Operation monitoring method and system for distributed power distribution network

Abstract

The invention relates to the technical field of power grid monitoring, in particular to an operation monitoring method and system for a distributed power distribution network, and the method comprises the steps: generating a plurality of multi-dimensional monitoring surfaces based on a topological structure, real-time electrical parameters and a load side dynamic response simulation result of the distributed power distribution network; determining a plurality of groups of electrical parameter time sequence chains and generating a parameter chain cluster based on the interaction data of the multi-dimensional monitoring surface and the distributed power distribution network and the load side dynamic response data; and the multi-dimensional monitoring surface is a monitoring carrier which is appointed by a user and covers a distribution network topology area and a load response area. According to the method, the load absorption capacity of the distribution network and the power characteristics of the operation state unit can be accurately evaluated by analyzing time series data such as load fluctuation, power change and voltage change, the dynamic response characteristics of the distribution network can be deeply analyzed, and possible load risks can be recognized in advance.

Description

Technical Field

[0001] This invention relates to the field of power grid monitoring technology, specifically to a method and system for monitoring the operation of a distributed distribution network. Background Technology

[0002] With increasing global attention and use of renewable energy, distributed energy has become an important component of energy systems. Traditional centralized power systems struggle to cope with these highly dispersed and dynamically changing energy sources. Distributed distribution networks offer more flexible access to and dispatch of these energy resources, but also increase the complexity of monitoring and management. Therefore, it is necessary to develop more sophisticated and intelligent methods for monitoring the operation of distributed distribution networks.

[0003] Currently, traditional methods often rely on only certain fixed monitoring points or single electrical parameters, which cannot achieve multi-dimensional real-time monitoring. They are prone to overlooking potential abnormal states or faults, and usually rely on static data or limited time-series data, which cannot accurately analyze dynamic characteristics such as load fluctuations, power changes, and voltage changes. This results in the inability to identify and assess load risks in a timely manner, long response times, and potential delays in fault handling.

[0004] Furthermore, traditional methods generally manage distribution networks through relatively simple threshold control strategies, but often neglect the dynamic changes in load absorption characteristics, spatial attributes, and state characteristic parameters. This makes it difficult to achieve refined and personalized power resource allocation, which can easily lead to low distribution network operating efficiency or instability. Moreover, when monitoring large-scale distribution networks, traditional monitoring point settings are often relied upon, making it difficult to accurately locate and dynamically adjust the monitoring area, and making it difficult to achieve precise regional control. This can easily exacerbate the impact of sudden faults on the distribution network. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for monitoring the operation of a distributed distribution network, comprising: Based on the topology of the distributed distribution network, real-time electrical parameters, and simulation results of load-side dynamic response, multiple multi-dimensional monitoring surfaces are generated. Based on the interaction data between the multi-dimensional monitoring surface and the distributed distribution network, as well as the load-side dynamic response data, multiple sets of electrical parameter time series chains are determined and a parameter chain cluster is generated; the multi-dimensional monitoring surface is a monitoring carrier specified by the user that covers the distribution network topology area and the load response area; the electrical parameter time series chain is the parameter acquisition time series data between the monitoring sub-unit of the multi-dimensional monitoring surface and the topology sub-unit of the distributed distribution network. Based on the parameter chain cluster, the distribution network operation status unit is analyzed, and the load absorption power characteristic value of each distribution network operation status unit is calculated. Based on the load absorption power characteristic value of the distribution network operation status unit and the spatial attributes of the multi-dimensional monitoring surface, the status characteristic parameters and preliminary operation control thresholds of each distribution network operation status unit are determined; the status characteristic parameters include at least power fluctuation intensity, continuous response duration and spatial correlation. Based on the state characteristic parameters and preliminary operation control thresholds of the target operation status units that meet the target conditions among the multiple distribution network operation status units, the operation control boundary conditions and final operation metrics of the target monitoring area are determined.

[0006] Preferably, based on the topology of the distributed distribution network, real-time electrical parameters, and load-side dynamic response simulation results, multiple multi-dimensional monitoring surfaces are generated, including: Obtain the topology, real-time electrical parameters, and load-side dynamic response simulation results of the distributed power distribution network; The topology of the distributed distribution network is modeled spatially using real-time electrical parameters to obtain the corresponding topological spatial domain. Based on the load fluctuation distribution of the topology spatial domain and the load-side dynamic response simulation results, a multi-dimensional monitoring surface corresponding to multiple spatial dimensions is generated; wherein, the multiple spatial dimensions include the feeder extension direction, node radiation direction and load response dimension under the distribution network topology spatial domain; the coverage of the multi-dimensional monitoring surface is determined based on the scale of the topology spatial domain and the range of load fluctuation distribution.

[0007] Preferably, based on the interaction data between the multi-dimensional monitoring surface and the distributed distribution network, as well as the load-side dynamic response data, multiple sets of electrical parameter time-series chains are determined and a parameter chain cluster is generated, including: Acquire multi-dimensional monitoring aspects of user input targets; The target multi-dimensional monitoring surface is interacted with the distributed distribution network in real time to obtain interactive data; power fluctuation, time series and voltage sag time series are extracted from the load-side dynamic response data. The interactive data, power fluctuations, timing segments, and voltage sag timing segments are subjected to timing correlation processing to obtain multiple sets of electrical parameter timing chains; Based on the time-series correlation algorithm, multiple sets of electrical parameter time-series chains are classified and aggregated to obtain at least one parameter chain cluster.

[0008] Preferably, the distribution network operation status unit is analyzed based on the parameter chain cluster, and the load absorption power characteristic value of each distribution network operation status unit is calculated, including: For each parameter chain cluster, an initial electrical parameter timing chain is selected from the parameter chain cluster; Based on connectivity determination rules, the timing connectivity relationship between the initial electrical parameter timing chain and other electrical parameter timing chains in the parameter chain cluster is determined; wherein, the connectivity determination rule is: according to the parameter change trend of the initial electrical parameter timing chain, directly connect with the parameters of the first and last timing nodes of the initial electrical parameter timing chain; According to the aforementioned timing connectivity, the initial electrical parameter timing chain is integrated with the other electrical parameter timing chains in an orderly manner to obtain a complete parameter timing chain; Based on the topological correspondence and parameter change characteristics of multiple sets of complete parameter time-series chains, multiple distribution network operation status units are generated through parsing. The line current parameters, node voltage parameters, and power component parameters are extracted from the complete parameter time chain, and the load absorption power characteristic value of each distribution network operation state unit is calculated by combining the cross-sectional properties of the branch lines.

[0009] Preferably, based on the load absorption power characteristic values ​​and spatial attributes of the multi-dimensional monitoring surfaces of the distribution network operation status units, the status characteristic parameters and preliminary operation control thresholds of each distribution network operation status unit are determined, including: For each distribution network operation status unit, the power fluctuation intensity of the distribution network operation status unit is determined based on the difference between the load absorption power characteristic value and the preset reference power. Based on the time span of parameter changes in the complete parameter time series, the continuous response duration of the distribution network operation status unit is determined; A topology-level mapping analysis is performed on multiple distribution network operation status units, and the parent-child relationship and spatial correlation degree of multiple distribution network operation status units are obtained by combining the spatial attributes of multi-dimensional monitoring surfaces; wherein, the spatial correlation degree is a natural number, and the spatial correlation degree of any distribution network operation status unit is greater than the spatial correlation degree of the parent of any distribution network operation status unit, and the difference is 1. Based on the power fluctuation intensity, continuous response duration and spatial correlation, state characteristic parameters of each distribution network operation state unit are generated. The voltage sag duration parameter is extracted from the load-side dynamic response data, and the preliminary operation control threshold of each distribution network operation status unit is calculated by combining the power fluctuation intensity and the impedance parameters of the branch lines.

