Active power distribution network resource optimization method and system based on operation state perception

Abstract

The invention discloses an active power distribution network resource optimization method and system based on operation state perception, and relates to the technical field of resource optimization, and the method comprises the steps: determining a node set in an operation monitoring state, and obtaining the voltage deviation characteristics of each node in a continuous time window and the current fluctuation characteristics of a connected line; generating core regulation and control equipment and adjustable operation intervals and constraint conditions thereof based on the node-line state characteristics, and forming alternative self-healing regulation and control strategies; the node state change and the equipment resource occupation condition after the alternative self-healing regulation and control strategy is triggered are obtained, and an equipment resource load change curve is formed; extracting a resource occupation fluctuation index and accumulated resource consumption, and generating a resource consumption data set; and optimizing the self-healing operation sequence based on the resource consumption data set, generating an equipment resource optimization scheme, and executing the self-healing operation in sequence, thereby realizing dynamic resource optimization configuration of the active power distribution network, and improving the utilization efficiency of equipment and the self-healing capability of the system.

Description

Technical Field

[0001] This invention relates to the field of resource optimization technology, specifically to an active distribution network resource optimization method and system based on operational status awareness. Background Technology

[0002] With the large-scale integration of distributed energy sources, energy storage devices, and smart switching equipment into distribution networks, traditional distribution networks are gradually evolving into active distribution networks. During the operation of an active distribution network, node voltage levels, line current loads, and the operating status of various control devices continuously change with load variations and fluctuations in distributed power output, resulting in significant dynamism and complexity in system operation. To ensure the safe and stable operation of the distribution network, it is typically necessary to rely on self-healing mechanisms to quickly adjust the network and restore operation in the event of anomalies or faults, while simultaneously making rational use of the regulation capabilities of critical equipment. Real-time sensing and analysis of node voltage deviation characteristics, line current fluctuation characteristics, and the operating status of critical equipment can provide a reference for self-healing operations and a basis for the optimal allocation of equipment resources, thereby improving system reliability and resource utilization efficiency.

[0003] However, in the process of resource optimization, existing distribution network operation and control methods, when relying on self-healing capabilities for response, still suffer from insufficient consideration of the resource occupation, dynamic consumption, and operational constraints of various core control equipment. During distribution network operation, the operating states of different nodes and lines vary, and the adjustable range and operational constraints of various core control equipment also differ. If a comprehensive perception of the operating state and resource consumption analysis are lacking in self-healing operations, it may lead to uneven equipment resource utilization, large fluctuations in resource occupation, excessive resource consumption, decreased optimization efficiency, or infeasibility of control schemes, thereby affecting the execution effect of self-healing operations and the overall operational stability of the distribution network. Therefore, it is necessary to propose an active distribution network resource optimization method and system based on operating state perception to address the aforementioned problems. Summary of the Invention

[0004] To address the aforementioned technical problems, this paper provides an active distribution network resource optimization method and system based on operational status awareness. This technical solution solves the problem mentioned in the background that during the operation of the distribution network, the operational status of different nodes and lines varies, and the adjustable range and operational constraints of various core control equipment are also different. If there is a lack of comprehensive awareness of the operational status and resource consumption analysis during self-healing operations, it may lead to uneven utilization of equipment resources, large fluctuations in resource occupation, excessive resource consumption, decreased optimization efficiency, or infeasibility of control schemes, thereby affecting the execution effect of self-healing operations and the overall operational stability of the distribution network.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: An active distribution network resource optimization method based on operational status awareness includes: The set of nodes in the active distribution network that are under operation monitoring is determined, and the voltage deviation characteristics of each node and the current fluctuation characteristics of the lines connected to the node are obtained within a continuous time window. The nodes are the operating nodes in the distribution network where monitoring devices have been deployed. Based on voltage offset characteristics and current fluctuation characteristics, a node-line association state matrix is ​​generated to record the correspondence between the state characteristics of each node and the connected line within a continuous time window. Based on the correspondence of state characteristics, the core control equipment is determined, and the adjustable operating range and constraints of each core control equipment are obtained, generating alternative self-healing control strategies corresponding to the resources of the core control equipment. For each alternative self-healing control strategy, obtain the node state changes, self-healing voltage offset characteristics, self-healing current fluctuation characteristics, and equipment resource occupancy after its triggering, and organize them in time sequence to form an equipment resource load change curve. In the equipment resource load change curve, the resource occupancy sequence and the corresponding maximum and average resource occupancy of each core control equipment during the execution of the self-healing operation sequence are extracted. The resource occupancy sequence is then corrected by threshold interval constraints to obtain the constrained resource occupancy sequence. The resource occupancy change rate is then obtained based on the constrained resource occupancy sequence and combined with the maximum and average resource occupancy to obtain the resource occupancy fluctuation index. The cumulative resource consumption is then obtained, and the resource consumption dataset corresponding to the self-healing operation is generated. Based on the resource occupancy fluctuation index and cumulative resource consumption in the resource consumption dataset, the candidate self-healing control strategies are ranked. The ranking results are then filtered in conjunction with the constraints of the core control equipment to obtain an optimized self-healing operation sequence. A corresponding equipment resource optimization scheme is generated, and then the self-healing operation is executed sequentially according to the equipment resource optimization scheme to optimize the equipment resources of the active distribution network.

