Self-adaptive repairing method and system for line loss of power distribution network

Abstract

The invention relates to the technical field of line loss repair, and provides a power distribution network line loss self-adaptive repair method and system. The method comprises the following steps: reading a line topology network, and determining ternary division characteristics; building an abnormity positioning module; carrying out loss assessment decision and probability calculation, and determining a line loss positioning single row; building a network reconstruction model, traversing line loss positioning single columns, performing region segmentation reconstruction and self-healing correction analysis, and determining reconstruction data; traversing the line loss positioning single column, reading non-self-healing line loss positioning data, and generating an exception repair instruction; and performing line loss repair management in combination with the reconstruction data and the exception repair instruction. According to the method, the technical problems of more invalid repair operations and low repair efficiency caused by incapability of accurately judging the line loss position and incapability of adaptively adjusting according to the real-time state of the power distribution network in a traditional repair method are solved, the accuracy and the repair efficiency of positioning the line loss of the power distribution network are improved through network reconstruction and abnormal traceability repair, and the repair efficiency of the power distribution network is improved. And invalid repair operation is reduced.

Description

Technical Field

[0001] The present application relates to the field of distribution network operation and maintenance technology, specifically to the field of line loss repair technology, and in particular to a distribution network line loss adaptive repair method and system. Background Art

[0002] With the rapid development of the power industry and the in-depth advancement of smart grid construction, line loss management in distribution networks has become an increasingly important issue. Line loss in distribution networks not only directly affects the economic benefits of power companies, but also affects the stable operation and energy efficiency of the entire power grid. However, with the continuous growth of power demand and the increasing complexity of the power grid structure, the line loss problem in distribution networks has become increasingly prominent. Due to the complexity of the distribution network, traditional line loss repair methods are difficult to cover all possible line loss points, resulting in some line loss problems being difficult to detect in time, and failing to perceive the operating status of the distribution network in real time. Therefore, it is impossible to adaptively adjust the repair strategy according to real-time data, resulting in low repair efficiency and even invalid repair operations. Summary of the invention

[0003] The present application provides a method and system for adaptively repairing line losses in a distribution network, aiming to solve the technical problems that traditional repair methods cannot accurately determine the location of line losses and cannot adaptively adjust according to the real-time status of the distribution network, resulting in a large number of invalid repair operations and low repair efficiency.

[0004] In view of the above problems, the present application provides a method and system for adaptively repairing line losses in a distribution network.

[0005] The first aspect disclosed in the present application provides a method for adaptively repairing line losses in a distribution network, the method comprising: reading a line topology network of a target distribution network, and determining a ternary division feature, wherein the ternary division feature comprises voltage-divided line loss, zone-divided line loss, and component-divided line loss; building an abnormality positioning module based on the line topology network and the ternary division feature, the abnormality positioning module comprising parallel ternary decision branches, and the abnormality positioning module having a built-in line loss probability calculation formula; transmitting distribution network data back, combining with the abnormality positioning module, making loss determination decisions and probability calculations, and determining a line loss positioning column; building a network reconstruction model based on network coupling characteristics, taking regional network reconstruction as the line loss repair principle, traversing the line loss positioning column, performing regional segmentation reconstruction and self-healing correction analysis, and determining reconstruction data, the reconstruction data comprising network configuration; traversing the line loss positioning column, reading non-self-healing line loss positioning data, and generating an abnormality repair instruction; combining the reconstruction data with the abnormality repair instruction, and performing line loss repair management on the target distribution network.

[0006] Another aspect disclosed in the present application provides a distribution network line loss adaptive repair system, the system comprising: a ternary division feature unit, the ternary division feature unit is used to read the line topology network of the target distribution network and determine the ternary division feature, wherein the ternary division feature includes voltage division line loss, partition line loss, and component division line loss; an abnormal positioning module building unit, the abnormal positioning module building unit is used to build an abnormal positioning module based on the line topology network and the ternary division feature, the abnormal positioning module includes parallel ternary decision branches, and the abnormal positioning module has a built-in line loss probability calculation formula; a line loss positioning single column determination unit, the line loss positioning single column determination unit is used to transmit distribution network data back, combined with the An abnormality positioning module performs loss determination and probability calculation to determine a line loss positioning column; a reconstruction data determination unit, which is used to build a network reconstruction model based on network coupling characteristics, take regional network reconstruction as the line loss repair principle, traverse the line loss positioning column, perform regional segmentation reconstruction and self-healing correction analysis, and determine reconstruction data, wherein the reconstruction data includes network configuration; an abnormality repair instruction generation unit, which is used to traverse the line loss positioning column, read non-self-healing line loss positioning data, and generate abnormality repair instructions; a line loss repair management unit, which is used to combine the reconstruction data with the abnormality repair instruction to perform line loss repair management on the target distribution network.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The above-mentioned distribution network line loss adaptive repair method first reads the line topology network of the distribution network, and constructs an abnormal location module based on the network structure and the three-element division characteristics of voltage division, division and component division. This module consists of three parallel decision branches, each of which has a corresponding line loss probability calculation formula built in, which is used to quickly and accurately determine the location and degree of line loss that may exist in the distribution network. Subsequently, the operation data of the distribution network is fed back in real time and combined with the abnormal location module to make loss determination decisions and probability calculations. Through this process, a specific line loss location column can be determined, that is, the specific location or area where the line loss problem exists. After that, a network reconstruction model is built based on the network coupling characteristics of the distribution network. This model takes regional network reconstruction as the principle, traverses the determined line loss location column, and performs regional segmentation reconstruction and self-healing correction analysis. This process aims to reduce or eliminate line loss problems and improve the operation efficiency of the distribution network by optimizing network configuration. During the traversal process, if it is found that some line loss problems cannot be self-healed through network reconstruction, these non-self-healing line loss location data will be read and corresponding abnormal repair instructions will be generated. Then, combined with network reconstruction data and abnormal repair instructions, line loss repair management is performed on the target distribution network. The entire process achieves accurate positioning, adaptive repair and efficient management of distribution network line loss problems, improving the operating efficiency and stability of the distribution network.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 The figure is a flow chart of a method for adaptively repairing line losses in a distribution network in an embodiment.