[0010] Preferably, based on the state characteristic parameters and preliminary operation control thresholds of the target operation state units among the multiple distribution network operation state units that meet the target conditions, the operation control boundary conditions and final operation metrics of the target monitoring area are determined, including: Obtain the target monitoring point; wherein the target monitoring point is determined by the location input by the user; In order of increasing power fluctuation intensity, the parameter acquisition points of multiple point distribution network operation status units are compared with the target monitoring point until the target operation status unit is determined. The comparison between the distribution network operation status unit and the target monitoring point is terminated; wherein, the parameter acquisition point set includes multiple time-series acquisition points, which are obtained by converting the distribution network operation status unit from the distribution network topology space domain to the monitoring display domain; if the parameter acquisition point set of any distribution network operation status unit includes the target monitoring point, the any distribution network operation status unit is determined as a target operation status unit that meets the target conditions; the target conditions are: the target operation status unit is the displayed parameter coverage range; wherein, the parameter coverage range includes the target monitoring point.

[0011] Preferably, determining the operation control boundary conditions and final operation metrics of the target monitoring area based on the state characteristic parameters and preliminary operation control thresholds of the target operation state units that meet the target conditions among the multiple distribution network operation state units further includes: In response to the spatial correlation degree of the target operating state unit being greater than a preset correlation threshold, the type of the target monitoring area is determined to be a stable operating area; the target monitoring area is a key control area specified by the user. For the stable operating region, at least one sub-level operating state unit of the target operating state unit is determined; Calculate the sum of power fluctuation intensity, the sum of continuous response duration, and the average of the preliminary operation control threshold of at least one sub-level operating state unit; The final power fluctuation index of the target monitoring area is determined based on the difference between the power fluctuation intensity of the target operating state unit and the sum of the power fluctuation intensities. The final response time index of the target monitoring area is determined based on the sum of the continuous response time of the target operating state unit and the total continuous response time. Based on the average value of the preliminary operation control threshold, the final power fluctuation index, and the final response time index, the operation control boundary conditions of the target monitoring area are determined. The final power fluctuation index and the final response time index are integrated into the final operational metric of the target monitoring area.

[0012] Preferably, determining the operation control boundary conditions and final operation metrics of the target monitoring area based on the state characteristic parameters and preliminary operation control thresholds of the target operation state units that meet the target conditions among the multiple distribution network operation state units further includes: In response to the fact that the spatial correlation degree of the target operating state unit is not greater than a preset correlation threshold, the type of the target monitoring area is determined to be an abnormal operating area; For the abnormal operation area, determine the sub-level abnormal operation units of the target operation state unit; Calculate the sum of power fluctuation intensity and the average of the preliminary operation control threshold of the sub-level abnormal operation units; The difference between the power fluctuation intensity of the target operating state unit and the sum of the power fluctuation intensities is determined as the fault fluctuation index of the abnormal operating area. Based on the product of the average value of the preliminary operation control threshold and the preset fault coefficient, the fault control boundary conditions of the abnormal operation area are determined. The fault fluctuation index and fault control boundary conditions are integrated into the abnormal operation assessment result of the abnormal operation area.

[0013] Preferably, the parameter acquisition points of multiple point distribution network operation status units are sequentially compared with the target monitoring point until the target operation status unit is determined, including: The multiple distribution network operation status units are sorted in ascending order of power fluctuation intensity; The parameter collection point set of each sorted distribution network operation status unit is matched spatially with the target monitoring point; the parameter collection point set is composed of the projection points of the complete parameter time sequence chain of the distribution network operation status unit in the monitoring display field; If the projection coverage of the parameter collection point set of the distribution network operation status unit includes the target monitoring point, then the distribution network operation status unit is determined as the target operation status unit.

[0014] A distributed distribution network operation monitoring system, applicable to the aforementioned distributed distribution network operation monitoring method, includes: The data monitoring module is used to generate multiple multi-dimensional monitoring surfaces based on the topology of the distributed distribution network, real-time electrical parameters, and load-side dynamic response simulation results. The parameter interaction module is used to determine multiple sets of electrical parameter time series chains and generate a parameter chain cluster based on the interaction data between the multi-dimensional monitoring surface and the distributed distribution network and the load-side dynamic response data; the multi-dimensional monitoring surface is a monitoring carrier that covers the distribution network topology area and the load response area specified by the user; the electrical parameter time series chain is the parameter acquisition time series data between the monitoring sub-unit of the multi-dimensional monitoring surface and the topology sub-unit of the distributed distribution network. The parameter parsing module is used to parse the distribution network operation status unit based on the parameter chain cluster and calculate the load absorption power characteristic value of each distribution network operation status unit. The operation control module is used to determine the state characteristic parameters and preliminary operation control thresholds of each distribution network operation status unit based on the load absorption power characteristic value and the spatial attributes of the multi-dimensional monitoring surface of the distribution network operation status unit; the state characteristic parameters include at least power fluctuation intensity, continuous response duration and spatial correlation. The operation measurement module is used to determine the operation control boundary conditions and final operation measurement of the target monitoring area based on the state characteristic parameters of the target operation state units that meet the target conditions among the multiple distribution network operation state units and the preliminary operation control threshold.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention enables multi-dimensional and meticulous real-time monitoring of the distribution network's operating status by utilizing a multi-dimensional monitoring approach based on the distribution network topology, real-time electrical parameters, and load-side dynamic response simulation results. This ensures the capture of any abnormal states or potential faults, providing accurate data support. By analyzing time-series data such as load fluctuations, power changes, and voltage variations, it can accurately assess the distribution network's load absorption capacity and the power characteristics of operating units. This method allows for in-depth analysis of the distribution network's dynamic response characteristics, enabling early identification of potential load risks. This invention provides preliminary control thresholds for distribution network operation by using load absorption power characteristic values, spatial attributes, and state characteristic parameters, thereby optimizing the allocation and management of power resources and improving the operating efficiency and stability of the distribution network. By classifying the operating status of the distribution network based on spatial correlation and load fluctuation characteristics, monitoring and control strategies can be dynamically adjusted, and potential faults can be warned in advance. This method can better manage and optimize the operating control boundary of the monitoring area and avoid excessive impact on the normal operation of the distribution network due to sudden faults. This invention can accurately determine the target operating state unit by precisely locating the target monitoring point and spatially matching the time-series acquisition points of the distribution network state unit. This method enables more precise regional monitoring in large distribution networks, providing targeted control measures. Furthermore, for abnormal operating areas, by calculating parameters such as power fluctuation intensity and control thresholds, it can promptly identify problem areas in operation and take corresponding control measures, avoiding potential fault hazards in the distribution network and ensuring the safe operation of the power system. Attached Figure Description

[0016] Figure 1 This is a schematic flowchart of the overall method in one embodiment of the present invention; Figure 2 This is a schematic diagram of the overall system architecture in one embodiment of the present invention.

