Distributed intelligent power distribution network reliability influence factor analysis method and system

Abstract

The invention discloses a distributed intelligent power distribution network reliability influence factor analysis method and system, and relates to the technical field of influence factor analysis, and the method comprises the following steps: obtaining the operation state of a power distribution network in a target region, and constructing a topology-control mode table; performing uncertainty labeling on the key equipment to form a structural uncertainty matrix; coupling the structural uncertainty matrix with node operation data, establishing a reliability model, calculating the sensitivity of each uncertain structural unit to the reliability index of the whole system, and generating a structural sensitivity vector; and combining the structural sensitivity vector with real-time operation data to form a dynamic reliability influence factor table, and constructing a topology-control mode table. According to the method, the dynamic reliability influence factor table is generated by establishing the structure-state coupling reliability model, so that the problem that key influence factors are inaccurately identified due to the fact that the influence of topological change and control mode switching on the system reliability is not comprehensively considered in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of influencing factor analysis, and more particularly, to a distributed intelligent power distribution network reliability influencing factor analysis method and system. BACKGROUND

[0002] With the deepening of new energy high penetration and power market reform, the distributed intelligent power distribution network, as a key link connecting power sources and users, presents complex characteristics of high dynamics and strong coupling in its operation state. The topology structure frequently changes due to events such as distributed power access and exit, sectionalizing switch action, network reconfiguration, and the control mode is flexibly adjusted among constant voltage control, droop control, power tracking control and other modes according to the running scene.

[0003] Reliability, as a core indicator to measure the power supply quality of the power distribution network, directly provides an important basis for power grid planning optimization, dispatching operation decision and equipment operation and maintenance. At present, the existing power distribution network reliability analysis methods mainly focus on the processing of parameter uncertainty, that is, through probability statistics, interval analysis and other means, the random characteristics of operating parameters such as component failure rate, repair time and load fluctuation are quantitatively analyzed, and then the system reliability level is evaluated.

[0004] However, the frequently changing topology structure and control mode in the distributed intelligent power distribution network will cause significant structural uncertainty. This uncertainty is caused by the dynamic change of network topology connection relationship and the dynamic matching of control strategy and control object, which is fundamentally different from traditional parameter uncertainty. The existing reliability analysis methods lack effective modeling and quantitative means for this kind of structural uncertainty, and cannot accurately depict the influence mechanism of the dynamic change of topology structure and control mode on system reliability, resulting in a serious deviation of the reliability evaluation results from the real operation state of the power distribution network, which makes it difficult to meet the fine and dynamic reliability analysis requirements of the distributed intelligent power distribution network, and restricts the scientificity and effectiveness of the power grid operation decision. Therefore, it is urgent to propose a distributed intelligent power distribution network reliability influencing factor analysis method that can effectively handle structural uncertainty.

[0005] In the above-mentioned disclosed technical solution, at least the following technical problems exist: the existing reliability analysis methods can only handle parameter uncertainty, and cannot handle the structural uncertainty caused by the time-varying topology structure and control mode in the distributed intelligent power distribution network, resulting in a serious deviation of the evaluation results from the real operation state. SUMMARY

[0006] In order to overcome the above-mentioned defects of the prior art, embodiments of the present application provide a distributed smart power distribution network reliability influence factor analysis method and system, by constructing a topology-control mode table, forming a structure uncertainty matrix and establishing a structure-state coupled reliability model, generating a structure sensitivity vector and a dynamic reliability influence factor table, to solve the problem that the prior art fails to comprehensively consider the influence of topology changes and control mode switching on system reliability, leading to inaccurate identification of key influence factors.

[0007] To achieve the above object, the present application provides the following technical solutions: On the one hand, the distributed smart power distribution network reliability influence factor analysis method comprises the following steps: obtaining the operating state of the target area power distribution network, and constructing a topology-control mode table; based on the topology-control mode table, annotating the key equipment with uncertainty, and forming a structure uncertainty matrix; aligning the structure unit uncertainty weight in the structure uncertainty matrix with the operating data of the corresponding node according to the time label, and calculating the system-level reliability index accordingly, establishing a structure-state coupled reliability model, and calculating the sensitivity of each uncertain structure unit to the overall system reliability index, and generating a structure sensitivity vector; combining the structure sensitivity vector with the real-time operating data to form a dynamic reliability influence factor table.