[0011] Figure 2 The present invention is a diagram of the architecture of a distribution network line loss adaptive repair system in one embodiment.

[0012] Explanation of the accompanying drawings: ternary division feature unit 1, abnormality positioning module building unit 2, line loss positioning single column determination unit 3, reconstruction data determination unit 4, abnormality repair instruction generation unit 5, line loss repair management unit 6. DETAILED DESCRIPTION

[0013] The embodiments of the present application provide a method and system for adaptively repairing line losses in a distribution network, thereby solving the technical problems that traditional repair methods cannot accurately determine the location of line losses and cannot adaptively adjust according to the real-time status of the distribution network, resulting in a large number of invalid repair operations and low repair efficiency.

[0014] 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, rather than 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.

[0015] It should be noted that the terms "including" and "having" and any variations thereof 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 explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0016] Embodiment 1, as Figure 1 As shown, the present application provides a method for adaptively repairing line losses in a distribution network, the method comprising: The line topology network of the target distribution network is read, and a three-element partitioning feature is determined, wherein the three-element partitioning feature includes voltage division line loss, area division line loss, and component division line loss.

[0017] With the rapid development of the electric power industry, electricity has become an indispensable energy source for the operation of modern society. As a link in the power system that is directly connected to users, the operating status of the distribution network directly affects the quality of users' electricity consumption and the economic benefits of the power system. However, with the rapid development of industrial production and transportation, the demand for electricity continues to grow, and the scale and complexity of the distribution network are also increasing, which has led to the increasingly prominent line loss problem in the distribution network. Line loss, that is, the energy loss caused by physical factors such as resistance, inductance and conductivity during the transmission and distribution of electricity, is an issue that cannot be ignored in the distribution network. It not only leads to energy waste, but also affects the stable operation of the power grid, reduces power supply efficiency, and may even cause safety accidents.

[0018] In an embodiment of the present application, in the line loss management of the target distribution network, the system terminal reads the line topology network of the target distribution network to fully understand the line structure of the target distribution network. The line topology network is a connection relationship diagram of each line and device in the target distribution network, which reflects the overall structure of the target distribution network. Subsequently, based on this topology network, the system terminal determines the ternary division characteristics of the target distribution network. The ternary division characteristics refer to the voltage division line loss, the partition line loss and the component line loss. Among them, the voltage division line loss refers to the energy loss generated in the target distribution network due to the different voltage levels during the transmission and distribution of electric energy. This loss can help understand the energy efficiency at each voltage level, thereby optimizing power transmission and distribution. The partition line loss refers to the energy loss generated by the target distribution network in a specific area when transmitting electric energy. By analyzing the partition line loss, the operating efficiency of the target distribution network in the area can be evaluated. The component line loss refers to the energy loss generated by each component in the target distribution network, such as transformers, cables, etc., during the transmission and distribution of electric energy. Understanding the line loss of these components helps to accurately identify components with low energy efficiency.

[0019] Based on the line topology network and the ternary division characteristics, an abnormality location module is constructed. The abnormality location module includes parallel ternary decision branches, and the abnormality location module has a built-in line loss probability calculation formula.

