A power distribution network unmonitored area state analysis method and device and medium

Abstract

The application discloses a state analysis method and device for an unmonitored area of a power distribution network and a medium; relates to the field of power grid management; and solves the problem that the state of a data node in a power distribution system in an area where power distribution terminals cannot be comprehensively deployed cannot be comprehensively monitored. The power grid data of a monitoring node in the power distribution system to which a power distribution terminal is pre-deployed is acquired, a target optimization function is constructed by using a state vector and a selection matrix, a target node is screened out, and the power grid state of the unmonitored area is determined by a calculation deduction method. The actual deployment of the power distribution terminal is replaced by the calculation deduction, the equipment procurement, installation and maintenance costs are reduced, the data acquisition is realized with low cost and high efficiency, the equipment investment and maintenance costs are reduced, and the intelligent level of the power distribution network is improved. The data missing problem caused by the fact that power distribution terminals are not deployed in some areas of the power distribution system is solved, the accuracy and efficiency of the state analysis of the power distribution system are improved, and the method has strong universality and flexibility.

Description

Technical Field

[0001] This application relates to the field of power grid management, and in particular to a method, device and medium for state analysis of unmonitored areas of a distribution network. Background Technology

[0002] As a crucial component of smart distribution networks, the deployment and management of intelligent distribution terminals directly impact the stability and reliability of the network. Optimizing distribution terminal deployment algorithms effectively reduces the impact of link failures and poor communication quality on services. However, this approach places high demands on distribution terminal deployment, requiring a large-scale, comprehensive deployment of these terminals.

[0003] However, in some regions with numerous and widespread distribution network points, the automation infrastructure suffers from incomplete coverage of remote sensing, remote control, and remote operation of medium-voltage switches, with some provinces having coverage rates below 50%, and low coverage of feeder automation lines. While full installation of data acquisition devices could achieve transparency, the construction and maintenance costs are high, making it impossible for some regions to fully deploy distribution terminals, resulting in incomplete power grid monitoring.

[0004] Therefore, it is evident that achieving status monitoring of power grid nodes in power distribution systems in areas where it is impossible to fully deploy power distribution terminals is a technical problem that urgently needs to be solved by those in this field. Summary of the Invention

[0005] The purpose of this application is to provide a method, device, and medium for state analysis of unmonitored areas of a distribution network, to solve the problem of incomplete state monitoring of power grid nodes in distribution systems in areas where it is impossible to fully deploy distribution terminals.

[0006] To address the aforementioned technical problems, this application provides a method for state analysis of unmonitored areas of a distribution network, comprising:

[0007] Acquire power grid data from monitoring nodes in the power distribution system that have pre-deployed power distribution terminals;

[0008] A state vector is generated based on the power grid data from the monitoring nodes, and a selection matrix is ​​determined based on all the monitoring nodes.

[0009] Based on the selection matrix and the state vector, the objective optimization function of the power distribution system is constructed.

[0010] Target nodes are selected from the monitoring nodes based on the optimization results of the target optimization function;

[0011] Based on the power grid data of the target node, determine the distribution transformer voltage, distribution transformer power, and line switch location information of the power grid nodes where the distribution terminal has not been deployed.

[0012] As an optional solution, in the above-mentioned method for state analysis of unmonitored areas of the distribution network, the step of acquiring power grid data from monitoring nodes with pre-deployed distribution terminals in the distribution system includes:

[0013] Obtain the preset sampling frequency;

[0014] The power distribution system is pre-deployed with power distribution terminals that acquire power grid data from each monitoring node according to the preset acquisition frequency; wherein, the power grid data includes: phase voltage, phase current, active power, reactive power, frequency, power factor, harmonic current, and harmonic voltage.

[0015] As an optional approach, in the above-mentioned state analysis method for unmonitored areas of the distribution network, the step of generating a state vector based on the power grid data from the monitoring nodes and determining a selection matrix based on all the monitoring nodes includes:

[0016] The power grid data from the monitoring node is input into a preset state estimation model;

[0017] The state vector is obtained by solving the preset state estimation model;

[0018] The preset state estimation model is as follows: ;

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

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

[0021] The selection matrix is ​​as follows: ;

[0022] In the formula, Indicates the first One power grid node; , and when When, it indicates the first No distribution terminal was deployed at any of the power grid nodes, when When, it indicates the first Each power grid node has deployed a distribution terminal; A positive integer representing the number of monitoring nodes.

[0023] As an optional approach, the state analysis method for the unmonitored areas of the distribution network described above involves constructing the objective optimization function of the distribution system based on the selection matrix and the state vector, including:

[0024] Based on the selection matrix, construct the objective optimization function of the power distribution system;

[0025] The representative error function of the objective optimization function is determined based on the state vector;

[0026] The objective optimization function is: ;

[0027] The representative error function is: ;

[0028] In the formula, Represent the objective optimization function; Represents the representative error function; This represents the state vector. This represents the selection matrix. This indicates the allowable error.

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

[0030] Based on the power grid data of the target node, determine the actual voltage on the secondary side of the power grid node where the distribution terminal has not been deployed and the transformer turns ratio on the secondary side.

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

[0032] The preset conversion formula is as follows: ;

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

[0034] Determine whether the preset voltage condition is met based on the calculated voltage on the primary side and the actual voltage on the primary side;

[0035] If so, the voltage information of the distribution transformer at the current power grid node is saved based on the calculated voltage of the primary side.

