containing distributed power sources.

[0006] In a first aspect, a fault location method for a distribution network including a distributed power source is provided, wherein each section switch in the distribution network including the distributed power source is equipped with a feeder terminal unit, and the method comprises:

[0007] Acquire a fault current information set; the fault current information set includes fault current information collected by multiple feeder terminal units in a distributed power distribution network;

[0008] According to the adaptive particle swarm algorithm, each fault current information in the fault current information set is calculated to determine the fault location in the distribution network containing distributed power sources; the adaptive particle swarm algorithm is an algorithm for optimizing the particle swarm algorithm based on adaptive inertia weight and dynamic adjustment learning factor.

[0009] Optionally, the adaptive particle swarm algorithm is obtained by:

[0010] For each initialized particle, the historical optimal position of the particle is set as the initial position of the particle;

[0011] According to the adaptive inertia weight and the dynamically adjusted learning factor, the initial velocity and initial position corresponding to each particle are updated;

[0012] Calculate the current fitness value of each particle based on the updated speed and position corresponding to each particle;

[0013] For each particle, the current fitness value of the particle is compared with the optimal fitness value of the particle to determine whether to update the optimal fitness value of the particle; the optimal fitness value is calculated based on the historical optimal position and the speed corresponding to the particle at the historical optimal position;

[0014] If the optimal fitness value of a particle is updated, the optimal fitness value of the particle is updated to the current fitness value;

[0015] If the optimal fitness value of a particle is not updated, the above steps are repeated until the current fitness value of the particle is greater than the optimal fitness value of the particle, or the preset number of iterations is reached;

[0016] According to the current optimal fitness value corresponding to each particle, the speed and position corresponding to each particle are determined to complete the adaptive optimization of the particle swarm algorithm.

[0017] Optionally, the initial velocity and initial position corresponding to each particle are updated according to the adaptive inertia weight and the dynamically adjusted learning factor, including:

[0018] For each particle, the update speed of the particle is calculated based on the adaptive inertia weight, dynamic adjustment factor, the best position in the particle swarm, and the initial speed, initial position and historical best position of the particle; the best position is the position of the best particle in the particle swarm;

[0019] According to the adaptive inertia weight, the initial position of the particle and the update speed, the update position of the particle is calculated.

[0020] Optionally, before updating the initial velocity and initial position corresponding to each particle according to the adaptive inertia weight and the dynamically adjusted learning factor, the method further includes:

[0021] Determine the maximum and minimum values ​​of the adaptive inertia weight, and the maximum and minimum values ​​of the dynamically adjusted learning factor;

[0022] For each iterative calculation of the current fitness value of the particle, compare the current fitness value of each particle with the average fitness value; the average fitness is the average of the current fitness values ​​of all particles;

[0023] If the current fitness value of a particle is greater than the average fitness value, the adaptive inertia weight corresponding to the particle is determined as the maximum value of the adaptive inertia weight;

[0024] If the current fitness value of a particle is less than the average fitness value, the value corresponding to the adaptive inertia weight of the particle is obtained according to the maximum and minimum values ​​of the adaptive inertia weight, the current fitness value of the particle, the average fitness value, and the minimum fitness value of all particles;

[0025] For each iterative calculation of the current fitness value of the particle, the value corresponding to the dynamic adjustment learning factor is calculated according to the maximum and minimum values ​​of the dynamic adjustment learning factor, the current iteration number and the preset iteration number.

[0026] Optionally, before acquiring the fault current information set, the method further includes:

[0027] Encoding the fault current information collected by the feeder terminal units in each section according to a first preset encoding rule to obtain section state encoding information corresponding to each section in the distribution network containing distributed power sources;

[0028] Encode the fault current information passing through the sectionalizer according to the second preset encoding rule to obtain node state encoding information corresponding to each sectionalizer in the distributed power distribution network;

[0029] Accordingly, each fault current information in the fault current information set is calculated according to the adaptive particle swarm algorithm to determine the fault location in the distribution network containing distributed power sources, including:

[0030] Determine the fault current information corresponding to each section according to the section status coding information corresponding to each section;

[0031] For each section, the fault current information corresponding to the section is calculated according to the adaptive particle swarm algorithm to determine whether each section has a fault;

[0032] If a section fails, the section switch located in the section is determined based on the node status coding information;

[0033] For each sectionalizer located in the section, the fault current information corresponding to the sectionalizer is calculated according to the adaptive particle swarm algorithm to determine whether the sectionalizer has a fault.

[0034] Optionally, each fault current information in the fault current information set is calculated according to an adaptive particle swarm algorithm to determine the fault location occurring in the distribution network including the distributed power source, including:

[0035] For each sectionalizing switch, the sectionalizing switch is used as the dividing point to divide the distribution network containing the distributed generation into an upstream section and a downstream section;

[0036] Based on the adaptive particle swarm algorithm, the number of power sources in the upstream section, the number of feeder sections, the power coefficient, the state information of the feeder section between the section switch and the power source in the upstream section, and the state information of the feeder section in the downstream section are calculated to obtain the switching function corresponding to the upstream section;

[0037] Based on the adaptive particle swarm algorithm, the number of power sources in the downstream section, the number of feeder sections, the power coefficient, the state information of the feeder section between the section switch and the power source in the downstream section, and the state information of the feeder section in the upstream section are calculated to obtain the switching function corresponding to the upstream section;

[0038] For each segment switch, the switch function corresponding to the upstream segment and the switch function corresponding to the upstream segment are calculated based on the adaptive particle swarm algorithm to obtain the switch function corresponding to the segment switch;

[0039] According to the switch functions corresponding to the respective section switches in the distribution network containing the distributed generation, the access status results corresponding to the access of the respective section switches to the system power supply are determined.

[0040] Optionally, after calculating each fault current information in the fault current information set according to the adaptive particle swarm algorithm to determine the fault location occurring in the distribution network including the distributed power source, the method further includes:

[0041] For each sectional switch, the fitness function value is calculated based on the state information and quantity information of the feeder section, the actual fault current information collected by the feeder terminal unit, and the expected fault current information; the expected fault current information is calculated based on the switch function corresponding to the sectional switch;

[0042] According to the fitness function value, it is determined whether the actual fault current information collected by the feeder terminal unit is consistent with the expected fault current information;

[0043] If they match, it is determined that the result of judging the connection status of the segment switch to the system power supply according to the switch function is accurate;

[0044] If they do not match, the adaptive inertia weight and the dynamic adjustment learning factor are adjusted to make the expected fault current information calculated based on the switching function consistent with the actual fault current information collected by the feeder terminal unit.