[0020] In one embodiment, based on the line topology network and ternary division characteristics of the target distribution network, the system terminal constructs an abnormal location module. The core of this module is three parallel decision branches, namely ternary decision branches, which independently analyze the line loss of voltage division, partition and component. Each decision branch integrates a line loss probability calculation formula, which can estimate the probability of line loss under specific conditions. Specifically, the system terminal collects sample distribution network voltage division data and sample voltage division line loss to construct a training set, a validation set and a test set. Subsequently, the structure of the input layer, hidden layer and output layer of the neural network is determined according to the complexity of the problem and the characteristics of the data, including the number of neurons in each layer, the activation function, etc. Then configure the hyperparameters of the neural network, such as the learning rate, batch size, number of iterations, etc. After that, the training set is input into the neural network to train the neural network model. During the training process, the predicted voltage division line loss of the neural network is calculated by forward propagation, and compared with the sample voltage division line loss, and the loss function value is calculated using the mean square error. Then, the gradient of the loss function to the neural network parameters is calculated using the back propagation algorithm, and the neural network parameters are updated to minimize the loss function. After each training cycle, the performance of the neural network is evaluated using the validation set, and the hyperparameters are adjusted or training is stopped as needed to prevent overfitting. After the training is completed, the system terminal uses the test set to evaluate the trained neural network model. The prediction accuracy, recall rate, F1 value and other indicators of the neural network are calculated to evaluate the performance of the neural network on unknown data. If the expected expectations are met, the current neural network is used as the voltage-dividing line loss decision branch. Otherwise, the structure of the neural network and the hyperparameters are adjusted. After the voltage-dividing line loss decision branch is constructed, the system terminal integrates the pre-constructed line loss probability calculation formula into the output layer of the voltage-dividing line loss decision branch to calculate the prior probability and posterior probability of the node corresponding to the current input distribution network data. After the line loss probability calculation formula is integrated, the system terminal also integrates the matching algorithm into the output layer. The matching algorithm can mark the calculated prior probability and posterior probability according to the node information, and mark the voltage-dividing line loss degree to the line topology network respectively, forming a voltage-dividing probability distribution network and a voltage-dividing line loss degree distribution network. These two networks will serve as the final output of the voltage-dividing line loss decision branch. Similarly, the system terminal uses the same method as above to train the partition line loss decision branch and the component line loss decision branch, and connects these three branches in parallel to form a ternary decision branch. Furthermore, the system terminal constructs an input layer and an output layer, and encapsulates them with the ternary decision branch to build an abnormality positioning module. Among them, the input layer has built-in data division rules. According to historical experience, the rules can divide the input distribution network data into distribution network voltage data, distribution network partition data, and distribution network component data for subsequent analysis of the ternary decision branch.The output layer stores a line topology network of the target distribution network, which is used to summarize the voltage-dividing probability distribution network and voltage-dividing line loss distribution network, the partition probability distribution network and the partition line loss distribution network, the component probability distribution network and the component line loss distribution network generated by the ternary decision branch to form a probability distribution network and a line loss distribution network as the final output of the abnormal location module. By building this abnormal location module, the system terminal can more quickly and accurately identify potential problem areas or components in the target distribution network, so as to take corresponding measures to reduce or avoid energy loss and improve the operating efficiency and stability of the target distribution network.

[0021] Furthermore, the present application provides a formula for calculating the line loss probability, including: The line loss probability calculation formula includes a priori probability and a posteriori probability, and obtaining the line loss probability calculation formula includes: ;in, is the prior probability, For the Visualize the eigenvalues ​​of features, For the Visualize the weights of features, is the total amount of visualized features; is the posterior probability, is the degradation probability matrix of the visualization feature, is the migration probability matrix of hidden features; For the The eigenvalues ​​of the hidden features, For the The weights of the hidden features, is the total amount of hidden features, For the Visualize the degradation probability of features, For the The transfer probability of hidden features.

[0022] Optionally, the line loss probability calculation formula is divided into a priori probability calculation and a posteriori probability calculation to calculate the probability of line loss. Two types of features are used, namely visual features and hidden features, and different weights are assigned to them. The line loss probability calculation formula is as follows: ;in, is the prior probability, which is the calculated value. For the The eigenvalues ​​of visualized features are features that can be directly observed or measured, such as current, voltage, etc. For the The weight of the visualization feature determines the influence of each visualization feature on the prior probability and posterior probability. is the total amount of visualized features. is the posterior probability, which is the calculated value. It is the degradation probability matrix of the visualization feature. Degradation refers to the possibility of the feature value changing from a normal state to an abnormal state. Each data inside represents the degradation probability of a visualization feature of the current node, which is obtained based on the historical data in advance. It is the migration probability matrix of hidden features, which is also obtained in advance based on historical data statistics. For the The eigenvalues ​​of hidden features are factors that cannot be directly observed but may have an impact on line loss, such as aging status, load fluctuation and other parameters. For the The weight of the hidden feature determines the influence of each hidden feature on the posterior probability. is the total amount of hidden features. For the Visualize the degradation probability of a feature. For the The migration probability of hidden features. The prior probability is obtained by multiplying the feature value of each visual feature by its corresponding weight, then adding all the products and dividing by the total number of visual features, providing a preliminary estimate based on the current observation data. The posterior probability not only takes into account the current observation data, but also the potential impact of hidden factors. This allows the posterior probability to more comprehensively reflect the possibility of line loss.

[0023] The distribution network data is transmitted back, and combined with the abnormal location module, loss determination decision and probability calculation are performed to determine the line loss location column.