[0036] As an optional solution, in the above-mentioned state analysis method for unmonitored areas of the distribution network, determining the distribution transformer power of the grid nodes where the distribution terminal is not deployed based on the grid data of the target node includes:

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

[0038] Based on the primary side output power, the primary side copper loss power, the primary side iron loss power, and the first preset power calculation formula, the deduced input power of the primary side is determined;

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

[0040] in, This represents the derived input power on the primary side. This indicates the primary side measured output power. Indicates the primary copper loss power. Indicates the primary side iron loss power;

[0041] The secondary-side output power, secondary-side copper loss power, and secondary-side iron loss power of the grid nodes that have not deployed the distribution terminal are determined based on the grid data of the target node.

[0042] Based on the secondary side output power, the secondary side copper loss power, the secondary side iron loss power, and the second preset power calculation formula, the deduced input power of the secondary side is determined;

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

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

[0045] The transformer power is determined based on the derived input power of the primary side and the derived input power of the secondary side.

[0046] Determine whether the power output of the distribution transformer meets the preset power conditions based on the power grid data of the target node;

[0047] If so, save the power information of the distribution transformer at the current power grid node.

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

[0049] Based on the grid data of the target node, the primary output power and primary voltage of the grid node where the distribution terminal is not deployed are determined, and the primary current is obtained by combining the preset current calculation formula.

[0050] Based on the grid data of the target node, the secondary output power and secondary voltage of the grid node where the distribution terminal is not deployed are determined, and the secondary current is obtained by combining the preset current calculation formula.

[0051] The preset current calculation formula is as follows: ;

[0052] In the formula, Indicates primary side current or secondary side current. This indicates the primary side output power or the secondary side output power. Indicates primary side voltage or secondary side voltage. Indicates the power factor;

[0053] Determine whether the primary current and secondary current meet the preset consistency conditions based on the switch position in the power distribution system.

[0054] If so, save the line switch information of the current power grid node.

[0055] To address the aforementioned technical problems, this application also provides a state analysis device for unmonitored areas of a power distribution network, comprising:

[0056] The acquisition module is used to acquire power grid data from monitoring nodes that have pre-deployed distribution terminals in the power distribution system.

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

[0058] A construction module is used to construct the objective optimization function of the power distribution system based on the selection matrix and the state vector.

[0059] A filtering module is used to filter target nodes from the monitoring nodes based on the optimization result of the target optimization function;

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

[0061] To address the aforementioned technical problems, this application also provides a state analysis device for unmonitored areas of a power distribution network, comprising:

[0062] Memory, used to store computer programs;

[0063] A processor is used to implement the steps of the above-described method for state analysis of unmonitored areas of the power distribution network when executing the computer program.

[0064] To address the aforementioned technical problems, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the aforementioned method for state analysis of unmonitored areas of a power distribution network.

[0065] The state analysis method for unmonitored areas of a distribution network provided in this application acquires grid data from monitoring nodes with pre-deployed distribution terminals in the distribution system. It then constructs an objective optimization function using state vectors and a selection matrix to select target nodes and determines the grid state of the unmonitored area through computational deduction. By replacing actual deployment of distribution terminals with computational deduction, it reduces equipment procurement, installation, and maintenance costs, achieving low-cost, high-efficiency data acquisition, reducing equipment investment and maintenance costs, and improving the intelligence level of the distribution network. It solves the problem of data loss caused by the lack of distribution terminals in some areas of the distribution system, improves the accuracy and efficiency of distribution system state analysis, and is not limited by specific distribution terminal types and deployment locations, exhibiting strong versatility and flexibility.

[0066] In addition, this application also provides an apparatus and medium that correspond to the above-mentioned state analysis method for unmonitored areas of the power distribution network, with the same effect. Attached Figure Description

[0067] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0068] Figure 1 A flowchart illustrating a method for state analysis of unmonitored areas of a power distribution network, as provided in this application embodiment;

[0069] Figure 2 A schematic diagram illustrating the simulation results of various deployment methods provided in this application embodiment;

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

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

[0072] Figure 5 A schematic diagram illustrating the distribution characteristics of 50 different deployment methods of RNL provided in this application embodiment;

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

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

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

[0076] Figure 9 A structural diagram of a state analysis device for an unmonitored area of ​​a power distribution network provided in this application embodiment;

[0077] Figure 10 A structural diagram of another state analysis device for an unmonitored area of ​​a power distribution network provided in an embodiment of this application. Detailed Implementation

[0078] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

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

[0080] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0081] As a crucial component of smart distribution networks, the deployment and management of intelligent distribution terminals directly impact the stability and reliability of the distribution network. Improving the accuracy and intelligence of data collection in distribution networks, and optimizing terminal deployment and data acquisition strategies, have become critical issues that urgently need to be addressed.

[0082] With the diversification and increasing penetration of smart devices in power distribution networks, the data they generate will grow exponentially. The deployment methods of smart distribution terminals will directly affect the effectiveness of power distribution network data processing and zonal control. Therefore, researching distribution terminal deployment methods helps improve system performance, real-time performance, and reliability, while reducing operation and maintenance costs.

[0083] Currently, there is increasing research focusing on this area. Based on the topological characteristics of the power distribution system and the spatial characteristics of residential areas, and combining the spatial information of resident nodes and electricity consumption patterns to form characteristic data, an improved density peak analysis algorithm is used to determine the number, address, and service range of power distribution terminals. This method addresses the issue of residential electricity consumption response and does not consider the impact of communication on power distribution terminal deployment; therefore, it is not suitable for data processing scenarios with high real-time requirements.

[0084] By establishing models of intelligent terminals, user transmission delay, and robustness, the intelligent distribution terminal deployment problem is transformed into a minimum dominance set optimization problem with constraints. Based on the overlap dominance to measure network robustness, a distribution terminal deployment algorithm based on overlap dominance is designed.