[0045] In a second aspect, a fault locating device for a distributed power distribution network is provided, wherein each section switch in the distributed power distribution network is equipped with a feeder terminal unit, and the device comprises:

[0046] An acquisition module is used to acquire a fault current information set; the fault current information set includes fault current information collected by multiple feeder terminal units in a distributed power distribution network;

[0047] The fault location module is used to calculate each fault current information in the fault current information set according to the adaptive particle swarm algorithm to determine the fault location in the distribution network containing distributed power sources; the adaptive particle swarm algorithm is an algorithm that optimizes the particle swarm algorithm according to the adaptive inertia weight and the dynamic adjustment learning factor.

[0048] According to a third aspect, an electronic device is provided, comprising: a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method in the first aspect or its various implementations.

[0049] According to a fourth aspect, a computer-readable storage medium is provided for storing a computer program, wherein the computer program enables a computer to execute the method according to the first aspect or its various implementations.

[0050] Through the technical solution provided in the present application, an adaptive particle swarm algorithm optimized by adaptive inertia weight and dynamically adjusted learning factor is adopted to calculate each fault current information in the fault current information set, so as to quickly and accurately determine the fault location in the distribution network containing distributed power sources.

[0051] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 An application scenario diagram provided for an embodiment of the present application;

[0054] Figure 2 A flow chart of a method for locating a fault in a distribution network containing distributed power sources provided in an embodiment of the present application;

[0055] Figure 3 A flowchart of another method for locating a fault in a distribution network containing distributed power sources provided in an embodiment of the present application;

[0056] Figure 4 A schematic diagram of a fault location device for a distributed power distribution network provided in an embodiment of the present application;

[0057] Figure 5 It is a schematic block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0059] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application 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 interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0060] As mentioned above, with the rapid development of distribution networks containing distributed power sources, the fault current information can be uploaded to the data acquisition and monitoring control system (SCADA) through the feeder terminal unit (FTU), and then the feeder automation system uses the particle swarm algorithm to locate the fault section of the distribution network containing distributed power sources. However, the traditional particle swarm algorithm uses fixed inertia weights and learning factors, so the traditional particle swarm algorithm has the disadvantages of local convergence and slow positioning speed in locating the fault section of the distribution network containing distributed power sources.

[0061] In order to solve the above technical problems, the inventive concept of the present application is: obtaining a fault current information set; the fault current information set includes fault current information collected by multiple feeder terminal units in a distribution network containing distributed power sources; calculating each fault current information in the fault current information set according to an adaptive particle swarm algorithm to determine the fault location in the distribution network containing distributed power sources; the adaptive particle swarm algorithm is an algorithm that optimizes the particle swarm algorithm according to an adaptive inertia weight and a dynamically adjusted learning factor. The present application uses an adaptive particle swarm algorithm optimized by an adaptive inertia weight and a dynamically adjusted learning factor to calculate each fault current information in the fault current information set to quickly and accurately determine the fault location in the distribution network containing distributed power sources.

[0062] It should be understood that the technical solution of the present application can be applied to the following scenarios, but is not limited to:

[0063] In some possible implementations, Figure 1 An application scenario diagram provided for an embodiment of the present application, such as Figure 1 As shown, the application scenario may include an electronic device 110 and a network device 120. The electronic device 110 may establish a connection with the network device 120 via a wired network or a wireless network.

[0064] Exemplarily, the electronic device 110 may be a desktop computer, a laptop computer, a tablet computer, etc., but is not limited thereto. The network device 120 may be a terminal device or a server, but is not limited thereto. In one embodiment of the present application, the electronic device 110 may send a request message to the network device 120, and the request message may be used to request to obtain a fault current information set. Further, the electronic device 110 may receive a response message sent by the network device 120, and the response message includes obtaining a fault current information set.

[0065] also, Figure 1 An electronic device 110 and a network device 120 are provided as an example, but other numbers of electronic devices and network devices may actually be included, and the present application does not impose any limitation on this.

[0066] In other possible implementations, the technical solution of the present application may also be executed by the above-mentioned electronic device 110, or the technical solution of the present application may also be executed by the above-mentioned network device 120, and the present application does not impose any limitation on this.

[0067] After introducing the application scenarios of the embodiments of the present application, the technical solution of the present application will be described in detail below:

[0068] Figure 2 A flow chart of a method for locating a fault in a distributed power distribution network provided in an embodiment of the present application. The method can be performed as follows: Figure 1 The electronic device 110 shown in the figure performs, but is not limited to this. Here, each section switch in the distributed power distribution network is equipped with a feeder terminal unit, such as Figure 2 As shown, the method may include the following steps:

[0069] S210: Obtain a fault current information set.

[0070] Here, the fault current information set includes fault current information respectively collected by a plurality of feeder terminal units in the distributed power distribution network.

[0071] It should be noted that the distribution network containing distributed power sources is equipped with collection and monitoring equipment such as circuit breakers, section switches, and tie switches, so as to collect and monitor the fault current information of the equipment to determine the location of the fault.

[0072] S220. Calculate each fault current information in the fault current information set according to an adaptive particle swarm algorithm to determine a fault location in the distribution network containing distributed power sources.

[0073] Here, the adaptive particle swarm algorithm is an algorithm that optimizes the particle swarm algorithm based on the adaptive inertia weight and the dynamically adjusted learning factor. For the traditional standard particle swarm algorithm, its inertia weight is a given constant value; and the adaptive inertia weight proposed in this application is in the process of continuous iterative optimization. The adaptive inertia weight needs to change accordingly with the change of the current fitness value of the particle, so as to better balance the search speed of the particle and improve the overall optimization ability of the particle. At the beginning of the iteration, the adaptive inertia weight is guaranteed to be large, and as the number of iterations increases, the adaptive inertia weight gradually decreases, so as to coordinate the relationship between global optimization and iteration speed in the iterative process. At the same time, this application aims at the defect that the traditional standard particle swarm algorithm is prone to fall into local extreme values ​​in the early stage, and improves the particle swarm algorithm by introducing adaptive inertia weight and dynamically adjusted learning factors, improves the optimization ability, and improves the efficiency and accuracy of power grid fault location.

[0074] When a fault occurs in a distribution network containing distributed generation, the fault current information collected from the feeder terminal units located in each section is uploaded to the supervisory control system (SCADA), which then calculates the fault current information through an adaptive particle swarm algorithm to determine the fault section.

