Method and system for analyzing reliability influencing factors of distributed intelligent power distribution network

Abstract

The application discloses a distributed intelligent power distribution network reliability influence factor analysis method and system, relates to the technical field of influence factor analysis, and comprises the following steps: obtaining the operation state of a target area power distribution network, and constructing a topology-control mode table; marking uncertainty of key equipment to form a structure uncertainty matrix; coupling the structure uncertainty matrix with node operation data, establishing a reliability model, calculating the sensitivity of each uncertain structure unit to the overall system reliability index, and generating a structure sensitivity vector; and combining the structure sensitivity vector with real-time operation data to form a dynamic reliability influence factor table. The topology-control mode table is constructed. By establishing a structure-state coupled reliability model and generating a dynamic reliability influence factor table, the application solves the problem that the influence of topology changes and control mode switching on system reliability is not comprehensively considered in the prior art, and the problem of inaccurate identification of key influence factors is solved.

Description

Technical Field

[0001] This invention relates to the field of influencing factor analysis technology, and more specifically, to a method and system for analyzing influencing factors on the reliability of distributed smart distribution networks. Background Technology

[0002] With the high penetration of new energy sources and the deepening of power market reform, the distributed smart distribution network, as a key link connecting power sources and users, exhibits complex characteristics of high dynamism and strong coupling in its operation. The topology changes frequently due to events such as the access and exit of distributed power sources, the action of segmented switches, and network reconfiguration. The control mode also flexibly adjusts between various methods such as constant voltage control, droop control, and power point tracking control as the operating scenario changes.

[0003] Reliability, as a core indicator for measuring the power supply quality of a distribution network, provides crucial data for power grid planning optimization, dispatching and operation decisions, and equipment maintenance and repair. Currently, existing distribution network reliability analysis methods mostly focus on handling parameter uncertainties. That is, they use probability statistics, interval analysis, and other methods to quantify the stochastic characteristics of operating parameters such as component failure rate, repair time, and load fluctuations, thereby assessing the system's reliability level.

[0004] However, the frequently changing topology and control modes in distributed smart distribution networks (DSDCs) introduce significant structural uncertainties. These uncertainties stem from the dynamic changes in network topology connections and the dynamic matching of control strategies and controlled objects, fundamentally differing from traditional parametric uncertainties. Existing reliability analysis methods lack effective modeling and quantification tools for this type of structural uncertainty, failing to accurately characterize the impact mechanism of dynamic changes in topology and control modes on system reliability. This leads to a significant deviation between reliability evaluation results and the actual operating state of the distribution network, making it difficult to meet the refined and dynamic reliability analysis requirements of DDCs and hindering the scientific rigor and effectiveness of power grid operation decisions. Therefore, there is an urgent need to propose a method for analyzing the reliability influencing factors of DDCs that can effectively handle structural uncertainties.

[0005] The above-disclosed technical solutions have at least the following technical problems: existing reliability analysis methods can only handle parameter uncertainties, but cannot handle structural uncertainties caused by the ever-changing topology and control modes in distributed smart distribution networks, resulting in evaluation results that deviate significantly from the actual operating state. Summary of the Invention

[0006] To overcome the aforementioned deficiencies in the prior art, embodiments of the present invention provide a method and system for analyzing the reliability influencing factors of distributed smart distribution networks. By constructing a topology-control mode table, forming a structural uncertainty matrix, and establishing a structure-state coupled reliability model, a structural sensitivity vector and a dynamic reliability influencing factor table are generated. This addresses the problem in the prior art that it fails to comprehensively consider the impact of topology changes and control mode switching on system reliability, leading to inaccurate identification of key influencing factors.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] On the one hand, the method for analyzing the reliability influencing factors of distributed smart distribution networks includes the following steps: obtaining the operating status of the distribution network in the target area and constructing a topology-control mode table; based on the topology-control mode table, marking the uncertainty of key equipment to form a structural uncertainty matrix; aligning the uncertainty weights of structural units in the structural uncertainty matrix with the operating data of the corresponding nodes according to time labels, and calculating the system-level reliability index accordingly, establishing a structure-state coupled reliability model, and calculating the sensitivity of each uncertain structural unit to the overall system reliability index to generate a structural sensitivity vector; combining the structural sensitivity vector with real-time operating data to form a dynamic reliability influencing factor table.

