State analysis method and device for unmonitored area of power distribution network and medium

Abstract

The invention discloses a state analysis method and device for an unmonitored area of a power distribution network and a medium. The method relates to the field of power grid management, and solves the problem that the state of data nodes in a power distribution system in a region where power distribution terminals cannot be comprehensively deployed cannot be comprehensively monitored. The method comprises the steps of obtaining power grid data of a monitoring node of a pre-deployed power distribution terminal in a power distribution system, constructing a target optimization function by using a state vector and a selection matrix, screening out a target node, and determining a power grid state of an unmonitored area through a calculation and deduction method. Actual deployment of the power distribution terminal is replaced by calculation and deduction, so that equipment purchase, installation and maintenance costs are reduced, low-cost and high-efficiency data acquisition is realized, equipment investment and maintenance costs are reduced, and the intelligent level of the power distribution network is improved. The problem of data missing caused by the fact that power distribution terminals are not deployed in partial areas in the power distribution system is solved, the accuracy and efficiency of state analysis of the power distribution system are improved, and high universality and flexibility are achieved.

Description

Technical Field

[0001] The present application relates to the field of power grid management, and in particular to a method, device and medium for analyzing the status of an unmonitored area of a distribution network. Background Art

[0002] As a crucial component of the smart distribution network, the deployment and management of smart distribution terminals directly impacts the stability and reliability of the distribution network. By optimizing the distribution terminal deployment algorithm, the impact of link failures and poor communication quality on services is effectively reduced. However, this solution places high demands on the deployment of distribution terminals, requiring the comprehensive deployment of a large number of distribution terminals.

[0003] However, in some regions, where distribution networks are numerous and widespread, automation construction faces incomplete coverage of the "three remote controls" for medium-voltage switches. In some provinces, coverage is less than 50%, and feeder automation line coverage is low. While transparency can be achieved by fully installing data collection devices, the high construction and operation and maintenance costs make full deployment of distribution terminals impossible in some regions, resulting in incomplete grid monitoring.

[0004] It can be seen that realizing status monitoring of grid nodes in the distribution system in areas where full deployment of distribution terminals is not possible is a technical problem that needs to be solved urgently by people in this field. Summary of the Invention

[0005] The purpose of this application is to provide a method, device and medium for analyzing the status of unmonitored areas of a distribution network, so as to solve the problem that the status of grid nodes cannot be fully monitored in distribution systems in areas where full deployment of distribution terminals is not possible.

[0006] To solve the above technical problems, the present application provides a method for analyzing the status of an unmonitored area of a distribution network, comprising:

[0007] Obtaining grid data from monitoring nodes pre-deployed with distribution terminals in the distribution system;

[0008] generating a state vector based on the power grid data of the monitoring nodes, and determining a selection matrix based on all the monitoring nodes;

[0009] Constructing a target optimization function of the power distribution system according to the selection matrix and the state vector;

[0010] Filtering a target node from the monitoring nodes according to an optimization result of the target optimization function;

[0011] Based on the power grid data of the target node, the distribution transformer voltage, distribution transformer power, and line switch position information of the power grid node where the power distribution terminal is not deployed are determined.

[0012] As an optional solution, in the above-mentioned method for analyzing the status of an unmonitored area of the distribution network, obtaining the grid data of the monitoring nodes in the distribution system where the distribution terminals are pre-deployed includes:

[0013] Get the preset acquisition frequency;

[0014] The distribution terminal pre-deployed in the distribution system is controlled to obtain the grid data of each monitoring node according to the preset collection frequency; wherein the grid data includes: each phase voltage, each phase current, active power, reactive power, frequency, power factor, harmonic current, and harmonic voltage.

[0015] As an optional solution, in the above-mentioned state analysis method for an unmonitored area of a distribution network, generating a state vector based on the grid data of the monitoring nodes and determining a selection matrix based on all the monitoring nodes include:

[0016] Inputting the power grid data of the monitoring node into a preset state estimation model;

[0017] Solving the preset state estimation model to obtain the state vector;

[0018] Wherein, the preset state estimation model is: ;

[0019] Wherein, X(t) represents the state vector at time t; H(t) represents the state estimation matrix; Y(t) represents the vector of the power grid data of the monitoring node at time t;

[0020] determining a selection matrix based on all the monitoring nodes;

[0021] Wherein, the selection matrix is: ;

[0022] Where, Indicates the grid nodes; , and when When There are no power distribution terminals deployed in the power grid nodes. When Power distribution terminals are deployed at each grid node; It is a positive integer, indicating the number of monitoring nodes.

[0023] As an optional solution, in the above-mentioned state analysis method for an unmonitored area of a distribution network, constructing a target optimization function of the distribution system based on the selection matrix and the state vector includes:

[0024] constructing a target optimization function of the power distribution system according to the selection matrix;

[0025] determining a representative error function of the target optimization function according to the state vector;

[0026] Wherein, the objective optimization function is: ;

[0027] The representative error function is: ;

[0028] Where, represents the target optimization function; represents the representative error function; represents the state vector, represents the selection matrix, Indicates the allowable error.

[0029] As an optional solution, in the above-mentioned method for analyzing the state of the unmonitored area of the distribution network, determining the distribution transformer voltage of the grid node where the distribution terminal is not deployed based on the grid data of the target node includes:

[0030] Determine, based on the grid data of the target node, an actual voltage on the secondary side of a grid node where the power distribution terminal is not deployed and a transformer turns ratio on the secondary side;

[0031] According to a preset conversion formula, a deduced voltage on the primary side corresponding to the secondary side of the grid node where the power distribution terminal is not deployed is obtained;

[0032] Wherein, the preset conversion formula is: ;

[0033] in, represents the deduced voltage on the primary side, represents the actual voltage on the secondary side, and K represents the transformer turns ratio;

[0034] Determining whether a preset voltage condition is met based on the deduced voltage on the primary side and the actual voltage on the primary side;

[0035] If so, save the current grid node distribution transformer voltage information based on the deduced voltage on the primary side.