[0006] In an optional embodiment, the step of generating a node-line associated state matrix based on voltage offset characteristics and current fluctuation characteristics, and recording the correspondence between the state characteristics of each node and the connected line within a continuous time window, specifically includes: Obtain network structure data of the power distribution network, the network structure data including node identification information, line identification information, and association configuration data between nodes and lines; The associated configuration data is matched with the corresponding node identification information and line identification information to obtain the matching results; Based on the matching results, determine the nodes connected to each line, and for each node, count all the lines it is connected to to form a set of corresponding nodes to lines. Based on the set of corresponding nodes to lines, traverse all nodes and lines to obtain the connection relationship between each node and line; Based on the connection relationship between nodes and lines, a node-line topology model is constructed. The node-line topology model uses distribution network nodes as model nodes and the lines connecting each node in the distribution network as model lines. The voltage offset characteristics of each node are mapped to the corresponding node in the topology model, and the current fluctuation characteristics of each line are mapped to the corresponding line in the topology model to obtain preliminary mapping data. The initial mapping data is standardized to obtain standardized mapping data. Based on the standardized mapping data, the state characteristics of each node and its directly connected lines within a continuous time window are paired to obtain the pairing results. The pairing results are recorded one by one to form a node-line association state matrix. The rows of the matrix correspond to each node in the node set, and the columns correspond to the set of lines directly connected to each node. Each element of the matrix contains the voltage offset characteristics of the node in a continuous time window and the current fluctuation characteristics of the line directly connected to the node in a continuous time window. Based on the row and column elements of the matrix, the voltage offset characteristics of each node are combined with the current fluctuation characteristics of its connected lines to generate a node-line state characteristic correspondence.

[0007] In an optional embodiment, the step of determining the core control equipment based on the correspondence of state features, obtaining the adjustable operating range and constraints of each core control equipment, and generating alternative self-healing control strategies corresponding to the resources of the core control equipment specifically includes: Obtain all nodes in the operational monitoring state and their connected lines; Based on the correspondence between node and line status characteristics, the voltage offset characteristics of each node and the current fluctuation characteristics of its connected lines within a continuous time window are determined. Nodes and lines whose voltage offset characteristics and current fluctuation characteristics of their connected lines simultaneously meet the preset anomaly judgment conditions are selected to obtain a set of candidate nodes and lines for self-healing analysis. Based on the candidate node-line set, determine the controllable equipment associated with the nodes and lines to form the core control equipment; Obtain the rated operating parameters, historical operating boundaries, and current operating status of each core control device; Based on the rated operating parameters, historical operating boundaries and current operating status of each core control device, the adjustable operating range of each core control device in self-healing operation is determined, forming an adjustable operating range; Based on the adjustable operating range, constraints that limit the operation of the equipment are set, including safety constraints, topology constraints, operational constraints, and time constraints, forming corresponding constraint conditions; Under the adjustable operating range and constraints, each core control device is sequentially generated with its allowed controllable actions, and the feasibility of each action under constraints is verified to obtain a self-healing operation that meets the constraints. The self-healing operations of each core control device are combined to generate a set of self-healing operations for self-healing analysis. By combining the self-healing operations that the core control equipment can perform under different states, alternative self-healing control strategies can be generated.

