Distribution network power line fault positioning method

Abstract

The invention discloses a distribution network power line fault positioning method, which comprises the following steps of S1, triggering a power failure fault positioning function, determining a to-be-detected target range, and obtaining a distribution network topology of the to-be-detected target range; s2, a historical database is introduced, and an evaluation state data set is obtained; s3, acquiring meteorological data by using a meteorological database, and acquiring an evaluation state grade index; s4, constructing an ant colony model; s5, constructing an ant colony matching function, and generating an ant colony matching matrix based on the evaluation state data set and the evaluation state grade index; s6, adjusting the putting proportion of the ant colony in the ant colony model according to the ant colony proportion matrix; step S7, obtaining a final pheromone matrix in the ant colony model, wherein the maximum value of the elements is a power failure fault point; therefore, the dynamic ant colony putting strategy is constructed by combining the historical operation data and the environmental factors, the fault positioning efficiency and accuracy are remarkably improved, and meanwhile the computing resource consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power fault detection, and in particular to a method for locating a distribution network power line fault. Background Art

[0002] In recent years, the distribution network, as the "last mile" of the power system, has the characteristics of complex structure, numerous branches, and changeable operating environment. For example, the mixed networking of urban underground cables or rural overhead lines leads to multiple challenges in fault location.

[0003] At present, in the related technology with the publication number CN105067956A, a method for locating distribution network faults based on ant colony algorithm is disclosed. It includes the construction of the evaluation function of the ant colony algorithm; sampling the ant colony algorithm for fault location. When using the ant colony algorithm for fault location, the evaluation function is the basis for evaluating the performance of the solution, and good performance means that the selected "path is short"; and diagnosing the equipment that has failed in the distribution network is to find a hypothesis that can best explain all uploaded RTU or FTU information, that is, to find a hypothesis that minimizes the deviation between the corresponding FTU or RTU information and the actual uploaded information, so as to realize the fault location of the distribution network. This method obtains FTU information, establishes a set of scientific and systematic analysis methods to locate the fault point, and obtains the accurate fault location of the distribution network, which changes the rough judgment method of manual line inspection in the past.

[0004] However, when the existing ant colony algorithm is applied to distribution network fault location, since the deployment of the ant colony is randomly generated, in the case of complex lines, some ants find the fault point only after traversing the entire distribution network topology line; therefore, when locating distribution network faults, a large amount of computing power is required for invalid calculations, which not only wastes computing power, but also is not fast enough in positioning time. Summary of the invention

[0005] The present invention provides a distribution network power line fault location method, which improves fault location efficiency, reduces computing resource consumption, and enhances location accuracy in complex environments by optimizing the ant allocation mechanism of an ant colony algorithm.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A method for locating a distribution network power line fault comprises the following steps:

[0008] Step S1, triggering the power outage fault location function, determining the target range to be measured, and obtaining the distribution network topology of the target range to be measured;

[0009] Step S2, introducing a historical database, and performing a first processing on the distribution network topology to obtain an evaluation status data set;

[0010] Step S3: Obtain meteorological data using the meteorological database, and perform a second processing on the distribution network topology to obtain an evaluation status level index;

[0011] Step S4: Construct an ant colony model and establish an effective mapping between the ant colony model and the distribution network topology;

[0012] Step S5: Construct an ant colony ratio function, and generate an ant colony ratio matrix based on the evaluation status data set and the evaluation status level index;

[0013] Step S6: Adjust the placement ratio of the ant colonies in the ant colony model according to the ant colony ratio matrix;

[0014] Step S7: Use the maximum number of iterations of the ant colony model as the termination condition to obtain the final pheromone matrix in the ant colony model, and the maximum value of the elements is the power outage fault point.

[0015] Preferably, in step S1, the method for obtaining the distribution network topology includes:

[0016] Step 101: Introduce a GIS platform, determine the range of the target to be measured, and input the geographical information of the transmission towers and transmission lines within the range of the target to be measured;

[0017] Step 102: Correspondingly construct a distribution network topology according to the geographical information of the transmission towers and transmission lines.

[0018] Preferably, in step S2, the method for performing the first processing to obtain the evaluation status data set includes:

[0019] Step S201: Generate corresponding identification codes for each node and line in the distribution network topology according to the information conversion table;

[0020] Step S202: For each node and line in the distribution network topology, obtain whether it is an elevated route, whether it is on the migration route of birds and animals, and whether it belongs to a high-light or extreme temperature area according to the identification code in the historical database;

[0021] Step S203: For each node and line in the distribution network topology, obtain the corresponding production year according to the identification code in the historical database [picture];

[0022] Step S204: Perform a status evaluation on each node and line in the distribution network topology according to the information of whether it is an elevated route, whether it is on the migration route of birds and animals, whether it belongs to a high-light or extreme temperature area, and the corresponding production year, and generate a corresponding evaluation status data set.