[0036] As an optional solution, in the above-mentioned method for analyzing the status of the unmonitored area of the distribution network, determining the distribution transformation power of the grid node where the distribution terminal is not deployed based on the grid data of the target node includes:

[0037] Determine, based on the grid data of the target node, the primary side output power, the primary side copper loss power, and the primary side iron loss power of the grid node where the power distribution terminal is not deployed;

[0038] Determining the deduced input power of the primary side based on the primary side output power, the primary side copper loss power, the primary side iron loss power and a first preset power calculation formula;

[0039] The first preset power calculation formula is: ;

[0040] in, represents the deduced input power on the primary side, Indicates the primary side measured output power, Indicates the primary side copper loss power, Indicates the primary side iron loss power;

[0041] Determine, based on the grid data of the target node, the secondary side output power, the secondary side copper loss power, and the secondary side iron loss power of the grid node where the power distribution terminal is not deployed;

[0042] Determining the deduced input power of the secondary side based on the secondary side output power, the secondary side copper loss power, the secondary side iron loss power and a second preset power calculation formula;

[0043] The second preset power calculation formula is: ;

[0044] in, represents the deduced input power on the secondary side, represents the secondary side output power, Indicates the secondary side copper loss power, Indicates the secondary side iron loss power;

[0045] Determine the distribution transformer power based on the deduced input power on the primary side and the deduced input power on the secondary side;

[0046] Determining whether the distribution transformer power meets a preset power condition based on the grid data of the target node;

[0047] If so, save the current grid node distribution and transformation power information.

[0048] As an optional solution, in the above-mentioned method for analyzing the status of an unmonitored area of the distribution network, determining the line switch location information of the grid node where the distribution terminal is not deployed based on the grid data of the target node includes:

[0049] Determine the primary side output power and primary side voltage of the grid node where the power distribution terminal is not deployed based on the grid data of the target node, and obtain the primary side current by combining with a preset current calculation formula;

[0050] Determine the secondary side output power and secondary side voltage of the grid node where the power distribution terminal is not deployed based on the grid data of the target node, and obtain the secondary side current by combining with a preset current calculation formula;

[0051] Wherein, the preset current calculation formula is: ;

[0052] Where, Indicates the primary side current or the secondary side current, Indicates the primary side output power or the secondary side output power, Indicates the primary side voltage or the secondary side voltage, Indicates power factor;

[0053] Determining whether the primary side current and the secondary side current meet a preset consistency condition according to the switch position in the power distribution system;

[0054] If yes, the line switch information of the current grid node is saved.

[0055] To solve the above technical problems, the present application further provides a state analysis device for an unmonitored area of a distribution network, comprising:

[0056] An acquisition module is used to acquire grid data of monitoring nodes in the power distribution system where distribution terminals are pre-deployed;

[0057] a processing module, configured to generate a state vector based on the power grid data of the monitoring nodes, and determine a selection matrix based on all the monitoring nodes;

[0058] A construction module, configured to construct a target optimization function of the power distribution system according to the selection matrix and the state vector;

[0059] A screening module, configured to screen out target nodes from the monitoring nodes according to an optimization result of the target optimization function;

[0060] A deduction module is used to determine the distribution transformer voltage, distribution transformer power, and line switch position information of the grid node where the distribution terminal is not deployed based on the grid data of the target node.

[0061] To solve the above technical problems, the present application further provides a state analysis device for an unmonitored area of a distribution network, comprising:

[0062] memory for storing computer programs;

[0063] A processor is configured to implement the steps of the above-mentioned method for analyzing the status of an unmonitored area of a distribution network when executing the computer program.

[0064] To solve the above technical problems, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for analyzing the status of unmonitored areas of the distribution network are implemented.

[0065] The state analysis method for unmonitored areas of the distribution network provided in the present application obtains the grid data of the monitoring nodes of the distribution terminals pre-deployed in the distribution system, constructs the target optimization function using the state vector and the selection matrix, screens out the target nodes, and determines the grid state of the unmonitored area by means of computational deduction. By replacing the actual deployment of distribution terminals with computational deduction, the equipment procurement, installation and maintenance costs are reduced, low-cost and high-efficiency data acquisition is achieved, equipment investment and maintenance costs are reduced, and the intelligence level of the distribution network is improved. It solves the problem of missing data caused by the non-deployment of distribution terminals in some areas of the distribution system, improves the accuracy and efficiency of the distribution system state analysis, is not limited to the specific type of distribution terminals and deployment locations, and has strong versatility and flexibility.

[0066] In addition, the present application also provides a device and a medium, which correspond to the above-mentioned status analysis method of the unmonitored area of the distribution network, and have the same effect as above. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0068] Figure 1 A flow chart of a method for analyzing the status of an unmonitored area of a distribution network is provided for an embodiment of the present application;

[0069] Figure 2 A schematic diagram of simulation results of the deployment performance of multiple methods provided in an embodiment of the present application;

[0070] Figure 3 A schematic diagram of RDC distribution characteristics of different deployment methods in 50 different simulation scenarios provided in an embodiment of the present application;

[0071] Figure 4 A schematic diagram of RDA distribution characteristics of different deployment methods in 50 different simulation scenarios provided in an embodiment of the present application;

[0072] Figure 5 A schematic diagram of the distribution characteristics of RNLs for 50 groups of different deployment methods provided in an embodiment of the present application;

[0073] Figure 6A schematic diagram of TR distribution characteristics of different deployment methods in 50 different simulation scenarios provided in an embodiment of the present application;

[0074] Figure 7 A schematic diagram of RRF distribution characteristics of different deployment methods under 50 different simulation scenarios provided in an embodiment of the present application;

[0075] Figure 8 A schematic diagram of cost-performance simulation results of different deployment methods provided in an embodiment of the present application;

[0076] Figure 9 A structural diagram of a device for analyzing the status of an unmonitored area of a distribution network provided in an embodiment of the present application;

[0077] Figure 10 This is a structural diagram of another device for analyzing the status of an unmonitored area of a distribution network provided in an embodiment of the present application. DETAILED DESCRIPTION

[0078] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0079] The core of this application is to provide a method, device and medium for status analysis of unmonitored areas of a distribution network.

[0080] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0081] As a crucial component of smart distribution networks, the deployment and management of smart distribution terminals directly impacts the stability and reliability of the network. Improving the accuracy and intelligence of data collection within the distribution network, as well as optimizing terminal deployment and data collection strategies, have become critical issues that require urgent resolution.

[0082] As the diversity and penetration of smart devices in distribution networks increases, the data they generate will grow exponentially. The deployment method of smart distribution terminals will directly affect the effectiveness of distribution network data processing and zoning control. Therefore, studying distribution terminal deployment methods can help improve system performance, real-time performance, and reliability, while reducing operation and maintenance costs.

[0083] Currently, a growing number of studies are focusing on this area. Based on the topological characteristics of the distribution system and the spatial characteristics of residential areas, this approach combines spatial information about resident nodes and electricity usage patterns to generate characteristic data. This approach uses an improved density peak analysis algorithm to determine the number, location, and service range of distribution terminal deployments. While this approach addresses the issue of residential electricity consumption response and takes into account the impact of communications on distribution terminal deployment, it is not suitable for data processing scenarios with high real-time requirements.