[0024] In one embodiment, the target distribution network generates a large amount of real-time data during operation, namely, distribution network data, which are transmitted back to the system terminal through the data acquisition device. Subsequently, the system terminal inputs these data into the abnormal location module for loss determination and probability calculation. The abnormal location module generates a probability distribution network and a line loss distribution network through collaborative analysis of the internal input layer, ternary decision branch and output layer. After that, the probability distribution network is combined with the line loss distribution network, and multiple screenings are performed to generate a line loss location column. This single column will record in detail the key information such as the description of the abnormal event, location information, possible causes, expected losses, and calculated probabilities. This single column can help operators quickly locate the problem to reduce losses and restore the normal operation of the target distribution network.

[0025] Furthermore, the present application provides the method for determining the line loss location, including: Based on the ternary decision branch, the distribution network data is analyzed and proofread in parallel to determine the probability distribution network and the line loss degree distribution network; based on the line loss degree distribution network, a primary screening is performed in combination with the standard line loss coefficient to determine the first loss assessment data, and a secondary screening is performed based on the probability distribution network to determine the second loss assessment data; the first loss assessment data and the second loss assessment data are fitted to determine the line loss positioning single column.

[0026] Preferably, the system terminal inputs the distribution network data into the abnormal location module for damage assessment decision and probability calculation. The abnormal location module divides the input distribution network data through the input layer to generate distribution network voltage data, distribution network partition data and distribution network component data. Subsequently, the ternary decision branch inside the abnormal location module calculates the corresponding data divided, and each decision branch generates a corresponding probability distribution network and line loss degree distribution network. After that, the output layer of the abnormal location module merges the probability distribution network generated by each decision branch with the line loss degree distribution network to determine the final probability distribution network and line loss degree distribution network. Then, the system terminal uses the standard line loss coefficient to screen the line loss degree distribution network. During the screening process, the system terminal compares the line loss degree of each node in the line loss degree distribution network with the standard line loss coefficient, extracts the nodes whose line loss degree is greater than or equal to the standard line loss coefficient, and adds them to the pre-constructed blank first damage assessment data. This standard line loss coefficient is determined based on historical data and safety criteria. This first damage assessment data represents the areas or nodes that are initially determined to have problems. After completing the first screening, the system terminal performs a second screening of the probability distribution network based on the prior probability threshold and the posterior probability threshold to determine the second loss assessment data. Finally, the first loss assessment data and the second loss assessment data are fitted, that is, the two sets of data are combined to form a more comprehensive and accurate line loss location column. This line loss location column contains a list of all areas or nodes that are identified as abnormal, which is used to guide subsequent corrections.

[0027] Further, the present application provides secondary screening based on a probability distribution network, including: Obtain a probability distribution network, wherein each network node includes a prior probability and a posterior probability; traverse the probability distribution network, and perform a threshold overlimit judgment based on a prior probability threshold and a posterior probability threshold; if the prior probability satisfies the prior probability threshold, and the posterior probability satisfies the posterior probability threshold, add the network node to the second damage assessment data; if either the prior probability or the posterior probability does not satisfy the probability threshold, identify the network node as a pseudo-outlier.

[0028] Optionally, after obtaining the probability distribution network, the system terminal parses the probability distribution network to understand the prior probability and posterior probability stored in each node. Subsequently, the probability distribution network is traversed, and in each traversal, the system terminal makes a threshold overlimit judgment on each network node. This judgment is based on a pre-set prior probability threshold and a posterior probability threshold. These two thresholds are set according to historical data and safety criteria to determine whether a node may have power loss. If the prior probability of a node meets the prior probability threshold, and the posterior probability also meets the posterior probability threshold, then it is judged that the node may have power loss, so it is added to the pre-constructed blank second damage assessment data. This second damage assessment data can determine which areas or nodes may have power loss problems. However, if any of the prior probability or posterior probability of a node does not meet the corresponding threshold, then the node is judged to be a pseudo-anomaly. These nodes may be caused by false alarms, noise or other reasons, and they do not really represent the occurrence of power loss. By identifying these points as pseudo-outliers, unnecessary attention and processing on them can be avoided, thereby improving the accuracy and efficiency of the entire damage assessment process.

[0029] A network reconstruction model is built based on network coupling characteristics, and regional network reconstruction is used as the line loss repair principle. The line loss positioning column is traversed to perform regional segmentation reconstruction and self-healing correction analysis to determine reconstruction data, which includes network configuration.