[0085] Both methods mentioned above take into account the impact of communication on the deployment of distribution terminals. However, they still have significant shortcomings for distribution system services with large dynamic changes, especially since the spatiotemporal correlation characteristics of sources and loads in the distribution network have a significant impact on the deployment of edge computing nodes. In addition, with the development of new distribution systems, they also face problems such as difficulty in accurate modeling, high computational complexity, and poor adaptability to dynamic environments.

[0086] To address the aforementioned problems, embodiments of this application provide a method for state analysis of unmonitored areas of a power distribution network, such as... Figure 1 As shown, it includes:

[0087] S11: Obtain power grid data from monitoring nodes in the power distribution system that have pre-deployed power distribution terminals;

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

[0089] S13: Construct the objective optimization function of the power distribution system based on the selection matrix and state vector;

[0090] S14: Select target nodes from the monitoring nodes based on the optimization results of the objective optimization function;

[0091] S15: Based on the grid data of the target node, determine the distribution transformer voltage, distribution transformer power, and line switch location information of the grid node without a distribution terminal.

[0092] This embodiment applies to a smart distribution network environment, where a certain number of distribution terminals have been pre-deployed in the distribution system to monitor grid data. These distribution terminals can collect key parameters such as voltage, current, and power, and transmit them to the data processing center via a communication network. However, due to limitations in cost, technology, or other factors, some areas in the distribution system still lack distribution terminals, and the grid status in these areas needs to be analyzed through calculation and extrapolation methods.

[0093] Step S11 acquires grid data from monitoring nodes pre-deployed with distribution terminals in the power distribution system. This involves obtaining grid data from these pre-deployed monitoring nodes. This data includes key parameters such as voltage, current, and power, which form the basis for subsequent analysis. It is necessary to ensure the distribution terminals are functioning properly and that the data is accurate and real-time. The distribution terminals should be able to stably collect and transmit grid data.

[0094] Distribution terminals collect power grid data through built-in sensors and transmit the data to a data processing center via communication networks (such as fiber optics or 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 power grid nodes, end-end power grid nodes, and multiple branch power grid nodes.

[0095] Step S12 generates a state vector based on the power grid data from the monitoring nodes and determines a selection matrix based on all monitoring nodes. This involves organizing the power grid data from the monitoring nodes into a state vector to represent the current state of the power distribution system. Simultaneously, the selection matrix is ​​determined based on the deployment of all monitoring nodes.

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

[0097] Step S13: Based on the selection matrix and state vector, construct the objective optimization function for the power distribution system. This function aims to improve the accuracy and efficiency of power 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 or maximizing coverage. For example, monitoring nodes can be selected to minimize the state estimation error of the power distribution system or to maximize the state inference accuracy of unmonitored areas.

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

[0100] Step S15 determines the distribution transformer voltage, distribution transformer power, and line switch location information of the grid nodes without distribution terminals based on the grid data of the target node. Using the grid data of the target node, the distribution transformer voltage, distribution transformer power, and line switch location information of the grid nodes without distribution terminals are determined by calculation and deduction.

[0101] The state analysis method for unmonitored areas of a distribution network provided in this application acquires grid data from monitoring nodes with pre-deployed distribution terminals in the distribution system. It constructs an objective optimization function using state vectors and a selection matrix to filter out target nodes and determines the grid state of unmonitored areas through computational deduction. This computational deduction replaces the actual deployment of distribution terminals, reducing equipment procurement, installation, and maintenance costs. It achieves low-cost, high-efficiency data acquisition, reduces equipment investment and maintenance costs, and improves the intelligence level of the distribution network. It solves the problem of data loss caused by the lack of distribution terminals in some areas of the distribution system, improves the accuracy and efficiency of distribution system state analysis, and is not limited by specific distribution terminal types and deployment locations, exhibiting strong versatility and flexibility.

[0102] According to the above embodiments, in one specific embodiment, acquiring power grid data from monitoring nodes in the power distribution system that have pre-deployed power distribution terminals includes:

[0103] Obtain the preset sampling frequency;

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

[0105] A preset acquisition frequency is obtained, and the pre-deployed distribution terminals in the power distribution system are controlled to acquire power grid data from each monitoring node according to this acquisition frequency. The preset acquisition frequency should be determined based on the actual needs of the power distribution system and the accuracy requirements of the data analysis. Different monitoring parameters may require different acquisition frequencies. For example, the acquisition frequency for voltage and current is usually higher, while the acquisition frequency for 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 acquisition frequency of each monitoring parameter is preset. This can be achieved through a power distribution management system or a remote configuration tool.

[0107] The power distribution management system sends data acquisition instructions to the power distribution terminals, including the acquisition frequency and a list of parameters to be monitored. The power distribution terminals then begin acquiring data according to the instructions and upload it to the data processing center at preset frequencies.

[0108] By precisely controlling the data acquisition process, high-precision monitoring of the power distribution system's status was achieved. Because different monitoring parameters have different requirements for the data acquisition frequency, the acquisition operation was performed at a preset acquisition frequency to ensure data accuracy and real-time performance.

[0109] By precisely controlling the data acquisition frequency and parameters, the accuracy of the acquired power grid data is ensured, providing a reliable foundation for subsequent status analysis.