[0075] The method provided in this embodiment can solve the problems of slow convergence speed and low positioning accuracy of the traditional particle swarm optimization algorithm. It adopts an adaptive particle swarm algorithm optimized by adaptive inertia weight and dynamic adjustment learning factor to calculate each fault current information in the fault current information set, so as to quickly and accurately determine the fault location in the distribution network containing distributed power sources.

[0076] In some possible implementations, see Figure 3 , the adaptive particle swarm algorithm is obtained in the following way, which can include the following steps:

[0077] S310: For each initialized particle, set the historical optimal position of the particle as the initial position of the particle.

[0078] It can be understood that before all particles in the initialization particle swarm are initialized, the particle swarm will record the historical optimal position corresponding to each particle. Therefore, after all particles in the particle swarm are initialized, that is, after the speed and position of each particle in the particle swarm are initialized, the historical optimal position corresponding to each particle before initialization is set as the initial position corresponding to each particle.

[0079] S320, updating the initial velocity and initial position corresponding to each particle according to the adaptive inertia weight and the dynamically adjusted learning factor.

[0080] Here, the adaptive inertia weight and the dynamic adjustment learning factor are both variables, wherein the adaptive inertia weight can change with the current fitness value of the particle; the dynamic adjustment learning factor can be the weight approaching the historical optimal position of each particle and the optimal position in the particle group.

[0081] In this step, by selecting adaptive inertia weights, the particle search speed of particles can be better balanced and the overall particle optimization ability can be improved; by selecting dynamic adjustment learning factors, particles can be as close as possible to their own historical optimal positions and the best positions in the particle swarm, thereby enhancing the global optimization ability of the particle swarm algorithm.

[0082] S330. Calculate the current fitness value of each particle according to the updated speed and position corresponding to each particle.

[0083] S340: For each particle, compare the current fitness value of the particle with the optimal fitness value of the particle to determine whether to update the optimal fitness value of the particle.

[0084] Here, the optimal fitness value is calculated based on the historical optimal position and the speed corresponding to the particle at the historical optimal position.

[0085] It should be noted that when the current fitness value of a particle is greater than the optimal fitness value of the particle, it can be determined that the fitness of the particle is better, and the result of the determination is to update the optimal fitness value of the particle.

[0086] S350: If the optimal fitness value of a particle is updated, the optimal fitness value of the particle is updated to the current fitness value.

[0087] S360: If the optimal fitness value of a particle is not updated, steps S320 to S340 are repeatedly performed until the current fitness value of the particle is greater than the optimal fitness value of the particle, or the preset number of iterations is reached.

[0088] It should be noted that when step S320 is repeatedly executed, the adaptive inertia weight and the dynamic adjustment learning factor may be adjusted so that the current fitness value obtained according to the updated speed and position is close to or greater than the optimal fitness value, thereby reducing the number of iterations.

[0089] S370, according to the current optimal fitness value corresponding to each particle, determine the speed and position corresponding to each particle to complete the adaptive optimization of the particle swarm algorithm.

[0090] In step S360, if the current fitness value of a particle is still less than its corresponding optimal fitness value after a preset number of iterations, the current optimal fitness value corresponding to the particle is selected as the optimal fitness value corresponding to the particle.

[0091] It should be noted that when the current fitness value of a particle is much greater than the average fitness value of all particles in the population, it indicates that the adaptive particle swarm algorithm is in the early stage of the search or the current particle distribution is relatively dispersed. When the current fitness value of a particle is not much different from the average fitness value of all particles in the population, it indicates that the adaptive particle swarm algorithm is in the middle and late stages of the search or the current particle distribution is relatively concentrated.

[0092] By using the above method to optimize the particle swarm algorithm through adaptive inertia weight and dynamic adjustment of learning factor, the local optimization ability and speed of the adaptive particle swarm algorithm can be enhanced, and the global and local optimization ability of the adaptive particle swarm algorithm can be ensured.

[0093] In some possible implementations, updating the initial velocity and initial position corresponding to each particle according to the adaptive inertia weight and the dynamically adjusted learning factor may include the following steps:

[0094] S410, for each particle, calculate the update speed corresponding to the particle according to the adaptive inertia weight, the dynamic adjustment factor, the best position in the particle swarm, and the initial speed, initial position and historical best position corresponding to the particle.

[0095] The optimal position is the position of the optimal particle in the particle swarm; here, the optimal particle may be determined according to the historical optimal position corresponding to each particle in the particle swarm before initialization.

[0096] Among them, the update speed corresponding to the particle is calculated by the following formula:

[0097]

[0098] Where: For particles Update speed; The best position in history; is the best position in the particle swarm; For particles The initial velocity; For particles Corresponding position; is the adaptive inertia weight, where Decreases linearly with the number of iterations; and To dynamically adjust the learning factor, the value is usually 2; and is a random number uniformly distributed between [0,1].

[0099] S420, calculating the updated position corresponding to the particle according to the adaptive inertia weight, the initial position corresponding to the particle and the update speed.

[0100] Here, the updated position corresponding to the particle is calculated by the following formula:

[0101]

[0102]

[0103] In the formula, For particles The updated position of For particles Corresponding position; is the adaptive inertia weight; For particles The update speed.

[0104] By adopting the above method, the speed and position of each particle in the particle swarm can be adaptively updated to enhance the local optimization ability and speed of the adaptive particle swarm algorithm, thereby improving the global and local optimization ability of the adaptive particle swarm algorithm.

[0105] In some possible implementations, before updating the initial velocity and initial position corresponding to each particle according to the adaptive inertia weight and the dynamically adjusted learning factor, the following steps may also be included:

[0106] S510: Determine the maximum value and the minimum value of the adaptive inertia weight, and dynamically adjust the maximum value and the minimum value of the learning factor.

[0107] Here, the use of adaptive inertia weight can avoid premature convergence of the algorithm. Since the adaptive inertia weight can change dynamically with the current fitness value of the particle, the adaptive inertia weight has a corresponding numerical range. Therefore, the maximum and minimum values ​​of the adaptive inertia weight can be determined according to the current fitness value of the particle.

[0108] S520, for each iterative calculation of the current fitness value of the particle, compare the current fitness value corresponding to each particle with the average fitness value; the average fitness is the average value of the current fitness values ​​of all particles.

[0109] S530: If the current fitness value of a particle is greater than the average fitness value, the adaptive inertia weight corresponding to the particle is determined as the maximum value of the adaptive inertia weight.