[0008] In an optional embodiment, for each candidate self-healing control strategy, the node state change, self-healing voltage offset characteristics, self-healing current fluctuation characteristics, and equipment resource occupancy after its triggering are obtained, and the data are organized in chronological order to form an equipment resource load change curve, specifically including: Based on each alternative self-healing control strategy, the state changes of each node after its triggering are obtained sequentially and recorded as the first node state data. The node state changes include the voltage value, switching state and operating parameter changes of the node before and after the operation. Based on the first node state data, the self-healing voltage offset feature of each node within a continuous time window is obtained, and this feature is associated with the corresponding first node state data to obtain second node state data containing node state changes and self-healing voltage offset features. The connected lines corresponding to each node are determined by the node-line association state matrix before the self-healing operation. After implementing the alternative self-healing control strategy, the self-healing current fluctuation characteristics of each line within a continuous time window are obtained and correlated with the second node status data to form the third node status data. Based on the status data of the third node, the resource consumption of the core control equipment involved in the alternative self-healing control strategy is recorded during the execution process; Organize the status data of the third node and the resource occupancy of the core control equipment in chronological order to form preliminary equipment resource load change data; The initial load change data is formatted in a unified manner to obtain standardized equipment resource load change data. Based on standardized equipment resource load change data, the resource occupancy of each core control equipment within a continuous time window is extracted sequentially according to the alternative self-healing control strategy, and the resource occupancy changes of each core control equipment are sorted out in chronological order to form equipment resource load change curves.

[0009] In an optional embodiment, the resource occupancy sequence and corresponding maximum and average resource occupancy values ​​of each core control device during the self-healing operation sequence execution are extracted from the device resource load change curve. The resource occupancy sequence is then corrected by threshold range constraints to obtain a constraint-corrected resource occupancy sequence. Subsequently, the resource occupancy change rate is obtained based on the constraint-corrected resource occupancy sequence, and combined with the maximum and average resource occupancy values ​​to obtain a resource occupancy fluctuation index. This is accumulated to obtain the cumulative resource consumption, thereby generating a resource consumption dataset corresponding to the self-healing operation. Specifically, this includes: Based on the equipment resource load change curve, identify the core control equipment that actually participates in the regulation during the execution of the alternative self-healing control strategy, and obtain a list of execution equipment. Based on the list of executing devices, extract the resource usage data of each core control device during the execution of the self-healing operation sequence to obtain the resource usage sequence of each device; Based on the resource occupancy sequence, the maximum and average resource occupancy values ​​of each core control device are obtained as statistical indicators of resource occupancy. Based on the maximum and average resource occupancy values ​​in the occupancy statistics, a resource occupancy threshold range is constructed, and the resource occupancy value at each time point in the resource occupancy sequence is compared with the resource occupancy threshold range to determine the resource occupancy status at each time point. When the resource occupancy value is within the resource occupancy threshold range, the original resource occupancy value is retained. When the resource occupancy value exceeds the resource occupancy threshold range, the resource occupancy value exceeding the upper threshold is adjusted to the upper limit of the threshold range, and the resource occupancy value below the lower threshold is adjusted to the lower limit of the threshold range, so as to achieve the limiting processing of the resource occupancy value and obtain the constrained and corrected resource occupancy sequence. The constrained resource occupancy sequence is divided into local time windows, and the resource occupancy fluctuation characteristics within each time window are extracted to characterize the intensity of resource occupancy changes. Based on the constrained and corrected resource occupancy sequence and resource occupancy fluctuation characteristics, the resource occupancy change rate of each core control device during the execution of the self-healing operation sequence is obtained; The maximum, average, and rate of change of resource usage are compiled to form a resource usage fluctuation index for each core control device. Based on the equipment resource load change curve corresponding to each alternative self-healing control strategy, the resource occupancy of each core control device during the execution of the self-healing operation sequence is accumulated to obtain the cumulative resource consumption of each core control device. By correlating resource usage fluctuation indicators with cumulative resource consumption, a resource consumption dataset corresponding to candidate self-healing operations is generated.