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

[0111] Input the power grid data from the monitoring nodes into the preset state estimation model;

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

[0113] The preset state estimation model is as follows: ;

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

[0115] The selection matrix is ​​determined based on all monitoring nodes;

[0116] The selection matrix is ​​as follows: ;

[0117] In the formula, Indicates the first One power grid node; , and when When, it indicates the first No distribution terminal was deployed at any of the power grid nodes, when When, it indicates the first Each power grid node has deployed a distribution terminal; A positive integer representing the number of monitoring nodes.

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

[0119] X(t) represents the state vector at time t, containing state parameters such as voltage magnitude and phase angle of the target node in the distribution network. H(t) represents the state estimation matrix, reflecting the nonlinear relationship between measurements and state variables. Y(t) represents the power grid data vector of the monitoring node at time t, containing 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 each power grid node has a distribution terminal unit (DTU) deployed. The selection matrix is ​​constructed based on the distribution network topology and monitoring requirements. An optimization algorithm determines which nodes need to deploy DTUs to achieve the best monitoring results. Assuming a distribution network with 100 nodes, 50 nodes are equipped with DTUs. Power grid data is collected in real time through these DTUs and input into a pre-defined state estimation model for solution. Based on the obtained state vectors and the constructed selection matrix, further analysis and deduction of transformer voltage, transformer power, and line switches are performed.

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

[0122] According to the above embodiments, in a specific embodiment, the objective optimization function of the power distribution system is constructed based on the selection matrix and the state vector, including:

[0123] Based on the selection matrix, construct the objective optimization function for the power distribution system;

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

[0125] The objective function is: ;

[0126] The representative error function is: ;

[0127] In the formula, Represent the objective optimization function; Represents the representative error function; Represents the state vector. Represents the selection matrix. This indicates the allowable error.

[0128] In a power distribution system, monitoring nodes have been identified, and the deployment status of each node is represented by a selection matrix. The selection matrix can also be a two-dimensional array, where each row represents a monitoring node and each column represents a decision variable (i.e., whether to deploy a power 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 0 indicates that it is not selected.

[0129] Because power distribution systems have numerous data nodes, and the data acquisition costs, data quality, and real-time requirements vary among different nodes, optimization algorithms are needed to find the optimal node deployment scheme. By using a selection matrix, an objective optimization function for the power distribution system is constructed. This function aims to find the optimal data node deployment scheme to minimize the cost of data acquisition and processing while meeting the requirements of data accuracy and real-time performance.

[0130] A state vector is a vector containing key state parameters of a power distribution system, such as voltage magnitude, phase angle, and power. It represents the overall state of the power distribution system at a given moment. A state vector can be a multi-dimensional array, where each element represents a specific state parameter. For example, in a three-phase power system, the state vector might contain three voltage magnitudes, three phase angles, and parameters such as active and reactive power. The representative error function is an index used to measure the quality of the solution to the objective optimization function. It represents the error between the data node deployment scheme obtained through the optimization algorithm and the actual optimal scheme. The state vector and the representative error function further refine the specific form of the objective optimization function.

[0131] Based on the representative error function, the objective optimization function is solved, and the target node is determined from the monitoring nodes based on the solution of the objective optimization function.

[0132] After determining the target node, the preferred method is to acquire the power grid data collected by the distribution terminal of the target node, and then perform data repair on the power grid data to obtain the repaired power grid data.

[0133] For example, because data may be lost or anomaly-prone during transmission, preprocessing of distribution network data is necessary. This preprocessing can be used to repair the distribution network data. Distribution terminals in the distribution system need to collect data such as voltage, current, power, topology, and equipment status. Equipment status may 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, it compares with historical data to detect whether there are any anomalies in the current data. The difference in status data at time tk is shown in the following formula: ;

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

[0136] After anomaly data is detected, data repair can be performed. First, a K-means clustering algorithm is used to cluster historical and current data to obtain similar data sets. After constructing an objective function and solving the objective function, a weighted average is calculated on the similar data sets to determine the imputation value for missing data and perform the repair.

[0137] For the abnormal data detected above, if it cannot be repaired at all, it will be directly removed, while for slightly abnormal data, it will be corrected based on adjacent data.

[0138] In this embodiment, the power distribution terminal in the power distribution system collects various types of data, then verifies the collected data and detects abnormal data; next, 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 simulations, ensuring the accuracy and reliability of the calculations and simulations, thereby improving the operating efficiency and safety of the intelligent power distribution terminal.

[0139] According to the above embodiments, in one specific embodiment, determining the distribution transformer voltage of a grid node without a deployed distribution terminal based on the grid data of the target node includes:

[0140] Determine the actual voltage on the secondary side and the turns ratio of the transformer on the secondary side of the grid node without a distribution terminal deployed based on the grid data of the target node;

[0141] According to the preset conversion formula, the derived voltage of the primary side corresponding to the secondary side of the power grid node without deployed distribution terminal is obtained;

[0142] The preset conversion formula is: ;

[0143] in, This represents the derived voltage on the primary side. This 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 calculated voltage on the primary side and the actual voltage on the primary side.

[0145] If so, the voltage information of the distribution transformer at the current power grid node is saved based on the calculated voltage of the primary side.

[0146] Based on the identified target nodes (the nodes where distribution terminals are deployed), it is necessary to extrapolate the voltage status of adjacent unmonitored nodes. The actual secondary voltage refers to the transformer secondary voltage value 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 number of turns in the primary winding to the number of turns in the secondary winding of the distribution transformer; for example, the turns ratio of a 10kV / 0.4kV distribution transformer is 25:1.

[0147] The primary side estimated voltage refers to the calculated voltage estimate of the high-voltage side of the transformer. The secondary side actual voltage is the measured value of the low-voltage side directly obtained from the distribution terminal. The primary side actual voltage also refers to the reference voltage value obtained from adjacent monitoring nodes or the upstream substation.