[0084] By establishing a model of smart terminals, user transmission delay and robustness, the smart distribution terminal deployment problem is transformed into a minimum dominating set optimization problem with constraints. The network robustness is measured based on coincidence domination, and a distribution terminal deployment algorithm based on coincidence domination is designed.

[0085] Both of these approaches consider the impact of communications on the deployment of distribution terminals, but they still have significant shortcomings for the dynamic and highly variable nature of distribution system services. In particular, the spatiotemporal correlation between sources and loads in distribution networks significantly impacts the deployment of edge computing nodes. Furthermore, the development of new distribution systems presents challenges such as difficulty in accurate modeling, high computational complexity, and poor adaptability to dynamic environments.

[0086] To solve the above problems, the present invention provides a method for analyzing the status of an unmonitored area of a distribution network. Figure 1 Shown, including:

[0087] S11: Acquire grid data of monitoring nodes pre-deployed with distribution terminals in the distribution system;

[0088] S12: Generate a state vector based on the grid data of the monitoring node, and determine a selection matrix based on all monitoring nodes;

[0089] S13: Constructing the target optimization function of the distribution system based on the selection matrix and state vector;

[0090] S14: Filtering target nodes from monitoring nodes according to the optimization result of the target optimization function;

[0091] S15: Determine the distribution transformer voltage, distribution transformer power, and line switch position information of the grid node where no distribution terminal is deployed based on the grid data of the target node.

[0092] This embodiment is applicable to smart distribution grid environments, where a certain number of distribution terminals have been pre-deployed within the distribution system to monitor grid data. These terminals can collect key parameters such as voltage, current, and power, and transmit them to a data processing center via a communication network. However, due to cost, technical, or other limitations, some areas of the distribution system still lack distribution terminals. The grid status in these areas requires analysis through computational inference.

[0093] Step S11 acquires grid data from monitoring nodes in the distribution system that have pre-deployed distribution terminals. This data, including key parameters such as voltage, current, and power, forms the basis for subsequent analysis. The distribution terminals must function properly and ensure data accuracy and real-time performance. The distribution terminals must be able to reliably collect and transmit grid data.

[0094] Distribution terminals collect grid data through built-in sensors and transmit it to a data processing center via communication networks (such as fiber optics and wireless). The data processing center receives and stores this data for subsequent analysis. Typically, monitoring nodes pre-deployed with distribution terminals include at least head-end grid nodes, end-point grid nodes, and multi-branch grid nodes.

[0095] Step S12 generates a state vector based on the grid data from the monitoring nodes and determines a selection matrix based on all the monitoring nodes. This process organizes the grid data from the monitoring nodes into a state vector that represents the current state of the distribution system. The selection matrix is also determined based on the deployment of all the monitoring nodes.

[0096] The state vector can be generated by arranging the grid data of the monitoring nodes in a certain order and format. The selection matrix is used to represent the deployment of the monitoring nodes. For example, when a monitoring node is deployed, the corresponding matrix element is 1, otherwise it is 0.

[0097] Step S13 constructs a target optimization function for the distribution system based on the selection matrix and state vector. This function aims to improve the accuracy and efficiency of distribution system state analysis by optimizing the selection of monitoring nodes.

[0098] The objective optimization function can be constructed based on objectives such as minimizing error, maximizing coverage, etc. For example, monitoring nodes can be selected to minimize the state estimation error of the distribution system, or to maximize the state deduction accuracy of the unmonitored area.

[0099] Step S14 selects target nodes from the monitoring nodes based on the optimization results of the target optimization function. By solving the target optimization function, the nodes most critical to the distribution system status analysis are selected from the monitoring nodes. These target nodes will be used for subsequent status deduction and unmonitored area analysis.

[0100] Step S15 determines the distribution transformer voltage, distribution transformer power, and line switch position information of the grid node where the distribution terminal is not deployed based on the grid data of the target node, and uses the grid data of the target node to determine the distribution transformer voltage, distribution transformer power, line switch position and other information of the grid node where the distribution terminal is not deployed through calculation and deduction.

[0101] The state analysis method for unmonitored areas of the distribution network provided by the embodiment of the present application obtains the grid data of the monitoring nodes of the distribution system where the distribution terminals are pre-deployed, constructs the target optimization function using the state vector and the selection matrix, screens out the target nodes, and determines the grid state of the unmonitored area by the method of computational deduction. By replacing the actual deployment of the distribution terminals with computational deduction, the equipment procurement, installation and maintenance costs are reduced, low-cost and high-efficiency data acquisition is achieved, equipment investment and maintenance costs are reduced, and the intelligence level of the distribution network is improved. The method solves the problem of missing data caused by the non-deployment of distribution terminals in some areas of the distribution system, improves the accuracy and efficiency of the distribution system state analysis, is not limited to the specific type of distribution terminals and deployment locations, and has strong versatility and flexibility.

[0102] According to the above embodiment, in a specific embodiment, obtaining grid data of a monitoring node in which a distribution terminal is pre-deployed in a power distribution system includes:

[0103] Get the preset acquisition frequency;

[0104] The control distribution system is pre-deployed with distribution terminals to obtain grid data from each monitoring node according to a preset collection frequency; the grid data includes: each phase voltage, each phase current, active power, reactive power, frequency, power factor, harmonic current, and harmonic voltage.

[0105] Obtain a preset collection frequency and control the pre-deployed distribution terminals in the distribution system to obtain grid data from each monitoring node based on this collection frequency. The preset collection frequency should be determined based on the actual needs of the distribution system and the accuracy requirements of the data analysis. Different monitoring parameters may require different collection frequencies. For example, the collection frequency of voltage and current is generally higher, while the collection frequency of power factor and harmonic analysis can be relatively lower.

[0106] Based on the characteristics of the power distribution system and the data analysis requirements, the collection frequency of each monitoring parameter can be pre-set. This can be achieved through the power distribution management system or remote configuration tools.

[0107] The distribution management system sends a collection instruction to the distribution terminal, including the collection frequency and a list of parameters to be monitored. The distribution terminal begins collecting data according to the instruction and uploads it to the data processing center at a preset frequency.

[0108] By precisely controlling the data collection process, we achieve high-precision monitoring of the distribution system status. Because different monitoring parameters require different data collection frequencies, we choose to perform data collection operations under the conditions of a preset collection frequency to ensure data accuracy and real-time performance.