[0009] In a preferred embodiment, the step of obtaining the operating status of the target area distribution network and constructing a topology-control mode table specifically involves: collecting the real-time operating status of nodes, lines, and controllable equipment within the target area; aligning the collected real-time operating data with a unified format and timestamp to ensure that different data sources can be directly used for subsequent topology and control mode construction; representing nodes and lines as a topology graph structure, where the node set represents distribution network nodes and controllable equipment, the edge set represents the electrical connectivity between nodes, and necessary attributes are added to describe the network status; and associating the topology graph structure with the control mode information of nodes and equipment to form a topology-control mode table.

[0010] In a preferred embodiment, the step of labeling key equipment with uncertainties based on the topology-control pattern table to form a structural uncertainty matrix specifically involves: analyzing the structural behavior of each electrical node and its associated control unit based on the topology-control pattern table to identify key equipment with multi-state characteristics or control switchable attributes, forming a set of structural units; labeling the key equipment with uncertainties based on the operational stability and control characteristics of each unit in the set of structural units to obtain a topology containing uncertain attributes; propagating the uncertainty labeling information of key equipment based on the electrical connectivity and control dependencies between nodes in the topology to obtain the coupling influence relationship between the structural layer and the control layer; and matrixing the key equipment and its associated units based on the coupling influence relationship to form a structural uncertainty matrix used to characterize the distribution characteristics of system structural uncertainty.

[0011] In a preferred embodiment, the step of performing structural behavior analysis on each electrical node and its associated control unit based on the topology-control mode table to identify key devices with multi-state characteristics or control switchable attributes specifically involves: establishing an association mapping model between nodes and control units based on the connectivity relationships of nodes, lines, and control units recorded in the topology-control mode table; analyzing the connectivity characteristics and control response characteristics of each node under different operating states through the association mapping model to identify a set of devices with state switching or control transferable characteristics; determining the structural characteristics of the identified set of devices to identify key devices that may trigger system structural reconfiguration when the operating topology or control mode changes; and extracting the corresponding connectivity paths and controlled node information based on the identification results of the key devices to form a set of structural units.

[0012] In a preferred embodiment, establishing the structure-state coupled reliability model specifically involves: based on the structural uncertainty matrix, mapping the uncertainty weights of each structural unit in the matrix to the node identifiers in the topology-control mode table to obtain the correspondence between the structural layer and the state layer; based on the correspondence, extracting node operation data and aligning the operation data with the uncertainty weights of the corresponding structural units according to time synchronization tags; based on time alignment, combining the uncertainty weights of the structural units and the fluctuation characteristics of the node operation data, calculating the instantaneous failure probability of the nodes, and then establishing a joint distribution model of node failure probability and topology state with the connectivity relationship between nodes as a constraint; jointly mapping the joint distribution model with the action state vector of the control unit to obtain the three-layer correlation matrix of structure-control-state; marginalizing the correlation matrix to extract the comprehensive failure probability distribution of the system under dynamic structural state, and then introducing system path constraints to calculate the system-level reliability index; continuously iteratively updating the above system-level reliability index with time series operation data to form a dynamic structure-state coupled reliability model.

[0013] In a preferred embodiment, the calculation of the sensitivity of each uncertain structural unit to the overall system reliability index and the generation of a structural sensitivity vector specifically involves: applying a small perturbation to each structural unit based on a structure-state coupled reliability model, maintaining synchronous updates of the control mode and node operation data, and obtaining the difference in system reliability index before and after the perturbation; calculating the ratio of the reliability index difference to the perturbation amplitude of the corresponding structural unit as the initial sensitivity of that structural unit; propagating and correcting the initial sensitivity along the physical connection path and control transmission path between nodes based on the electrical connectivity and control dependency chain in the topology-control mode table; and normalizing the sensitivity result after propagation correction to generate a structural sensitivity vector.

[0014] In a preferred embodiment, the initial sensitivity is propagated and corrected along the physical connection path and control transfer path between nodes based on the electrical connectivity and control dependency chain in the topology-control pattern table. Specifically, this involves: establishing a composite adjacency matrix between nodes based on the topology-control pattern table; linearly propagating the initial sensitivity of each structural unit along the physical connection path according to the composite adjacency matrix, using path impedance or power transfer coefficient as a propagation attenuation factor; mapping the dependency chain between control units to a control transfer matrix, and using control response delay and action confidence as correction weights to nonlinearly correct the sensitivity propagated along the control path; introducing node operating state-related parameters as dynamic weighting factors based on the physical path propagation results and control path correction results, and fusing and weighting the two types of results to obtain the node-level corrected sensitivity; and normalizing the node-level corrected sensitivity to form the propagated and corrected structural sensitivity distribution.