[0148] When the error meets the requirements, the simulation results are stored in the real-time database to ensure the reliability of the simulated voltage and avoid erroneous data affecting the system's judgment.

[0149] According to the above embodiments, in one specific embodiment, determining the distribution transformer power of a grid node without a deployed distribution terminal 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 nodes without deployed distribution terminals based on the grid data of the target node;

[0151] Based on the primary side output power, primary side copper loss power, primary side iron loss power and the first preset power calculation formula, the deduced input power of the primary side is determined;

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

[0153] in, This represents the derived input power on the primary side. This indicates the primary side measured output power. Indicates the primary 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 nodes without deployed distribution terminals based on the grid data of the target node;

[0155] Based on the secondary side output power, secondary side copper loss power, secondary side iron loss power and the second preset power calculation formula, the deduced input power of the secondary side is determined;

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

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

[0158] The transformer power is determined based on the derived input power of the primary side and the derived input power of 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 power information of the distribution transformer at the current power grid node.

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

[0162] The estimated input power refers to the active power input to the high-voltage side of the transformer. It completes the power balance calculation on the high-voltage side and provides data for power state analysis.

[0163] Secondary side output power refers to the active power output from the low-voltage side of the transformer; secondary side copper loss power refers to the resistance loss of the low-voltage winding of the transformer; secondary side iron loss power refers to the manifestation of the transformer core loss on the low-voltage side. The estimated secondary side input power refers to the estimated value of the active power input to the low-voltage side of the transformer.

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

[0165] According to the above embodiments, in one specific embodiment, determining the line switch location information of a power grid node without a deployed distribution terminal based on the power grid data of the target node includes:

[0166] Based on the grid data of the target node, determine the primary output power and primary voltage of the grid node without a distribution terminal, and obtain the primary current by combining the preset current calculation formula.

[0167] Based on the grid data of the target node, determine the secondary output power and secondary voltage of the grid node without a distribution terminal, and obtain the secondary current by combining the preset current calculation formula.

[0168] The preset current calculation formula is as follows: ;

[0169] In the formula, Indicates primary side current or secondary side current. This indicates the primary side output power or the secondary side output power. Indicates primary side voltage or secondary side voltage. Indicates the 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 so, save the line switch information of the current power grid node.

[0172] Based on the grid data of the target node, calculate the primary and secondary currents of the grid nodes without deployed distribution terminals. Current is the most direct electrical quantity reflecting the switching status of the line.

[0173] Based on the switch positions in the power distribution system, determine whether the primary and secondary currents meet preset consistency conditions. If the currents meet the consistency conditions, save the line switch information of the current power grid node for subsequent analysis and use. The preset consistency conditions can be set according to actual conditions, such as the deviation range of current values.

[0174] In the calculation of line switches, the power, voltage and status of the primary side of each terminal node (distribution transformer) can be determined. Based on this, the current is 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 section, pole-mounted disconnector, conductor section, off-site medium voltage user access point, pole-mounted user transformer, pole-mounted transformer, pole-mounted circuit breaker, distribution transformer-user, distribution transformer-double winding, pole-mounted load switch) are calculated upwards. The calculation is then carried out to the first line switch. Combined with the measurement data of the line switch, relevant data are obtained through the Electricity Metering System (TMR) and Energy Management System (EMS). The calculation results are verified step by step from top to bottom.

[0175] Furthermore, this application can also be used to evaluate specific terminal deployment schemes to achieve lower deployment costs while ensuring other performance aspects. Specifically:

[0176] S21: Obtain simulation parameters;

[0177] S22: Process the simulation parameters using the methods in steps S11-S16 to obtain the simulation transformer voltage derivation results, simulation transformer power derivation results, and simulation line switch derivation results of the simulation parameters, and determine the first simulation result corresponding to the simulation parameters.

[0178] Simulation results include data acquisition coverage, computational simulation accuracy, redundancy rate, and response time.

[0179] S23: Simulation parameters are processed 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 acquisition frequency, a deployment optimization method considering terminal energy consumption, and a terminal deployment optimization method considering communication network limitations, respectively, to obtain the second simulation result, the third simulation result, the fourth simulation result, the fifth simulation result, and the sixth simulation result;

[0180] S24: Compare the first simulation result, 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 (OOD) involves deploying smart distribution terminals at all nodes, with a high data acquisition frequency. The simulation method for the hybrid deployment optimization based on terminal types (HDT) involves deploying different types of terminals, optimizing the distribution of electrical and non-electrical quantity terminals. The simulation method for the terminal deployment optimization based on data collection frequency (DCF) involves high-frequency data acquisition in key areas and low-frequency acquisition in general areas. The simulation method for the deployment optimization considering energy consumption of terminals (DCE) involves power-efficient terminals, reducing the operating frequency of high-energy-consuming terminals. The simulation method for the terminal deployment considering communication network constraints (DCC) involves using a combination of multiple communication technologies to optimize network transmission. To facilitate analysis and comparison of simulation results, one of the computational derivation methods for smart distribution networks in this embodiment can be designated as DMC, and compared with other methods to evaluate DMC.