[0008] In a preferred embodiment, the obtaining of the operating state of the target area power distribution network and the construction of the topology-control mode table are specifically: collecting the real-time operating state of the nodes, lines and controllable devices in the target area; aligning the collected real-time operating data in a unified format and time stamp to ensure that different data sources can be directly used for subsequent topology and control mode construction; representing the nodes and lines as a topology graph structure, wherein the node set represents the power distribution network nodes and controllable devices, the edge set represents the electrical connection relationship between nodes, and necessary attributes are added to describe the network state; associating the topology graph structure with the control mode information of the nodes and devices to form a topology-control mode table.

[0009] In a preferred embodiment, the key equipment is annotated with uncertainty based on the topology-control mode table to form a structure uncertainty matrix, specifically: based on the topology-control mode table, the structural behavior of each electrical node and its associated control unit is analyzed, and the key equipment with multi-state characteristics or control switchable attributes is identified to form a structure unit set; based on the operation stability and control characteristics of each unit in the structure unit set, the key equipment is annotated with uncertainty to obtain a topology structure containing uncertainty attributes; based on the electrical connection relationship and control dependency relationship between nodes in the topology structure, the uncertainty annotation information of the key equipment is propagated to obtain the coupling influence relationship between the structure layer and the control layer; based on the coupling influence relationship, the key equipment and its associated unit are matrixed and arranged to form a structure uncertainty matrix for representing the structure uncertainty distribution characteristics of the system.

[0010] In a preferred embodiment, the structural behavior of each electrical node and its associated control unit is analyzed based on the topology-control mode table to identify the key equipment with multi-state characteristics or control switchable attributes, specifically: based on the connection relationship of nodes, lines and control units recorded in the topology-control mode table, an association mapping model of nodes and control units is established; through the association mapping model, the connection characteristics and control response characteristics of each node under different operating states are analyzed, and a device set with state switching or control migratory characteristics is identified; the identified device set is subjected to structural characteristic determination to determine the key equipment that may cause system structure reconstruction when the operating topology or control mode changes; based on the identification result of the key equipment, the corresponding connection path and controlled node information are extracted to form a structure unit set.

[0011] In a preferred embodiment, the structure-state coupling reliability model is established, specifically: based on the structure uncertainty matrix, the uncertainty weights of each structure unit in the matrix are mapped with the node identifiers in the topology-control mode table to obtain the correspondence relationship between the structure layer and the state layer; based on the correspondence relationship, the node operation data are extracted and aligned with the uncertainty weights of the corresponding structure units according to the time synchronization label; based on the time alignment, the node instantaneous failure probability is calculated in combination with the uncertainty weights of the structure units and the fluctuation characteristics of the node operation data, and a joint distribution model of the node failure probability and the topology state is established with the connection relationship between nodes as a constraint; the joint distribution model is jointly mapped with the action state vector of the control unit to obtain a structure-control-state three-layer association matrix; the association matrix is edge processed to extract the comprehensive failure probability distribution of the system under the dynamic structure state, and the system-level reliability index is calculated by introducing the system path constraint condition; the above system-level reliability index and the time series operation data are continuously iterated and updated to form a dynamic structure-state coupling reliability model.

[0012] In a preferred embodiment, the calculating each uncertainty structure unit calculates the sensitivity of the overall system reliability index, and generates a structure sensitivity vector, specifically: based on the structure-state coupling reliability model, a small disturbance is applied to each structure unit, the control mode is updated synchronously with the node operation data, and the difference value of the system reliability index before and after the disturbance is obtained; the ratio of the reliability index difference value to the corresponding structure unit disturbance amplitude is calculated as the initial sensitivity of the structure unit; based on the electrical connection relationship and control dependency chain in the topology-control mode table, the initial sensitivity is propagated and corrected along the physical connection path and control transmission path between nodes; the propagated and corrected sensitivity result is normalized to generate the structure sensitivity vector.

[0013] In a preferred embodiment, the initial sensitivity is propagated and corrected along the physical connection path and control transmission path between nodes based on the electrical connection relationship and control dependency chain in the topology-control mode table, specifically: based on the topology-control mode table, a composite adjacency matrix between nodes is established; according to the composite adjacency matrix, the initial sensitivity of each structure 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 into a control transmission matrix, and the control response delay and action confidence are used as correction weights to nonlinearly correct the sensitivity propagated along the control path; based on the physical path propagation result and the control path correction result, the node operation state related parameters are introduced as dynamic weight factors to fuse and weight the two types of results to obtain the node-level corrected sensitivity; the node-level corrected sensitivity is normalized to form the propagated and corrected structure sensitivity distribution.