[0109] By precisely controlling the data acquisition frequency and parameters, the acquired power grid data is ensured to be accurate, providing a reliable basis for subsequent status analysis.

[0110] According to the above embodiment, in a specific embodiment, generating a state vector based on the grid data of the monitoring nodes and determining a selection matrix based on all the monitoring nodes includes:

[0111] Inputting the power grid data of the monitoring node into a preset state estimation model;

[0112] Solve the preset state estimation model to obtain the state vector;

[0113] Among them, the preset state estimation model is: ;

[0114] Where X(t) represents the state vector at time t; H(t) represents the state estimation matrix; Y(t) represents the vector of power grid data of the monitoring node at time t;

[0115] Determine the selection matrix based on all monitoring nodes;

[0116] Among them, the selection matrix is: ;

[0117] Where, Indicates the grid nodes; , and when When There are no power distribution terminals deployed in the power grid nodes. When Power distribution terminals are deployed at each grid node; It is a positive integer, indicating the number of monitoring nodes.

[0118] The multi-dimensional grid data (voltage / current / power, etc.) of the monitoring node is input into a preset state estimation model, which is used to estimate the state of the entire distribution system based on the monitoring data.

[0119] X(t) represents the state vector at time t, which includes state parameters such as the voltage amplitude and phase angle at the target node in the distribution network. H(t) represents the state estimation matrix, which reflects the nonlinear relationship between the measured and state variables. Y(t) represents the grid data vector at the monitoring node at time t, which includes real-time data collected from the monitoring node. By solving the state estimation model, the state vector of the distribution network at time t is obtained.

[0120] The selection matrix indicates whether a distribution terminal is deployed at each grid node. The selection matrix is constructed based on the distribution network topology and monitoring requirements. An optimization algorithm is used to determine which nodes require distribution terminals for optimal monitoring. Assuming a distribution network consisting of 100 nodes, 50 of them are equipped with distribution terminals. Real-time grid data is collected through these terminals and fed into a pre-defined state estimation model for solution. Based on the resulting state vector and the constructed selection matrix, further analysis of distribution transformer voltage, distribution transformer power, and line switches was performed.

[0121] By constructing the selection matrix and solving the target optimization function, the optimal deployment of distribution terminals is achieved, reducing equipment investment and maintenance costs.

[0122] According to the above embodiment, in a specific embodiment, constructing a target optimization function of the power distribution system based on the selection matrix and the state vector includes:

[0123] According to the selection matrix, the target optimization function of the distribution system is constructed;

[0124] Determine a representative error function of the target optimization function based on the state vector;

[0125] Among them, the objective optimization function is: ;

[0126] The representative error function is: ;

[0127] Where, represents the target optimization function; represents the representative error function; represents the state vector, represents the selection matrix, Indicates the allowable error.

[0128] Monitoring nodes have been identified in the power distribution system, and the deployment status of each node is represented by a selection matrix. The selection matrix can also be specifically formulated as a two-dimensional array, where each row represents a monitoring node and each column represents a decision variable (i.e., whether to deploy a distribution terminal). For example, for a system with n monitoring nodes, the selection matrix can be an n×1 column vector, where an element value of 1 indicates that the corresponding node is selected, and a value of 0 indicates that it is not selected.

[0129] Because the distribution system contains numerous data nodes, and each node has varying data collection costs, data quality, and real-time requirements, an optimization algorithm is needed to find the optimal node deployment solution. By selecting a matrix, a target optimization function for the distribution system is constructed. This function aims to find the optimal data node deployment solution that minimizes data collection and processing costs while meeting data accuracy and real-time requirements.

[0130] A state vector is a vector that contains key state parameters of a distribution system, such as voltage amplitude, phase angle, and power. It represents the overall state of the distribution system at a certain moment. The state vector can be specifically a multidimensional array, where each element represents a specific state parameter. For example, in a three-phase power system, the state vector may contain three voltage amplitudes, three phase angles, and parameters such as active power and reactive power. The representative error function is an indicator used to measure the quality of the solution to the objective optimization function. It represents the error between the data node deployment plan obtained by the optimization algorithm and the actual optimal plan. The specific form of the objective optimization function is further refined through the state vector and the representative error function.

[0131] According to the representative error function, the target optimization function is solved, and according to the solution result of the target optimization function, the target node is determined from the monitoring nodes.

[0132] After the target node is determined, preferably, the power grid data collected by the power distribution terminal of the target node is obtained, and data repair is performed on the power grid data to obtain the repaired power grid data.

[0133] For example, because data may be lost or anomalies may occur during transmission, distribution network data needs to be preprocessed. During this preprocessing process, data repair can be performed on the distribution network data. Distribution terminals in the distribution system need to collect data such as voltage, current, power, topology, and device status. Device status can include status parameters of equipment such as transformers, circuit breakers, and lines.

[0134] For example, for power distribution terminals, real-time status data uses a verification method based on historical data, that is, historical data is used for comparison to detect whether there are any abnormalities in the current data. The difference in status data at time tk is shown in the following formula: ;

[0135] in Indicates the difference in status data, represents the state data at time k, Represents the state data at time k, if If the predetermined threshold is exceeded, the status data at this time can be considered abnormal.

[0136] After discovering abnormal data, the data can be repaired accordingly. First, the K-means clustering algorithm is used to cluster the historical data and the current data to obtain similar data sets. After constructing the objective function and solving the objective function, the similar data sets are weighted averaged to calculate the filling value of the missing data for repair.

[0137] For the abnormal data detected above, if it is completely unrepairable, it will be directly eliminated, while for slightly abnormal data, it will be corrected based on the adjacent data.

[0138] In this embodiment, the distribution terminals in the power distribution system collect various types of data, then verify the collected data and detect abnormal data; secondly, the abnormal data is repaired to ensure data integrity and rationality; finally, the processed data is stored in the database to provide basic data for subsequent calculations and deductions, ensuring the accuracy and reliability of the calculations and deductions, thereby improving the operating efficiency and safety of the intelligent distribution terminals.

[0139] According to the above embodiment, in a specific embodiment, determining the distribution transformer voltage of a grid node where no distribution terminal is deployed based on the grid data of the target node includes:

[0140] Determine, based on the grid data of the target node, the actual voltage on the secondary side of the grid node where no distribution terminal is deployed and the turns ratio of the transformer on the secondary side;

[0141] According to the preset conversion formula, the deduced voltage on the primary side corresponding to the secondary side of the grid node where no distribution terminal is deployed is obtained;

[0142] Among them, the preset conversion formula is: ;

[0143] in, represents the deduced voltage on the primary side, represents the actual voltage on the secondary side, and K represents the transformer turns ratio;

[0144] Determine whether the preset voltage condition is met based on the deduced voltage on the primary side and the actual voltage on the primary side;

[0145] If so, save the current grid node distribution transformer voltage information based on the deduced voltage on the primary side.