[0017] In the diagram: 1. Data monitoring module; 2. Parameter interaction module; 3. Parameter parsing module; 4. Operation control module; 5. Operation measurement module. Detailed Implementation

[0018] 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1, please refer to Figure 1 This invention provides a technical solution: a method for monitoring the operation of a distributed power distribution network, comprising: S1. Based on the topology of the distributed distribution network, real-time electrical parameters, and load-side dynamic response simulation results, multiple multi-dimensional monitoring surfaces are generated. S2. Based on the interaction data between the multi-dimensional monitoring surface and the distributed distribution network, as well as the dynamic response data of the load side, determine multiple sets of electrical parameter time series chains and generate a parameter chain cluster; the multi-dimensional monitoring surface is the monitoring carrier that covers the distribution network topology area and the load response area specified by the user; the electrical parameter time series chain is the parameter acquisition time series data between the monitoring sub-unit of the multi-dimensional monitoring surface and the topology sub-unit of the distributed distribution network. S3. Based on the parameter chain cluster analysis, analyze the distribution network operation status unit and calculate the load absorption power characteristic value of each distribution network operation status unit; S4. Based on the load absorption power characteristic value of the distribution network operation status unit and the spatial attributes of the multi-dimensional monitoring surface, determine the status characteristic parameters and preliminary operation control thresholds of each distribution network operation status unit; the status characteristic parameters shall include at least the power fluctuation intensity, continuous response duration and spatial correlation. S5. Based on the state characteristic parameters and preliminary operation control thresholds of the target operation state units that meet the target conditions among multiple distribution network operation state units, determine the operation control boundary conditions and final operation metrics of the target monitoring area.

[0020] It should be noted that multiple monitoring surfaces are generated based on the distribution network topology, real-time electrical parameters (such as voltage and current), and dynamic response simulation results on the load side (i.e., consumer-side electricity demand). For example, suppose there is a city's distribution network; the distribution network topology may include multiple substations, transmission lines, and various branch networks; real-time electrical parameters include the current and voltage of these facilities; load response simulation results refer to the changes in electricity demand in different areas; through this information, a multi-dimensional monitoring surface covering the entire distribution network can be generated; for example, different monitoring surfaces can be formed based on load changes and electrical parameters in different areas to analyze the operating status of the distribution network. Based on the interaction data between multi-dimensional monitoring surfaces and distributed distribution networks, electrical parameter time-series chains are determined and parameter chain clusters are generated. Each time-series chain records the data acquisition between a monitoring sub-unit and a distribution network sub-unit. For example, in the monitoring surface of the previous step, suppose the voltage and current fluctuations of a region are monitored. The time-series data of these fluctuations (i.e., the changes in voltage and current over time) form an electrical parameter time-series chain. Through multiple such chains, a cluster can be generated to represent the dynamic response between each monitoring sub-unit and the distribution network in that region. Based on parameter chain clustering, the operating status units of the distribution network are analyzed, and the load absorption power characteristic value of each unit is calculated. For example, by analyzing the electrical parameter time series chain, the load absorption status of each distribution network unit (such as a substation or branch network) can be determined. For example, if the load demand in a region suddenly increases, the changes in current and voltage will reflect the load absorption characteristics of that unit. The load absorption power characteristic value can represent the intensity of power demand. Based on the load absorption power characteristic values ​​and the spatial attributes of multi-dimensional monitoring surfaces, the state characteristic parameters of each distribution network operation unit are determined, and preliminary operation control thresholds are set. These state characteristic parameters include power fluctuation intensity, duration of response, and spatial correlation. For example, if the current fluctuation in a certain area is large, the power fluctuation intensity of that area can be calculated, representing the severity of load demand fluctuations. Duration of response indicates the duration of power demand in that area. Spatial correlation measures the correlation between the power demand of that area and other areas. For instance, if the power demand changes of a certain area and neighboring areas are highly correlated, the spatial correlation between them can be calculated. Based on these parameters, preliminary operation control thresholds can be set, such as the maximum allowable value for power fluctuation intensity. Based on the state characteristic parameters of the target operating state unit that meets the target conditions and the preliminary operating control threshold, the operating control boundary conditions of the target monitoring area are determined, and the operating metrics are finally determined. For example, suppose we want to optimize the power supply of a certain urban area, with the goal of ensuring that power overload does not occur during high load periods. Based on the previous state characteristic parameters (such as power fluctuation intensity, continuous response duration, etc.) and the initially set control threshold, an operating control boundary can be determined. For example, when the current fluctuation exceeds a certain intensity, measures are taken to limit power demand. The final operating metrics will be evaluated based on these boundary conditions to ensure the stable operation of the distribution network under different load conditions.

[0021] In an optional embodiment, based on the topology of the distributed distribution network, real-time electrical parameters, and load-side dynamic response simulation results, multiple multi-dimensional monitoring surfaces are generated, including: Obtain the topology, real-time electrical parameters, and load-side dynamic response simulation results of the distributed power distribution network; Spatial data modeling is performed on the topology of the distributed distribution network in conjunction with real-time electrical parameters to obtain the corresponding topological spatial domain; Based on the load fluctuation distribution of the topology spatial domain and load-side dynamic response simulation results, a multi-dimensional monitoring surface corresponding to multiple spatial dimensions is generated. Among them, the multiple spatial dimensions include the feeder extension direction, node radiation direction and load response dimension under the distribution network topology spatial domain. The coverage of the multi-dimensional monitoring surface is determined based on the scale of the topology spatial domain and the range of load fluctuation distribution.

[0022] It should be noted that the data collected includes the distribution network topology, real-time electrical parameters (such as voltage and current), and dynamic response simulation results on the load side. For example, suppose there is a city's distribution network. The topology includes the connection methods of various substations, feeders, switches, and other facilities in the distribution network. The real-time electrical parameters can be data such as voltage, current, and power of these facilities. The dynamic response simulation results on the load side refer to the changes in electricity consumption at the consumer end (such as residential and commercial users) at different time periods, such as a sharp increase in electricity demand during peak hours or a sudden decrease in electricity consumption in certain areas. By combining the topology of the distribution network with real-time electrical parameters, spatial data modeling is performed to obtain the topological spatial domain of the distribution network. For example, a city distribution network consists of multiple substations, feeders, distribution boxes, and electrical equipment. Real-time electrical parameters (such as current and voltage) reflect the electrical status of these facilities. By combining this data, a spatial data model can be established to describe the spatial distribution of each distribution network facility (e.g., the voltage and current distribution of a substation, or the current distribution of a feeder). This will help to understand the electrical status of different areas of the distribution network, facilitating further analysis and optimization. By analyzing the topological spatial domain and load-side dynamic response simulation results, a multi-dimensional monitoring surface with multiple spatial dimensions is generated. These spatial dimensions include feeder extension direction, node radiation direction, and load response dimension. For example: Feeder extension direction: Feeders in a distribution network transmit power from substations to various user areas; the extension direction of the feeder determines the power transmission path; for example, one feeder extends eastward while another extends westward; the voltage and current changes of each feeder may differ, thus monitoring will vary depending on the feeder direction. Node radiation direction: Nodes in a distribution network (such as substations or distribution boxes) connect to different feeders through radiation directions; the electrical parameters (voltage, current, etc.) of these nodes are also affected by load fluctuations; for example, if the power demand in a certain area suddenly increases, the voltage and current changes of nodes near that area will be more significant. Based on the load-side dynamic response simulation results, the spatial distribution of load fluctuations can be taken into consideration to analyze the changing patterns of power demand; for example, during peak summer periods, the power demand in a certain area may experience short-term, drastic fluctuations, and the load response dimension will reflect the intensity and duration of these fluctuations. The coverage of a multi-dimensional monitoring surface is determined based on the size of the topological spatial domain and the distribution range of load fluctuations. For example, in a distribution network, if the load fluctuation in a certain area is large (such as the peak electricity consumption caused by air conditioning use in a commercial area), a monitoring surface needs to be set up to capture the changes in electrical parameters (such as voltage and current) in that area. The coverage of this monitoring surface will be determined based on the topology of that area (e.g., the feeder range from the substation to the commercial area) and the range of load fluctuations (e.g., the maximum power demand fluctuation range of the commercial area). If the power demand fluctuation in this area is large, the monitoring surface needs to cover a larger area.