[0014] In a preferred embodiment, the structure sensitivity vector is combined with real-time operation data to form a dynamic reliability influence factor table, specifically: based on the structure sensitivity vector, the sensitivity value of each structure unit under the reference operation condition is extracted as the static reference sensitivity coefficient; real-time operation data of each node and associated lines are collected and used to construct a state correction function; the reference sensitivity coefficient of each structure unit is weighted and fused with the corresponding state correction function to obtain time-varying reliability influence values; The time-varying reliability influence values are associated and stored according to time sequence and structure unit identification to form a dynamic reliability influence factor table.

[0015] In a preferred embodiment, after the structure sensitivity vector is combined with the real-time operation data to form the dynamic reliability influence factor table, it further comprises: based on the dynamic reliability influence factor table, extracting key structure influence factors, specifically: extracting the time sequence influence value sequence of each structure unit in the dynamic reliability influence factor table according to a predetermined time window; taking the time average value and the time standard deviation of the time sequence influence value sequence as the steady-state influence quantity and the fluctuation influence quantity of the structure unit in the time window, respectively; according to the preset steady-state threshold and the fluctuation threshold, candidate key unit judgment is performed; taking the candidate key unit as the base point, according to the connection relationship and the control dependency relationship in the topology-control mode table, the adjacent units directly connected or directly controlled dependent on the candidate key unit are identified, and the adjacent units and the candidate key unit are taken as an influence cluster; for each influence cluster, the time average value and the time standard deviation of each unit in the cluster are combined according to the connection priority order in the topology-control mode table to form a key structure influence factor item; and a plurality of key structure influence factor items are associated and output in a set format to form a key reliability influence factor set.

[0016] In another aspect, the distributed intelligent power distribution network reliability influence factor analysis system comprises the following modules: A topology-control mode table construction module is used to obtain the operation state of the target regional power distribution network and construct a topology-control mode table; a structure uncertainty matrix generation module is used to label the uncertainty of key equipment based on the topology-control mode table to form a structure uncertainty matrix; a structure sensitivity vector generation module is used to align the structure unit uncertainty weight in the structure uncertainty matrix with the operation data of the corresponding node according to the time label, calculate the system-level reliability index, establish a structure-state coupled reliability model, and calculate the sensitivity of each uncertain structure unit to the overall system reliability index to generate a structure sensitivity vector; and a reliability influence factor table formation module is used to combine the structure sensitivity vector with the real-time operation data to form a dynamic reliability influence factor table.

[0017] The technical effects and advantages of the distributed intelligent power distribution network reliability influence factor analysis method and system of the present application are as follows: 1. The present application effectively captures and quantifies the structure uncertainty of the distributed intelligent power distribution network by constructing a topology-control mode table and a structure uncertainty matrix, breaking through the limitation of traditional methods that can only handle parameter uncertainty; combining structure-state coupled modeling and dynamic reliability influence factor table, accurately associating structure changes and operation state, realizing dynamic characterization of reliability influence factors, improving analysis accuracy, and providing strong support for power distribution network optimization decision. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The figure is a flowchart of the distributed intelligent power distribution network reliability influence factor analysis method of the present application. Figure 2 Fig. 1 is a structural schematic diagram of a distributed intelligent power distribution network reliability influencing factor analysis system according to the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort fall within the protection scope of the present application.

[0020] Embodiment 1, Figure 1 The distributed intelligent power distribution network reliability influencing factor analysis method according to the present application includes the following steps: S1, obtaining an operation state of a target area power distribution network, and constructing a topology-control mode table; In this embodiment, the topology-control mode table is used to represent the network structure and control behavior that changes at any time due to the access of a distributed power source, power outage, island operation or control strategy adjustment.