[0015] In a preferred embodiment, the step of combining the structural sensitivity vector with real-time operating data to form a dynamic reliability impact factor table specifically involves: extracting the sensitivity values ​​of each structural unit under baseline operating conditions based on the structural sensitivity vector, which are used as static baseline sensitivity coefficients; collecting and constructing state correction functions based on real-time operating data of each node and associated lines; and performing item-by-item weighted fusion of the baseline sensitivity coefficients of each structural unit with the corresponding state correction functions to obtain time-varying reliability impact values.

[0016] The time-varying reliability impact values ​​are associated and stored with the structural unit identifier according to the time series, forming a dynamic reliability impact factor table.

[0017] In a preferred embodiment, after combining the structural sensitivity vector with real-time operational data to form a dynamic reliability impact factor table, the method further includes: extracting key structural impact factors based on the dynamic reliability impact factor table, specifically: extracting the time-series impact value sequence of each structural unit from the dynamic reliability impact factor table according to a predetermined time window; using the time average and time standard deviation of the time-series impact value sequence as the steady-state impact and fluctuation impact of the structural unit within the time window; judging candidate key units according to preset steady-state thresholds and fluctuation thresholds; using the candidate key units as base points, identifying adjacent units that have direct connectivity or direct control dependence with them according to the connectivity and control dependency relationships in the topology-control mode table, and taking the adjacent units and candidate key units together as an impact cluster; for each impact cluster, merging the time average and time standard deviation of each unit in the cluster according to the connectivity priority order in the topology-control mode table to form a key structural impact factor item; and associating and outputting several key structural impact factor items according to a set format to form a key reliability impact factor set.

[0018] On the other hand, the distributed smart distribution network reliability influencing factor analysis system includes the following modules:

[0019] The topology-control mode table construction module is used to obtain the operating status of the distribution network in the target area and construct the topology-control mode table. The structural uncertainty matrix generation module is used to mark the uncertainty of key equipment based on the topology-control mode table and form a structural uncertainty matrix. The structural sensitivity vector generation module is used to align the uncertainty weights of structural units in the structural uncertainty matrix with the operating data of the corresponding nodes according to time labels, calculate the system-level reliability index, establish a structure-state coupled reliability model, and calculate the sensitivity of each uncertain structural unit to the overall system reliability index, generating a structural sensitivity vector. The reliability impact factor table formation module is used to combine the structural sensitivity vector with real-time operating data to form a dynamic reliability impact factor table.

[0020] The technical effects and advantages of the method and system for analyzing factors affecting the reliability of distributed smart distribution networks in this invention are as follows:

[0021] 1. This invention effectively captures and quantifies the structural uncertainty of distributed smart distribution networks by constructing a topology-control mode table and a structural uncertainty matrix, breaking through the limitation of traditional methods that can only handle parameter uncertainty. By combining structure-state coupling modeling and a dynamic reliability influencing factor table, it accurately correlates structural changes with operating states, realizes dynamic characterization of reliability influencing factors, improves the accuracy of analysis, and provides strong support for distribution network optimization decisions. Attached Figure Description

[0022] Figure 1This is a flowchart illustrating the method for analyzing the factors affecting the reliability of distributed smart distribution networks according to the present invention.

[0023] Figure 2 This is a schematic diagram of the distributed smart distribution network reliability influencing factor analysis system of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0025] Example 1, Figure 1 The present invention provides a method for analyzing the factors affecting the reliability of distributed smart distribution networks, comprising the following steps:

[0026] S1, obtain the operating status of the target area distribution network and construct the topology-control mode table;

[0027] In this embodiment, the topology-control mode table is used to represent the network structure and control behavior that change at any time due to distributed power supply access, power outages, islanded operation, or control strategy adjustments.

[0028] The process of obtaining the operating status of the target area's distribution network and constructing a topology-control mode table specifically involves:

[0029] The system collects the real-time operating status of nodes, lines, circuit breakers, controllable loads, and distributed power sources within the target area. The real-time operating status includes node voltage, current, switch status, protection device operation status, line load and fault status, controllable load connection and disconnection status, and distributed power source output power and control mode.

[0030] The collected heterogeneous data is standardized in terms of unit and format, and the timestamps are aligned with the topology nodes to ensure that different data sources can be directly used for subsequent topology and control pattern construction.

[0031] Based on standardized heterogeneous data, nodes and lines are represented as a topology graph structure, where the node set represents distribution network nodes and controllable equipment, the edge set represents the electrical connectivity between nodes and line status, and each edge is given attributes such as real-time load, line capacity and protection configuration.