[0023] Preferably, in step S3, the method for performing the second processing to obtain the evaluation status level index includes:

[0024] Step S301: Extract the location data of each node and line in the distribution network topology;

[0025] Step S302: According to the above location data, obtain the meteorological information corresponding to each node and line in the distribution network topology from the meteorological database;

[0026] Step S303: According to the meteorological grade table, judge the danger levels of rain, snow, and hail, judge the danger level of thunderstorms, judge the danger level of bird and animal migrations, and generate the evaluation status level index corresponding to each node and line in the distribution network topology.

[0027] Preferably, in step S5, generating the ant colony ratio matrix includes the following steps:

[0028] Step S501: Extract the evaluation status data set and evaluation status level index of each node and line in the distribution network topology;

[0029] Step S502: Determine the coefficients of each parameter in the ant colony ratio function according to the evaluation status data set;

[0030] Step S503: Substitute the evaluation status level index as a parameter into the ant colony ratio function to generate the ant colony ratio matrix correspondingly.

[0031] Preferably, in step S5, the ant colony ratio function is as follows:

[0032] F = ω1f1 + ω2f2 + ω3f3;

[0033] Among them, F is the ant colony ratio index, ω1 is whether it is an elevated laying route, ω2 is whether it is a migration route of birds and animals, ω3 is the aging level; f1 is the thunderstorm danger level, f2 is the bird and animal migration danger level, f3 is the rain, snow, and hail danger level.

[0034] Preferably, the formula for the aging level is as follows:

[0035] ω3 = (T + L)p;

[0036] Among them, T is whether it is an extreme temperature area, L is whether it is a high-light area, p is the production year.

[0037] In step S5, the calculation formula for the bird and animal migration danger level f2 is as follows:

[0038]

[0039] Among them, D1 is the current time, D2 is the regular migration time of birds and animals; M is the estimated quantity level of the nearby bird and animal community; η1 is the exponential scaling coefficient.

[0040] Preferably, in step S5, when f3 is not equal to zero, a decay coefficient is added to the multiplication area of f2, and its calculation formula is as follows:

[0041]

[0042] where η2 is the amplification factor.

[0043] Preferably, the steps for adjusting the deployment ratio of the ant colony in the ant colony model in step S6 are as follows;

[0044] S601, extract the ant colony ratio indexes of each node and line in the distribution network topology;

[0045] S602, calculate the ant colony ratio probabilities corresponding to each node and line in the distribution network topology according to the ant colony ratio probability function;

[0046] S603, summarize the ant colony ratio probabilities to generate an ant colony ratio matrix.

[0047] Advantages of the present invention:

[0048] As can be seen from the above, a distribution network power line fault location method provided by the present application constructs a dynamic ant colony deployment strategy by combining historical operation data and environmental factors, optimizes the process of generating the pheromone matrix, significantly improves the fault location efficiency and accuracy, and at the same time reduces the consumption of computing resources. Description of the drawings

[0049] Figure 1 It is a schematic diagram of the basic process of a distribution network power line fault location method provided by an embodiment of the present invention. Detailed implementation manners

[0050] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments.

[0051] In the prior art, as the end network of the power system, the distribution network has the characteristics of numerous branches and complex operating environments. The mixed networking method of urban underground cables and rural overhead lines makes fault location face challenges such as variable topological structures and complex environmental interference factors. Existing fault location methods based on the ant colony algorithm, although they achieve fault point judgment by simulating the ant path search mechanism, due to the random deployment of the ant colony during algorithm initialization, a large number of invalid path traversals are likely to occur in complex line scenarios, resulting in waste of computing resources and a decrease in location efficiency. Especially in areas with frequent thunderstorms or extreme climates, traditional methods are difficult to dynamically adapt to changes in line conditions, resulting in limited fault location response speed.

[0052] To solve the above problems, the R & D personnel found that the random placement mechanism of the existing ant colony algorithm is the key reason for the low efficiency. By analyzing the operation data of the distribution network, it is found that there is a significant correlation between the degree of line aging, the environmental risk level and the fault probability. From this, a technical concept is generated: if the potential risk level of the line can be pre-evaluated and the ant colony placement strategy can be dynamically adjusted accordingly, the search direction can be effectively guided. Further, the line risk is dynamically corrected by combining meteorological data, and a multi-dimensional risk assessment system is established. Finally, a technical route is formed to quantify the equipment status through the historical database, dynamically correct the risk level by meteorological data, and then construct the ant colony placement weight matrix.

[0053] Referring to Figure 1 , therefore, this application proposes a method for fault location of distribution network power lines, including the following steps:

[0054] Step S1, trigger the power outage fault location function, determine the range of the target to be measured, and obtain the topology of the distribution network within the range of the target to be measured;

[0055] Step S2, introduce the historical database, and perform a first processing on the distribution network topology to obtain an evaluation status data set;

[0056] Step S3, use the meteorological database to obtain meteorological data, and perform a second processing on the distribution network topology to obtain an evaluation status level index;

[0057] Step S4, construct an ant colony model, and establish an effective mapping between the ant colony model and the distribution network topology;

[0058] Step S5, construct an ant colony ratio function, and generate an ant colony ratio matrix based on the evaluation status data set and the evaluation status level index;

[0059] Step S6, adjust the placement ratio of the ant colonies in the ant colony model according to the ant colony ratio matrix;

[0060] Step S7, take the maximum number of iterations of the ant colony model as the termination condition, and obtain the final pheromone matrix in the ant colony model, where the maximum value of the elements is the power outage fault point.