[0110] S540: If the current fitness value of a particle is less than the average fitness value, the value corresponding to the adaptive inertia weight of the particle is obtained according to the maximum and minimum values ​​of the adaptive inertia weight, the current fitness value of the particle, the average fitness value, and the minimum fitness value of all particles.

[0111] It should be noted that the particle swarm will change dynamically at different stages of the adaptive particle swarm algorithm, so it is necessary to introduce an adaptive proportional selection strategy to calculate the probability of particles being selected at different stages. Using the adaptive proportional selection strategy, during the iterative evolution process, the value of the contraction factor is dynamically adjusted according to the distance between the particle position and the global optimum to achieve the control goal of adaptively and dynamically adjusting the adaptive inertia weight.

[0112] In the early stage of the adaptive particle swarm algorithm, the adaptive inertia weight is kept at a large value. Due to the randomness of the particle swarm, the minimum value of the adaptive inertia weight is far from the average fitness value, so the weight should be appropriately reduced to expand the search range and avoid premature search; in the middle stage, the value of the adaptive inertia weight is gradually reduced at a certain speed to gradually enhance its local search ability; in the later stage, the value of the adaptive inertia weight is small, and the smaller adaptive inertia weight value can perform a fine search on the area near the extreme value, so that the particles converge to the global optimal position, so the adaptive strategy can achieve control between the global search ability and the local search ability.

[0113] Among them, the calculation formula of the adaptive inertia weight corresponding to the particle is as follows:

[0114]

[0115] In the formula and are the minimum and maximum values ​​of the inertia weight respectively; is the average fitness value of all particles at the nth iteration; is the current fitness value of the particle at the nth iteration; is the minimum fitness value of all particles at the nth iteration; is the average fitness value of all particles at the nth iteration.

[0116] S550, for each iterative calculation of the current fitness value of the particle, calculate the value corresponding to the dynamic adjustment learning factor according to the maximum value and the minimum value of the dynamic adjustment learning factor, the current number of iterations and the preset number of iterations.

[0117] It should be noted that the dynamically adjusted learning factors include individual learning factors and social learning factors, and both individual learning factors and social learning factors are conducive to the optimization of the particle swarm algorithm. Here, according to the different requirements for individual learning factors and social learning factors in different optimization stages, dynamic adjustment parameters are introduced to change the individual learning factors and social learning factors in real time, so as to achieve a more accurate optimization effect.

[0118] Here, the calculation formulas for individual learning factor and social learning factor are as follows:

[0119]

[0120] Where: is the individual learning factor; is the social learning factor; , To randomly generate a random number between 0 and 1; and are the minimum and maximum values ​​of the individual learning factor, respectively; and are the minimum and maximum values ​​of the social learning factor, respectively; and They are the current number of iterations and the preset number of iterations respectively.

[0121] Here, the individual learning factor , social learning factor Represent the weights of approaching the individual optimum and the global optimum, respectively, where It reflects the learning of information from one’s own experience; It reflects the learning of the experience information of other particles in the particle swarm. In the early stage of particle swarm iteration, the significance of individual experience information is greater than that of global experience information; in the later stage of particle swarm iteration, particles should be ensured to converge quickly to the global optimum. Therefore, it should be ensured that Large, late iteration Larger.

[0122] In the particle optimization process of the algorithm, the number of iterations focusing on local optimization and global optimization is the same, ensuring the weight of local and global. and The value of can pay more attention to global optimization in the early stage to reduce the risk of falling into local optimality; in the middle stage, more attention is paid to local optimization to accelerate the convergence speed of the algorithm and make the whole optimization process more efficient; in the later stage of the algorithm, the global search ability of the algorithm is maximized through global optimization again, which is conducive to accurately finding the optimal solution.

[0123] In the particle swarm optimization process, in the search front Take the larger value, Taking a smaller value makes the particle as close to its historical optimal position as possible, thereby enhancing the algorithm's global optimization ability; in the latter part, it is just the opposite. Take the smaller value, Taking a larger value will make the particles approach the optimal position in the particle swarm and enhance the local optimization ability of the algorithm.

[0124] By adopting the above method, according to the current fitness value corresponding to the particle, the adaptive inertia weight is improved, and the dynamic adjustment learning factor is optimized, so as to further improve the optimization ability of the adaptive particle swarm algorithm, thereby improving the efficiency and accuracy of power grid fault location.

[0125] In some possible implementations, before obtaining the fault current information set, the following steps may also be included:

[0126] S610. Encode the fault current information collected by the feeder terminal units in each section according to a first preset encoding rule to obtain section status encoding information corresponding to each section in the distribution network containing distributed power sources.

[0127] Here, the first preset coding rule may include encoding the fault current information corresponding to each section according to the state information corresponding to each section.

[0128] The status information of segment A is used Take for example, where =1 means that the status information of segment A is a fault; =0 indicates that the status information of segment A is fault-free. Therefore, when encoding the fault current information corresponding to segment A, the fault current information corresponding to segment A is encoded as 1 or 0. Specifically, when the status information of segment A is a fault, the fault current information corresponding to segment A is encoded as 1; when the status information of segment A is fault-free, the fault current information corresponding to segment A is encoded as 0.

[0129] S620: Encode the fault current information passing through the sectionalizer according to a second preset encoding rule to obtain node state encoding information corresponding to each sectionalizer in the distribution network containing the distributed power source.

[0130] Here, the second preset encoding rule may include encoding the fault current information passing through the section switches according to the fault current flow state corresponding to each section switch.

[0131] Taking the flow state of the fault current through the section switch A as represented by lj as an example, wherein lj=1 indicates that the flow state of the fault current through the section switch A is that there is a fault current flowing; lj=0 indicates that the flow state of the fault current through the section switch A is that there is no fault current flowing. Therefore, when encoding the fault current information corresponding to the section switch A, the fault current information corresponding to the section switch A is encoded as 1 or 0. Specifically, when the fault current flow state of the section switch A is that there is a fault current flowing, the fault current information corresponding to the section switch A is encoded as 1; when the fault current flow state of the section switch A is that there is no fault current flowing, the fault current information corresponding to the section switch A is encoded as 0.

[0132] By adopting the above method, the fault current information at the feeder section and the section switch can be encoded before obtaining the fault current information set, so that when calculating the fault current information, the section and / or section switch where the fault current is located can be quickly determined, thereby achieving rapid positioning of the fault current.