[0021] The obtaining of the operation state of the target area power distribution network and the construction of the topology-control mode table are specifically as follows: The real-time operation states of nodes, lines, circuit breakers, controllable loads and distributed power sources in the target area are collected, wherein the real-time operation states include node voltage, current, switch state, protection device action state, line load and fault state, controllable load access and disconnection state, and distributed power source output power and control mode; The collected heterogeneous data are unified in units and standardized in formats, and the time stamp is mapped and aligned with the topology node, so as to ensure that different data sources can be directly used for subsequent topology and control mode construction; Based on the standardized heterogeneous data, the nodes and lines are represented as a topology graph structure, wherein the node set represents the power distribution network nodes and controllable devices, the edge set represents the electrical connection relationship between nodes and the line state, and each edge is additionally attached with real-time load, line capacity and protection configuration and other attributes; Based on the topology graph structure, the control mode attributes are additionally attached to each node and device, including circuit breaker / switch control logic, distributed power source control mode, controllable load response strategy, and node-to-node control dependency relationship, so as to associate the topology information with the control behavior; 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 changes due to the access of a distributed power source, power outage, island operation or control strategy adjustment in the target area, and a unique identifier and a state update time stamp are defined for each unit in the table.

[0022] S2, annotating the key devices based on the topology-control mode table to form a structure uncertainty matrix; In the embodiment, the key devices include a variable topology section, a switchable control device, and a device that can have a state transition.

[0023] It should be noted that the structure uncertainty matrix is an uncertainty expression tool for quantifying the influence of key devices and their control modes on system reliability under the change of topology structure in a distributed intelligent power distribution network. The potential influence of the changing network structure and control mode on system reliability is captured. The key units (nodes, lines, controllable devices) in the topology-control mode table are mapped to a quantifiable matrix. Each matrix element represents the uncertainty degree of the corresponding unit and its possible influence range. The structure-state coupling reliability model is provided with input, so that the reliability analysis not only considers parameter uncertainty, but also considers topology and control uncertainty.

[0024] In the embodiment, the structure uncertainty matrix is formed by annotating the key devices based on the topology-control mode table, specifically: Based on the topology-control mode table, the structural behavior of each electrical node and its associated control unit is analyzed to identify key devices with multi-state characteristics or control switchable properties, forming a structure unit set to represent the variability of the network at the structure level. For each structure unit, based on the historical failure frequency, action delay distribution, and real-time parameters of the control link, the uncertainty characteristics of each key device are mapped to the topology structure in the form of weight labels. For the uncertainty labels of the annotated devices, the uncertainty propagation calculation is performed according to their electrical connection path in the topology and control logic dependency, so that the annotated information forms a coupling influence domain between the topology-control layers. Based on the propagated uncertainty influence domain, the weight labels of the key devices and their associated units are restructured into a matrix form. The matrix elements are used to quantify the uncertainty coupling strength between the structure units, forming the structure uncertainty matrix.

[0025] The uncertainty weight is specifically:

[0026] wherein, is the uncertainty weight, , , and are the weight allocation coefficients of fault uncertainty, action uncertainty, and communication uncertainty (obtained based on correlation analysis), is the historical failure occurrence probability (statistically obtained) of the device i, is the mean value of the device action delay, is the maximum action delay in the system, is the communication delay of the control link corresponding to the device, is the maximum communication delay in the system.

[0027] The structural behavior of each electrical node and its associated control unit is analyzed based on the topology-control mode table, and key devices with multi-state characteristics or control switchable properties are identified, specifically: Based on the connection relationship of nodes, lines and control units recorded in the topology-control mode table, the electrical connection structure and control dependency chain between nodes are extracted, and an association mapping model between nodes and control units is established; On the basis of the association mapping model, the power flow direction and control command response path of each node under different connected states are analyzed to identify a candidate set of devices that may have state switching, power backfeeding or control migration behavior; Based on the historical operation data and control strategy configuration file of each device in the device candidate set, the state transition frequency, action delay and control command trigger condition are obtained, and a structural behavior feature vector of the device is established based thereon, which is used to quantify the multi-state operation characteristics of the device; The structural behavior feature vector is input into a preset multi-state judgment model, and if the structural variability index output by the model exceeds a preset threshold, the corresponding device is identified as a key device, and the control dependency degree between the key device and the adjacent node is recorded; Based on the identification result of the key device, the connected path and the controlled node information of the key device are further extracted to form a structural unit set, which includes the structural variability parameters and control logic dependency description of each key device, and is used to represent the dynamic variability of the power distribution network at the structural level, and is used as a target set for subsequent uncertainty labeling.

[0028] The purpose of the uncertainty labeling performed by the present application is that the distributed intelligent power distribution network has dynamic behaviors such as real-time access / exit of distributed power sources, circuit breaker switching and island operation; traditional reliability analysis only considers parameter fluctuations (such as load or power generation power), and ignores topology and control mode changes; uncertainty labeling can clearly indicate which devices and control units may affect system reliability under a specific state, thereby providing a basis for sensitivity analysis and dynamic reliability factor calculation.