[0146] After determining the target node (the node where the distribution terminal is deployed), the voltage status of adjacent unmonitored nodes needs to be deduced. The actual secondary voltage refers to the transformer secondary voltage directly measured through the distribution terminal, such as the 380V or 220V low-voltage side voltage. The transformer turns ratio refers to the ratio of the primary and secondary windings of the distribution transformer. For example, the turns ratio of a 10kV / 0.4kV distribution transformer is 25:1.

[0147] The deduced primary voltage is the estimated voltage on the high-voltage side of the transformer, obtained through calculation. The actual secondary voltage is the low-voltage side measurement directly obtained from the distribution terminal. The actual primary voltage refers to the reference voltage value obtained from adjacent monitoring nodes or upstream substations.

[0148] When the error meets the requirements, the deduction results will be stored in the real-time database to ensure the reliability of the deduced voltage and avoid erroneous data affecting system judgment.

[0149] According to the above embodiment, in a specific embodiment, determining the distribution and transformation power of a grid node where no distribution terminal is deployed based on the grid data of the target node includes:

[0150] Determine the primary side output power, primary side copper loss power, and primary side iron loss power of the grid node where no distribution terminal is deployed based on the grid data of the target node;

[0151] Determining the deduced input power of the primary side based on the primary side output power, the primary side copper loss power, the primary side iron loss power, and a first preset power calculation formula;

[0152] The first preset power calculation formula is: ;

[0153] in, represents the deduced input power on the primary side, Indicates the primary side measured output power, Indicates the primary side copper loss power, Indicates the primary side iron loss power;

[0154] Determine the secondary side output power, secondary side copper loss power, and secondary side iron loss power of the grid node where no distribution terminal is deployed based on the grid data of the target node;

[0155] Determine the deduced input power on the secondary side based on the secondary side output power, the secondary side copper loss power, the secondary side iron loss power, and a second preset power calculation formula;

[0156] The second preset power calculation formula is: ;

[0157] in, represents the deduced input power on the secondary side, represents the secondary side output power, Indicates the secondary side copper loss power, Indicates the secondary side iron loss power;

[0158] Determine the distribution transformer power based on the deduced input power on the primary side and the deduced input power on the secondary side;

[0159] Determine whether the distribution transformer power meets the preset power conditions based on the grid data of the target node;

[0160] If so, save the current grid node distribution and transformation power information.

[0161] The primary side output power refers to the active power output on the high-voltage side of the transformer, usually measured in kW; the primary side copper loss power refers to the power loss caused by the resistance of the transformer high-voltage winding; the primary side iron loss power refers to the power loss caused by hysteresis and eddy current in the transformer core.

[0162] The deduced input power refers to the estimated value of the active power input on the high-voltage side of the transformer, which completes the high-voltage side power balance calculation and provides data for power status analysis.

[0163] Secondary output power refers to the active power output from the transformer's low-voltage side. Secondary copper loss refers to the resistive loss of the transformer's low-voltage winding. Secondary iron loss refers to the core loss of the transformer on the low-voltage side. Deduced secondary input power refers to the estimated active power input to the transformer's low-voltage side.

[0164] This embodiment uses an innovative power deduction method to significantly reduce the cost of distribution network status perception while ensuring calculation accuracy, providing an effective means for refined management of smart distribution networks.

[0165] According to the above embodiment, in a specific embodiment, determining the line switch location information of the grid node where no distribution terminal is deployed based on the grid data of the target node includes:

[0166] Determine the primary-side output power and primary-side voltage of the grid node where no distribution terminal is deployed based on the grid data of the target node, and obtain the primary-side current by combining the preset current calculation formula;

[0167] Determine the secondary-side output power and secondary-side voltage of the grid node where no distribution terminal is deployed based on the grid data of the target node, and obtain the secondary-side current by combining the preset current calculation formula;

[0168] Among them, the preset current calculation formula is: ;

[0169] Where, Indicates the primary side current or the secondary side current, Indicates the primary side output power or the secondary side output power, Indicates the primary side voltage or the secondary side voltage, Indicates power factor;

[0170] Determine whether the primary and secondary currents meet the preset consistency conditions based on the switch positions in the power distribution system;

[0171] If yes, the line switch information of the current grid node is saved.

[0172] Based on the grid data of the target node, the primary and secondary currents of the grid node without the distribution terminal are calculated. Current is the most direct electrical quantity reflecting the status of the circuit breaker.

[0173] Based on the switch positions in the power distribution system, the primary and secondary currents are determined to meet preset consistency conditions. If the currents meet the consistency conditions, the circuit breaker information for the current grid node is saved for subsequent analysis and use. The preset consistency conditions can be set based on actual conditions, such as the deviation range of the current value.

[0174] In the deduction calculation of line switches, the power, voltage, and status of the primary side of each terminal node (distribution transformer) can be solved. On this basis, the current is deduced and calculated. Then, according to the order of the distribution transformer in the line and line segment, the power, voltage, status, and current of the line nodes (including: busbar, off-site-cable segment, pole-disconnector, conductor segment, off-site-medium-voltage user access point, pole-user transformer, pole-transformer, pole-circuit breaker, distribution transformer-user, distribution transformer-double winding, pole-load switch) are deduced upward to the head-end line switch. Then, combined with the measurement data of the line switch, relevant data is obtained through the electric energy automatic metering system (TMR) and energy management system (EMS), and the deduction results are verified step by step from top to bottom.

[0175] In addition, this application can also be used to evaluate the specific terminal deployment solution to achieve a lower deployment cost while ensuring other performance.

[0176] S21: Get simulation parameters;

[0177] S22: Processing the simulation parameters using the method in steps S11 to S16 to obtain a simulated distribution transformer voltage deduction result, a simulated distribution transformer power deduction result, and a simulated line switch deduction result of the simulation parameters, and determining a first simulation result corresponding to the simulation parameters;

[0178] The simulation results include data acquisition coverage, computational deduction accuracy, computational deduction precision, redundancy rate, and response time;

[0179] S23: simulating the simulation parameters using a one-to-one smart terminal deployment method, a hybrid deployment optimization method based on terminal type, a smart terminal deployment optimization method based on data collection frequency, a deployment optimization method considering terminal energy consumption, and a terminal deployment optimization method considering communication network limitations, respectively, to obtain a second simulation result, a third simulation result, a fourth simulation result, a fifth simulation result, and a sixth simulation result;

[0180] S24: Compare the first simulation result with the second simulation result, the third simulation result, the fourth simulation result, the fifth simulation result, and the sixth simulation result, and evaluate the first simulation result.