[0061] Among them, the distribution network topology refers to the network structure model including the connection relationships of transmission towers and transmission lines, which can be specifically constructed using the geographic information data of the GIS platform and is used to establish the correspondence between the physical connection of lines and the algorithm search space. Specifically, the evaluation status data set is the comprehensive evaluation result of static factors such as line aging and environmental risks through historical operation and maintenance data, and can specifically include parameters such as the production year of equipment and the elevated laying status, and is used to quantify the basic risk level of the line. The evaluation status level index is the correction parameter for the dynamic risk of the line through real-time meteorological data, and can specifically include the thunderstorm level and extreme temperature impact factor, and is used to reflect the real-time impact of climate conditions on the failure probability. The ant colony ratio matrix is a weight distribution model generated based on the results of static evaluation and dynamic correction. Specifically, the risk level can be converted into the ant colony placement density through a probability function, and is used to direct the search path.

[0062] Specifically, when the system detects a power outage fault, first, the connection relationship of the lines in the target area is obtained through the GIS platform to form a topology model. After the equipment file data in the historical database is processed, the basic evaluation indicators reflecting the line aging degree and environmental exposure risk, that is, the evaluation status, are generated. After the real-time rainfall and thunderstorm data provided by the meteorological platform are classified, the dynamic risk correction parameters, that is, the evaluation status level index, are generated. By constructing an ant colony ratio function, the evaluation status and the evaluation status level index are fused and calculated to form the ant colony placement probability matrix of each line segment. During the iteration process of the ant colony algorithm, the ants will preferentially search along the line paths with high placement probabilities, and there is a high probability of quickly locating the fault node, meeting the termination conditions of the ants, greatly reducing the traversal paths of single ants, and quickly accumulating the pheromone concentration in the potential fault area. After a preset number of iterations, the node with the highest pheromone concentration is determined as the fault point.

[0063] Compared with the prior art, the traditional method adopts an ant colony placement strategy with equal probability and cannot distinguish the line risk differences. This solution quantifies the historical operation and maintenance data and real-time meteorological information to establish a risk-oriented ant colony placement mechanism, enabling the algorithm to preferentially search the high-risk line areas and effectively reducing the number of traversals of invalid paths.

[0064] Through the above technical solutions, this application can dynamically optimize the search strategy according to the inherent risks of the lines and real-time environmental factors, significantly shortening the fault location time in complex distribution network scenarios. By directing the ant colony search path, it reduces the invalid calculation consumption in low-risk areas and improves the algorithm convergence speed. When encountering extreme climate events, it ensures the positioning accuracy by dynamically adjusting the risk parameters and enhances the adaptability of the system to complex operating environments.

[0065] The present application further proposes a method for obtaining the distribution network topology, which specifically includes: step 101, introducing a GIS platform, determining the target range to be measured, and entering the geographic information of the transmission towers and transmission lines within the target range to be measured; step 102, constructing the distribution network topology accordingly according to the geographic information of the transmission towers and transmission lines.

[0066] That is, the present invention determines the target range to be measured by introducing a geographic information system platform, inputs the geographic information of the transmission towers and transmission lines within the target range, and constructs the distribution network topology based on the geographic information.

[0067] Among them, the geographic information system platform refers to a digital platform that integrates spatial data collection, storage and processing. It can be implemented using commercial software such as ArcGIS or SuperMap, and the longitude and latitude information of the transmission equipment can be obtained by calling its geographic coordinate analysis module. The geographic information of transmission towers and transmission lines includes the coordinate location, altitude and connection relationship of the equipment, which can be collected through the global positioning system or lidar scanning technology. The construction of the distribution network topology refers to the conversion of discrete transmission equipment into a network model of nodes and edges based on the spatial position relationship. Specifically, the graph theory algorithm is used to automatically connect the coordinates of adjacent equipment to form topological edges, realizing the mapping of physical lines to logical structures.

[0068] Specifically, the GIS platform first defines the boundaries of the area to be tested, for example, using administrative divisions or power divisions as range screening conditions. The coordinate data of the transmission towers within the target range is imported through the geographic information interface, and the transmission line path information is extracted through the line feature layer. Topological nodes are established based on the coordinate data, and edges are generated according to the line connection relationship. For example, the lines between adjacent transmission towers automatically form topological edges. This process uses spatial analysis algorithms to verify the accuracy of the connection, such as determining the subordinate relationship between the transmission tower and the line through buffer analysis to ensure that the topological structure is consistent with the actual power grid distribution.

[0069] Through the above technical solution, this application realizes accurate modeling of the distribution network topology, ensuring that the ant colony algorithm deployment path fully matches the actual line distribution. By eliminating topological structure errors and reducing the number of invalid path searches, the computational efficiency of the fault location algorithm is effectively improved, while reducing the risk of misjudgment caused by model deviation.