[0010] In an optional embodiment, based on the resource occupancy fluctuation index and cumulative resource consumption in the resource consumption dataset, the candidate self-healing control strategies are ranked, and the ranking results are filtered in combination with the constraints of the core control equipment to obtain an optimized self-healing operation sequence. A corresponding equipment resource optimization scheme is then generated, and the self-healing operations are executed sequentially according to the equipment resource optimization scheme to optimize the equipment resources of the active power distribution network. Specifically, this includes: Based on the resource consumption dataset, we analyze the resource consumption of each alternative self-healing regulation strategy. Based on resource consumption, the candidate self-healing control strategies are prioritized to obtain an operation order list and generate the corresponding operation sequence. By combining the rated operating parameters, adjustable operating range and corresponding constraints of each core control equipment, the feasibility of the sorted operation sequence is verified, operation combinations that do not meet the equipment constraints are eliminated, and feasible operation sequences are obtained. Adjust and optimize the feasible operation sequence to generate an optimized self-healing operation sequence. Based on the optimized self-healing operation sequence, a corresponding equipment resource optimization plan is generated, clarifying the execution order and parameter settings of each core control device in each operation; The equipment resource optimization scheme is input into the control system, and the self-healing operation is executed sequentially on each core control equipment in the active power distribution network to implement equipment resource regulation. During the self-healing operation, the status changes of each node and the resource occupancy of the core control equipment are monitored in real time, and dynamic adjustments are made for abnormal or excessive equipment operation. After completing all self-healing operations, record the final node status and resource usage of each device to generate the device resource optimization execution results.

[0011] Furthermore, an active distribution network resource optimization system based on operational status awareness is proposed to implement any of the resource optimization methods mentioned above, including: The operation status sensing module is used to acquire the voltage deviation characteristics of each node, the current fluctuation characteristics of each line, and the resource occupancy data of the core control equipment in the active distribution network, and generate a node-line status feature dataset. The topology state modeling module is used to construct a node-line association state matrix based on the node-line state feature dataset, forming a correspondence between the state features of nodes and connected lines. The core equipment identification module is used to filter core control equipment according to the correspondence between node and line status characteristics, obtain the rated operating parameters, historical operating boundaries and current operating status of each core control equipment, determine the adjustable operating range and constraints of each core control equipment, and generate alternative self-healing control strategies. The resource load analysis module is used to obtain the changes in resource occupancy and node status of core control equipment after the execution of the alternative self-healing control strategy, form the equipment resource load change curve, and perform standardized processing on it. The resource consumption assessment module is used to extract the maximum, average and change rate of resource occupancy of core control equipment based on the standardized equipment resource load change curve, cumulatively calculate the cumulative resource consumption, and generate a resource consumption dataset corresponding to candidate self-healing operations. The optimization decision execution module is used to adjust and optimize the alternative self-healing control strategies based on the resource consumption dataset, generate an optimized self-healing operation sequence, and execute the operations in sequence to realize the dynamic optimization configuration of equipment resources, while recording the final node status and equipment resource usage.

[0012] In an optional embodiment, the topology state modeling module includes: A node status acquisition unit is used to acquire the voltage offset characteristics and operating status data of each node within a continuous time window. A line status acquisition unit is used to acquire the current fluctuation characteristics of each line within a continuous time window. A state association generation unit is used to pair the state features of a node with the state features of its directly connected lines to form a node-line state feature correspondence. An association matrix generation unit is used to construct a node-line association state matrix based on the correspondence between node-line state features.

[0013] In an optional embodiment, the core device identification module includes: The candidate node-line filtering unit is used to filter nodes and lines that meet preset anomaly judgment conditions based on the correspondence between node-line status features, forming a candidate node-line set. The core control equipment determination unit is used to identify controllable equipment associated with nodes and lines based on the candidate node-line set, thereby forming core control equipment. An adjustable operating range determination unit is used to obtain the rated operating parameters, historical operating boundaries and current operating status of each core control device, and to determine the adjustable operating range and constraints. A candidate operation generation unit is used to combine the self-healing operations that the core control equipment can execute under different operating states to generate alternative self-healing control strategies.

[0014] In an optional embodiment, the resource consumption assessment module includes: The resource indicator extraction unit is used to extract the maximum value, average value and rate of change of resource occupancy of each core control device based on the resource occupancy of each core control device during the execution of the alternative self-healing control strategy. A resource consumption calculation unit is used to cumulatively calculate the cumulative resource consumption of each core control device during the execution of alternative self-healing control strategies. The resource consumption dataset generation unit is used to associate the resource occupancy indicators of each core control device with the cumulative resource consumption to form a resource consumption dataset corresponding to the candidate self-healing operation.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This paper proposes a resource optimization method and system for active distribution networks based on operational status awareness. By acquiring node voltage offset characteristics and line current fluctuation characteristics, it constructs a node-line state characteristic correspondence, identifies core control equipment and its adjustable operating range, and optimizes alternative self-healing control strategies based on resource consumption data. This achieves efficient allocation of equipment resources and dynamic optimization of self-healing operations. Using this method, the self-healing efficiency of active distribution networks can be improved, the utilization rate of core control equipment resources can be balanced, the risk of resource waste can be reduced, and the stability and reliability of the system under complex operating conditions can be enhanced, providing technical support for the safe and efficient operation of distribution networks. Attached Figure Description