[0023] In an optional embodiment, based on the interaction data between the multi-dimensional monitoring surface and the distributed distribution network, and the load-side dynamic response data, multiple sets of electrical parameter time series chains are determined and a parameter chain cluster is generated, including: Acquire multi-dimensional monitoring aspects of user input targets; The target multi-dimensional monitoring surface is interacted with the distributed distribution network in real time to obtain interactive data; power fluctuation, time series and voltage sag time series are extracted from the load side dynamic response data; Time-series correlation processing is performed on interactive data, power fluctuations, timing segments, and voltage sag timing segments to obtain multiple sets of electrical parameter timing chains; Based on the time-series correlation algorithm, multiple sets of electrical parameter time-series chains are classified and aggregated to obtain at least one parameter chain cluster.

[0024] It should be noted that, based on the user's requirements, a multi-dimensional monitoring surface is defined and obtained, typically including multiple spatial dimensions, such as feeder direction, voltage, current, and load response. For example, suppose a user wants to monitor a city's power distribution network, paying particular attention to the current fluctuations and voltage changes of a major feeder. The user's target multi-dimensional monitoring surface might include: Feeder direction: monitoring feeders from substations to residential areas; Load response: monitoring power demand fluctuations in specific residential areas; Voltage and current: monitoring electrical parameters at various nodes, especially voltage and current fluctuations during peak hours. The system interacts with the defined monitoring points and the real-time electrical parameters of the distribution network to obtain real-time data, such as voltage, current, and power. For example, once the monitoring points are determined, the system will interact with sensors and smart devices in the distribution network to collect real-time electrical data, such as current data monitored on a feeder or voltage data at a node (such as a substation or distribution box). This data will be collected in real time by sensors, transmitted, and fed back to the system. From the dynamic response data on the load side, power fluctuations, time series, and voltage sag time series are extracted. These are key electrical events that reflect load fluctuations, power system stress, and potential power quality issues. For example: Power fluctuations: During peak hours, significant fluctuations in power demand in a region (such as air conditioning being turned on and off) cause power fluctuations; the system extracts these periods of power fluctuation. Time series: Refers to the dynamic pattern of power system load changes within a specific time period; for example, if power demand in a region increases rapidly from 3 pm to 4 pm, the system extracts data for this period. Voltage sag time series: Voltage sags may occur in the power system (e.g., voltage drops during peak electricity consumption), which can affect the normal operation of equipment; the system monitors and extracts voltage drop time series. Time-series correlation processing is performed on data such as power fluctuations, load changes, and voltage sags. This is done to understand the relationships between different electrical parameters and how they change over time. For example, within an hour, power demand changes from a stable state to a drastic fluctuation; at the same time, voltage begins to sag. The system will correlate power fluctuations, load changes, and voltage sags to form a time-series chain. The time-series chain is a set of electrical parameters that change over time, reflecting the interrelationships between various electrical parameters within the system. Based on time-series correlation algorithms (such as K-means clustering, DBSCAN, etc.), multiple electrical parameter time-series chains are classified to form parameter chain clusters. Each cluster represents a specific electrical event or pattern, such as system load peaks, load fluctuations, short-term voltage sags, etc. For example, suppose the following time-series chains are obtained within the same time period: Time-series chain 1: large power fluctuations, voltage sags; Time-series chain 2: stable load, voltage sags; Time-series chain 3: large load increase, voltage sags. Using time-series correlation algorithms, these time-series chains are classified and aggregated, which may result in two clusters: Cluster 1: patterns related to load fluctuations and voltage sags; Cluster 2: voltage change patterns under stable or increased load conditions. These clusters will help analysts identify the occurrence patterns of different electrical events, such as whether voltage sags are always accompanied by load fluctuations, or whether voltage sags frequently occur in certain specific periods.

[0025] In an optional embodiment, the distribution network operation status unit is parsed based on the parameter chain cluster, and the load absorption power characteristic value of each distribution network operation status unit is calculated, including: For each parameter chain cluster, select an initial electrical parameter timing chain from the parameter chain cluster; Based on connectivity determination rules, the temporal connectivity relationship between the initial electrical parameter timing chain and other electrical parameter timing chains in the parameter chain cluster is determined; wherein, the connectivity determination rule is: according to the parameter change trend of the initial electrical parameter timing chain, it is directly connected to the parameters of the first and last timing nodes of the initial electrical parameter timing chain; According to the timing connectivity, the initial electrical parameter timing chain is integrated with other electrical parameter timing chains in an orderly manner to obtain the complete parameter timing chain; Based on the topological correspondence and parameter change characteristics of multiple complete parameter time-series chains, multiple distribution network operation status units are generated through analysis. The line current parameters, node voltage parameters, and power component parameters are extracted from the complete parameter time series, and the load absorption power characteristic value of each distribution network operation state unit is calculated by combining the cross-sectional properties of the branch lines.

[0026] It should be noted that an "initial timing chain" is selected from each parameter chain cluster. This initial chain will serve as the basis for further analysis. For example, suppose there is a cluster containing multiple timing chains, such as a power fluctuation timing chain, a voltage sag timing chain, and a load fluctuation timing chain. The power fluctuation timing chain can be selected as the initial timing chain for subsequent analysis. Based on certain "connectivity determination rules," the relationship between the initial timing chain and other timing chains in the cluster is determined, and it is understood how they are interconnected through changes in time and electrical parameters. The connectivity determination rule is as follows: according to the parameter change trend of the initial timing chain, the "first and last nodes" of the initial timing chain are connected to other timing chains to form a timing connectivity relationship. For example, suppose the initial timing chain is a "power fluctuation timing chain." At the end of this timing chain, the power suddenly increases. Based on the trend of the timing chain (such as an increase in current and a decrease in voltage), it can be determined that this timing chain is connected to another "voltage sag timing chain." This connection indicates that there may be a certain correlation between power fluctuations and voltage sags, suggesting that load changes have led to a temporary drop in voltage. Objective: To systematically integrate the initial timing chain with other relevant timing chains to form a "complete parameter timing chain," which comprehensively reflects the changes in electrical parameters over time. For example, continuing the previous example, when the power fluctuation timing chain (initial chain) is connected with the voltage sag timing chain (other chains), a complete parameter timing chain is obtained. This chain describes the entire process from power fluctuation to voltage sag. Ultimately, this complete timing chain may contain multiple parameters, such as power, current, and voltage, describing the comprehensive changes in the power system over this period. By analyzing the topological relationships (i.e., how they are connected) of multiple complete time-series chains and the characteristics of electrical parameter changes, different "operating state units" of the distribution network can be defined. For example, suppose there are multiple complete time-series chains describing the changes in power demand in different areas (e.g., load fluctuations and voltage sags in area A, current changes in area B, etc.). The topological relationships between these time-series chains can be used to construct an "operating state unit" of the distribution network. For instance, the load fluctuations and voltage changes in areas A and B are closely related, so these two areas can be classified into one operating state unit. Key electrical parameters (such as current, node voltage, power components, etc.) can be extracted from complete time-series chains for further analysis of the distribution network's operating status. For example, in a complete time-series chain, the current change of a certain feeder (such as the current increase from noon to afternoon) and the voltage change of certain nodes (such as voltage sags) may be extracted. In addition, power component data, such as active power and reactive power, can also be extracted. By combining the characteristics of the branch lines of the distribution network (such as line cross-section and materials), the load absorption power characteristic value of each distribution network operating unit can be calculated. This helps to assess the load carrying capacity of the line. For example, suppose that in area A, the distribution network is connected to other areas through a high-voltage line. This line has a large cross-section and good materials, allowing for a high load carrying capacity. The absorption power characteristic value of the line under different load conditions can be calculated to determine whether the line can carry the expected load demand. If the load absorption power characteristic value of the line exceeds the maximum carrying capacity, it may be necessary to upgrade the line or increase the reserve capacity.