[0181] The simulation method for the "one-to-one" smart terminal deployment method (OOD) is to deploy smart distribution terminals on all nodes, with a high frequency of data collection. The simulation method for the hybrid deployment optimization method based on terminal types (HDT) is to deploy different types of terminals and optimize the distribution of electrical terminals and non-electrical terminals. The simulation method for the smart terminal deployment optimization method based on data collection frequency (DCF) is to collect data at high frequencies in key areas and at low frequencies in general areas. The simulation method for the deployment optimization method considering energy consumption of terminals (DCE) is to optimize terminals based on power consumption and reduce the operating frequency of terminals with high energy consumption. The simulation method for the terminal deployment optimization method considering communication network constraints (DCC) is to use a combination of multiple communication technologies to optimize network transmission. To facilitate the analysis and comparison of simulation results, a calculation and deduction method for smart distribution networks in this embodiment can be denoted as DMC and compared with other methods to evaluate DMC.

[0182] Figure 2 A schematic diagram of simulation results of the deployment performance of multiple methods provided in the embodiment of the present application is shown as follows: Figure 2As shown in the figure, data collection coverage (RDC), computational simulation accuracy (RDA), network load ratio (RNL), redundancy rate (RRF), and response time (TR) are selected as performance indicators. RDC represents the overall data collection capability of the distribution network under different deployment methods; RDA indicates the accuracy of data obtained through computational simulation, reflecting the effectiveness of the simulation model; RNL measures the usage of the distribution network communication network, indicating the network bandwidth and load pressure; RRF indicates the system's ability to maintain normal operation in the event of terminal failure, reflecting the system's fault tolerance; TR is the average time it takes for a terminal to collect and upload data, reflecting the system's rapid response capability.

[0183] Specifically, Figure 3 A schematic diagram of RDC distribution characteristics of different deployment methods in 50 different simulation scenarios provided in the embodiment of this application, according to Figure 2 and Figure 3 As can be seen, since the OOD method deploys one intelligent terminal per node and does not optimize data collection points, its coverage remains at 100%. However, compared to the HDT, DCF, DCE, and DCC methods, the proposed DMC deployment method improves data coverage by 0.67%, 1.02%, 4.92%, and 1.57%, respectively. This is because HDT maintains a high coverage rate by deploying different types of terminals and optimizing the network structure; while DCE, due to its strict control over terminal usage and its optimization goal of minimizing energy efficiency, has the lowest coverage rate among all methods.

[0184] Figure 4 This is a schematic diagram of RDA distribution characteristics of different deployment methods in 50 different simulation scenarios provided by the embodiment of this application. Figure 4 In the 2018 study, the DMC deployment method improved average computational accuracy by 1.09%, 1.74%, 3.87%, and 2.22% compared to the HDT, DCF, DCE, and DCC methods, respectively. The DMC method achieved the highest accuracy by dynamically adjusting computational accuracy. In contrast, the DCE and DCC methods compromised computational accuracy to reduce energy consumption and the number of terminals.

[0185] Figure 5 A schematic diagram of the distribution characteristics of RNLs for 50 groups of different deployment methods provided in this application embodiment. Figure 5In the data presented, the proposed DMC deployment method reduced average network load by 35.09%, 25.56%, 15.19%, and 5.53% compared to the OOD, HDT, DCF, DCE, and DCC methods, respectively. However, compared to the DCC method, the proposed DMC method had a 4.8% higher network load. This is because DCC's optimization goal is to reduce network load. By optimizing communication technology and reducing data transmission volume, this method sacrifices other performance factors to achieve optimal network load performance. Furthermore, DCE reduces communication requirements by reducing terminal operating frequencies, resulting in a lower network load.

[0186] Figure 6 A schematic diagram of TR distribution characteristics of different deployment methods in 50 different simulation scenarios provided in the embodiment of this application. Figure 6 In the data presented, the proposed DMC deployment method reduced average response time by 41.91%, 33.71%, 25.61%, 8.18%, and 14.77% compared to the OOD, HDT, DCF, DCE, and DCC methods, respectively. Because the proposed DMC method optimizes the processing of collected data and establishes a computational deduction process, it has the shortest response time, meaning it can respond more quickly to changes in the system.

[0187] Figure 7 The following is a schematic diagram of RRF distribution characteristics of different deployment methods in 50 different simulation scenarios provided in the embodiment of this application. Figure 2 and Figure 7 As can be seen, compared to the OOD, HDT, DCF, and DCC methods, the proposed DMC deployment method improves redundancy by 3.30%, 1.20%, 2.08%, and 0.88%, respectively; however, compared to the DCE method, the proposed DMC method reduces network load by 0.71%. Although the OOD method uses a "one-to-one" terminal deployment scheme, it does not optimize the deployment and computational inference methods, resulting in the lowest redundancy rate among all methods. HDT, by deploying different types of terminals and optimizing the network structure, also achieves good redundancy. DCC also achieves a high redundancy rate due to its thorough optimization of the communication network. The DCE method optimizes the operating status of distribution terminals, reducing terminal utilization. This means that more terminals are in a dormant state, allowing for more resilience in the event of terminal failure, resulting in the highest redundancy performance.

[0188] Optionally, the total number of deployed terminals (ND), deployment cost (CD), operation and maintenance workload (OM), energy consumption (EC), and carbon emissions (CE) can be selected as performance indicators to evaluate the cost performance of the DMC method. ND measures the actual number of deployed terminals and reflects the use of hardware resources. CD includes the procurement and installation costs of terminal equipment, reflecting the overall investment cost, measured in 10,000 yuan per year. OM represents the annual operation and maintenance time required, reflecting the consumption of maintenance resources, measured in hours per year. EC represents the annual energy consumption of the terminal, reflecting the energy-saving performance of the system, measured in kilowatt-hours per year. CE represents the carbon dioxide emissions generated by the energy system during production, transportation, use, and recycling, representing the environmental impact of the system, measured in tons per year.