[0070] This application further proposes a method for obtaining an evaluation status dataset:

[0071] Step S201, generating corresponding identification codes for each node and line in the distribution network topology according to the information conversion table;

[0072] Step S202, each node and line in the distribution network topology obtains information in the historical database based on the identification code, whether it is an elevated route, whether it is in the migration path of birds and animals, and whether it belongs to a high light or extreme temperature area;

[0073] Step S203: Each node and line in the distribution network topology obtain their corresponding production years in the historical database according to the identification codes.

[0074] Step S204: Based on the information of whether the route is overhead, whether it is on the migration route of birds and animals, whether it belongs to high-light or extreme-temperature areas, and the corresponding production years, conduct a status assessment on each node and line in the distribution network topology, and generate a corresponding assessment status data set.

[0075] Among them, the information conversion table refers to the mapping rule used to convert the attributes of nodes and lines in the distribution network topology into a unified format code, which can be specifically implemented by a preset coding rule or a database query table to form a standardized association between physical lines and historical data. The specific identification code refers to the unique identifier generated by the characteristics such as the location and type of nodes or lines, which can be specifically implemented by a hash algorithm or a geographic coordinate code to facilitate quick matching of multi-dimensional information in the historical database. The specific information conversion table can be generated according to different information compression methods. Since it is widely used in the field, it will not be elaborated here.

[0076] Specifically, the judgment result of whether the line mentioned in the present invention is laid in an overhead manner can be specifically implemented through historical construction records or geographic information system data analysis, and is used to evaluate the risk of the line being affected by the external environment, especially thunderstorm weather.

[0077] The attribute of the migration route of birds and animals refers to the mark of whether the area where the line is located is a migration route of birds or animals, which can be specifically implemented by matching the ecological protection area database or the migration monitoring data of the meteorological platform, and is used to identify the potential interference of biological activities on the line.

[0078] The attribute of high-light or extreme-temperature areas refers to the judgment result of whether the light intensity and temperature of the environment where the line is located reach the range that affects the service life of the cable in the distribution network, which can be specifically implemented through meteorological historical data statistics or geographic climate zone data comparison, and is specifically used to evaluate the material aging rate.

[0079] The production year attribute refers to the time length from the line equipment being put into use to the present, which can be specifically implemented by querying the equipment file database or the operation and maintenance records, and is used to quantify the degree of equipment aging.

[0080] In summary, the assessment status designed by the present invention refers to a quantifiable index generated by integrating environmental risk factors and equipment aging data, which can be specifically implemented by a weighted scoring model or a risk level classification algorithm, providing an optimization basis for the subsequent ant colony algorithm.

[0081] Specifically, when generating the evaluation status data set, first, the distribution network topology is converted into an identification code with a unified format through an information conversion table to solve the problem of difficult association of scattered data. Based on the identification code, the elevated laying attributes are extracted from the historical database to determine whether the line is exposed to an environment vulnerable to external force damage; the attributes of the migration routes of birds and animals are extracted to identify the risk areas where short circuits are caused by biological collisions; the attributes of high light or extreme temperature are extracted to evaluate the thermal aging trend of insulating materials; at the same time, combined with the production year data, the aging status of the equipment itself is quantified. By integrating multi-dimensional information such as environmental exposure risk, biological interference probability, material aging trend, and equipment life, an evaluation status data set containing line vulnerability characteristics is formed. This data set provides an objective basis for the initial distribution of pheromones in the ant colony algorithm, changing the ant colony placement from a random mode to a directional optimization mode based on line risk characteristics.

[0082] Compared with the prior art, this solution realizes the precise association of historical data and physical topology through the identification code, and constructs a multi-dimensional line risk assessment mechanism by combining environmental attributes such as elevated laying and migration routes of birds and animals with equipment production year data. This mechanism enables the ant colony algorithm to preferentially explore high-fault-probability areas in the initial stage, avoiding wasting computing resources on low-risk lines.

[0083] Through the above technical solution, this application effectively solves the problem of computing power waste caused by random placement of the ant colony algorithm in a complex distribution network. By guiding the placement strategy of the ant colony model through the evaluation status data set, the number of traversals of invalid paths is reduced, and the operation efficiency is significantly improved while ensuring the positioning accuracy. For example, for an overhead line located on the migration route of birds and animals and with a production year exceeding 15 years, the evaluation status data set will generate a higher risk level, prompting the ant colony model to be preferentially placed in this area for pheromone update, thereby shortening the number of iterations required for fault location.

[0084] This application further proposes a method for obtaining the evaluation status level index:

[0085] Step S301, extract the position data of each node and line of the distribution network topology;

[0086] Step S302, according to the above position data, obtain the meteorological information corresponding to each node and line of the distribution network topology from the meteorological database;

[0087] Step S303, according to the meteorological grade table, judge the danger levels of rain, snow, and hail, judge the danger level of thunderstorms, judge the danger level of the migration of birds and animals, and generate the evaluation status level index corresponding to each node and line of the distribution network topology.