[0016] Figure 1 This is a flowchart of an active distribution network resource optimization method based on operational status awareness proposed in this invention; Figure 2 This is a flowchart of the node-line state mapping process in this invention; Figure 3 This is a flowchart illustrating the self-healing and resource trajectory of the core control device in this invention. Figure 4 This is a system framework diagram of an active distribution network resource optimization system based on operational status awareness proposed in this invention. Detailed Implementation

[0017] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0018] Reference Figure 1 - Figure 4As shown in the figure, an active distribution network resource optimization method based on operational status awareness in an embodiment of the present invention includes: The set of nodes in the active distribution network that are under operation monitoring is determined, and the voltage deviation characteristics of each node and the current fluctuation characteristics of the lines connected to the node are obtained within a continuous time window. The nodes are the operating nodes in the distribution network where monitoring devices have been deployed. Understandably, when determining the set of nodes in an active distribution network that are under operational monitoring, these nodes refer to those nodes in the distribution network that have been equipped with monitoring devices. These nodes are capable of real-time acquisition of voltage, current, and other operating parameters, typically including key substation nodes, feeder terminal nodes, and nodes with significant loads or generator connections. These nodes, through built-in sensors or monitoring devices, can continuously acquire voltage deviation characteristics and monitor the current fluctuation characteristics of their connected lines in real time, thus providing fundamental data for subsequent self-healing analysis.

[0019] Based on voltage offset characteristics and current fluctuation characteristics, a node-line association state matrix is ​​generated to record the correspondence between the state characteristics of each node and the connected line within a continuous time window. Specifically, within each continuous time window, the voltage offset characteristics of each node and the current fluctuation characteristics of the lines connected to the node are first acquired. This continuous time window can be set according to the needs of distribution network monitoring, for example, it can be of different lengths such as 10 milliseconds, 1 second, or 5 minutes, to adapt to node state changes at different time scales. Shorter time windows can be used to capture the characteristics of instantaneous disturbances or rapid fluctuations, such as lightning strikes or instantaneous voltage fluctuations; longer time windows can be used to observe long-term stable state changes, such as continuous load changes on lines. The types of disturbances or state changes mentioned here are only used to illustrate the basis for selecting the length of the time window and do not limit the technical solution to fault scenarios. By collecting node and line characteristics within a unified time window, a complete node-line associated state matrix can be generated, accurately reflecting the state changes of each node and its connected lines over continuous time, providing basic data for subsequent identification of core control equipment and generation of self-healing operations.

[0020] Based on the correspondence of state characteristics, the core control equipment is determined, and the adjustable operating range and constraints of each core control equipment are obtained, generating alternative self-healing control strategies corresponding to the resources of the core control equipment. For each alternative self-healing control strategy, obtain the node state changes, self-healing voltage offset characteristics, self-healing current fluctuation characteristics, and equipment resource occupancy after its triggering, and organize them in time sequence to form an equipment resource load change curve. In the equipment resource load change curve, the resource occupancy sequence and the corresponding maximum and average resource occupancy of each core control equipment during the execution of the self-healing operation sequence are extracted. The resource occupancy sequence is then corrected by threshold interval constraints to obtain the constrained resource occupancy sequence. The resource occupancy change rate is then obtained based on the constrained resource occupancy sequence and combined with the maximum and average resource occupancy to obtain the resource occupancy fluctuation index. The cumulative resource consumption is then obtained, and the resource consumption dataset corresponding to the self-healing operation is generated. Based on the resource occupancy fluctuation index and cumulative resource consumption in the resource consumption dataset, the candidate self-healing control strategies are ranked. The ranking results are then filtered in conjunction with the constraints of the core control equipment to obtain an optimized self-healing operation sequence. A corresponding equipment resource optimization scheme is generated, and then the self-healing operation is executed sequentially according to the equipment resource optimization scheme to optimize the equipment resources of the active distribution network.