[0027] In an optional embodiment, based on the load absorption power characteristic values ​​of the distribution network operation status units and the spatial attributes of the multi-dimensional monitoring surfaces, the status characteristic parameters and preliminary operation control thresholds of each distribution network operation status unit are determined, including: For each distribution network operation status unit, the power fluctuation intensity of the distribution network operation status unit is determined based on the difference between the load absorption power characteristic value and the preset reference power. Based on the time span of parameter changes in the complete parameter time series, the continuous response duration of the distribution network operation status unit is determined; A topological hierarchical mapping analysis was performed on multiple distribution network operation status units, and the parent-child relationship and spatial correlation degree of multiple distribution network operation status units were obtained by combining the spatial attributes of multi-dimensional monitoring surfaces. Among them, the spatial correlation degree is a natural number, and the spatial correlation degree of any distribution network operation status unit is greater than the spatial correlation degree of any distribution network operation status unit's parent, with a difference of 1. Based on power fluctuation intensity, continuous response duration and spatial correlation, state characteristic parameters of each distribution network operation state unit are generated. The voltage sag duration parameter is extracted from the load-side dynamic response data, and the preliminary operation control threshold of each distribution network operation status unit is calculated by combining the power fluctuation intensity and the impedance parameters of the branch lines.

[0028] It should be noted that the "power fluctuation intensity" of the distribution network operation status unit is determined by comparing the difference between the characteristic value of the load absorbed power and the preset benchmark power (usually the expected load level). For example, suppose that in a certain area of ​​the distribution network, the preset benchmark power is 100kW, while the actual measured load absorbed power is 120kW. In this case, the power fluctuation intensity can be obtained by calculating the difference (120kW-100kW=20kW). The greater the fluctuation intensity, the stronger the power fluctuation in that area. The "continuous response duration" of a distribution network operating state unit is calculated as the time span required from the onset of a change to the restoration of stability within a complete parameter time series. For example, suppose that in a certain time series, the voltage in a certain area drops from 220V to 190V within 2 minutes, and then gradually recovers to 220V within 5 minutes; in this case, the continuous response duration is 7 minutes (2 minutes of drop time + 5 minutes of recovery time). This duration reflects the continuity of the distribution network system's response to load fluctuations. This study analyzes the parent-child relationships among multiple distribution network operation status units through topology mapping and calculates their "spatial correlation degree" by combining spatial attributes. Spatial correlation degree is a natural number reflecting the strength of spatial connections between status units. A spatial correlation degree difference of 1 means that the spatial correlation degree of the parent status unit is always greater than that of the child status unit. For example, suppose there are two distribution network operation status units A and B, where A is the parent and B is the child. A has a spatial correlation degree of 2, B has a spatial correlation degree of 1, and the difference between them is 1. This indicates that status unit A is at a higher topological level than status unit B, and they are spatially interconnected; A's correlation degree is higher than B's. Based on the power fluctuation intensity, continuous response duration, and spatial correlation of the distribution network operation status units, "state characteristic parameters" are generated for each status unit to help to make more accurate assessments of each unit. For example, suppose that the power fluctuation intensity of distribution network operation status unit A is 20kW, the continuous response duration is 7 minutes, and the spatial correlation is 2; the power fluctuation intensity of status unit B is 10kW, the continuous response duration is 5 minutes, and the spatial correlation is 1. Then, the state characteristic parameters of A may include a comprehensive assessment of these data, such as A having poor system stability, while B may be more stable. The voltage sag duration is extracted from the load-side dynamic response data and combined with the power fluctuation intensity and the impedance parameters of the branch lines to calculate a preliminary operation control threshold. This threshold is used to determine the maximum load fluctuation and voltage sag that the distribution network can withstand during operation. For example, suppose the load-side dynamic response data of a certain distribution network operation status unit shows that the voltage sag duration is 3 seconds, while the power fluctuation intensity in this area is 20kW and the line impedance is 0.2Ω. Based on these data, a preliminary operation control threshold can be obtained through certain calculation methods. For example, suppose the threshold is 150kW, which means that when the load fluctuation intensity exceeds 150kW, the distribution network may experience voltage instability and control measures need to be initiated.

[0029] In an optional embodiment, based on the state characteristic parameters and preliminary operation control thresholds of the target operation state units among multiple distribution network operation state units that meet the target conditions, the operation control boundary conditions and final operation metrics of the target monitoring area are determined, including: Obtain the target monitoring point; where the target monitoring point is determined by the location input by the user; According to the order of power fluctuation intensity from small to large, the parameter collection points of multiple distribution network operation status units are compared with the target monitoring points in turn until the target operation status unit is determined. The comparison between the distribution network operation status unit and the target monitoring point ends; the parameter acquisition point set includes multiple time-series acquisition points, which are obtained by converting the distribution network operation status unit from the distribution network topology space domain to the monitoring display domain; if the parameter acquisition point set of any distribution network operation status unit includes the target monitoring point, then any distribution network operation status unit is determined as a target operation status unit that meets the target conditions; the target conditions are: the target operation status unit is the displayed parameter coverage area; wherein, the parameter coverage area includes the target monitoring point.

[0030] It should be noted that the monitoring location input by the user will be considered the "target monitoring point"; the target monitoring point is the specific location of interest to the user, which can be a certain area or device in the distribution network; sorting by power fluctuation intensity: the parameter collection points of multiple distribution network operation status units are arranged in ascending order of power fluctuation intensity; each distribution network operation status unit has an associated power fluctuation intensity value, which represents the power fluctuation situation of that unit; the lower the intensity, the smaller the power fluctuation of the unit, and the more stable the operation; comparing the parameter collection point set of the distribution network operation status unit with the target monitoring point: the parameter collection point set of the distribution network operation status unit consists of multiple "time-series collection points," these time-series collection points... The data collection points are the data transformed from the distribution network topology spatial domain to the monitoring and display domain. Each time-series collection point corresponds to parameter data (such as voltage, current, power, etc.) at a specific moment in the distribution network, and these collection points are compared with the target monitoring points specified by the user. The comparison process determines the target operating state unit: if the parameter collection point set of a certain distribution network operating state unit includes the target monitoring point, it means that this distribution network operating state unit meets the target conditions. The target conditions are: the displayed parameter coverage of the target operating state unit must include the target monitoring point, indicating that the parameter data of the distribution network unit can accurately reflect the state of the target monitoring point. The comparison ends once a matching target monitoring point is found... The comparison process ends after identifying the operating status unit. For example, suppose there is a distribution network system containing multiple operating status units. Each status unit collects power fluctuations, load data, and other electrical parameters at different times. Now, a user wants to monitor a specific area or device and sets a target monitoring point (e.g., a distribution box in area B). The user inputs the location of the distribution box in area B as the target monitoring point, which is the key point they need to monitor. The distribution network system has multiple status units, and the power fluctuation intensity of each status unit is as follows: Area A: power fluctuation intensity 5kW, Area B: power fluctuation intensity 8kW, Area C: power fluctuation intensity 1kW. The system sorts these state units by power fluctuation intensity, resulting in the following order: Region A -> Region B -> Region C. Next, the system checks whether the parameter acquisition point sets of these regions cover the target monitoring point (the location of the distribution box in Region B). Region A's time-series acquisition point set does not include the distribution box in Region B, so it is excluded. Region B's time-series acquisition point set includes the monitoring data of the distribution box in Region B, meeting the target condition. During the comparison process, Region B's parameter acquisition point set includes the target monitoring point; therefore, Region B is identified as the target operating state unit. Once Region B is identified as the target operating state unit, the comparison process ends, and there is no need to check subsequent regions.