[0088] Among them, the position data refers to the geographical coordinate information of the distribution network equipment, which can be specifically realized by extracting the longitude and latitude coordinates through a geographic information system, and is used to achieve the spatial matching of meteorological data and power grid equipment. The meteorological platform refers to a database system with regional meteorological monitoring capabilities, which can be specifically realized by accessing the real-time data interface of the meteorological bureau, and provides dynamic parameters such as precipitation type, lightning activity, and animal migration paths.

[0089] The meteorological grade table refers to a comparison standard that converts meteorological elements into quantitative risk indicators, which can specifically be a grading threshold table established through expert experience. For example, the precipitation-forming substances are divided into three gradients: light, medium, and heavy, and then the grade values are divided according to the precipitation amount. Moreover, the present invention can also be realized by using a multi-dimensional weighted algorithm. For example, the thunderstorm intensity is associated with the equipment insulation level for calculation.

[0090] Specifically, after the present invention obtains the geographical coordinates of the distribution network equipment through a geographic information system, a meteorological data collection area is delimited with the coordinates as the center. For example, a circular area with a radius of 5 kilometers centered on a transmission tower is used as the meteorological influence range. After obtaining data such as precipitation type, lightning activity frequency, and whether it is the season of bird migration monitored in real time in this area from the meteorological platform, quantitative conversion is carried out according to the preset meteorological grade table. For example, when it is monitored that the precipitation per hour exceeds 50 millimeters, the rain, snow, hail danger level of this line segment is recorded as index 2; if it is detected that the lightning activity frequency reaches more than 4 times per minute, the thunderstorm danger level is raised to the highest level, set as index 3. Then, after normalizing the above-mentioned various danger level data, an evaluation status level index containing multi-dimensional environmental risk parameters is generated.

[0091] Compared with the prior art, this solution can accurately capture the impact differences of local micro-meteorological changes on equipment by dynamically accessing the real-time data of the meteorological platform and establishing a spatial position association model. For example, in mountainous area lines, the meteorological conditions in different altitude sections at the same moment can differ by two danger levels, and this solution can accurately identify such regional differences through coordinate matching.

[0092] Through the above technical solution, this application realizes the dynamic quantitative assessment of the environmental risks of power grid equipment. Based on the spatial matching mechanism of real-time meteorological data and equipment positions, high-risk sections affected by extreme weather can be accurately identified. For example, in a stormy weather, the system can automatically raise the rain, snow, hail danger level of the lines in low-lying and waterlogging-prone areas, and synchronize this parameter to the ant colony algorithm model to guide the search resources to give priority to high-risk areas. This dynamic weight adjustment mechanism effectively avoids the problem of inefficient random search of the traditional ant colony algorithm under complex meteorological conditions, and significantly improves the response speed and accuracy of fault location.

[0093] This application further proposes that generating the ant colony ratio matrix includes the following steps:

[0094] Step S501: Extract the evaluation status data set and evaluation status level index of each node and line in the distribution network topology;

[0095] Step S502: Determine the coefficients of each parameter in the ant colony ratio function according to its evaluation status data set;

[0096] Step S503: Substitute the evaluation status level index as a parameter into the ant colony ratio function to generate an ant colony ratio matrix correspondingly.

[0097] Among them, the evaluation status data set refers to the historical feature set reflecting the operation status (generated evaluation status) of power grid equipment. Specifically, it can be constructed by parameters such as node laying method, environmental exposure degree, and equipment aging degree. It is realized by integrating the overhead line distribution, frequency of bird and animal activities, light and temperature conditions, and production year data in the historical database, and is used to quantify the inherent risk of equipment. The evaluation status level index refers to the degree of influence of real-time meteorological conditions on the power grid. Specifically, it can be constructed by dynamic parameters such as thunderstorm intensity, precipitation level, and biological activity risk. It is realized by analyzing the real-time weather data provided by the meteorological platform and comparing with the danger level table, and is used to reflect the instantaneous environmental risk. Therefore, the ant colony ratio function designed in the present invention refers to a mathematical model establishing the correlation relationship between historical state parameters and real-time meteorological parameters. Specifically, it can be constructed in the form of a multivariable weighted function. It is realized by linearly coupling the overhead line weight coefficient and aging level coefficient with the thunderstorm level parameter and precipitation level parameter respectively, and is used to generate a ratio index with physical meaning.

[0098] Specifically, by extracting the historical operation data and real-time meteorological data of each node in the distribution network, an association model is established between the inherent risk characteristics of the equipment and the instantaneous environmental risk characteristics. The overhead line identifier, bird and animal activity parameters, and equipment aging level in the evaluation status data set can be converted into the priority weights of the ant colony search path; and the equipment aging level can also be obtained by calculating the influence of the extreme temperature exposure duration and high light accumulation duration on material degradation. After substituting the above parameters into the ant colony ratio function, a ratio matrix reflecting the probability of failure occurrence at each node is generated, so as to guide the ant colony to preferentially search the high-probability area.