[0133] Accordingly, calculating each fault current information in the fault current information set according to the adaptive particle swarm algorithm to determine the fault location occurring in the distribution network containing distributed power sources may include the following steps:

[0134] S710. Determine the fault current information corresponding to each section according to the section status coding information corresponding to each section.

[0135] Here, according to the section status coding information corresponding to each section, it is possible to facilitate identification of the fault current information corresponding to each section, so as to accelerate the positioning of the fault position in the distribution network containing distributed power sources.

[0136] S720. For each section, calculate the fault current information corresponding to the section according to the adaptive particle swarm algorithm to determine whether a fault occurs in each section.

[0137] S730: If a section fails, determine the section switch in the section according to the node status coding information.

[0138] S740. For each sectionalizing switch located in the section, calculate the fault current information corresponding to the sectionalizing switch according to the adaptive particle swarm algorithm to determine whether the sectionalizing switch fails.

[0139] By using the above method, after determining that a section has failed, the section switch located in the section is determined based on the node status coding information, and the fault current information corresponding to the section switch is calculated based on the adaptive particle swarm algorithm, so that the location of the section switch where the failure occurs can be quickly determined.

[0140] In some possible implementations, calculating each fault current information in the fault current information set according to the adaptive particle swarm algorithm to determine the fault location in the distribution network containing distributed power sources may include the following steps:

[0141] S810. For each sectionalizing switch, the distribution network containing the distributed generation is divided into an upstream section and a downstream section with the sectionalizing switch as a dividing point.

[0142] Since the structure of the distribution network containing distributed power sources is more complex, the direction of the short-circuit current on the distribution network containing distributed power sources is no longer single and constant, so the switching function of the traditional distribution network is no longer applicable. Therefore, the traditional switching function is redefined here, with the segmented switch as the dividing point, and the distribution network topology structure is divided into an upstream section and a downstream section according to the dividing point.

[0143] S820. Based on the adaptive particle swarm algorithm, the number of power sources in the upstream section, the number of feeder sections, the power coefficient, the status information of the feeder section between the segmented switch and the power source in the upstream section, and the status information of the feeder section in the downstream section are calculated to obtain the switching function corresponding to the upstream section.

[0144] Here, the switching function corresponding to the upstream section can be calculated by the following formula:

[0145]

[0146] Where: is the switching function corresponding to the upstream section; Status information of the feeder section between the section switch and the power source in the upstream section; Status information of the feeder section in the downstream section; is the power coefficient in the upstream section, which is 1 if there is power connected to the distribution network, otherwise it is 0; is the number of power sources in the upstream section; is the number of power sources in the downstream section.

[0147] S830. Based on the adaptive particle swarm algorithm, the number of power sources in the downstream section, the number of feeder sections, the power coefficient, the status information of the feeder section between the segmented switch and the power source in the downstream section, and the status information of the feeder section in the upstream section are calculated to obtain the switching function corresponding to the downstream section.

[0148] Here, the switching function corresponding to the downstream section can be calculated by the following formula:

[0149]

[0150] Where: is the switching function corresponding to the downstream section; Status information of the feeder section between the section switch and the power supply in the downstream section; Status information of the feeder section in the upstream section; is the power coefficient in the downstream section, which is 1 if there is power connected to the distribution network, otherwise it is 0; is the number of power sources in the downstream section; is the number of power supplies in the upstream section.

[0151] S840. For each segment switch, a switch function corresponding to the upstream segment and a switch function corresponding to the downstream segment are calculated based on an adaptive particle swarm algorithm to obtain a switch function corresponding to the segment switch.

[0152] Here, the switching function corresponding to the segmented switch can be calculated by the following formula:

[0153]

[0154] Where: is the switching function corresponding to the segment switch; is the switching function corresponding to the upstream section; is the switching function corresponding to the downstream section.

[0155] S850. Determine, according to the switch functions corresponding to the respective section switches in the distribution network including the distributed power source, the access status results corresponding to the access of the respective section switches to the system power source.

[0156] Here, by using the switching functions corresponding to each segmented switch, it is possible to determine whether there is a short-circuit current in the upstream and downstream areas of the segmented switch, and determine the access status results corresponding to the segmented switch being connected to the system power supply, so as to determine whether the segmented switch is the location where a fault occurs.

[0157] By adopting the above method, the switching function corresponding to the sectional switch can be redefined for the complex structure of the distribution network containing distributed power sources. That is, the sectional switch is used as the demarcation point, and the topological structure of the distribution network is divided into two parts according to the demarcation point. The switching functions corresponding to the upstream section and the downstream section are calculated, and then the switching function corresponding to the sectional switch is determined based on the switching function corresponding to the upstream section and the switching function corresponding to the downstream section. In this way, the access status results of each sectional switch to the system power supply can be determined for single and multiple faults in the multi-power distribution network.

[0158] In some possible implementations, after calculating each fault current information in the fault current information set according to the adaptive particle swarm algorithm and determining the fault location in the distribution network containing the distributed power source, the following steps may also be included:

[0159] S910. For each section switch, calculate a fitness function value according to the state information and quantity information of the feeder section, the actual fault current information collected by the feeder terminal unit, and the expected fault current information.

[0160] Here, the expected fault current information is calculated according to the switching function corresponding to the section switch.

[0161] The calculation formula of the fitness function value is as follows:

[0162]

[0163] Where: is the fitness function; The status information of the feeder section; is the actual fault current information, is the expected fault current information; is a judgment item, where is the quantity information of the feeder section, is the judgment coefficient, and its value range is ,here, is taken as 0.6.

[0164] S920. Determine, based on the fitness function value, whether the actual fault current information collected by the feeder terminal unit is consistent with the expected fault current information.

[0165] It should be noted that the size of the fitness function value can represent the quality of the access situation result obtained in step S850. The smaller the fitness function value, the more accurate the access situation result is, and the more it can be explained that the actual value uploaded by the feeder terminal unit is consistent with the expected value derived from the switching function; the minimum value of the fitness function corresponds to the global optimal solution of the access situation result.

[0166] Among them, when the fitness function value is less than the preset threshold, it can be determined that the actual fault current information collected by the feeder terminal unit is consistent with the expected fault current information; when the fitness function value is greater than the preset threshold, it is determined that the actual fault current information collected by the feeder terminal unit is inconsistent with the expected fault current information.

[0167] S930: If they are consistent, determine that the result of judging the connection status corresponding to the connection of the segment switch to the system power supply according to the switch function is accurate.