[0030] In one embodiment, the system terminal first defines the goal of network reconstruction, that is, reconfigures and optimizes the network structure for the specific area where the problem occurs, so as to restore the normal operation of the target distribution network and reduce or eliminate the line loss problem. Subsequently, the coupling relationship in the network is analyzed using the distribution network data. This includes identifying key nodes and lines, evaluating the degree of mutual influence between them, and determining their position and role in the network. Then, according to the analysis results of the network coupling characteristics, the network reconstruction model is set, such as the threshold of neurons, coupling strength, voltage level, current size, etc. The setting of these parameters will directly affect the performance and accuracy of the model. After that, according to the electrical coupling method, the connection relationship between nodes and lines is realized in the model. That is, the generator, transformer, load and other nodes are connected through the line. Then, according to the mesh topology structure, the connection mode between nodes and lines is constructed in the model, and then the network reconstruction model is constructed. The mesh topology structure is often selected for power network reconstruction due to its high reliability and fault tolerance. After the network reconstruction model is built, in order to carry out effective repair, the system terminal takes regional network reconstruction as the principle, that is, on the premise of maintaining the stable operation of the overall network, the network structure is reconfigured and optimized for the specific area where the problem occurs. The system terminal traverses the line loss location column and segments and reconstructs these areas in the network reconstruction model according to the network coupling characteristics. Segmentation refers to temporarily isolating the problem area from the network reconstruction model for separate analysis and processing. Reconstruction is to reconfigure the network structure of the problem area based on the segmentation results to determine multiple distribution transformer data. During the reconstruction process, self-healing correction analysis is also required. Self-healing correction refers to splicing the distribution transformer data through collision avoidance analysis to determine the network configuration when a fault or problem occurs in the target distribution network, so as to restore the normal operation of the target distribution network. After obtaining the network configuration, the system terminal organizes the network configuration to form reconstruction data, which contains specific information about the network configuration, such as node location, line connection, equipment parameters, etc. These reconstruction data will be used to guide the actual network repair work to restore the normal operation and reduce or eliminate line loss problems.

[0031] Furthermore, the present application provides the regional segmentation reconstruction and self-healing correction analysis, including: Based on the network coupling characteristics, the line loss positioning column is traversed to determine multiple loss-determining distribution transformer areas, wherein the coupling degree within the distribution transformer area is higher than the coupling threshold, and the coupling degree between the distribution transformer areas is lower than the coupling threshold; the multiple loss-determining distribution transformer areas are traversed to perform regional network reconstruction to determine multiple distribution transformer data, wherein the regional network reconstruction analysis method is multi-objective optimization; collision avoidance analysis is performed on the multiple distribution transformer data, and the network configuration is determined by splicing.

[0032] Preferably, the system terminal determines the degree of coupling in the network by analyzing the network coupling characteristics. Subsequently, the single column data of the line loss location is traversed, and the area with a coupling degree higher than the preset coupling threshold is identified as the distribution transformer area. These areas are tightly coupled internally, but the coupling degree between the areas is low, thus forming a relatively independent network area. After obtaining multiple distribution transformer areas with rated loss, the system terminal traverses all distribution transformer areas with rated loss and assigns a unique identifier to each area. Then, the network structure of each area is initialized, which includes the location of the node, the connection of the line, etc. After that, according to the goal of network reconstruction, multiple objective functions are set, such as improving network efficiency, reducing line loss, maintaining model complexity, etc. Then, according to the importance of each objective function, the corresponding weight is assigned. Then, the network configuration of each distribution transformer area with rated loss is used as a chromosome, and the fitness of each chromosome is evaluated according to the weighted value of the objective function. The chromosome with higher fitness is selected to enter the next generation. Two chromosomes are randomly selected for crossover operation to generate new offspring chromosomes, that is, new network configurations. This helps to explore new areas in the solution space. The offspring chromosomes are subjected to random mutation operations to introduce new genes, that is, new network configuration parameters. This helps maintain the diversity of the population and avoid falling into local optimality. Further, the system terminal repeats the selection, crossover and mutation process to generate a new generation of chromosome population. The fitness of the new generation of chromosomes is then evaluated, and the chromosomes with the optimal solution or close to the optimal solution are recorded. Subsequently, the above optimization process is iterated until the fitness improvement is no longer significant. After that, the optimal or close to the optimal chromosome is selected from the final generation of chromosomes as the distribution transformer data of each region, and the key parameters of these chromosomes, such as node location, line connection, equipment parameters, etc., are extracted to determine multiple distribution transformer data. Then, all distribution transformer data are traversed to identify possible collision points between different regions. Collision points include node overlap, line intersection, equipment conflict, etc. Then, the severity of the identified collision points is evaluated. The severity is determined based on factors such as the degree of impact of the collision point on network performance, repair cost, time, etc. The higher the severity, the higher the processing priority of the corresponding collision point. After the priority is determined, the system terminal re-performs multi-objective optimization on multiple distribution transformer data according to the determined priority to avoid conflicts and ensure that each region can reach the optimal state. Finally, the distribution transformer data of each area after collision avoidance analysis are spliced ​​to form a complete network configuration. This configuration not only takes into account the influence of network coupling characteristics, but also optimizes through multi-objective optimization and collision avoidance analysis, which can effectively reduce line losses and improve the stability and efficiency of the target distribution network.

[0033] Furthermore, the present application provides for adjusting the regional scale of the multiple loss-determining distribution transformer regions, including: Based on the correction requirements, the scale of the multiple damage-rated distribution transformer areas is adjusted, wherein the scale adjustment methods include regional expansion and regional contraction.