[0182] Figure 2 This application provides a schematic diagram illustrating simulation results of the deployment performance of various methods in an embodiment, as shown below. Figure 2As shown, the following metrics were selected as performance indicators: Data Acquisition Coverage (RDC), Calculation and Inference Accuracy (RDA), Network Load Rate (RNL), Redundancy Rate (RRF), and Response Time (TR). RDC characterizes the overall data acquisition capability of the distribution network under different deployment methods; RDA represents the accuracy of data obtained through calculation and simulation, reflecting the effectiveness of the simulation model; RNL measures the usage of the distribution network communication network, representing the network bandwidth and load pressure; RRF represents the system's ability to maintain normal operation when terminals fail, reflecting the system's fault tolerance; and TR represents the average time for terminals to collect and upload data, reflecting the system's rapid response capability.

[0183] Specifically, Figure 3 This application provides a schematic diagram illustrating the RDC distribution characteristics of 50 different deployment methods under different simulation scenarios, based on an embodiment of the present application. Figure 2 and Figure 3 As can be seen, since the OOD method deploys one smart terminal per node and does not optimize the 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 high coverage by deploying different types of terminals and optimizing the network structure; while DCE, due to strict control over terminal usage and minimizing energy efficiency as its optimization goal, has the lowest coverage among all methods.

[0184] Figure 4 This is a schematic diagram illustrating the RDA distribution characteristics of different deployment methods under 50 different simulation scenarios, as provided in an embodiment of this application. Figure 4 In terms of average computational accuracy, the DMC deployment method improved 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 the computational accuracy. In contrast, the DCE and DCC methods compromised computational accuracy to reduce energy consumption and the number of terminals.

[0185] Figure 5 This diagram illustrates the distribution characteristics of 50 different deployment methods of RNLs provided in this embodiment of the application. Figure 5In terms of average network load, compared to OOD, HDT, DCF, DCE, and DCC methods, the proposed DMC deployment method reduces network load by 35.09%, 25.56%, 15.19%, and 5.53%, respectively; however, compared to the DCC method, the proposed DMC method has a 4.8% higher network load. This is because DCC's optimization goal is to reduce network load and decrease data transmission volume by optimizing communication technology; therefore, this method sacrifices other performance aspects to achieve optimal network load performance. Furthermore, DCE reduces communication requirements by decreasing terminal operating frequency, resulting in a lower network load.

[0186] Figure 6 This is a schematic diagram illustrating the TR distribution characteristics of 50 different deployment methods under different simulation scenarios, as provided in an embodiment of this application. Figure 6 In terms of average response time, compared with OOD, HDT, DCF, DCE, and DCC methods, the proposed DMC deployment method reduces the average response time by 41.91%, 33.71%, 25.61%, 8.18%, and 14.77%, respectively. Since the proposed DMC method optimizes both the processing of collected data and establishes a computational and extrapolation process, it achieves the shortest response time, meaning that the proposed method can respond to changes in the system more quickly.

[0187] Figure 7 This is a schematic diagram illustrating the RRF distribution characteristics of 50 different deployment methods under different simulation scenarios, provided as an embodiment of this application. According to... Figure 2 and Figure 7 It can be seen that compared with the OOD, HDT, DCF, and DCC methods, the proposed DMC deployment method improves the redundancy rate by 3.30%, 1.20%, 2.08%, and 0.88%, respectively; however, compared with the DCE method, the network load of the proposed DMC method is reduced by 0.71%. Although the OOD method adopts a "one-to-one" terminal deployment scheme, it does not optimize the deployment method and calculation inference method, so its redundancy rate is the lowest among all methods. HDT, by deploying different types of terminals and optimizing the network structure, also performs well in terms of redundancy rate; while DCC has a relatively high redundancy rate due to its thorough optimization of the communication network. The DCE method optimizes the operating status of the distribution terminals, reduces the terminal utilization rate, which means that more terminals are in a dormant state, and can more easily cope with terminal failures, thus achieving the best redundancy rate performance.

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

[0189] In a specific embodiment, such as Figure 8 As shown, Figure 8 This diagram illustrates the cost-performance simulation results of different deployment methods provided in this application embodiment. Figure 8 As shown, in terms of the number of terminals deployed, compared to OOD, HDT, DCF, and DCC, the proposed DMC method reduces the number of terminals required by 41%, 27.16%, 18.06%, and 9.23%, respectively. Regarding deployment cost, compared to OOD, HDT, DCF, and DCE, the proposed DMC method saves 32.21%, 10.19%, 15.57%, and 5.37% in deployment cost, respectively. Since OOD deployment requires a smart distribution terminal at each node, it requires the highest number of terminals and has the highest deployment cost, and 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, mainly by reducing reliance on terminals through extrapolation techniques and energy-saving measures. As for deployment cost, HDT uses a hybrid type of terminal, resulting in a higher terminal count but lower cost than DCF. Similarly, DCC also uses different types of terminals and considers the characteristics of the communication network, achieving the lowest deployment cost. Although the DMC mentioned above is fully equipped with smart terminals, the number of them is relatively small, achieving a low deployment cost while ensuring other performance characteristics.

[0190] In terms of operation and maintenance workload, compared with OOD, HDT, DCF, and DCE, the proposed DMC method reduces the operation and maintenance workload by 44.26%, 19.05%, 12.82%, and 2.78%, respectively. The proposed DMC and DCC methods achieve the lowest operation and maintenance workload by reducing terminal working time and energy consumption. ODD, due to the large number of deployed terminals, has the largest operation and maintenance workload. The DCF and DCE methods can also effectively reduce the operation and maintenance workload compared with the OOD method by dynamically adjusting terminal requirements. Compared with OOD, HDT, DCF, DCE, and DCC, the proposed DMC method reduces the operation and maintenance workload by 46.53%, 34.15%, 23.94%, 11.48%, and 16.92%, respectively. Since the proposed DMC method considers energy consumption optimization and data collection efficiency of smart terminals from the beginning, it not only reduces the number of terminals but also reduces overall energy consumption through energy efficiency optimization measures, achieving overall energy consumption optimization compared with other methods. This is of great significance for large-scale power distribution networks, saving a significant amount of operating costs.