[0168] S940: If they are not consistent, the adaptive inertia weight and the dynamic adjustment learning factor are adjusted to make the expected fault current information obtained by calculating based on the switching function consistent with the actual fault current information collected by the feeder terminal unit.

[0169] In the case of inconsistency, the adaptive inertia weight and the dynamic adjustment learning factor are adjusted to make the expected fault current information calculated by the switching function calculated based on the adaptive particle swarm algorithm consistent with the actual fault current information, so as to ensure the accuracy of the access result of the segmented switch to the system power supply determined by the switching function calculated based on the adaptive particle swarm algorithm.

[0170] By adopting the above method, it is determined whether the actual fault current information collected by the feeder terminal unit is consistent with the expected fault current information according to the fitness function value, so as to determine whether the access situation result corresponding to the section switch being connected to the system power supply according to the switch function is accurate. When the access situation result corresponding to the section switch being connected to the system power supply according to the switch function is inaccurate, the adaptive inertia weight and the dynamic adjustment learning factor are adjusted to make the expected fault current information calculated by the switch function based on the adaptive particle swarm algorithm in step S850 consistent with the actual fault current information collected by the feeder terminal unit, thereby ensuring the accuracy of the final access situation result of the section switch being connected to the system power supply.

[0171] It can be seen that on the basis of the traditional particle swarm algorithm, this application adopts dynamic particle velocity and position update strategy, adaptive inertia weight and dynamic adjustment learning factor to improve the algorithm, effectively balancing the global and local search capabilities of the algorithm, and applying the improved particle swarm algorithm to the switching function for simulation testing, verifying the high efficiency and accuracy of the algorithm.

[0172] Figure 4 Schematic diagram of a fault location device for a distributed power distribution network according to an embodiment of the present invention. Each section switch in the distributed power distribution network is equipped with a feeder terminal unit, such as Figure 4 As shown, the device comprises:

[0173] The acquisition module 1010 is used to acquire a fault current information set; the fault current information set includes fault current information collected by multiple feeder terminal units in the distributed power distribution network;

[0174] The fault location module 1020 is used to calculate each fault current information in the fault current information set according to the adaptive particle swarm algorithm to determine the fault location in the distribution network containing distributed power sources; the adaptive particle swarm algorithm is an algorithm for optimizing the particle swarm algorithm according to the adaptive inertia weight and the dynamic adjustment learning factor.

[0175] In some possible implementations, the device further includes:

[0176] A current position setting module is used to set the current position of each initialized particle as the historical optimal position of the particle to determine the optimal position in the particle group;

[0177] The first updating module is used to update the speed and position corresponding to each particle according to the adaptive inertia weight and the dynamic adjustment learning factor;

[0178] A fitness value calculation module is used to calculate the current fitness value of each particle according to the updated speed and position corresponding to each particle;

[0179] The second updating module is used to compare the current fitness value of each particle with the optimal fitness value of the particle to determine whether to update the optimal fitness value of the particle; the optimal fitness value is calculated based on the historical optimal position and the speed corresponding to the particle at the historical optimal position;

[0180] A third updating module is used for updating the optimal fitness value of a particle to the current fitness value if the optimal fitness value of the particle is updated;

[0181] An iteration module, for repeating the above steps if the optimal fitness value of a particle is not updated, until the current fitness value of the particle is greater than the optimal fitness value of the particle, or a preset number of iterations is reached;

[0182] The speed and position determination module is used to determine the speed and position of each particle according to the current optimal fitness value of each particle, so as to complete the adaptive optimization of the particle swarm algorithm.

[0183] In some possible implementations, the first update module includes:

[0184] The speed update unit is used to calculate the update speed corresponding to each particle according to the adaptive inertia weight, the dynamic adjustment factor, the best position in the particle group, and the initial speed, initial position and historical best position of the particle; the best position is the position of the best particle in the particle group;

[0185] The position update unit is used to calculate the update position corresponding to the particle according to the adaptive inertia weight, the initial position corresponding to the particle and the update speed.

[0186] In some possible implementations, the device further includes:

[0187] A numerical determination module, used to determine the maximum and minimum values ​​of the adaptive inertia weight, and to dynamically adjust the maximum and minimum values ​​of the learning factor;

[0188] A comparison module is used to compare the current fitness value of each particle with the average fitness value for each iterative calculation of the current fitness value of the particle; the average fitness is the average value of the current fitness values ​​of all particles;

[0189] A first adaptive inertia weight determination module, configured to determine the adaptive inertia weight corresponding to a particle as the maximum adaptive inertia weight if the current fitness value of the particle is greater than the average fitness value;

[0190] The second adaptive inertia weight determination module is used to obtain the value corresponding to the adaptive inertia weight of a particle according to the maximum and minimum values ​​of the adaptive inertia weight, the current fitness value of the particle, the average fitness value, and the minimum fitness value of all particles if the current fitness value of a particle is less than the average fitness value;

[0191] The dynamic adjustment learning factor determination module is used to calculate the value corresponding to the dynamic adjustment learning factor according to the maximum and minimum values ​​of the dynamic adjustment learning factor, the current number of iterations and the preset number of iterations for each iterative calculation of the current fitness value of the particle.

[0192] In some possible implementations, the apparatus further includes:

[0193] A section state coding determination unit is used to encode the fault current information collected by the feeder terminal units in each section according to a first preset coding rule to obtain section state coding information corresponding to each section in the distribution network containing distributed power sources;

[0194] A node state coding determination unit, configured to encode the fault current information passing through the segment switch according to a second preset coding rule, and obtain node state coding information corresponding to each segment switch in the distribution network containing the distributed power source;

[0195] In some implementations, the fault location module 1020 includes:

[0196] A fault current information determining unit, used to determine the fault current information corresponding to each section according to the section state coding information corresponding to each section;

[0197] The section fault judgment unit is used to calculate the fault current information corresponding to each section according to the adaptive particle swarm algorithm to determine whether each section has a fault;

[0198] A section switch determination unit, for determining a section switch located in a section according to the node status coding information if a section fails;

[0199] The section switch fault judgment unit is used to calculate the fault current information corresponding to each section switch located in the section according to the adaptive particle swarm algorithm to determine whether the section switch has a fault.