[0034] Optionally, factors such as network status and load conditions may change over time, so these rated-damage distribution transformer areas also need to be adjusted or updated accordingly. When the network status or load conditions near a rated-damage distribution transformer area change, causing the boundary of the area to expand outward to better cover potential problem areas, the system terminal performs regional expansion. This involves incorporating certain equipment or lines that did not originally belong to the area into the new rated-damage distribution transformer area. On the contrary, if the network status or load conditions within a rated-damage distribution transformer area improves, or an area that was originally thought to have problems is found to be normal after inspection, then the boundary of the area may need to shrink inward to reduce unnecessary waste of resources and operation and maintenance costs.

[0035] The line loss location column is traversed, non-self-healing line loss location data is read, and an abnormality repair instruction is generated.

[0036] In one embodiment, when traversing the line loss location column, the system terminal also traverses the non-self-healing line loss location data. Non-self-healing line loss refers to the power loss caused by equipment failure, line aging, power theft, etc. in the target distribution network, and this loss cannot be automatically restored by the adjustment or repair of the target distribution network itself. The traversal process involves checking each line loss location data and analyzing whether it indicates a persistent power loss problem that requires manual intervention. Once such non-self-healing line loss location data is identified, the system terminal will trace the cause of the line loss and determine the abnormal repair instruction based on the repair time limit. This abnormal repair instruction includes specific repair measures, repair time, required materials, personnel allocation and other information, which provide clear guidance and reference for operators to help quickly and accurately find and repair problems, thereby restoring the normal operation of the target distribution network.

[0037] Furthermore, the present application provides the generating exception repair instruction, including: For the non-self-healing line loss location data, the cause of the line loss is traced to determine the repair plan set; the repair time limit is set based on the repair priority according to the line loss level and network impact, wherein the repair priority is positively correlated with the line loss level and network impact; the repair plan set and the repair time limit are mapped to generate abnormal repair instructions.

[0038] Preferably, for the non-self-healing line loss location data, the system terminal performs line loss cause tracing. The purpose of this step is to find out the root cause of the line loss. Through in-depth analysis of these causes, the nature of the problem can be better understood, providing an important basis for the subsequent formulation of repair measures. Subsequently, based on the tracing results, the system terminal determines the repair plan set. These plan sets contain a variety of repair schemes for different causes, each of which details the repair steps, required materials, personnel configuration, etc. By preparing a variety of plans, the most suitable repair plan can be flexibly selected according to the actual situation to improve the repair efficiency. After determining the repair plan set, the system terminal sets the repair time limit according to the line loss level and network impact. The line loss level reflects the severity of the line loss, while the network impact measures the impact range of the problem on the entire target distribution network. The repair priority is positively correlated with the two, that is, the more severe the line loss and the wider the impact range, the higher its repair priority and the more urgent the repair time limit. After that, the system terminal maps the repair plan set with the repair time limit to generate an abnormal repair instruction. These instructions specify key information such as the repair measures to be taken, operators, completion time, etc. By sending these instructions to operators, it is possible to ensure that the repair tasks are carried out quickly and accurately, thereby restoring the normal operation of the target distribution network in the shortest time.

[0039] The reconstruction data and the abnormal repair instruction are combined to perform line loss repair management on the target distribution network.

[0040] In one embodiment, during the line loss repair management process of the target distribution network, the system terminal combines the reconstruction data with the generated abnormal repair instructions to perform targeted repairs on the target distribution network. By implementing these instructions, the line loss problem in the target distribution network can be effectively solved, and the operating efficiency and stability of the target distribution network can be improved. At the same time, the operating status of the target distribution network can be continuously monitored, new problems can be discovered and repaired in a timely manner, forming a dynamic cycle management process.

[0041] In summary, the embodiments of the present application have at least the following technical effects: The embodiment of the present application reads the line topology network of the target distribution network and determines the three-element division characteristics including voltage division, partition, and component division. Based on these characteristics and the line topology network, an abnormal positioning module is constructed, which includes parallel three-element decision branches for making damage determination decisions and probability calculations in combination with distribution network data, thereby determining the line loss location column. Subsequently, a network reconstruction model is constructed using the network coupling characteristics, and the line loss location column is subjected to regional segmentation reconstruction and self-healing correction analysis to determine the reconstruction data, including the network configuration. In this process, the damage distribution transformer area is divided based on the coupling degree between regions, and the regional network is reconstructed through multi-objective optimization. After that, the line loss location column is traversed to identify the non-self-healing line loss location data, and the line loss cause is traced to determine the corresponding repair plan set. According to the line loss level and network impact, the repair priority and repair time limit are set for each repair plan. Then, the target distribution network is managed for line loss repair in combination with the reconstruction data and the abnormal repair instruction. In this process, the repair plan is dynamically adjusted and optimized in combination with the real-time data of the distribution network to achieve efficient line loss repair. In addition, it also includes the steps of adjusting the scale of the distribution transformer area to meet different correction requirements, and adjusting the scale of the area by expanding or shrinking the area. These technical effects jointly solve the technical problems that traditional repair methods cannot accurately determine the line loss location and cannot adaptively adjust according to the real-time status of the distribution network, resulting in a large number of invalid repair operations and low repair efficiency. It realizes network reconstruction and abnormal tracing and repair, improves the accuracy of distribution network line loss positioning and repair efficiency, and reduces the effect of invalid repair operations.