[0031] In an optional embodiment, based on the state characteristic parameters of the target operating state units among multiple distribution network operating state units that meet the target conditions and the preliminary operating control threshold, the operating control boundary conditions and final operating metrics of the target monitoring area are determined, further including: If the spatial correlation of the target operating state unit is greater than the preset correlation threshold, the target monitoring area is determined to be a stable operating area; the target monitoring area is a key control area specified by the user. For a stable operating region, at least one sub-level operating state unit of the target operating state unit is identified; Calculate the sum of power fluctuation intensity, the sum of continuous response duration, and the average of the preliminary operation control threshold of at least one sub-level operating state unit; The final power fluctuation index of the target monitoring area is determined by the difference between the power fluctuation intensity of the target operating state unit and the sum of the power fluctuation intensities. The final response time index for the target monitoring area is determined by the sum of the continuous response time of the target operating state unit and the total continuous response time. Based on the average value of the initial operation control threshold, the final power fluctuation index, and the final response time index, the operation control boundary conditions of the target monitoring area are determined. The final power fluctuation index and the final response time index are integrated into the final operational metric for the target monitoring area.

[0032] It should be noted that the spatial correlation degree of the target operating state unit represents the degree of correlation between that unit and the target monitoring area. Typically, this correlation degree is assessed based on factors such as distribution network topology, data flow, and load fluctuations. If the spatial correlation degree between the target operating state unit and the target monitoring area is greater than a preset correlation threshold, then the target monitoring area is determined to be a stable operating area, meaning that the power system in that area is operating relatively stably under its current state. If the correlation degree of the target operating state unit meets certain conditions, the target monitoring area is designated as a key control area, meaning that this area is considered to require special attention. For stable operating areas, it is necessary to... Further analysis is needed on the sub-level operating state units of the target operating state unit. A sub-level operating state unit refers to a smaller operating unit or device within the target operating state unit. For example, assuming a large substation is the target operating state unit, it contains multiple sub-level devices (such as transformers, circuit breakers, and distribution cabinets). It is necessary to calculate the sum of power fluctuation intensity, the sum of continuous response duration, and the average of the initial operating control threshold for at least one sub-level operating state unit. These indicators help assess the stability of different devices within the area. The sum of power fluctuation intensity is the sum of the power fluctuations of all sub-level units. The sum of continuous response duration is the sum of the response fluctuations or anomalies of all sub-level units. The sum of the durations; the average of the initial operation control thresholds: the initial threshold of the sub-unit, i.e., the maximum allowable value of the set power fluctuation intensity or response time; based on the difference between the power fluctuation intensity of the target operating state unit and the sum of the power fluctuation intensities of all sub-units, the final power fluctuation index of the target monitoring area can be determined; this reflects the difference between the degree of power fluctuation in the target area and the fluctuation of other equipment in the area; a large difference may indicate that the target area has high volatility, and monitoring and management may need to be strengthened; based on the difference between the continuous response time of the target operating state unit and the sum of the continuous response times of all sub-units, the target monitoring... The final response time index for the region; this means that if the response time of the target region is significantly longer or shorter than that of the sub-units, it will affect the operational status assessment of the target monitoring region; based on the average preliminary operational control threshold, the final power fluctuation index, and the final response time index, the operational control boundary conditions of the target monitoring region can be determined; these conditions define the maximum power fluctuation range and the most permissible response time of the region, providing further control standards; finally, the final power fluctuation index and the final response time index will be integrated to form the final operational metric of the target monitoring region, that is, the quantitative assessment of the overall operational stability and control status of the region.

[0033] In an optional embodiment, based on the state characteristic parameters of the target operating state units among multiple distribution network operating state units that meet the target conditions and the preliminary operating control threshold, the operating control boundary conditions and final operating metrics of the target monitoring area are determined, further including: If the spatial correlation degree of the target operating state unit is not greater than the preset correlation threshold, the type of the target monitoring area is determined to be an abnormal operating area; For abnormal operating regions, identify the sub-level abnormal operating units of the target operating state unit; Calculate the sum of power fluctuation intensity and the average of the initial operation control threshold of the abnormal operation unit at the sub-level; The difference between the power fluctuation intensity of the target operating state unit and the sum of the power fluctuation intensity is determined as the fault fluctuation index of the abnormal operating area. Based on the product of the average preliminary operation control threshold and the preset fault coefficient, the fault control boundary conditions of the abnormal operation area are determined. The fault fluctuation index and fault control boundary conditions are integrated into the abnormal operation assessment results of the abnormal operation area.

[0034] It should be noted that the correlation between the target operating status unit and the target monitoring area is important. If the correlation between the target operating status unit and the target area is not greater than the preset correlation threshold, it indicates that an anomaly has occurred in the area. Here, a correlation not greater than the preset threshold means that the operating status of the area is relatively unstable, so the target monitoring area is identified as an abnormal operating area. Once the target area is identified as an abnormal operating area, further analysis of the sub-level abnormal operating units within the target operating status unit is required. These sub-level abnormal operating units refer to equipment or subsystems exhibiting abnormalities within this area. For example, if a transformer in a substation fails, that transformer would be considered a sub-level abnormal operating unit. The sum of the power fluctuation intensity of all sub-level abnormal operating units needs to be calculated, which means summing up the power fluctuations occurring in each sub-level unit (such as transformers, circuit breakers, etc.). The initial average operating control threshold refers to the average of the initially set control thresholds for all sub-level abnormal operating units, which typically reflects the maximum power fluctuation capability or response time of these devices. The fault fluctuation index is calculated based on the difference between the power fluctuation intensity of the target operating unit and the sum of the power fluctuation intensities of the sub-units. Assuming the power fluctuation intensity of the target operating unit is 12kW and the sum of the power fluctuation intensities of the sub-units is 30kW, then the fault fluctuation index = 30kW - 12kW = 18kW. This difference reflects the degree of power fluctuation mismatch between the target area and the sub-units, and thus indicates the degree of abnormality in the area. Fault control boundary conditions are calculated based on the product of the average initial operating control threshold and a preset fault coefficient. The preset fault coefficient is a predefined constant, typically used to represent the fault tolerance of equipment or area. Assuming the average initial operating control threshold is 10kW and the preset fault coefficient is 1.5, the fault control boundary condition is: 10kW × 1.5 = 15kW. This indicates that the maximum allowable fluctuation range of the area under fault conditions is 15kW. Integrating the fault fluctuation index and the fault control boundary condition yields the abnormal operation assessment result for the area. This provides a basis for further control decisions, such as whether load regulation, equipment shutdown, or other maintenance operations are necessary. For example, if the fault fluctuation index is 18kW and the fault control boundary condition is 15kW, it indicates that the power system in the area has exceeded the normal control range and immediate emergency measures may be required.