[0032] Based on the topology graph structure, control mode attributes are attached to each node and device, including circuit breaker / switch control logic, distributed power supply control mode, controllable load response strategy, and control dependency relationship between nodes, so that topology information and control behavior are associated.

[0033] The obtained node, edge, and control mode information are integrated to form a topology-control mode table, which is used to describe the network structure and control behavior that dynamically change in the target area due to distributed power supply access, power outage, islanded operation, or control strategy adjustment. Each unit in the table is defined with a unique identifier and a status update timestamp.

[0034] S2, based on the topology-control mode table, marks the uncertainties of key equipment to form a structural uncertainty matrix;

[0035] In this embodiment, the key equipment includes a variable topology segment, a switchable control device, and equipment that may undergo state transitions.

[0036] It should be noted that the structural uncertainty matrix is ​​a tool for quantifying the impact of changes in topology on the reliability of key equipment and their control modes in distributed smart distribution networks. It captures the potential impact of constantly changing network structures and control modes on system reliability; maps key units (nodes, lines, controllable devices) in the topology-control mode table to a quantifiable matrix, where each matrix element represents the degree of uncertainty of the corresponding unit and its potential impact range; and provides input for subsequent structure-state coupled reliability models, enabling reliability analysis to consider not only parameter uncertainties but also topology and control uncertainties.

[0037] In this embodiment, the step of labeling key equipment with uncertainties based on the topology-control mode table to form a structural uncertainty matrix specifically involves:

[0038] Based on the topology-control pattern table, structural behavior analysis is performed on each electrical node and its associated control unit to identify key devices with multi-state characteristics or control switchable attributes, forming a set of structural units to characterize the variability of the network at the structural level.

[0039] For each structural unit, based on its historical fault frequency, action delay distribution and real-time parameters of the control link, the uncertainty characteristics of each key device are mapped to the topology in the form of weight labels.

[0040] For the uncertain labels of the labeled devices, uncertainty propagation calculations are performed based on their electrical connectivity paths and control logic dependencies in the topology, so that the labeling information forms a coupled influence domain between the topology and control layers.

[0041] Based on the uncertainty influence domain after propagation, the weight labels of key equipment and its associated units are restructured into a matrix form. The matrix elements are used to quantify the uncertainty coupling strength between each structural unit, forming a structural uncertainty matrix.

[0042] The uncertainty weights are specifically:

[0043]

[0044] in, For uncertain weights, , , These are the weighting coefficients for fault uncertainty, action uncertainty, and communication uncertainty (obtained based on correlation analysis). Let be the historical failure probability of device i (obtained statistically). This represents the average delay in device operation. This represents the maximum action delay within the system. This refers to the communication latency of the control link corresponding to the device. This represents the maximum communication latency within the system.

[0045] Based on the topology-control pattern table, structural behavior analysis is performed on each electrical node and its associated control unit to identify key devices with multi-state characteristics or control switchable attributes. Specifically:

[0046] Based on the connectivity relationships of nodes, lines and control units recorded in the topology-control pattern table, the electrical connection structure and control dependency chain between each node are extracted, and an association mapping model between nodes and control units is established.

[0047] Based on the aforementioned association mapping model, the power flow direction and control command response path of each node under different connectivity states are analyzed to identify a candidate set of devices that may exhibit state switching, power backfeeding, or control migration behaviors.

[0048] Based on the historical operating data and control strategy configuration files of each device in the candidate device set, we obtain its state transition frequency, action delay and control command triggering conditions, and establish the structural behavior feature vector of the device to quantify the multi-state operating characteristics of the device.

[0049] The structural behavior feature vector is input into a preset multi-state determination model. If the structural variability index output by the model exceeds a preset threshold, the corresponding device is identified as a critical device, and its control dependency with adjacent nodes is recorded.

[0050] Based on the identification results of key equipment, the information of its connection path and controlled node is further extracted to form a set of structural units. The set contains the structural variability parameters and control logic dependency description of each key equipment, which is used to characterize the dynamic variability of the distribution network at the structural level and serve as the target set for subsequent uncertainty labeling.

[0051] The purpose of uncertainty labeling in this invention is as follows: Distributed smart distribution networks have dynamic behaviors such as real-time access / exit of distributed power sources, circuit breaker switching, and islanding operation; traditional reliability analysis only considers parameter fluctuations (such as load or power generation) and ignores topology and control mode changes; uncertainty labeling can clarify which devices and control units may affect system reliability under specific conditions, thus providing a basis for sensitivity analysis and dynamic reliability factor calculation.