[0042] Embodiment 2 is based on the same inventive concept as a method for adaptively repairing line loss in a distribution network in the above embodiment. Figure 2 As shown, the present application provides a distribution network line loss adaptive repair system, the system comprising: Ternary division feature unit 1: The ternary division feature unit 1 is used to read the line topology network of the target distribution network and determine the ternary division feature, wherein the ternary division feature includes voltage division line loss, partition line loss, and component division line loss; abnormal location module building unit 2: The abnormal location module building unit 2 is used to build an abnormal location module based on the line topology network and the ternary division feature, and the abnormal location module includes parallel ternary decision branches, and the abnormal location module has a built-in line loss probability calculation formula; line loss location single column determination unit 3: The line loss location single column determination unit 3 is used to transmit distribution network data back, and in combination with the abnormal location module, perform loss determination decision and probability calculation. , determine the line loss location single column; reconstruction data determination unit 4: the reconstruction data determination unit 4 is used to build a network reconstruction model based on network coupling characteristics, take regional network reconstruction as the line loss repair principle, traverse the line loss location single column, perform regional segmentation reconstruction and self-healing correction analysis, and determine the reconstruction data, the reconstruction data includes network configuration; abnormal repair instruction generation unit 5: the abnormal repair instruction generation unit 5 is used to traverse the line loss location single column, read the non-self-healing line loss location data, and generate abnormal repair instructions; line loss repair management unit 6: the line loss repair management unit 6 is used to combine the reconstruction data and the abnormal repair instruction to perform line loss repair management on the target distribution network.

[0043] Furthermore, the line loss location single column determination unit 3 is also used to perform the following method: Based on the ternary decision branch, the distribution network data is analyzed and proofread in parallel to determine the probability distribution network and the line loss degree distribution network; based on the line loss degree distribution network, a primary screening is performed in combination with the standard line loss coefficient to determine the first loss assessment data, and a secondary screening is performed based on the probability distribution network to determine the second loss assessment data; the first loss assessment data and the second loss assessment data are fitted to determine the line loss positioning single column.

[0044] Furthermore, the line loss location single column determination unit 3 is also used to perform the following method: The line loss probability calculation formula includes a priori probability and a posteriori probability, and obtaining the line loss probability calculation formula includes: ;in, is the prior probability, For the Visualize the eigenvalues ​​of features, For the Visualize the weights of features, is the total amount of visualized features; is the posterior probability, is the degradation probability matrix of the visualization feature, is the migration probability matrix of hidden features; For the The eigenvalues ​​of the hidden features, For the The weights of the hidden features, is the total amount of hidden features, For the Visualize the degradation probability of features, For the The transfer probability of hidden features.

[0045] Furthermore, the line loss location single column determination unit 3 is also used to perform the following method: Obtain a probability distribution network, wherein each network node includes a prior probability and a posterior probability; traverse the probability distribution network, and perform a threshold overlimit judgment based on a prior probability threshold and a posterior probability threshold; if the prior probability satisfies the prior probability threshold, and the posterior probability satisfies the posterior probability threshold, add the network node to the second damage assessment data; if either the prior probability or the posterior probability does not satisfy the probability threshold, identify the network node as a pseudo-outlier.

[0046] Furthermore, the reconstructed data determination unit 4 is further configured to execute the following method: Based on the network coupling characteristics, the line loss positioning column is traversed to determine multiple loss-determining distribution transformer areas, wherein the coupling degree within the distribution transformer area is higher than the coupling threshold, and the coupling degree between the distribution transformer areas is lower than the coupling threshold; the multiple loss-determining distribution transformer areas are traversed to perform regional network reconstruction to determine multiple distribution transformer data, wherein the regional network reconstruction analysis method is multi-objective optimization; collision avoidance analysis is performed on the multiple distribution transformer data, and the network configuration is determined by splicing.

[0047] Furthermore, the reconstructed data determination unit 4 is further configured to execute the following method: Based on the correction requirements, the scale of the multiple damage-rated distribution transformer areas is adjusted, wherein the scale adjustment method includes regional expansion and regional contraction.

[0048] Furthermore, the abnormality repair instruction generating unit 5 is also used to execute the following method: For the non-self-healing line loss location data, the cause of the line loss is traced to determine the repair plan set; the repair time limit is set based on the repair priority according to the line loss level and network impact, wherein the repair priority is positively correlated with the line loss level and network impact; the repair plan set and the repair time limit are mapped to generate abnormal repair instructions.