[0189] In a specific embodiment, Figure 8 As shown, Figure 8 This is a schematic diagram of the cost performance simulation results of different deployment methods provided in the embodiment of this application. Figure 8 As shown in the figure, the proposed DMC method reduces the number of terminals required by OOD, HDT, DCF, and DCC by 41%, 27.16%, 18.06%, and 9.23%, respectively. In terms of deployment cost, the proposed DMC method reduces deployment costs by 32.21%, 10.19%, 15.57%, and 5.37%, respectively, compared to OOD, HDT, DCF, and DCE. Because OOD deployment requires the deployment of intelligent distribution terminals at every node, it requires the highest number of terminals and has the highest deployment cost. Furthermore, all data in this method relies on physical terminals for collection. In contrast, the proposed DMC and DCE methods effectively reduce the number of terminals, primarily by reducing reliance on terminals through derivation techniques and energy-saving measures. Regarding deployment cost, HDT uses a mix of terminal types, resulting in lower costs despite having a larger number of terminals than DCF. Similarly, DCC also uses different types of terminals and takes into account the characteristics of the communication network, achieving the lowest deployment cost. Although the proposed DMC is fully deployed with intelligent terminals, their number is relatively small, thus achieving a lower deployment cost while ensuring other performances.

[0190] In terms of O&M workload, the proposed DMC method reduced O&M workload by 44.26%, 19.05%, 12.82%, and 2.78%, respectively, compared to OOD, HDT, DCF, and DCE. The proposed DMC and DCC methods achieved the lowest O&M workload by reducing terminal operating time and energy consumption. ODD, however, had the highest O&M workload due to its large number of deployed terminals. The DCF and DCE methods also effectively reduced O&M workload compared to the OOD method by dynamically adjusting terminal demand. Compared to OOD, HDT, DCF, DCE, and DCC, the proposed DMC method reduced O&M workload by 46.53%, 34.15%, 23.94%, 11.48%, and 16.92%, respectively. Because the proposed DMC method considers energy optimization and the data collection efficiency of smart terminals from the outset, it not only reduces the number of terminals but also reduces overall energy consumption through energy efficiency optimization measures, achieving the best overall energy consumption compared to other methods. This is of great significance for large-scale distribution networks and saves a lot of operating costs.

[0191] Compared to OOD, HDT, DCF, DCE, and DCC, the proposed DMC method reduced carbon emissions by 42.86%, 42.03%, 41.18%, 27.27%, and 38.46%, respectively. Because the proposed DMC method incorporates carbon emission minimization as one of its objective functions during modeling, it achieves the lowest carbon emissions compared to other methods, significantly reducing energy system carbon emissions and fulfilling the original intention of low-carbon energy system design.

[0192] In the above embodiments, a method for analyzing the status of an unmonitored area of a distribution network is described in detail. This application also provides corresponding embodiments of a device for analyzing the status of an unmonitored area of a distribution network. It should be noted that this application describes the embodiments of the device from two perspectives: one is based on the functional module perspective, and the other is based on the hardware perspective.

[0193] Based on the perspective of functional modules, Figure 9 This is a structural diagram of a state analysis device for an unmonitored area of a distribution network provided in an embodiment of the present application, such as Figure 9 As shown, a state analysis device for an unmonitored area of a distribution network includes:

[0194] An acquisition module 11 is configured to acquire grid data of monitoring nodes in a power distribution system where power distribution terminals are pre-deployed;

[0195] The processing module 12 is used to generate a state vector based on the power grid data of the monitoring nodes and determine a selection matrix based on all the monitoring nodes;

[0196] A construction module 13 is used to construct a target optimization function of the power distribution system according to the selection matrix and the state vector;

[0197] A screening module 14 is used to screen out target nodes from monitoring nodes according to the optimization result of the target optimization function;

[0198] The deduction module 15 is used to determine the distribution transformer voltage, distribution transformer power, and line switch position information of the grid node where the distribution terminal is not deployed based on the grid data of the target node.

[0199] Since the embodiments of the apparatus part correspond to the embodiments of the method part, please refer to the description of the embodiments of the method part for the embodiments of the apparatus part, and they will not be repeated here.

[0200] Figure 10 This is a structural diagram of another state analysis device for an unmonitored area of a distribution network provided in an embodiment of the present application, such as Figure 10 As shown, the state analysis device for the unmonitored area of the power distribution network includes: a memory 30 for storing a computer program;

[0201] The processor 31 is configured to implement the steps of the method for obtaining user operation habit information in the above embodiment (status analysis method for unmonitored areas of a power distribution network) when executing a computer program.

[0202] The device for analyzing the status of the unmonitored area of the power distribution network provided in this embodiment may include but is not limited to a mobile terminal, a personal computer, a workstation, and the like.

[0203] The processor 31 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 31 may be implemented in at least one of the following hardware forms: a digital signal processor (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 31 may also include a main processor and a coprocessor. The main processor is used to process data in the awake state, also known as a central processing unit (CPU); the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 31 may be integrated with a graphics processing unit (GPU), which is responsible for rendering and drawing content required to be displayed on the display screen. In some embodiments, the processor 31 may also include an artificial intelligence (AI) processor, which is used to handle computational operations related to machine learning.

[0204] The memory 30 may include one or more computer-readable storage media, which may be non-transitory. The memory 30 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In this embodiment, the memory 30 is at least used to store the following computer program 301, wherein, after the computer program is loaded and executed by the processor 31, it can implement the relevant steps of the status analysis method for the unmonitored area of the distribution network disclosed in any of the aforementioned embodiments. In addition, the resources stored in the memory 30 may also include an operating system 302 and data 303, etc., and the storage method may be temporary storage or permanent storage. Among them, the operating system 302 may include Windows, Unix, Linux, etc. The data 303 may include but is not limited to data involved in implementing the status analysis method for the unmonitored area of the distribution network.

[0205] In some embodiments, the status analysis device for the unmonitored area of the power distribution network may further include a display screen 32 , an input and output interface 33 , a communication interface 34 , a power supply 35 , and a communication bus 36 .

[0206] Those skilled in the art will understand that Figure 10 The structure shown in the figure does not constitute a limitation on the status analysis device for the unmonitored area of the power distribution network, and may include more or fewer components than shown in the figure.

[0207] The state analysis device for an unmonitored area of a distribution network provided in an embodiment of the present application includes a memory and a processor. When the processor executes a program stored in the memory, it can implement the following method: a state analysis method for an unmonitored area of a distribution network.

[0208] Finally, the present application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the embodiment of the method for analyzing the status of an unmonitored area of a power distribution network.

[0209] It is understandable that if the method in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and executes all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0210] The computer-readable storage medium provided in this embodiment stores a computer program thereon. When a processor executes the program, the following method can be implemented: a method for analyzing the status of an unmonitored area of a distribution network.

[0211] The above is a detailed introduction to the state analysis method, device and medium for the unmonitored area of the distribution network provided by the present application. The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of this application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of this application.