[0052] S3, align the uncertainty weights of structural units in the structural uncertainty matrix with the running data of the corresponding nodes according to the time label, calculate the system-level reliability index based on this, establish a structure-state coupled reliability model, calculate the sensitivity of each uncertain structural unit to the overall system reliability index, generate a structural sensitivity vector, and calculate the sensitivity of each uncertain structural unit to the overall system reliability index.

[0053] It should be noted that the structure-state coupled reliability model is used to describe the reliability indicators of each node and line under different structural configurations and control modes, and supports quantitative calculation of the impact of topology changes and control switching on system reliability.

[0054] In this embodiment, establishing the structure-state coupled reliability model specifically involves:

[0055] Based on the structural uncertainty matrix, the uncertainty weights of each structural unit in the matrix are mapped one-to-one with the node identifiers in the topology-control mode table to obtain the correspondence between the structural layer and the state layer.

[0056] Based on the correspondence, real-time operating data of each node is extracted, including state parameters such as voltage, current, power flow direction and power factor. The operating data is then aligned with the uncertainty weight of the corresponding structural unit according to the time synchronization tag to ensure the spatiotemporal consistency of the data.

[0057] Based on time alignment, the instantaneous failure probability of a node is calculated according to the uncertainty weight of the structural unit and the fluctuation range of the node's operating data. With the electrical connectivity between nodes as a constraint, a joint distribution model of node failure probability and topology state is established. The joint distribution model is used to characterize the state-related failure characteristics caused by the propagation of structural disturbances in the network.

[0058] The joint distribution model is jointly mapped with the action state vector of the control unit to obtain a three-layer correlation matrix of structure-control-state, where each matrix element is used to characterize the conditional probability of node failure caused by structural changes under a given control strategy.

[0059] Based on this, the correlation matrix is ​​marginalized to extract the comprehensive failure probability distribution of the system under any dynamic structural state, and system power supply path constraints are introduced to calculate system-level reliability indicators.

[0060] The system-level reliability indicators and time-series operational data are continuously iteratively updated to form a dynamic structure-state coupled reliability model. This model can reflect the real-time impact of structural uncertainties on the evolution of system reliability under different control mode switching. The dynamic structure-state coupled reliability model serves as the input basis for subsequent sensitivity analysis, used to identify the reliability impact sensitivity of uncertain structural units and track their dominant propagation path.

[0061] The instantaneous failure probability of the node is calculated using the following formula:

[0062]

[0063] The specific calculation formula for the system-level reliability index is as follows:

[0064]

[0065] in, Let be the instantaneous failure probability of the node. The node baseline failure rate constant (obtained from historical statistics). For structural uncertainty weights, The fluctuation range of node operation data (such as voltage standard deviation). The allowable fluctuation threshold for nodes is set according to the equipment's rated parameters. As a system-level reliability indicator, This represents the node criticality coefficient.

[0066] The method for obtaining the node criticality coefficient includes:

[0067] Based on the topology-control mode table, analyze the electrical connections and control dependencies of each node;

[0068] The importance of a node in both the structural and control layers is determined by considering its connection redundancy (number of backup power paths), load concentration (proportion of connected loads to node power), and control transmission depth (node's hierarchical position in control logic).

[0069] After normalizing the above three types of indicators, a weighted fusion is performed to obtain the node criticality coefficient, which is used to characterize the degree of influence of the node on the system reliability under the current topology and control strategy.

[0070] The calculation of the sensitivity of each uncertain structural unit to the overall system reliability index, generating a structural sensitivity vector, is as follows:

[0071] Based on the structure-state coupled reliability model, a small perturbation is applied to each structural unit, so that its uncertainty weight changes within a preset perturbation range, and the control mode and node operation data are kept updated synchronously to form the difference in system reliability index before and after the perturbation.

[0072] The ratio of the reliability index difference to the disturbance amplitude of the corresponding structural unit is used as the initial sensitivity measure of the structural unit, which is used to characterize the degree of direct impact of the state change of the unit on the system reliability.

[0073] Based on the electrical connectivity and control dependency chain in the topology-control pattern table, the initial sensitivity is propagated and corrected along the physical connection path and control transmission path between nodes to reflect the transmission effect of structural disturbances in multi-layer networks.

[0074] The sensitivity results after propagation correction are normalized to generate a structural sensitivity vector. Each component of the vector is used to describe the dynamic influence intensity of each structural unit on the system-level reliability index and can be used as the basis for generating the subsequent dynamic reliability influence factor table.