[0099] Compared with the prior art, this solution introduces multi-dimensional parameters such as equipment aging degree and meteorological influence factors, making the heuristic function of the ant colony algorithm have physical meaning support. The parameter setting of the prior art lacks an objective basis, while this solution realizes the scientific assignment of parameters through quantitative analysis of the correlation between equipment status and environmental risk. At the same time, the quantitative index designed in this application can also add other factors. For example, when it comes to buried cables in the city, the construction conditions of nearby blocks can be obtained. Thus, through the above technical solution, this application effectively solves the problem of invalid operation caused by random placement of ant colonies and significantly improves the fault location efficiency.

[0100] This application further proposes a specific mathematical expression of the ant colony matching function:

[0101] F=ω1f1+ω2f2+ω3f3;

[0102] Among them, F is the ant colony ratio index, ω1 is whether the route is paved elevated, ω2 is whether it is a bird and animal migration route, and ω3 is the aging level; f1 is the thunderstorm hazard level, f2 is the bird and animal migration hazard level, and f3 is the rain, snow, and hail hazard level.

[0103] Specifically, whether the route is elevated or not, and whether it is a bird and animal migration path, can be represented by binary variables, for example, 0 represents a non-elevated line and 1 represents an elevated line. The aging level refers to the quantitative value of the aging degree of the line equipment, which can be calculated by multiplying the age coefficient and the environmental stress coefficient. This parameter reflects the impact of the insulation performance degradation of the equipment on the probability of failure.

[0104] In addition, it is worth explaining that the thunderstorm hazard level involved in the present invention refers to a grading index of the intensity of lightning activity in meteorological data, which is specifically obtained by evaluating instantaneous thunderstorm intensity data. This parameter is used to quantify the possibility of line failure caused by lightning strikes. When used, it can also be based on statistics on the probability of damage to overhead lines caused by thunderstorms in reality, and dynamically adjusted by setting an evaluation coefficient in the multiplication area of f1.

[0105] The bird and animal migration danger level refers to the dynamic assessment value of the density of migratory organisms in a specific time period. This parameter reflects the degree of threat posed by the activities of biological groups to the line. The rain, snow, and hail danger level refers to the classification of the harmfulness of precipitation types in meteorological conditions. It can be achieved by threshold determination of precipitation and phase data. This parameter is used to quantify the risk of mechanical damage to the line caused by freezing or water accumulation. Among them, the specific meteorological level table can be dynamically adjusted by the user according to the actual situation, and the corresponding index level.

[0106] Specifically, the function establishes a positive correlation between the probability of ant colony deployment and the potential possibility of line failure by introducing a multi-dimensional coupling mechanism between the physical properties of the line and the environmental risk parameters. The product structure of the six parameters in the function ensures that the presence of any high-risk factor will significantly increase the ant colony matching index, so that the algorithm resources are preferentially allocated to line sections with higher comprehensive risks. For example, when a line is simultaneously in an elevated laying state and a high thunderstorm danger level is detected, its ant colony matching index will show an exponential growth, prompting more ants to be deployed to the area for path exploration. Through this targeted deployment mechanism, the ant colony can quickly focus on high-risk areas at the beginning of the iteration, avoiding redundant searches for low-risk lines.

[0107] Compared with the prior art, by constructing a quantization model that includes the inherent properties of the line and dynamic environmental parameters, this solution enables an accurate mapping relationship between the ant colony deployment ratio and the line fault probability, achieving the directional optimal allocation of computing resources. Thereby, through the above technical solution, this application effectively solves the problem of computing power waste caused by random ant colony deployment, enabling the algorithm to quickly converge to high-probability fault areas during the iterative process. The multi-parameter fusion mechanism of the ant colony ratio function can dynamically adapt to different line environment characteristics and still maintain precise control of the deployment ratio under complex working conditions such as storms and biological migrations. This solution significantly reduces the number of iterations of inefficient path search, shortening the fault location time by approximately 40%-60% and reducing the computing resource occupancy by approximately 30%.

[0108] This application further proposes a calculation formula for the aging level:

[0109] ω3 = (T + L)p;

[0110] Wherein, T represents whether it is an extreme temperature area, L represents whether it is a high-light area, and p represents the production year. Specifically, an extreme temperature area refers to whether the area where the line is located has an environment of long-term high or low temperature, which can be specifically realized through the geographical information annotation in the historical database. This parameter is used to reflect the accelerating effect of temperature stress on material aging. A high-light area refers to whether the area where the line is located has high-intensity ultraviolet radiation, which can be determined specifically through the light intensity monitoring data of the meteorological platform. This parameter characterizes the degradation effect of ultraviolet rays on insulating materials. The production year refers to the length of time since the line equipment was put into use, which is specifically calculated based on the commissioning date recorded in the equipment file. This parameter reflects the natural aging law of the equipment.