[0029] S3, align the structure unit uncertainty weight in the structure uncertainty matrix with the operation data of the corresponding node according to the time label, calculate the system-level reliability index, establish a structure-state coupling reliability model, calculate the sensitivity of each uncertain structure unit to the overall system reliability index, generate a structure sensitivity vector, and calculate the sensitivity of each uncertain structure unit to the overall system reliability index, generate a structure sensitivity vector; It should be noted that the structure-state coupling reliability model is used to describe the reliability indexes of each node and line under different structure configurations and control modes, and supports quantitative calculation of the influence of topology changes and control switching on system reliability.

[0030] In this embodiment, the establishment of the structure-state coupling reliability model is specifically: Based on the structure uncertainty matrix, the uncertainty weight of each structure unit in the matrix is one-to-one mapped with the node identifier in the topology-control mode table, to obtain the corresponding relationship between the structure layer and the state layer; Based on the corresponding relationship, real-time operation data of each node is extracted, including voltage, current, power flow direction, power factor and other state parameters, and the operation data is aligned with the corresponding structure unit uncertainty weight according to the time synchronization label, to ensure the spatiotemporal consistency of the data; On the basis of time alignment, the instantaneous failure probability of the node is calculated according to the uncertainty weight of the structure unit and the fluctuation amplitude of the node operation data, and a joint distribution model of node failure probability and topology state is established with the electrical connection relationship between nodes as a constraint, the joint distribution model is used to represent the state-related failure characteristics caused by the propagation of structure disturbance in the network; The joint distribution model is jointly mapped with the action state vector of the control unit to obtain a structure-control-state three-layer association matrix, wherein each matrix element is used to depict the conditional probability of node failure caused by structure change under a given control strategy; On this basis, the edge of the association matrix is processed, the comprehensive failure probability distribution of the system under any dynamic structure state is extracted, and the system power supply path constraint condition is introduced to calculate the system-level reliability index; The system-level reliability index is continuously iterated and updated with the time series operation data to form a dynamic structure-state coupling reliability model, which can reflect the real-time influence of structure uncertainty on system reliability evolution under different control mode switching; the dynamic structure-state coupling reliability model is used as the input basis for subsequent sensitivity analysis, to identify the reliability influence sensitivity of the uncertain structure unit and track the dominant propagation path.

[0031] The instantaneous failure probability of the node is specifically calculated as:

[0032] The system-level reliability index, and the specific calculation formula is:

[0033] Wherein, is the instantaneous failure probability of the node, is the node reference failure rate constant (obtained from historical statistics), is the structure uncertainty weight, is the fluctuation amplitude of the node operating data (such as voltage standard deviation), is the node allowable fluctuation threshold, which is set according to the rated parameters of the equipment, is the system-level reliability index, is the node criticality coefficient.

[0034] The method for obtaining the node criticality coefficient comprises: According to the topology-control mode table, the electrical connection and control dependency of each node are counted; According to the connection redundancy (number of backup power supply paths), load concentration (connection load proportion of node power), and control transmission depth (node level position in control logic), the importance of the node in the structure and control double layers is comprehensively determined; After normalization processing and weighted fusion of the above three types of indexes, the node criticality coefficient is obtained, which is used to represent the influence degree of the node on the system reliability under the current topology structure and control strategy.

[0035] The sensitivity of each uncertain structure unit to the overall system reliability index is calculated, and a structure sensitivity vector is generated, specifically: Based on the structure-state coupled reliability model, a small disturbance is applied to each structure unit, so that its uncertainty weight changes within a preset disturbance amplitude, and the control mode and node operating data are updated synchronously to form the difference of the system reliability index before and after the disturbance; The reliability index difference and the disturbance amplitude ratio of the corresponding structure unit are used as the initial sensitivity measure of the structure unit, which is used to represent the direct influence degree of the unit state change on the system reliability; Based on the electrical connection relationship and control dependency chain in the topology-control mode 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 structure disturbance in the multi-layer network; The normalized processing is performed on the propagated and corrected sensitivity result to generate a structure sensitivity vector, and each component of the vector is used to describe the dynamic influence strength of each structure unit on the system-level reliability index, which can be used as the basis for generating the subsequent dynamic reliability influence factor table.