[0049] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0050] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0051] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for adaptively repairing line losses in a distribution network, characterized in that: The method comprises: Read the line topology network of the target distribution network and determine the three-element partitioning characteristics, wherein the three-element partitioning characteristics include voltage division line loss, area division line loss, and component division line loss; Based on the line topology network and the ternary division characteristics, an abnormality location module is built, the abnormality location module includes parallel ternary decision branches, and the abnormality location module has a built-in line loss probability calculation formula; The distribution network data is transmitted back, and combined with the abnormal location module, loss determination decision and probability calculation are performed to determine the line loss location column; A network reconstruction model is built based on network coupling characteristics, and regional network reconstruction is used as the line loss repair principle. The line loss positioning column is traversed to perform regional segmentation reconstruction and self-healing correction analysis to determine reconstruction data, which includes network configuration; Traversing the line loss location column, reading non-self-healing line loss location data, and generating an abnormality repair instruction; The reconstruction data and the abnormal repair instruction are combined to perform line loss repair management on the target distribution network.

2. A method for adaptively repairing line losses in a distribution network as claimed in claim 1, characterized in that: The determining of the line loss positioning column comprises: Based on the ternary decision branches, the distribution network data is analyzed and verified in parallel to determine a probability distribution network and a line loss distribution network; Based on the line loss degree distribution network, a primary screening is performed in combination with a standard line loss coefficient to determine the first loss assessment data, and a secondary screening is performed based on a probability distribution network to determine the second loss assessment data; The first loss estimation data and the second loss estimation data are fitted to determine the line loss positioning single column.

3. A method for adaptively repairing line losses in a distribution network as claimed in claim 2, characterized in that: The line loss probability calculation formula includes a priori probability and a posteriori probability, and obtaining the line loss probability calculation formula includes: ;in, is the prior probability, For the Visualize the eigenvalues ​​of features, For the Visualize the weights of features, is the total amount of visualized features; is the posterior probability, is the degradation probability matrix of the visualization feature, is the migration probability matrix of hidden features; For the The eigenvalues ​​of the hidden features, For the The weights of the hidden features, is the total amount of hidden features, For the Visualize the degradation probability of features, For the The transfer probability of hidden features.

4. A method for adaptively repairing line losses in a distribution network as claimed in claim 3, characterized in that: Secondary screening based on probability distribution network, including: Obtain a probability distribution network, wherein each network node includes a prior probability and a posterior probability; Traversing the probability distribution network, and performing threshold over-limit determination based on a priori probability threshold and a posterior probability threshold; If the prior probability satisfies the prior probability threshold, and the posterior probability satisfies the posterior probability threshold, adding the network node to the second damage assessment data; If either the prior probability or the posterior probability does not meet the probability threshold, the network node is identified as a pseudo outlier.

5. A method for adaptively repairing line losses in a distribution network as claimed in claim 1, characterized in that: The regional segmentation reconstruction and self-healing correction analysis includes: Based on the network coupling characteristics, traverse the line loss location column to determine multiple loss determination distribution transformer areas, wherein the coupling degree within the distribution transformer area is higher than the coupling threshold, and the coupling degree between the distribution transformer areas is lower than the coupling threshold; Traversing the plurality of distribution transformer areas for damage assessment, reconstructing the regional network, and determining a plurality of distribution transformer data, wherein the regional network reconstruction analysis method is a multi-objective optimization method; A collision avoidance analysis is performed on the plurality of distribution transformer data, and the network configuration is determined by splicing.

6. A method for adaptively repairing line losses in a distribution network as claimed in claim 5, characterized in that: Based on the correction requirements, the scale of the multiple damage-rated distribution transformer areas is adjusted, wherein the scale adjustment method includes regional expansion and regional contraction.

7. A method for adaptively repairing line losses in a distribution network as claimed in claim 1, characterized in that: The generating of the abnormality repair instruction comprises: For non-self-healing line loss location data, trace the cause of the line loss and determine the repair plan set; The repair time limit is set based on the repair priority according to the line loss level and the network impact, wherein the repair priority is positively correlated with the line loss level and the network impact; The repair plan set and the repair time limit are mapped to generate an abnormal repair instruction.

8. A distribution network line loss adaptive repair system, characterized in that: The steps for implementing the method for adaptively repairing line losses in a distribution network as claimed in any one of claims 1 to 7 include: A three-element partition feature unit: reads the line topology network of the target distribution network and determines the three-element partition feature, wherein the three-element partition feature includes voltage division line loss, area division line loss, and component division line loss; An abnormality location module building unit: based on the line topology network and the ternary division characteristics, building an abnormality location module, the abnormality location module includes parallel ternary decision branches, and the abnormality location module has a built-in line loss probability calculation formula; Line loss location single column determination unit: returns distribution network data, combines the abnormal location module, makes loss determination decisions and probability calculations, and determines the line loss location single column; Reconstruction data determination unit: build a network reconstruction model based on network coupling characteristics, take regional network reconstruction as the line loss repair principle, traverse the line loss positioning column, perform regional segmentation reconstruction and self-healing correction analysis, and determine reconstruction data, wherein the reconstruction data includes network configuration; An abnormal repair instruction generating unit: traverses the line loss location column, reads the non-self-healing line loss location data, and generates an abnormal repair instruction; Line loss repair management unit: combining the reconstruction data and the abnormal repair instruction to perform line loss repair management on the target distribution network.

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