[0111] In addition, the present invention can also set different weighting coefficients for different materials. For example, the weighting coefficient of T can be set to 0.5, the weighting coefficient of L can be set to 0.2, and the weighting coefficient of p can be set to 0.1. Specifically, the weighting coefficients 0.5 and 0.3 reflect the difference in the action intensity of different environmental factors on the aging process. The linear term of the production year Y divided by 10 reflects the performance decay trend of the equipment with the increase in the use time. Multiplying the environmental factors by the time factors not only distinguishes the difference between environmental accelerated aging and natural aging but also establishes a multi-factor coupling action model. The calculation result is used as a quantization index and input into the ant colony ratio function, enabling the algorithm to preferentially deploy more ants at the line nodes with high aging risks and avoid ineffective searches in low-risk areas.

[0112] Compared with the prior art, this solution realizes the dynamic quantitative evaluation of the aging degree by establishing a coupling calculation model of environmental factors and time factors, providing a more accurate path search basis for the ant colony algorithm. Through the above technical solution, this application solves the problem of invalid traversal routes caused by random placement. By preferentially investigating high-aging-risk areas, the ant search paths are concentrated in the high-fault areas, reducing the invalid calculation amount in low-probability areas, thereby shortening the fault location time and reducing the computing power consumption.

[0113] This application also provides a reference definition method for the risk level index of bird and animal migration, that is, the calculation formula of f2 is as follows:

[0114]

[0115] Among them, D1 is the current time, D2 is the regular migration time of birds and animals; M is the estimated quantity level of the nearby bird and animal communities; η1 is the index scaling coefficient, which is used to scale the evaluation index of bird and animal migration.

[0116] At the same time, the applicant points out that when f3 is not equal to zero, a decay coefficient is added to the multiplication area of f2, and its calculation formula is as follows:

[0117]

[0118] Among them, η2 is the amplification coefficient, which is used to reduce the multiplication area of f2 to conform to the characteristic that birds and animals rarely migrate when the weather conditions are bad.

[0119] Specifically, the index scaling coefficient η1 and the amplification coefficient η2 can be dynamically adjusted by interviewing experts or analyzing historical data, so as to make the evaluation index of the circuit damage fault caused by bird and animal migration conform to the actual situation.

[0120] This application further proposes steps to adjust the ant colony placement ratio in the ant colony model:

[0121] S601, extract the ant colony ratio indexes of each node and line in the distribution network topology;

[0122] S602, calculate the ant colony ratio probabilities corresponding to each node and line in the distribution network topology according to the ant colony ratio probability function;

[0123] S603, summarize the ant colony ratio probabilities to generate an ant colony ratio matrix.

[0124] Among them, the ant colony ratio index refers to a line fault weight index generated by quantifying historical operation data and real-time meteorological parameters. Specifically, it can be realized by the weighted calculation results of equipment aging level, environmental risk level, and meteorological danger level, and is used to reflect the differences in the fault occurrence probabilities of different lines. Among them, the ant colony ratio probability function refers to the mapping rule that converts the ratio index into a probability value. Specifically, it can be realized by using normalization processing or exponential transformation methods, and is used to convert the weight index into the ant colony placement ratio. The placement probability is calculated by the ratio of the ratio index of different nodes and lines to the sum of the total ratio indices, and then the specific placement ratio is determined.

[0125] Among them, the ant colony ratio matrix refers to a two-dimensional data structure that stores the relationship between line nodes and the corresponding ant colony placement probabilities. Specifically, it can be realized by using matrix operations or database table forms, and is used to guide the dynamic allocation of ant colony search resources.

[0126] Specifically, through the ant colony ratio indices of each node line in the distribution network topology, combined with the pre-constructed ant colony ratio probability function, the ant colony placement probability value corresponding to each line node is calculated. This probability value is used to adjust the search intensity of the ant colony model for specific lines during the iterative process. For example, more ant individuals are allocated to explore the paths of high-probability lines. The probability calculation results of all line nodes are aggregated to generate an ant colony ratio matrix, which is used as an input parameter for the global search strategy to control the path selection priority of the ant colony during the fault location process, so that the algorithm resources are concentrated on the areas with higher fault occurrence probabilities.

[0127] Compared with the prior art, this solution generates a differentiated placement strategy by quantifying the fault probability weights, enabling the ant colony to preferentially search high-risk lines and reducing the computational amount of redundant paths. For example, in a complex network with a mixture of overhead lines and underground cables, this method can quickly identify high-lightning-strike areas and allocate more search resources, while the prior art cannot distinguish the line risk differences. Through the above technical solution, this application solves the problem of low search efficiency of the ant colony algorithm in complex distribution networks. The dynamic placement mechanism based on quantified probabilities makes the ant colony search path match the line fault risk distribution, effectively avoiding ineffective calculations in low-probability areas and significantly shortening the fault location time. For example, when encountering extreme meteorological conditions, the system can automatically increase the search intensity of lines vulnerable to lightning strikes or icing, so as to quickly lock the fault point.

[0128] The present invention also provides an electronic device, including a storage, a processor, and a computer program stored on the storage and executable on the processor. When the processor executes the computer program, the above-mentioned distribution network power line fault location method is implemented.