[0035] In an optional embodiment, the parameter acquisition points of multiple point distribution network operation status units are sequentially compared with the target monitoring point until the target operation status unit is determined, including: Multiple distribution network operation status units are sorted in order of increasing power fluctuation intensity; The parameter acquisition point set of each sorted distribution network operation status unit is matched spatially with the target monitoring point; the parameter acquisition point set consists of the projection points of the complete parameter time sequence chain of the distribution network operation status unit in the monitoring display field; If the projection coverage of the parameter collection point set of the distribution network operation status unit includes the target monitoring point, then the distribution network operation status unit is determined as the target operation status unit.

[0036] It should be noted that a distribution network operation status unit refers to each independent operation unit in the distribution network (such as substations, distribution lines, etc.). These units will generate certain power fluctuations during operation. All these units are sorted in ascending order of power fluctuation intensity. Units with smaller power fluctuation intensity indicate less fluctuation and are likely to be more stable, while units with larger fluctuations may have greater risks. The purpose of sorting is to help prioritize the units with smaller fluctuations and gradually shift attention to the units with larger fluctuations, facilitating more precise monitoring and adjustment. Each distribution network operation status unit has a parameter acquisition point, which is obtained by time-series acquisition of the unit's operating parameters. The time-series chain refers to recording all operating data (such as voltage, power, load, etc.) of the unit within a certain time period. The projection points of these parameter acquisition points refer to the points displayed in the system display domain. The coordinates in the diagram represent the projected location of the parameter acquisition point in space. The target monitoring point refers to a specific location of interest, which may be a device, area, or a specific monitoring target within the system. Through spatial location matching, the projected points of the parameter acquisition point set of each distribution network operation status unit are compared with the target monitoring point to see if there is any overlap. If the projected points of a distribution network unit cover the target monitoring point, then the unit is considered to potentially affect the target monitoring point. If the projected coverage of the parameter acquisition point set of a distribution network operation status unit includes the target monitoring point, then the distribution network operation status unit is identified as a target operation status unit. This means that the status of the unit (e.g., power fluctuations, load changes, etc.) is closely related to the target monitoring point and may affect the target point. These target operation status units will be the focus of monitoring, and subsequent analysis and optimization will concentrate on these units.

[0037] Example 2, please refer to Figure 2 This invention provides a technical solution: an operation monitoring system for a distributed distribution network, applicable to the aforementioned operation monitoring method for a distributed distribution network, comprising: Data monitoring module 1 is used to generate multiple multi-dimensional monitoring surfaces based on the topology of the distributed distribution network, real-time electrical parameters, and load-side dynamic response simulation results. The parameter interaction module 2 is used to determine multiple sets of electrical parameter time series chains and generate a parameter chain cluster based on the interaction data between the multi-dimensional monitoring surface and the distributed distribution network and the load-side dynamic response data; the multi-dimensional monitoring surface is the monitoring carrier that covers the distribution network topology area and the load response area specified by the user; the electrical parameter time series chain is the parameter acquisition time series data between the monitoring sub-unit of the multi-dimensional monitoring surface and the topology sub-unit of the distributed distribution network. Parameter parsing module 3 is used to parse the distribution network operation status unit based on the parameter chain cluster and calculate the load absorption power characteristic value of each distribution network operation status unit; The operation control module 4 is used to determine the state characteristic parameters and preliminary operation control thresholds of each distribution network operation status unit based on the load absorption power characteristic value and spatial attributes of the multi-dimensional monitoring surface; the state characteristic parameters include at least the power fluctuation intensity, continuous response duration and spatial correlation. The operation measurement module 5 is used to determine the operation control boundary conditions and final operation measurement of the target monitoring area based on the state characteristic parameters and preliminary operation control thresholds of the target operation status units that meet the target conditions among multiple distribution network operation status units.

[0038] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A method for monitoring the operation of a distributed distribution network, characterized in that, include: Based on the topology of the distributed distribution network, real-time electrical parameters, and simulation results of load-side dynamic response, multiple multi-dimensional monitoring surfaces are generated. Based on the interaction data between the multi-dimensional monitoring surface and the distributed distribution network, as well as the load-side dynamic response data, multiple sets of electrical parameter time series chains are determined and a parameter chain cluster is generated; the multi-dimensional monitoring surface is a monitoring carrier specified by the user that covers the distribution network topology area and the load response area; the electrical parameter time series chain is the parameter acquisition time series data between the monitoring sub-unit of the multi-dimensional monitoring surface and the topology sub-unit of the distributed distribution network. Based on the parameter chain cluster, the distribution network operation status unit is analyzed, and the load absorption power characteristic value of each distribution network operation status unit is calculated. Based on the load absorption power characteristic value of the distribution network operation status unit and the spatial attributes of the multi-dimensional monitoring surface, the status characteristic parameters and preliminary operation control thresholds of each distribution network operation status unit are determined; the status characteristic parameters include at least power fluctuation intensity, continuous response duration and spatial correlation. Based on the state characteristic parameters and preliminary operation control thresholds of the target operation status units that meet the target conditions among the multiple distribution network operation status units, the operation control boundary conditions and final operation metrics of the target monitoring area are determined.

2. The method for monitoring the operation of a distributed distribution network according to claim 1, characterized in that, Based on the topology of the distributed distribution network, real-time electrical parameters, and load-side dynamic response simulation results, multiple multi-dimensional monitoring surfaces are generated, including: Obtain the topology, real-time electrical parameters, and load-side dynamic response simulation results of the distributed power distribution network; The topology of the distributed distribution network is modeled spatially using real-time electrical parameters to obtain the corresponding topological spatial domain. Based on the load fluctuation distribution of the topology spatial domain and the load-side dynamic response simulation results, a multi-dimensional monitoring surface corresponding to multiple spatial dimensions is generated; wherein, the multiple spatial dimensions include the feeder extension direction, node radiation direction and load response dimension under the distribution network topology spatial domain; the coverage of the multi-dimensional monitoring surface is determined based on the scale of the topology spatial domain and the range of load fluctuation distribution.

3. The method for monitoring the operation of a distributed distribution network according to claim 2, characterized in that, Based on the interaction data between the multi-dimensional monitoring surface and the distributed distribution network, as well as the load-side dynamic response data, multiple sets of electrical parameter time-series chains are determined and a parameter chain cluster is generated, including: Acquire multi-dimensional monitoring aspects of user input targets; The target multi-dimensional monitoring surface is interacted with the distributed distribution network in real time to obtain interactive data; power fluctuation, time series and voltage sag time series are extracted from the load-side dynamic response data. The interactive data, power fluctuations, timing segments, and voltage sag timing segments are subjected to timing correlation processing to obtain multiple sets of electrical parameter timing chains; Based on the time-series correlation algorithm, multiple sets of electrical parameter time-series chains are classified and aggregated to obtain at least one parameter chain cluster.