[0036] In this embodiment, the initial sensitivity is propagated and corrected along the physical connection path and the control transmission path between nodes based on the electrical connection relationship and the control dependency chain in the topological-control mode table, specifically: Based on the topological-control mode table, a composite adjacency matrix between nodes is established to describe the physical connection path and the control dependency path of each node; 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; At the same time, the dependency chain between control units is mapped into a control transmission matrix, and the control response delay and action confidence are used as correction weights to nonlinearly correct the sensitivity propagated along the control path; On the basis of the physical path propagation result and the control path correction result, the node operation state fluctuation rate is introduced as a dynamic weight factor to fuse and weight the two types of propagation results, and the node-level corrected sensitivity is obtained; The node-level corrected sensitivity is normalized to form a structural sensitivity distribution after propagation and correction, which is used to quantify the comprehensive influence of uncertain structural units on system reliability indicators.

[0037] S4, the structural sensitivity vector is combined with real-time operation data to form a dynamic reliability influence factor table.

[0038] The structural sensitivity vector is used to quantify the influence degree 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, system failure rate), and the larger the value, the more significant the uncertainty of the unit on the system reliability.

[0039] The dynamic reliability influence factor table is a comprehensive table obtained by introducing real-time operation data (voltage, current, power flow, temperature, control state, etc.) on the basis of the structural sensitivity vector, which is used to describe the dynamic influence intensity of each structural unit on the system reliability under the current operating state.

[0040] In this embodiment, the structural sensitivity vector is combined with real-time operation data to form a dynamic reliability influence factor table, specifically: Based on the structural sensitivity vector, the sensitivity value of each structural unit under the reference operating condition is extracted as a static reference sensitivity coefficient; Collect real-time operation data of each node and associated lines, including voltage deviation, power fluctuation rate, and control action state parameters; According to the fluctuation amplitude of node operation data, control response delay and power transfer state, a state correction function is constructed to describe the time-varying adjustment characteristics of the reference sensitivity under different operating states; The reference sensitivity coefficient of each structural unit is weighted and fused with the corresponding state correction function to obtain a time-varying reliability influence value. The time-varying reliability influence value is stored in association with the time sequence and the structural unit identifier to form a dynamic reliability influence factor table, which is used to represent the dynamic influence strength of each structural unit on the overall reliability of the system under real-time operating conditions.

[0041] The static reference sensitivity coefficient is specifically:

[0042] The state correction function is specifically:

[0043] wherein, is a static reference sensitivity coefficient, is a structural unit i in the uncertainty weight the change amount of the system reliability index when a slight disturbance occurs, is a disturbance amplitude, is a system reference reliability index, is a structural unit reference uncertainty weight, is a state correction function, is a node voltage deviation, is a node power fluctuation rate, is a control response delay, , , are respectively rated reference values.

[0044] The distributed intelligent power distribution network reliability influence factor analysis method further comprises: extracting key structural influence factors based on the dynamic reliability influence factor table.

[0045] The key structural influence factors are extracted based on the dynamic reliability influence factor table, and the method specifically comprises: extracting a time sequence influence value sequence of each structural unit from the dynamic reliability influence factor table according to a predetermined time window; respectively taking the time average and the time standard deviation of the sequence as the steady-state influence amount and the fluctuation influence amount of the structural unit within the time window; according to a pre-set steady-state threshold and a fluctuation threshold, if the time average is not less than the steady-state threshold or the time standard deviation is not less than the fluctuation threshold, the structural unit is listed as a candidate key unit; Taking the candidate key unit as a base point, using the electrical connection relationship and control dependency relationship recorded in the topology-control mode table, adjacent units having direct electrical connection or direct control dependency with each candidate key unit are identified, and the identified adjacent units and the candidate key unit are collectively taken as an influence cluster; For each influence cluster, the time average value and the time standard deviation of each unit in the cluster are merged according to the connection priority order recorded in the topology-control mode table, and the merging result and the cluster identifier of the influence cluster are output as a key structure influence factor item; The plurality of key structure influence factor items are associated and output in a set format to form a key reliability influence factor set, which 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, for subsequent topology adjustment or control strategy priority processing.