[0200] In some implementations, the fault location module 1020 includes:

[0201] A section division unit is used for dividing the distribution network containing the distributed power source into an upstream section and a downstream section for each section switch, taking the section switch as a dividing point;

[0202] A first switch function calculation unit is used to calculate the number of power sources in the upstream section, the number of feeder sections, the power coefficient, the state information of the feeder section between the section switch and the power source in the upstream section, and the state information of the feeder section in the downstream section based on an adaptive particle swarm algorithm to obtain a switch function corresponding to the upstream section;

[0203] The second switch function calculation unit is used to calculate the number of power sources in the downstream section, the number of feeder sections, the power coefficient, the state information of the feeder section between the sectional switch and the power source in the downstream section, and the state information of the feeder section in the upstream section based on the adaptive particle swarm algorithm to obtain the switch function corresponding to the downstream section;

[0204] A third switch function calculation unit is used to calculate the switch function corresponding to the upstream section and the switch function corresponding to the downstream section for each segment switch based on the adaptive particle swarm algorithm to obtain the switch function corresponding to the segment switch;

[0205] The access situation result determination unit is used to determine the access situation result corresponding to the access of each section switch to the system power supply according to the switch function corresponding to each section switch in the distribution network containing distributed power sources.

[0206] In some possible implementations, the device further includes:

[0207] The fitness function value calculation module is used to calculate the fitness function value for each section switch according to the state information and quantity information of the feeder section, the actual fault current information collected by the feeder terminal unit, and the expected fault current information; the expected fault current information is calculated according to the switch function corresponding to the section switch;

[0208] A fitness function value judgment module is used to judge whether the actual fault current information collected by the feeder terminal unit is consistent with the expected fault current information according to the fitness function value;

[0209] A result determination module is used to determine that the result of judging the connection status of the segment switch to the system power supply according to the switch function is accurate if the result is consistent;

[0210] The adjustment module is used to adjust the adaptive inertia weight and the dynamic adjustment learning factor if they are not consistent, so that the expected fault current information calculated based on the switching function is consistent with the actual fault current information collected by the feeder terminal unit.

[0211] It should be understood that the embodiments of the fault location device for a distribution network containing distributed power sources and the embodiments of the fault location method for a distribution network containing distributed power sources may correspond to each other, and similar descriptions may refer to the embodiments of the fault location method for a distribution network containing distributed power sources. To avoid repetition, they will not be described here. Specifically, Figure 4 The device shown can execute the above-mentioned embodiment of the fault location method for a distribution network containing distributed power sources, and the aforementioned and other operations and / or functions of each module in the device are respectively for realizing the corresponding processes in the above-mentioned fault location method for a distribution network containing distributed power sources. For the sake of brevity, they will not be repeated here.

[0212] In the above, the device of the embodiment of the present invention is described from the perspective of the functional module in conjunction with the accompanying drawings. It should be understood that the functional module can be implemented in hardware form, can be implemented by instructions in software form, and can also be implemented by a combination of hardware and software modules. Specifically, the steps of the fault location method and detection method embodiment of the distributed power distribution network in the embodiment of the present invention can be completed by the hardware integrated logic circuit and / or software instructions in the processor, and the steps of the fault location method and detection method of the distributed power distribution network disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor to execute, or a combination of hardware and software modules in the decoding processor. Optionally, the software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory, and combines its hardware to complete the steps in the above-mentioned fault location method and detection method embodiment of the distributed power distribution network.

[0213] Figure 5 is a schematic block diagram of an electronic device 110 according to an embodiment of the present invention.

[0214] like Figure 5 As shown, the fault location device 110 of the distributed power distribution network may include:

[0215] The memory 111 and the processor 112, the memory 111 is used to store the computer program and transmit the program code to the processor 112. In other words, the processor 112 can call and run the computer program from the memory 111 to implement the method in the embodiment of the present invention.

[0216] For example, the processor 112 may be configured to execute the above method embodiments according to instructions in the computer program.

[0217] In some embodiments of the present invention, the fault location device 110 of the distributed power distribution network may include but is not limited to:

[0218] General-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0219] In some embodiments of the present invention, the memory 111 includes but is not limited to:

[0220] Volatile memory and / or non-volatile memory. Among them, the non-volatile memory can be read-only memory (ROM), programmable ROM (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM) or flash memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct memory bus random access memory (Direct Rambus RAM, DR RAM).

[0221] In some embodiments of the present invention, the computer program may be divided into one or more modules, which are stored in the memory 111 and executed by the processor 112 to complete the method provided by the present invention. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the controller.

[0222] like Figure 5 As shown, the fault location device 110 for the distributed power distribution network may also include:

[0223] The transceiver 113 may be connected to the processor 112 or the memory 111 .

[0224] The processor 112 may control the transceiver 113 to communicate with other devices, specifically, to send information or data to other devices, or to receive information or data sent by other devices. The transceiver 113 may include a transmitter and a receiver. The transceiver 113 may further include an antenna, and the number of antennas may be one or more.

[0225] It should be understood that the various components in the fault location device for the distributed power distribution network are connected via a bus system, wherein the bus system includes a power bus, a control bus and a status signal bus in addition to a data bus.

[0226] The present invention also provides a computer storage medium having a computer program stored thereon, which, when executed by a computer, enables the computer to perform the method of the above method embodiment. In other words, an embodiment of the present invention also provides a computer program product containing instructions, which, when executed by a computer, enables the computer to perform the method of the above method embodiment.

[0227] When software is used for implementation, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integration. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (Digital Video Disc, DVD)), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0228] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0229] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the module is only a logical function division. There may be other division methods in actual implementation, such as multiple modules 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 devices or modules, which can be electrical, mechanical or other forms.

[0230] The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. For example, each functional module in each embodiment of the present application may be integrated into a processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0231] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in this application, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for locating a fault in a distribution network containing distributed power sources, characterized in that: A feeder terminal unit is installed at each section switch in the distribution network containing distributed power sources, and the method comprises: Acquire a fault current information set; the fault current information set includes fault current information respectively collected by a plurality of feeder terminal units in the distribution network containing distributed power sources; The individual fault current information in the fault current information set is calculated according to an adaptive particle swarm algorithm to determine the fault location in the distribution network containing distributed power sources; the adaptive particle swarm algorithm is an algorithm for optimizing the particle swarm algorithm according to an adaptive inertia weight and a dynamically adjusted learning factor.