4. The method for monitoring the operation of a distributed distribution network according to claim 3, characterized in that, Based on the parameter chain cluster analysis of the distribution network operation status unit, and the calculation of the load absorption power characteristic value of each distribution network operation status unit, including: For each parameter chain cluster, an initial electrical parameter timing chain is selected from the parameter chain cluster; Based on connectivity determination rules, the timing connectivity relationship between the initial electrical parameter timing chain and other electrical parameter timing chains in the parameter chain cluster is determined; wherein, the connectivity determination rule is: according to the parameter change trend of the initial electrical parameter timing chain, directly connect with the parameters of the first and last timing nodes of the initial electrical parameter timing chain; According to the aforementioned timing connectivity, the initial electrical parameter timing chain is integrated with the other electrical parameter timing chains in an orderly manner to obtain a complete parameter timing chain; Based on the topological correspondence and parameter change characteristics of multiple sets of complete parameter time-series chains, multiple distribution network operation status units are generated through parsing. The line current parameters, node voltage parameters, and power component parameters are extracted from the complete parameter time chain, and the load absorption power characteristic value of each distribution network operation state unit is calculated by combining the cross-sectional properties of the branch lines.

5. The method for monitoring the operation of a distributed distribution network according to claim 4, characterized in that, Based on the load absorption power characteristic values ​​and spatial attributes of the multi-dimensional monitoring surfaces of the distribution network operation status units, the status characteristic parameters and preliminary operation control thresholds of each distribution network operation status unit are determined, including: For each distribution network operation status unit, the power fluctuation intensity of the distribution network operation status unit is determined based on the difference between the load absorption power characteristic value and the preset reference power. Based on the time span of parameter changes in the complete parameter time series, the continuous response duration of the distribution network operation status unit is determined; A topology-level mapping analysis is performed on multiple distribution network operation status units, and the parent-child relationship and spatial correlation degree of multiple distribution network operation status units are obtained by combining the spatial attributes of multi-dimensional monitoring surfaces; wherein, the spatial correlation degree is a natural number, and the spatial correlation degree of any distribution network operation status unit is greater than the spatial correlation degree of the parent of any distribution network operation status unit, and the difference is 1. Based on the power fluctuation intensity, continuous response duration and spatial correlation, state characteristic parameters of each distribution network operation state unit are generated. The voltage sag duration parameter is extracted from the load-side dynamic response data, and the preliminary operation control threshold of each distribution network operation status unit is calculated by combining the power fluctuation intensity and the impedance parameters of the branch lines.

6. The method for monitoring the operation of a distributed distribution network according to claim 5, characterized in that, Based on the state characteristic parameters and preliminary operation control thresholds of the target operation state units that meet the target conditions among the multiple distribution network operation state units, the operation control boundary conditions and final operation metrics of the target monitoring area are determined, including: Obtain the target monitoring point; wherein the target monitoring point is determined by the location input by the user; In order of increasing power fluctuation intensity, the parameter acquisition points of multiple distribution network operation status units are compared with the target monitoring point until the target operation status unit is determined. The comparison between the distribution network operation status unit and the target monitoring point is terminated; wherein, the parameter acquisition point set includes multiple time-series acquisition points, which are obtained by converting the distribution network operation status unit from the distribution network topology space domain to the monitoring display domain; if the parameter acquisition point set of any distribution network operation status unit includes the target monitoring point, the any distribution network operation status unit is determined as a target operation status unit that meets the target conditions; the target conditions are: the target operation status unit is the displayed parameter coverage range; wherein, the parameter coverage range includes the target monitoring point.

7. The method for monitoring the operation of a distributed distribution network according to claim 6, characterized in that, Based on the state characteristic parameters and preliminary operation control thresholds of the target operation state units that meet the target conditions among the multiple distribution network operation state units, the operation control boundary conditions and final operation metrics of the target monitoring area are determined, including: In response to the spatial correlation degree of the target operating state unit being greater than a preset correlation threshold, the type of the target monitoring area is determined to be a stable operating area; the target monitoring area is a key control area specified by the user. For the stable operating region, at least one sub-level operating state unit of the target operating state unit is determined; Calculate the sum of power fluctuation intensity, the sum of continuous response duration, and the average of the preliminary operation control threshold of at least one sub-level operating state unit; The final power fluctuation index of the target monitoring area is determined based on the difference between the power fluctuation intensity of the target operating state unit and the sum of the power fluctuation intensities. The final response time index of the target monitoring area is determined based on the sum of the continuous response time of the target operating state unit and the total continuous response time. Based on the average value of the preliminary operation control threshold, the final power fluctuation index, and the final response time index, the operation control boundary conditions of the target monitoring area are determined. The final power fluctuation index and the final response time index are integrated into the final operational metric of the target monitoring area.

8. The method for monitoring the operation of a distributed distribution network according to claim 7, characterized in that, Based on the state characteristic parameters and preliminary operation control thresholds of the target operation state units that meet the target conditions among the multiple distribution network operation state units, the operation control boundary conditions and final operation metrics of the target monitoring area are determined, including: In response to the fact that the spatial correlation degree of the target operating state unit is not greater than a preset correlation threshold, the type of the target monitoring area is determined to be an abnormal operating area; For the abnormal operation area, determine the sub-level abnormal operation units of the target operation state unit; Calculate the sum of power fluctuation intensity and the average of the preliminary operation control threshold of the sub-level abnormal operation units; The difference between the power fluctuation intensity of the target operating state unit and the sum of the power fluctuation intensities is determined as the fault fluctuation index of the abnormal operating area. Based on the product of the average value of the preliminary operation control threshold and the preset fault coefficient, the fault control boundary conditions of the abnormal operation area are determined. The fault fluctuation index and fault control boundary conditions are integrated into the abnormal operation assessment result of the abnormal operation area.

9. The method for monitoring the operation of a distributed distribution network according to claim 8, characterized in that, The parameter acquisition points of multiple point distribution network operation status units are sequentially compared with the target monitoring point until the target operation status unit is determined, including: The multiple distribution network operation status units are sorted in ascending order of power fluctuation intensity; The parameter collection point set of each sorted distribution network operation status unit is matched spatially with the target monitoring point; the parameter collection point set is composed of the projection points of the complete parameter time sequence chain of the distribution network operation status unit in the monitoring display field; If the projection coverage of the parameter collection point set of the distribution network operation status unit includes the target monitoring point, then the distribution network operation status unit is determined as the target operation status unit.

10. A distributed distribution network operation monitoring system, applicable to the distributed distribution network operation monitoring method according to any one of claims 1-9, characterized in that, include: The data monitoring module is used to generate multiple multi-dimensional monitoring surfaces based on the topology of the distributed distribution network, real-time electrical parameters, and load-side dynamic response simulation results. The parameter interaction module is used to determine multiple sets of electrical parameter time series chains and generate a parameter chain cluster based on the interaction data between the multi-dimensional monitoring surface and the distributed distribution network and the load-side dynamic response data; the multi-dimensional monitoring surface is a monitoring carrier that covers the distribution network topology area and the load response area specified by the user; the electrical parameter time series chain is the parameter acquisition time series data between the monitoring sub-unit of the multi-dimensional monitoring surface and the topology sub-unit of the distributed distribution network. The parameter parsing module is used to parse the distribution network operation status unit based on the parameter chain cluster and calculate the load absorption power characteristic value of each distribution network operation status unit. The operation control module is used to determine the state characteristic parameters and preliminary operation control thresholds of each distribution network operation status unit based on the load absorption power characteristic value and the spatial attributes of the multi-dimensional monitoring surface of the distribution network operation status unit; the state characteristic parameters include at least power fluctuation intensity, continuous response duration and spatial correlation. The operation measurement module is used to determine the operation control boundary conditions and final operation measurement of the target monitoring area based on the state characteristic parameters of the target operation state units that meet the target conditions among the multiple distribution network operation state units and the preliminary operation control threshold.

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