[0021] Furthermore, based on voltage offset characteristics and current fluctuation characteristics, a node-line association state matrix is ​​generated to record the correspondence between the state characteristics of each node and the connected line within a continuous time window, specifically including: Obtain network structure data of the power distribution network, which includes node identification information, line identification information, and the association configuration data between nodes and lines; The associated configuration data is matched with the corresponding node identification information and line identification information to obtain the matching results; Based on the matching results, determine the nodes connected to each line, and for each node, count all the lines it is connected to to form a set of corresponding nodes to lines. Based on the set of corresponding nodes to lines, traverse all nodes and lines to obtain the connection relationship between each node and line; Based on the connection relationship between nodes and lines, a node-line topology model is constructed. The node-line topology model uses distribution network nodes as model nodes and the lines connecting each node in the distribution network as model lines. Specifically, the process begins by acquiring network structure data from the distribution network management system or monitoring platform. This data includes unique identifiers for each node and each line, as well as the association configuration data between nodes and lines. The association configuration data records the starting and ending node information for each line. After data acquisition, the starting and ending node information for each line is matched with the node identifier information. This involves traversing the node identifier list to find nodes that match the starting and ending points of the lines, and recording the matching results as the node-line association. For example, the starting point of a line corresponds to node one, and the ending point corresponds to node two, forming a mapping relationship. Subsequently, the matching results are summarized and statistically analyzed. For each node, all lines directly connected to it are integrated to generate a set of node-line correspondences. For example, if node one directly connects to lines one, three, and five, this is recorded as the set of lines corresponding to that node. After completing the statistical analysis of the line sets for all nodes, the entire distribution network node and line sets are traversed. Based on the node-line correspondence, the connection relationship between each node and line is determined, i.e., which nodes each line connects to, and which lines each node connects to. This connection relationship provides the foundation for subsequent topology analysis. Based on the above connection relationships, a node-line topology model is constructed. In this model, each distribution network node is mapped to a node in the topology model, and each line is mapped to a line connecting the corresponding node in the model. This topology model can intuitively represent the electrical connection relationships between nodes, line distribution, and network structure characteristics, providing a basic framework for further node state mapping, voltage offset, and current fluctuation analysis. For example, in practical applications, node two connects line one and line two, line one connects node one and node two, and line two connects node two and node three. This forms a node-line correspondence network in the topology model, where node two shows its connection to line one and line two. This ensures accurate tracking of node state changes and resource occupancy of core control equipment in subsequent self-healing operation sequence analysis.

[0022] The voltage offset characteristics of each node are mapped to the corresponding node in the topology model, and the current fluctuation characteristics of each line are mapped to the corresponding line in the topology model to obtain preliminary mapping data. The initial mapping data is standardized to obtain standardized mapping data. Based on the standardized mapping data, the state characteristics of each node and its directly connected lines within a continuous time window are paired to obtain the pairing results. The pairing results are recorded one by one to form a node-line association state matrix. The rows of the matrix correspond to each node in the node set, and the columns correspond to the set of lines directly connected to each node. Each element of the matrix contains the voltage offset characteristics of the node in a continuous time window and the current fluctuation characteristics of the line directly connected to the node in a continuous time window. Based on the row and column elements of the matrix, the voltage offset characteristics of each node are combined with the current fluctuation characteristics of its connected lines to generate a node-line state characteristic correspondence.

[0023] Specifically, after constructing the node-line topology model, the voltage offset features collected by each node within a continuous time window are mapped to the corresponding node positions in the topology model, so that each model node carries its voltage change information within different time windows. Simultaneously, the current fluctuation features collected by each line within the same continuous time window are mapped to the corresponding line positions in the topology model, so that each model line corresponds to its current fluctuation in the time dimension, thus forming preliminary mapping data containing node voltage states and line current states. Since the dimensions and value ranges of the operating parameters of different nodes and lines differ, to avoid the unbalanced impact of individual node or line characteristics on subsequent analysis, the above preliminary mapping data is standardized. For example, the voltage offset and current fluctuation features within each time window are converted into dimensionless relative change features, making the state characteristics of different nodes and lines comparable, resulting in standardized mapping data. Based on this, according to the established connection relationships in the topology model, each node is matched one-to-one with its directly connected lines, and the node voltage offset features within each continuous time window are paired with the corresponding line current fluctuation features, obtaining the state characteristic pairing results of nodes and lines within the same time window. Subsequently, the pairing results are recorded one by one according to the correspondence between nodes and lines, constructing a node-line association state matrix. The rows of the matrix represent the nodes in operational monitoring status, and the columns represent the set of lines directly connected to each node. Each element in the matrix contains the voltage offset characteristics of the node within a continuous time window and the current fluctuation characteristics of its directly connected lines within the same time window. This matrix structure systematically reflects the joint operational status of nodes and lines in the time dimension. Furthermore, based on the elements in each row and column of the matrix, the voltage offset characteristics of each node are combined with the current fluctuation characteristics of its connected lines to form a clear node-line state characteristic correspondence, providing a unified and clear state characteristic foundation for subsequent identification of core control equipment, self-healing operation analysis, and equipment resource optimization.