2. The method according to claim 1, characterized in that The adaptive particle swarm algorithm is obtained by the following method, including: For each initialized particle, setting the historical optimal position of the particle as the initial position of the particle; According to the adaptive inertia weight and the dynamically adjusted learning factor, the initial velocity and initial position corresponding to each of the particles are updated; Calculate the current fitness value of each particle according to the updated speed and position corresponding to each particle; For each particle, a current fitness value of the particle is compared with an optimal fitness value of the particle to determine whether to update the optimal fitness value of the particle; the optimal fitness value is calculated based on the historical optimal position and the speed corresponding to the particle at the historical optimal position; If the optimal fitness value of a particle is updated, the optimal fitness value of the particle is updated to the current fitness value; If the optimal fitness value of a particle is not updated, the above steps are repeated until the current fitness value of the particle is greater than the optimal fitness value of the particle, or the preset number of iterations is reached; According to the current optimal fitness value corresponding to each particle, the speed and position corresponding to each particle are determined to complete the adaptive optimization of the particle swarm algorithm.

3. The method according to claim 2, characterized in that The updating of the initial velocity and initial position corresponding to each of the particles according to the adaptive inertia weight and the dynamically adjusted learning factor includes: For each particle, an update speed corresponding to the particle is calculated according to the adaptive inertia weight, the dynamic adjustment factor, the best position in the particle swarm, and the initial speed, initial position and historical best position corresponding to the particle; the best position is the position of the best particle in the particle swarm; The updated position corresponding to the particle is calculated according to the adaptive inertia weight, the initial position corresponding to the particle and the update speed.

4. The method according to claim 2, characterized in that: Before updating the initial velocity and initial position corresponding to each of the particles according to the adaptive inertia weight and the dynamically adjusted learning factor, the method further includes: Determining a maximum value and a minimum value of the adaptive inertia weight, and a maximum value and a minimum value of the dynamically adjusted learning factor; For each iterative calculation of the current fitness value of the particles, compare the current fitness value corresponding to each particle with the average fitness value; the average fitness is the average value of the current fitness values ​​of all the particles; If the current fitness value of a particle is greater than the average fitness value, the adaptive inertia weight corresponding to the particle is determined as the maximum value of the adaptive inertia weight; If the current fitness value of a particle is less than the average fitness value, the value corresponding to the adaptive inertia weight corresponding to the particle is obtained according to the maximum value and the minimum value of the adaptive inertia weight, the current fitness value of the particle, the average fitness value, and the minimum fitness value of all the particles; For each iterative calculation of the current fitness value of the particle, a value corresponding to the dynamic adjustment learning factor is calculated according to the maximum value and the minimum value of the dynamic adjustment learning factor, the current iteration number and the preset iteration number.

5. The method according to claim 1, characterized in that Before obtaining the fault current information set, the method further includes: Encoding the fault current information collected by the feeder terminal units in each section according to a first preset coding rule to obtain section state coding information corresponding to each section in the distribution network containing distributed power sources; Encode the fault current information passing through the segment switch according to a second preset encoding rule to obtain node state encoding information corresponding to each segment switch in the distribution network containing distributed power sources; Accordingly, each fault current information in the fault current information set is calculated according to the adaptive particle swarm algorithm to determine the fault location occurring in the distribution network containing the distributed power source, including: Determining the fault current information corresponding to each of the sections according to the section status coding information corresponding to each of the sections; For each of the sections, the fault current information corresponding to the section is calculated according to the adaptive particle swarm algorithm to determine whether a fault occurs in each of the sections; If a failure occurs in one of the sections, determining the section switch located in the section according to the node status coding information; For each sectionalizer located in the section, fault current information corresponding to the sectionalizer is calculated according to the adaptive particle swarm algorithm to determine whether the sectionalizer has a fault.

6. The method according to claim 1, characterized in that Calculating each fault current information in the fault current information set according to an adaptive particle swarm algorithm to determine the fault location occurring in the distribution network including the distributed power source, including: For each of the sectionalizing switches, the distribution network containing the distributed power source is divided into an upstream section and a downstream section with the sectionalizing switch as a dividing point; Based on the adaptive particle swarm algorithm, the number of power sources in the upstream section, the number of feeder sections, the power coefficient, the state information of the feeder section between the section switch and the power source in the upstream section, and the state information of the feeder section in the downstream section are calculated to obtain the switching function corresponding to the upstream section; Based on the adaptive particle swarm algorithm, the number of power sources in the downstream section, the number of feeder sections, the power coefficient, the state information of the feeder section between the section switch and the power source in the downstream section, and the state information of the feeder section in the upstream section are calculated to obtain the switching function corresponding to the downstream section; For each of the segmented switches, a switch function corresponding to the upstream segment and a switch function corresponding to the downstream segment are calculated based on the adaptive particle swarm algorithm to obtain a switch function corresponding to the segmented switch; According to the switch functions corresponding to the respective sectional switches in the distribution network containing the distributed power source, the access status results corresponding to the access of the respective sectional switches to the system power source are determined.

7. The method according to claim 6, characterized in that After calculating each fault current information in the fault current information set according to the adaptive particle swarm algorithm to determine the fault location in the distribution network containing the distributed power source, the method further includes: For each of the sectionalized switches, a fitness function value is calculated according to the state information and quantity information of the feeder section, the actual fault current information collected by the feeder terminal unit, and the expected fault current information; the expected fault current information is calculated according to the switch function corresponding to the sectionalized switch; According to the fitness function value, determining whether the actual fault current information collected by the feeder terminal unit is consistent with the expected fault current information; If they are consistent, it is determined that the result of judging the connection status of the segment switch to the system power supply according to the switch function is accurate; If they do not match, the adaptive inertia weight and the dynamic adjustment learning factor are adjusted to make the expected fault current information calculated based on the switching function consistent with the actual fault current information collected by the feeder terminal unit.

8. A fault location device for a distribution network containing distributed power sources, characterized in that: A feeder terminal unit is installed at each section switch in the distribution network containing distributed power sources, and the device includes: An acquisition module, used for acquiring a fault current information set; the fault current information set includes fault current information respectively collected by a plurality of feeder terminal units in the distribution network containing distributed power sources; A fault location module is used to calculate each fault current information in the fault current information set according to an adaptive particle swarm algorithm to determine the fault location in the distribution network containing distributed power sources; the adaptive particle swarm algorithm is an algorithm that optimizes the particle swarm algorithm according to an adaptive inertia weight and a dynamically adjusted learning factor.

9. An electronic device, characterized in that: include: A processor and a memory, the memory being used to store a computer program, and the processor being used to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program, wherein the computer program enables a computer to execute the method according to any one of claims 1 to 7.

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