[0212] It should also be noted that, in this specification, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

Claims

1. A method for analyzing the status of an unmonitored area of a distribution network, characterized in that: include: Obtaining grid data from monitoring nodes pre-deployed with distribution terminals in the distribution system; generating a state vector based on the power grid data of the monitoring nodes, and determining a selection matrix based on all the monitoring nodes; Constructing a target optimization function of the power distribution system according to the selection matrix and the state vector; Filtering a target node from the monitoring nodes according to an optimization result of the target optimization function; Based on the power grid data of the target node, the distribution transformer voltage, distribution transformer power, and line switch position information of the power grid node where the power distribution terminal is not deployed are determined.

2. The method for analyzing the status of an unmonitored area of a distribution network according to claim 1, characterized in that: The obtaining of grid data of a monitoring node in the power distribution system where a power distribution terminal is pre-deployed includes: Get the preset acquisition frequency; The distribution terminal pre-deployed in the distribution system is controlled to obtain the grid data of each monitoring node according to the preset collection frequency; wherein the grid data includes: each phase voltage, each phase current, active power, reactive power, frequency, power factor, harmonic current, and harmonic voltage.

3. The method for analyzing the status of an unmonitored area of a distribution network according to claim 1, wherein: Generating a state vector based on the power grid data of the monitoring nodes and determining a selection matrix according to all the monitoring nodes includes: Inputting the power grid data of the monitoring node into a preset state estimation model; Solving the preset state estimation model to obtain the state vector; Wherein, the preset state estimation model is: ; Wherein, X(t) represents the state vector at time t; H(t) represents the state estimation matrix; Y(t) represents the vector of the power grid data of the monitoring node at time t; determining a selection matrix based on all the monitoring nodes; Wherein, the selection matrix is: ; Where, Indicates the grid nodes; , and when When There are no power distribution terminals deployed in the power grid nodes. When Power distribution terminals are deployed at each grid node; It is a positive integer, indicating the number of monitoring nodes.

4. The method for analyzing the status of an unmonitored area of a distribution network according to claim 3, characterized in that: Constructing a target optimization function of the power distribution system according to the selection matrix and the state vector, including: constructing a target optimization function of the power distribution system according to the selection matrix; determining a representative error function of the target optimization function according to the state vector; Wherein, the objective optimization function is: ; The representative error function is: ; Where, represents the target optimization function; represents the representative error function; represents the state vector, represents the selection matrix, Indicates the allowable error.

5. The method for analyzing the status of an unmonitored area of a distribution network according to claim 4, characterized in that: Determining the distribution transformer voltage of a power grid node where the power distribution terminal is not deployed based on the power grid data of the target node includes: Determine, based on the grid data of the target node, an actual voltage on the secondary side of a grid node where the power distribution terminal is not deployed and a transformer turns ratio on the secondary side; According to a preset conversion formula, a deduced voltage on the primary side corresponding to the secondary side of the grid node where the power distribution terminal is not deployed is obtained; Wherein, the preset conversion formula is: ; in, represents the deduced voltage on the primary side, represents the actual voltage on the secondary side, and K represents the transformer turns ratio; Determining whether a preset voltage condition is met based on the deduced voltage on the primary side and the actual voltage on the primary side; If so, save the current grid node distribution transformer voltage information based on the deduced voltage on the primary side.

6. The method for analyzing the status of an unmonitored area of a distribution network according to claim 5, characterized in that: Determining the distribution and transformation power of a power grid node where the power distribution terminal is not deployed based on the power grid data of the target node includes: Determine, based on the grid data of the target node, the primary side output power, the primary side copper loss power, and the primary side iron loss power of the grid node where the power distribution terminal is not deployed; Determining the deduced input power of the primary side based on the primary side output power, the primary side copper loss power, the primary side iron loss power and a first preset power calculation formula; The first preset power calculation formula is: ; in, represents the deduced input power on the primary side, Indicates the primary side measured output power, Indicates the primary side copper loss power, Indicates the primary side iron loss power; Determine, based on the grid data of the target node, the secondary side output power, the secondary side copper loss power, and the secondary side iron loss power of the grid node where the power distribution terminal is not deployed; Determining the deduced input power of the secondary side based on the secondary side output power, the secondary side copper loss power, the secondary side iron loss power and a second preset power calculation formula; The second preset power calculation formula is: ; in, represents the deduced input power on the secondary side, represents the secondary side output power, Indicates the secondary side copper loss power, Indicates the secondary side iron loss power; Determine the distribution transformer power based on the deduced input power on the primary side and the deduced input power on the secondary side; Determining whether the distribution transformer power meets a preset power condition based on the grid data of the target node; If so, save the current grid node distribution and transformation power information.

7. The method for analyzing the status of an unmonitored area of a distribution network according to claim 6, characterized in that: Determining the line switch location information of the grid node where the power distribution terminal is not deployed based on the grid data of the target node includes: Determine the primary side output power and primary side voltage of the grid node where the power distribution terminal is not deployed based on the grid data of the target node, and obtain the primary side current by combining with a preset current calculation formula; Determine the secondary side output power and secondary side voltage of the grid node where the power distribution terminal is not deployed based on the grid data of the target node, and obtain the secondary side current by combining with a preset current calculation formula; Wherein, the preset current calculation formula is: ; Where, Indicates the primary side current or the secondary side current, Indicates the primary side output power or the secondary side output power, Indicates the primary side voltage or the secondary side voltage, Indicates power factor; Determining whether the primary side current and the secondary side current meet a preset consistency condition according to the switch position in the power distribution system; If yes, the line switch information of the current grid node is saved.

8. A device for analyzing the status of an unmonitored area of a distribution network, characterized in that: include: An acquisition module is used to acquire grid data of monitoring nodes in the power distribution system where distribution terminals are pre-deployed; a processing module, configured to generate a state vector based on the power grid data of the monitoring nodes, and determine a selection matrix based on all the monitoring nodes; A construction module, configured to construct a target optimization function of the power distribution system according to the selection matrix and the state vector; A screening module, configured to screen out target nodes from the monitoring nodes according to an optimization result of the target optimization function; A deduction module is used to determine the distribution transformer voltage, distribution transformer power, and line switch position information of the grid node where the distribution terminal is not deployed based on the grid data of the target node.

9. A device for analyzing the status of an unmonitored area of a distribution network, characterized in that: include: memory for storing computer programs; A processor, configured to implement the steps of the method for analyzing the status of an unmonitored area of a distribution network as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for analyzing the status of an unmonitored area of a distribution network according to any one of claims 1 to 7.

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