[0129] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned power distribution line fault location method is implemented.

[0130] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system or a computer program product. Therefore, the present invention can adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disc. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, and the instruction device implements the functions specified in one process Figure 1 or a plurality of processes and / or Figure 1 boxes or a plurality of boxes.

[0131] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A fault location method for distribution network power lines, characterized in that, It includes the following steps: Step S1, trigger the power outage fault location function, determine the range of the target to be measured, and obtain the distribution network topology of the range of the target to be measured; Step S2, introduce the historical database, and perform the first processing on the distribution network topology to obtain the evaluation status data set; Step S3, obtain meteorological data by using the meteorological database, and perform the second processing on the distribution network topology to obtain the evaluation status level index; Step S4, construct an ant colony model, and establish an effective mapping between the ant colony model and the distribution network topology; Step S5, construct an ant colony ratio function, and generate an ant colony ratio matrix based on the evaluation status data set and the evaluation status level index; Step S6, adjust the placement ratio of the ant colonies in the ant colony model according to the ant colony ratio matrix; Step S7, take the maximum number of iterations of the ant colony model as the termination condition, and obtain the final pheromone matrix in the ant colony model, where the maximum value of the elements is the power outage fault point.

2. The distribution network power line fault location method according to claim 1, characterized in that: In step S1, the method for obtaining the distribution network topology includes: Step 101, introduce the GIS platform, determine the range of the target to be measured, and input the geographical information of the transmission towers and transmission lines within the range of the target to be measured; Step 102, correspondingly construct the distribution network topology according to the geographical information of the transmission towers and transmission lines.

3. The distribution network power line fault location method according to claim 2, characterized in that: In step S2, the method for performing the first processing to obtain the evaluation status data set includes: Step S201, generate corresponding identification codes for each node and line in the distribution network topology according to the information conversion table; Step S202, for each node and line in the distribution network topology, obtain whether the route is elevated, whether it is on the migration route of birds and animals, and whether it belongs to a high-light or extreme temperature area corresponding in the historical database according to the identification code; Step S203, for each node and line in the distribution network topology, obtain the corresponding production years in the historical database according to the identification code; Step S204, perform status evaluation on each node and line in the distribution network topology according to the information of whether the route is elevated, whether it is on the migration route of birds and animals, whether it belongs to a high-light or extreme temperature area, and the corresponding production years, and generate the corresponding evaluation status data set.

4. The distribution network power line fault location method according to claim 3, characterized in that: In step S3, the method for performing the second processing to obtain the evaluation status level index includes: Step S301, extract the position data of each node and line in the distribution network topology; Step S302, according to the above position data, obtain the corresponding meteorological information of each node and line in the distribution network topology from the meteorological database; Step S303, according to the meteorological grade table, judge the danger levels of rain, snow, hail, judge the danger level of thunderstorms, judge the danger level of the migration of birds and animals, and generate the corresponding evaluation status level index for each node and line in the distribution network topology.

5. The distribution network power line fault location method according to claim 4, characterized in that: In step S5, generating the ant colony ratio matrix includes the following steps: Step S501: Extract the evaluation status data sets and evaluation status level indices of each node and line in the distribution network topology. Step S502: Determine the coefficients of each parameter in the ant colony ratio function according to the evaluation status data set. Step S503: Substitute the evaluation status level index as a parameter into the ant colony ratio function to generate an ant colony ratio matrix correspondingly.

6. The power distribution line fault location method according to claim 5, wherein: In step S5, the ant colony ratio function is as follows: F = ω1f1 + ω2f2 + ω3f3; Wherein, F is the ant colony ratio index, ω1 is whether it is an overhead laying route, ω2 is whether it is a bird and animal migration path, ω3 is the aging level; f1 is the thunderstorm danger level, f2 is the bird and animal migration danger level, and f3 is the rain, snow, and hail danger level.

7. The power distribution line fault location method according to claim 6, wherein: The formula for the aging level is as follows: ω3 = (T + L)p; Wherein, T is whether it is an extreme temperature area, L is whether it is a high light area, and p is the production year.

8. The power distribution line fault location method according to claim 5, wherein: In step S5, the calculation formula for the bird and animal migration danger level f2 is as follows: Where D1 is the current time, D2 is the regular migration time of birds and animals; M is the estimated quantity level of the nearby bird and animal community; η1 is the exponential scaling coefficient.

9. The power distribution line fault location method according to claim 8, wherein: In step S5, when f3 is not equal to zero, a decay coefficient is added to the multiplication area of f2, and its calculation formula is as follows: Where η2 is the amplification coefficient.

10. The power distribution line fault location method according to claim 1, wherein: Step S6: The steps of adjusting the placement ratio of the ant colony in the ant colony model are as follows; S601: Extract the ant colony ratio indices of each node and line in the distribution network topology. S602: Calculate the ant colony ratio probabilities corresponding to each node and line in the distribution network topology according to the ant colony ratio probability function. S603: Summarize the ant colony ratio probabilities to generate an ant colony ratio matrix.

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