[0191] In terms of carbon emissions, compared with OOD, HDT, DCF, DCE, and DCC, the proposed DMC method reduces carbon emissions by 42.86%, 42.03%, 41.18%, 27.27%, and 38.46%, respectively. Since the proposed DMC method incorporates carbon emission minimization as one of the objective functions in its modeling, it achieves carbon emission minimization compared to other methods, significantly reducing the carbon emissions of the energy system and realizing the initial design goal of a low-carbon energy system.

[0192] In the above embodiments, the state analysis method for unmonitored areas of the distribution network has been described in detail. This application also provides embodiments corresponding to the state analysis device for unmonitored areas of the distribution network. It should be noted that this application describes the embodiments of the device from two perspectives: one is based on functional modules, and the other is based on hardware.

[0193] From the perspective of functional modules Figure 9 A structural diagram of a state analysis device for an unmonitored area of ​​a power distribution network provided in this application embodiment is shown below. Figure 9 As shown, a status analysis device for an unmonitored area of ​​a power distribution network includes:

[0194] The acquisition module 11 is used to acquire power grid data from monitoring nodes that have pre-deployed power distribution terminals in the power distribution system;

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

[0196] Module 13 is used to construct the objective optimization function of the power distribution system based on the selection matrix and the state vector.

[0197] The filtering module 14 is used to filter target nodes from the monitoring nodes based on the optimization results of the target optimization function;

[0198] The simulation module 15 is used to determine the distribution transformer voltage, distribution transformer power, and line switch location information of grid nodes without deployed distribution terminals based on the grid data of the target node.

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

[0200] Figure 10 A structural diagram of another state analysis device for an unmonitored area of ​​a power distribution network provided in this application embodiment is shown below. Figure 10 As shown, the status analysis device for unmonitored areas of the distribution network includes: a memory 30 for storing computer programs;

[0201] The processor 31 is used to execute a computer program to implement the steps of the method for obtaining user operation habit information as described in the above embodiment (State Analysis Method for Unmonitored Areas of Distribution Network).

[0202] The status analysis device for unmonitored areas of the distribution network provided in this embodiment may include, but is not limited to, mobile terminals, personal computers, workstations, etc.

[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 using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 31 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 31 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 31 may also include an Artificial Intelligence (AI) processor, which handles 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 high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 30 is used to store at least the following computer program 301, which, after being loaded and executed by the processor 31, is capable of implementing the relevant steps of the state analysis method for unmonitored areas of the distribution network disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 30 may also include an operating system 302 and data 303, and the storage method may be temporary or permanent storage. The operating system 302 may include Windows, Unix, Linux, etc. The data 303 may include, but is not limited to, the data involved in implementing the state analysis method for unmonitored areas of the distribution network.

[0205] In some embodiments, the status analysis device for unmonitored areas of the power distribution network may further include a display screen 32, an input / 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 does not constitute a limitation on the condition analysis device for unmonitored areas of the distribution network and may include more or fewer components than shown.

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

[0208] Finally, this 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 above embodiment of the state analysis method for unmonitored areas of the power distribution network.

[0209] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, 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. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0210] The computer-readable storage medium provided in this embodiment stores a computer program. When the processor executes the program, it can implement the following method: a method for analyzing the state of unmonitored areas of a power distribution network.

[0211] The foregoing provides a detailed description of the state analysis method, apparatus, and medium for unmonitored areas of the power distribution network provided in this application. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0212] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for state analysis of unmonitored areas of a distribution network, characterized in that, include: Acquire power grid data from monitoring nodes in the power distribution system that have pre-deployed power distribution terminals; A state vector is generated based on the power grid data from the monitoring nodes, and a selection matrix is ​​determined based on all the monitoring nodes. Based on the selection matrix and the state vector, the objective optimization function of the power distribution system is constructed. Target nodes are selected from the monitoring nodes based on the optimization results of the target optimization function; Based on the power grid data of the target node, determine the distribution transformer voltage, distribution transformer power, and line switch location information of the power grid nodes where the distribution terminal has not been deployed; The step of generating a state vector based on the power grid data from the monitoring nodes and determining a selection matrix based on all the monitoring nodes includes: The power grid data from the monitoring node is input into a preset state estimation model; The state vector is obtained by solving the preset state estimation model; The preset state estimation model is as follows: ; In the formula, X(t) represents the state vector at time t; H(t) represents the state estimation matrix; and Y(t) represents the vector of power grid data of the monitoring node at time t. Determine the selection matrix based on all the monitoring nodes; The selection matrix is ​​as follows: ; In the formula, Indicates the first One power grid node; , and when When, it indicates the first No distribution terminal was deployed at any of the power grid nodes, when When, it indicates the first Each power grid node has deployed a distribution terminal; A positive integer representing the number of monitoring nodes; The objective optimization function of the power distribution system is constructed based on the selection matrix and the state vector, including: Based on the selection matrix, construct the objective optimization function of the power distribution system; The representative error function of the objective optimization function is determined based on the state vector; The objective optimization function is: ; The representative error function is: ; In the formula, Represent the objective optimization function; Represents the representative error function; This represents the state vector. This represents the selection matrix. This indicates the allowable error.