[0024] Furthermore, based on the correspondence of state characteristics, core control equipment is identified, and the adjustable operating range and constraints of each core control equipment are obtained. Alternative self-healing control strategies corresponding to the resources of the core control equipment are then generated, specifically including: Obtain all nodes in the operational monitoring state and their connected lines; Based on the correspondence between node and line status characteristics, the voltage offset characteristics of each node and the current fluctuation characteristics of its connected lines within a continuous time window are determined. Nodes and lines whose voltage offset characteristics and current fluctuation characteristics of their connected lines simultaneously meet the preset anomaly judgment conditions are selected to obtain a set of candidate nodes and lines for self-healing analysis. Based on the candidate node-line set, determine the controllable equipment associated with the nodes and lines to form the core control equipment; Obtain the rated operating parameters, historical operating boundaries, and current operating status of each core control device; Based on the rated operating parameters, historical operating boundaries and current operating status of each core control device, the adjustable operating range of each core control device in self-healing operation is determined, forming an adjustable operating range; Based on the adjustable operating range, constraints that limit the operation of the equipment are set, including safety constraints, topology constraints, operational constraints, and time constraints, forming corresponding constraint conditions; Under the adjustable operating range and constraints, each core control device is sequentially generated with its allowed controllable actions, and the feasibility of each action under constraints is verified to obtain a self-healing operation that meets the constraints. The self-healing operations of each core control device are combined to generate a set of self-healing operations for self-healing analysis. By combining the self-healing operations that the core control equipment can perform under different states, alternative self-healing control strategies can be generated.

[0025] Specifically, after acquiring all distribution network nodes and their connected lines under operational monitoring, the voltage deviation characteristics of each node within a continuous time window and the current fluctuation characteristics of its connected lines are jointly determined based on the node-line status characteristic correspondence. Preset anomaly determination conditions are: the voltage deviation of a node within a continuous time window exceeds a positive or negative percentage threshold of the rated voltage (e.g., ±5% or ±10%), or the node voltage exhibits a sudden change trend within a short period, while the current fluctuation amplitude of its connected lines exceeds a positive or negative threshold of the line's rated current (e.g., ±10%), or the current shows a rapid rise or fall trend exceeding a set rate of change. When both the node voltage deviation characteristics and the connected line current fluctuation characteristics meet the above conditions, it is determined that the operating status of the node and line may affect the stability of the distribution network, requiring adjustment through self-healing, and it is included in the candidate node-line set for self-healing analysis. Based on this, and according to the candidate node-line set, controllable devices with direct electrical connections to these nodes and lines are further identified. These include switching devices used to change the direction of power flow, such as circuit breakers and disconnectors; voltage regulating devices used to adjust node voltage levels, such as on-load tap-changing transformers; and energy storage devices used to regulate power output, such as batteries. All of these controllable devices can adjust the distribution network's operating state through control commands without relying on manual intervention. Therefore, they are identified as core control devices for self-healing operations, thus forming a clearly defined core control system. Here, self-healing operation refers to the process of restoring or approaching normal operating states such as voltage and current by executing corresponding control actions on the core control devices after detecting abnormal node and line operating states. Essentially, it is a proactive adjustment and repair of the distribution network's operating state.