[0075] In this embodiment, the initial sensitivity is propagated and corrected along the physical connection path and control transmission path between nodes based on the electrical connectivity and control dependency chain in the topology-control mode table, specifically as follows:

[0076] Based on the topology-control mode table, a composite adjacency matrix between nodes is established to describe the physical connection path and control dependency path of each node.

[0077] According to the composite adjacency matrix, the initial sensitivity of each structural unit is linearly propagated along the physical connection path, and the path impedance or power transfer coefficient is used as the propagation attenuation factor.

[0078] Meanwhile, the dependency chain between control units is mapped to a control transfer matrix, and the sensitivity of propagation along the control path is nonlinearly corrected by using control response delay and action confidence as correction weights.

[0079] Based on the physical path propagation results and the control path correction results, the node operating state volatility is introduced as a dynamic weighting factor to fuse and weight the two types of propagation results, thus obtaining the node-level correction sensitivity.

[0080] The node-level correction sensitivity is normalized to form a propagated correction structural sensitivity distribution, which is used to quantify the comprehensive impact of uncertain structural units on system reliability indicators.

[0081] S4 combines the structural sensitivity vector with real-time operational data to form a dynamic reliability impact factor table.

[0082] The structural sensitivity vector is used to quantify the impact of each uncertain structural unit (such as switchable nodes, controllable circuit breakers, tie lines, etc.) on the overall system reliability indicators (such as power supply reliability and system failure rate). The larger the value, the more significant the impact of the uncertainty of the unit on the system reliability.

[0083] The dynamic reliability impact factor table is a comprehensive table obtained by introducing real-time operating data (voltage, current, power flow, temperature, control status, etc.) on the basis of the structural sensitivity vector. It is used to describe the dynamic impact intensity of each structural unit on the system reliability under the current operating state.

[0084] In this embodiment, the process of combining the structural sensitivity vector with real-time operational data to form a dynamic reliability impact factor table specifically involves:

[0085] Based on the structural sensitivity vector, the sensitivity values ​​of each structural unit under the reference operating conditions are extracted and used as the static reference sensitivity coefficient.

[0086] Collect real-time operating data of each node and associated line, including parameters such as voltage deviation, power fluctuation rate and control action status;

[0087] Based on the fluctuation range of node operation data, control response delay, and power transfer status, a state correction function is constructed to describe the time-varying adjustment characteristics of the reference sensitivity under different operating states.

[0088] The reference sensitivity coefficients of each structural unit are weighted and fused with the corresponding state correction functions to obtain the time-varying reliability impact value.

[0089] The time-varying reliability impact values ​​are associated and stored with the structural unit identifiers according to the time series to form a dynamic reliability impact factor table, which is used to characterize the dynamic impact intensity of each structural unit on the overall system reliability under real-time operating conditions.

[0090] The static benchmark sensitivity coefficient is specifically:

[0091]

[0092] The state correction function is specifically as follows:

[0093]

[0094] in, The static baseline sensitivity coefficient. For structural unit i under uncertainty weights The change in system reliability indicators when a minor disturbance occurs. For the disturbance amplitude, As the system's baseline reliability index, As the baseline uncertainty weight for structural units, This is the state correction function. For node voltage deviation, For node power fluctuation, To control response latency, , , These are the rated reference values.

[0095] The method for analyzing the reliability influencing factors of distributed smart distribution networks of the present invention further includes: extracting key structural influencing factors based on a dynamic reliability influencing factor table.

[0096] The key structural influencing factors extracted based on the dynamic reliability influencing factor table are as follows:

[0097] Extract the time-series impact value sequence of each structural unit from the dynamic reliability impact factor table according to a predetermined time window;

[0098] For the time-series influence value sequence, the time average and time standard deviation of the sequence are respectively used as the steady-state influence and fluctuation influence of the structural unit within the time window;

[0099] Based on the preset steady-state threshold and fluctuation threshold, if the time average value is not less than the steady-state threshold or the time standard deviation is not less than the fluctuation threshold, then the structural unit is listed as a candidate key unit.

[0100] Using the candidate key unit as the base point, and utilizing the electrical connectivity and control dependency relationships recorded in the topology-control mode table, the adjacent units that have direct electrical connectivity or direct control dependency with each candidate key unit are identified, and the identified adjacent units and the candidate key units are treated together as an influence cluster.

[0101] For each affected cluster, the time average and time standard deviation of each unit in the cluster are merged according to the connectivity priority order recorded in the topology-control mode table, and the merged result and the cluster identifier of the affected cluster are output as key structural influencing factors.