[0046] Embodiment 2, Figure 2 The distributed intelligent power distribution network reliability influence factor analysis system of the application is given, which comprises the following modules: The topology-control mode table construction module is used to obtain the operation state of the target regional power distribution network and construct a topology-control mode table; The structure uncertainty matrix generation module is used to label the key equipment based on the topology-control mode table, and form a structure uncertainty matrix; The structure sensitivity vector generation module is used to align the structure unit uncertainty weight in the structure uncertainty matrix with the operation data of the corresponding node according to the time label, and calculate the system-level reliability index, establish a structure-state coupled reliability model, and calculate the sensitivity of each uncertain structure unit to the overall system reliability index, and generate a structure sensitivity vector; The reliability influence factor table formation module is used to combine the structure sensitivity vector with the real-time operation data to form a dynamic reliability influence factor table.

[0047] The above formulas are all de-dimensioned to calculate their numerical values. The formulas are obtained by collecting a large amount of data to simulate the most recent real situation. The preset parameters in the formulas are set by a person skilled in the art according to the actual situation.

[0048] The above embodiments can be realized wholly or partially by software, hardware, firmware or any other combination. When realized by software, the above embodiments can be realized in the form of a computer program product, wholly or partially.

[0049] Those skilled in the art can understand that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized 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 realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0050] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0051] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0052] Finally: the above is only the preferred embodiment of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

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 marked on key equipment to form a structural uncertainty matrix; Align the uncertainty weights of structural units in the structural uncertainty matrix with the corresponding node's operational 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. 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 uncertainty labeling of key equipment based on the topology-control mode table forms a structural uncertainty matrix, specifically as follows: 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. 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, 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 that characterizes the distribution characteristics of system structural uncertainty.

4. The method for analyzing factors affecting the reliability of distributed smart distribution networks according to claim 3, characterized in that, 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: Based on the connectivity relationships of nodes, lines, and control units recorded in the topology-control mode table, an association mapping model between nodes and control units is established. By using an association mapping model, we analyze the connectivity and control response characteristics of each node under different operating states, and identify a set of devices with state switching or control transferable characteristics. Structural characteristics are determined from the identified set of devices to identify key devices that may trigger system restructuring when the operating topology or control mode changes. Based on the identification results of key equipment, the corresponding connection paths and controlled node information are extracted to form a set of structural units.

5. The method for analyzing factors affecting the reliability of distributed smart distribution networks according to claim 4, characterized in that, The establishment of the structure-state coupled reliability model specifically involves: Based on the structural uncertainty matrix, the uncertainty weights of each structural unit in the matrix are mapped 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, node operation data is extracted, and the operation data is aligned with the uncertainty weight of the corresponding structural unit according to the time synchronization label to ensure the spatiotemporal consistency of data. Based on time alignment, the instantaneous failure probability of nodes is calculated by combining the uncertainty weight of structural units and the fluctuation characteristics of node operation data. Then, a joint distribution model of node failure probability and topology state is established with the connectivity relationship between nodes as a constraint. By jointly mapping the joint distribution model with the action state vector of the control unit, a three-layer correlation matrix of structure-control-state is obtained; The correlation matrix is ​​marginalized to extract the comprehensive failure probability distribution of the system under dynamic structural state. Then, system path constraints are introduced to calculate the system-level reliability index. The above system-level reliability indicators and time-series operational data are continuously iterated and updated to form a dynamic structure-state coupled reliability model.

6. The method for analyzing factors affecting the reliability of distributed smart distribution networks according to claim 5, characterized in that, The calculation of the sensitivity of each uncertain structural unit to the overall system reliability index, generating a structural sensitivity vector, is as follows: Based on the structure-state coupled reliability model, a small perturbation is applied to each structural unit while keeping the control mode and node operation data updated synchronously, and the difference in system reliability index before and after the perturbation is obtained. The ratio of the reliability index difference to the disturbance amplitude of the corresponding structural unit is calculated as the initial sensitivity of the structural unit. Based on the electrical connectivity and control dependency chain in the topology-control mode table, the initial sensitivity is propagated and corrected along the physical connection path and control transmission path between nodes. The sensitivity results after propagation correction are normalized to generate a structural sensitivity vector.

7. The method for analyzing factors affecting the reliability of distributed smart distribution networks according to claim 6, 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.

8. The method for analyzing factors affecting the reliability of distributed smart distribution networks according to claim 7, 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.

9. The method for analyzing factors affecting the reliability of distributed smart distribution networks according to claim 8, 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.

10. A system using the distributed smart distribution network reliability influencing factor analysis method as described in any one of claims 1-9, 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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