2. The method for state analysis of unmonitored areas of a distribution network according to claim 1, characterized in that, The acquisition of power grid data from monitoring nodes of pre-deployed distribution terminals in the power distribution system includes: Obtain the preset sampling frequency; The power distribution system is pre-deployed with power distribution terminals that acquire power grid data from each monitoring node according to the preset acquisition frequency; wherein, the power grid data includes: phase voltage, phase current, active power, reactive power, frequency, power factor, harmonic current, and harmonic voltage.

3. The method for state analysis of unmonitored areas of a distribution network according to claim 1, characterized in that, Determining the distribution transformer voltage of grid nodes where the distribution terminal is not deployed based on the grid data of the target node includes: Based on the power grid data of the target node, determine the actual voltage on the secondary side of the power grid node where the distribution terminal has not been deployed and the transformer turns ratio on the secondary side. According to the preset conversion formula, the deduced voltage of the primary side corresponding to the secondary side of the power grid node where the distribution terminal is not deployed is obtained; The preset conversion formula is as follows: ; in, This represents the derived voltage on the primary side. This represents the actual voltage on the secondary side, and K represents the transformer turns ratio; Determine whether the preset voltage condition is met based on the calculated voltage on the primary side and the actual voltage on the primary side; If so, the voltage information of the distribution transformer at the current power grid node is saved based on the calculated voltage of the primary side.

4. The method for state analysis of unmonitored areas of a distribution network according to claim 3, characterized in that, Determining the distribution transformer power of grid nodes without the deployed distribution terminal based on the grid data of the target node includes: Based on the power grid data of the target node, determine the primary side output power, primary side copper loss power, and primary side iron loss power of the power grid node where the distribution terminal is not deployed; Based on the primary side output power, the primary side copper loss power, the primary side iron loss power, and the first preset power calculation formula, the deduced input power of the primary side is determined; The first preset power calculation formula is as follows: ; in, This represents the derived input power on the primary side. This indicates the primary side measured output power. Indicates the primary copper loss power. Indicates the primary side iron loss power; The secondary-side output power, secondary-side copper loss power, and secondary-side iron loss power of the grid nodes that have not deployed the distribution terminal are determined based on the grid data of the target node. Based on the secondary side output power, the secondary side copper loss power, the secondary side iron loss power, and the second preset power calculation formula, the deduced input power of the secondary side is determined; The second preset power calculation formula is: ; in, This represents the derived input power on the secondary side. Indicates the secondary side output power. Indicates the secondary copper loss power. Indicates the secondary side iron loss power; The transformer power is determined based on the derived input power of the primary side and the derived input power of the secondary side. Determine whether the power output of the distribution transformer meets the preset power conditions based on the power grid data of the target node; If so, save the power information of the distribution transformer at the current power grid node.

5. The method for state analysis of unmonitored areas of a power distribution network according to claim 4, characterized in that, Determining the line switch location information of power grid nodes where the distribution terminal is not deployed based on the power grid data of the target node includes: Based on the grid data of the target node, the primary output power and primary voltage of the grid node where the distribution terminal is not deployed are determined, and the primary current is obtained by combining the preset current calculation formula. Based on the grid data of the target node, the secondary output power and secondary voltage of the grid node where the distribution terminal is not deployed are determined, and the secondary current is obtained by combining the preset current calculation formula. The preset current calculation formula is as follows: ; In the formula, Indicates primary side current or secondary side current. This indicates the primary side output power or the secondary side output power. Indicates primary side voltage or secondary side voltage. Indicates the power factor; Determine whether the primary current and secondary current meet the preset consistency conditions based on the switch position in the power distribution system. If so, save the line switch information of the current power grid node.

6. A status analysis device for an unmonitored area of ​​a power distribution network, characterized in that, include: The acquisition module is used to acquire power grid data from monitoring nodes that have pre-deployed distribution terminals in the power distribution system. The processing module is used to generate a state vector based on the power grid data of the monitoring nodes, and to determine a selection matrix based on all the monitoring nodes; A construction module is used to construct the objective optimization function of the power distribution system based on the selection matrix and the state vector. A filtering module is used to filter target nodes from the monitoring nodes based on the optimization result of the target optimization function; The deduction module is used to determine the distribution transformer voltage, distribution transformer power, and line switch location information of the power grid nodes where the distribution terminal is not deployed based on the power grid data of the target node; The step of generating a state vector based on the power grid data from the monitoring nodes and determining a selection matrix based on all the monitoring nodes includes: The power grid data from the monitoring node is input into a preset state estimation model; The state vector is obtained by solving the preset state estimation model; The preset state estimation model is as follows: ; In the formula, X(t) represents the state vector at time t; H(t) represents the state estimation matrix; and Y(t) represents the vector of power grid data of the monitoring node at time t. Determine the selection matrix based on all the monitoring nodes; The selection matrix is ​​as follows: ; In the formula, Indicates the first One power grid node; , and when When, it indicates the first No distribution terminal was deployed at any of the power grid nodes, when When, it indicates the first Each power grid node has deployed a distribution terminal; A positive integer representing the number of monitoring nodes; The objective optimization function of the power distribution system is constructed based on the selection matrix and the state vector, including: Based on the selection matrix, construct the objective optimization function of the power distribution system; The representative error function of the objective optimization function is determined based on the state vector; The objective optimization function is: ; The representative error function is: ; In the formula, Represent the objective optimization function; Represents the representative error function; This represents the state vector. This represents the selection matrix. This indicates the allowable error.

7. A status analysis device for an unmonitored area of ​​a power distribution network, characterized in that, include: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the steps of the state analysis method for unmonitored areas of a power distribution network as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the state analysis method for unmonitored areas of a power distribution network as described in any one of claims 1 to 5.

Images