[0102] Several key structural influencing factors are associated and output according to a set format to form a set of key reliability influencing factors. The set of key reliability influencing factors is used to indicate the structural units and their associated clusters that have a significant impact on the system-level power supply reliability within the time window, so as to be prioritized for subsequent topology adjustments or control strategies.

[0103] Example 2, Figure 2 The present invention provides a distributed smart distribution network reliability influencing factor analysis system, comprising the following modules:

[0104] Topology-Control Mode Table Construction Module: Used to obtain the operating status of the target area's distribution network and construct the topology-control mode table;

[0105] Structural uncertainty matrix generation module: used to annotate the uncertainty of key equipment based on the topology-control mode table and form a structural uncertainty matrix;

[0106] The structural sensitivity vector generation module is used to align the uncertainty weights of structural units in the structural uncertainty matrix with the corresponding node's running data according to time labels, calculate the system-level reliability index based on this, establish a structure-state coupled reliability model, calculate the sensitivity of each uncertain structural unit to the overall system reliability index, and generate a structural sensitivity vector.

[0107] Reliability Impact Factor Table Generation Module: This module combines structural sensitivity vectors with real-time operational data to generate a dynamic reliability impact factor table.

[0108] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0109] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0110] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0111] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0112] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0113] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for analyzing factors affecting the reliability of distributed smart distribution networks, characterized in that, Includes the following steps: Obtain the operating status of the distribution network in the target area and construct a topology-control mode table; Based on the topology-control mode table, uncertainties are labeled for key equipment to form a structural uncertainty matrix. This includes: analyzing the structural behavior of each electrical node and its associated control unit based on the topology-control mode table, identifying key equipment with multi-state characteristics or control switchable attributes, and forming a set of structural units. This includes: establishing an association mapping model between nodes and control units based on the connectivity relationships of nodes, lines, and control units recorded in the topology-control mode table; analyzing the connectivity and control response characteristics of each node under different operating states through the association mapping model, identifying a set of equipment with state switching or control transferable characteristics; determining the structural characteristics of the identified set of equipment to identify key equipment that may trigger system structural reconfiguration when the operating topology or control mode changes; and extracting the corresponding connectivity paths and controlled node information based on the identification results of the key equipment to form a set of structural units. Based on the operational stability and control characteristics of each unit in the structural unit set, uncertainty is labeled for key equipment to obtain a topology containing uncertainty attributes; based on the electrical connectivity and control dependencies between nodes in the topology, the uncertainty labeling information of key equipment is propagated to obtain the coupling influence relationship between the structural layer and the control layer; based on the coupling influence relationship, the key equipment and its associated units are matrixed to form a structural uncertainty matrix for characterizing the distribution characteristics of system structural uncertainty. The structural uncertainty matrix is ​​aligned with the operational data of corresponding nodes by time labels, and a system-level reliability index is calculated accordingly. A structure-state coupled reliability model is established, including: mapping the uncertainty weights of each structural unit in the matrix to node identifiers in the topology-control mode table to obtain the correspondence between the structural layer and the state layer; extracting node operational data based on this correspondence and aligning the operational data with the uncertainty weights of corresponding structural units by time synchronization labels to ensure spatiotemporal consistency; calculating the instantaneous failure probability of nodes by combining the uncertainty weights of structural units and the fluctuation characteristics of node operational data, and establishing a joint distribution model of node failure probability and topology state with node connectivity as a constraint; jointly mapping the joint distribution model with the action state vector of the control unit to obtain a three-layer correlation matrix of structure-control-state; marginalizing the correlation matrix to extract the comprehensive failure probability distribution of the system under dynamic structural states, and then introducing system path constraints to calculate the system-level reliability index; continuously iterating and updating the above system-level reliability index with time-series operational data to form a dynamic structure-state coupled reliability model. The sensitivity of each uncertain structural unit to the overall system reliability index is calculated, generating a structural sensitivity vector. This includes: applying a small perturbation to each structural unit based on the structure-state coupled reliability model, keeping the control mode and node operation data updated synchronously, and obtaining the difference in system reliability index before and after the perturbation; calculating the ratio of the reliability index difference to the perturbation amplitude of the corresponding structural unit as the initial sensitivity of that structural unit; propagating and correcting the initial sensitivity along the physical connection path and control transmission path between nodes based on the electrical connectivity and control dependency chain in the topology-control mode table; normalizing the sensitivity result after propagation correction to generate a structural sensitivity vector; the structural sensitivity vector is used to characterize the dynamic influence intensity of each uncertain structural unit on the system-level reliability index. By combining the structural sensitivity vector with real-time operational data, a dynamic reliability impact factor table is formed.

2. The method for analyzing factors affecting the reliability of distributed smart distribution networks according to claim 1, characterized in that, The process of obtaining the operating status of the target area's distribution network and constructing a topology-control mode table specifically involves: Collect the real-time operating status of nodes, lines, and controllable equipment within the target area; The collected real-time running data is formatted and timestamp-aligned to ensure that different data sources can be directly used for subsequent topology and control mode construction. The nodes and lines are represented as a topology graph structure, where the set of nodes represents the distribution network nodes and controllable devices, the set of edges represents the electrical connectivity between nodes, and necessary attributes are added to describe the network state. The topology graph structure is associated with the control mode information of nodes and devices to form a topology-control mode table.

3. The method for analyzing factors affecting the reliability of distributed smart distribution networks according to claim 2, characterized in that, The electrical connectivity and control dependency chain in the topology-control pattern table are used to propagate and correct the initial sensitivity along the physical connection path and control transmission path between nodes. Specifically: Based on the topology-control pattern table, a composite adjacency matrix between nodes is established; Based on the composite adjacency matrix, the initial sensitivity of each structural unit is linearly propagated along the physical connection path, and the path impedance or power transfer coefficient is used as the propagation attenuation factor. The dependency chain between control units is mapped to a control transfer matrix, and the sensitivity of propagation along the control path is nonlinearly corrected by using control response delay and action confidence as correction weights. Based on the physical path propagation results and control path correction results, node operating status-related parameters are introduced as dynamic weighting factors to fuse and weight the two types of results, thus obtaining the node-level correction sensitivity. The node-level correction sensitivity is normalized to form the propagated correction structure sensitivity distribution.

4. The method for analyzing factors affecting the reliability of distributed smart distribution networks according to claim 3, characterized in that, The process of combining structural sensitivity vectors with real-time operational data to form a dynamic reliability impact factor table is as follows: Based on the structural sensitivity vector, the sensitivity values ​​of each structural unit under the reference operating conditions are extracted and used as the static reference sensitivity coefficient. Collect and construct a status correction function based on the real-time operating data of each node and associated lines; The reference sensitivity coefficients of each structural unit are weighted and fused with the corresponding state correction functions to obtain the time-varying reliability impact value. The time-varying reliability impact values ​​are associated and stored with the structural unit identifier according to the time series, forming a dynamic reliability impact factor table.

5. The method for analyzing factors affecting the reliability of distributed smart distribution networks according to claim 4, characterized in that, After combining the structural sensitivity vector with real-time operational data to form a dynamic reliability impact factor table, the method further includes: extracting key structural influencing factors based on the dynamic reliability impact factor table, specifically: Extract the time-series impact value sequence of each structural unit from the dynamic reliability impact factor table according to a predetermined time window; The time average and time standard deviation of the time series influence value sequence are respectively used as the steady-state influence and fluctuation influence of the structural unit within the time window; Candidate key units are determined based on preset steady-state and fluctuation thresholds; Based on the candidate key unit as the base point, and according to the connectivity and control dependency relationships in the topology-control pattern table, the adjacent units that have direct connectivity or direct control dependency with it are identified, and the adjacent units and the candidate key units are used together as the influence cluster. For each affected cluster, the time average and time standard deviation of each unit within the cluster are merged according to the connectivity priority order in the topology-control mode table to form the key structural influencing factor item; Several key structural influencing factors are linked and output according to a set format to form a set of key reliability influencing factors.

6. A system using the distributed smart distribution network reliability influencing factor analysis method as described in any one of claims 1-5, characterized in that, Includes the following modules: Topology-Control Mode Table Construction Module: Used to obtain the operating status of the target area's distribution network and construct the topology-control mode table; Structural uncertainty matrix generation module: used to annotate the uncertainty of key equipment based on the topology-control mode table and form a structural uncertainty matrix; The structural sensitivity vector generation module is used to align the uncertainty weights of structural units in the structural uncertainty matrix with the corresponding node's running data according to time labels, calculate the system-level reliability index based on this, establish a structure-state coupled reliability model, calculate the sensitivity of each uncertain structural unit to the overall system reliability index, and generate a structural sensitivity vector. Reliability Impact Factor Table Generation Module: This module combines structural sensitivity vectors with real-time operational data to generate a dynamic reliability impact factor table.

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