[0026] After identifying the core control equipment, the rated operating parameters, historical operating boundaries, and current operating status information of each core control equipment are further obtained. The rated operating parameters characterize the designed operating range of the equipment under normal operating conditions, the historical operating boundaries reflect the acceptable adjustment limits of the equipment during long-term operation, and the current operating status describes the actual load level of the equipment at the current moment. Based on the above information, the adjustability of each core control equipment in self-healing operation is analyzed to clarify its operable range without affecting safe and stable operation, thus forming the corresponding adjustable operating range. On this basis, constraints restricting equipment operation are further set, including safety constraints to prevent equipment overload or malfunction, topological constraints to maintain the rationality of the distribution network structure, operational constraints to regulate the sequence and mode of equipment actions, and time constraints to limit the duration of operations and response timing. Under the combined constraints of the adjustable operating range and various conditions, controllable actions are generated for each core control device. These controllable actions include specific operations that adjust the operating state of the equipment, such as adjusting the power flow by changing the on / off state of switching equipment, correcting node voltage deviations by changing the operating level of voltage regulators, and balancing line current loads by adjusting the output level of power control equipment. Subsequently, each controllable action undergoes a constraint feasibility check to determine whether it meets safety constraints, topology constraints, operational constraints, and time constraints under the current operating state. Actions that do not meet the constraints are eliminated, and those that do meet the requirements are retained as executable self-healing operations. Furthermore, the self-healing operations corresponding to different core control devices are combined to form a set of self-healing operations for self-healing analysis. Based on this, and combined with the self-healing operations that the core control devices can execute under their respective operating states (including normal load state, light load state, high load state, and fault response state), as well as coordination rules such as device type, node-line association, anomaly severity, and operation priority, the self-healing operations are sequentially combined. According to the combination rules, a logically complete alternative self-healing control strategy covering all possible operating scenarios is generated, which is ultimately used for subsequent resource consumption assessment and optimization analysis.

[0027] Furthermore, for each alternative self-healing control strategy, the node state changes, self-healing voltage offset characteristics, self-healing current fluctuation characteristics, and equipment resource occupancy after triggering are obtained, and these are organized in chronological order to form equipment resource load change curves, specifically including: Based on each alternative self-healing control strategy, the state changes of each node after its triggering are obtained in sequence and recorded as the first node state data. The node state changes include the voltage value, switch status and operating parameter changes of the node before and after the operation. Based on the first node state data, the self-healing voltage offset feature of each node within a continuous time window is obtained, and this feature is associated with the corresponding first node state data to obtain second node state data containing node state changes and self-healing voltage offset features. The connected lines corresponding to each node are determined by the node-line association state matrix before the self-healing operation. After implementing the alternative self-healing control strategy, the self-healing current fluctuation characteristics of each line within a continuous time window are obtained and correlated with the second node status data to form the third node status data. Based on the status data of the third node, the resource consumption of the core control equipment involved in the alternative self-healing control strategy is recorded during the execution process; Specifically, after the alternative self-healing control strategy is implemented, the voltage changes of each node within a continuous time window are first compared and analyzed based on the first node state data collected before and after the self-healing operation. By analyzing the magnitude and trend of the node voltage changes before and after the self-healing operation, the self-healing voltage offset characteristics of the nodes within the continuous time window are extracted to reflect the regulating effect of the self-healing operation on the node voltage state. For example, if the node voltage is consistently low before the self-healing operation and gradually rises and stabilizes after the operation, a positive self-healing voltage offset characteristic is formed within the corresponding time window. Subsequently, this self-healing voltage offset characteristic is correlated with the corresponding node operating state change information to form second node state data that simultaneously includes node state changes and self-healing voltage offset characteristics. Based on this, the node-line association state matrix constructed before the self-healing operation is used to determine the line relationships directly connected to each node in the distribution network topology, thereby clarifying the line range corresponding to each node. Furthermore, after implementing the alternative self-healing control strategy, the current changes of each line within a continuous time window are obtained. By analyzing the fluctuation amplitude and trend of the line current before and after the self-healing operation, the self-healing current fluctuation characteristics of the corresponding line are extracted. These characteristics are then correlated with the second node state data to form third node state data that simultaneously reflects node voltage changes and line current changes. For example, after the self-healing operation is executed, if the peak current of a certain line decreases significantly and the fluctuation amplitude decreases, a negative self-healing current fluctuation characteristic is formed within the corresponding time window, indicating that the line load has been alleviated.

[0028] Organize the status data of the third node and the resource occupancy of the core control equipment in chronological order to form preliminary equipment resource load change data; The initial load change data is formatted in a unified manner to obtain standardized equipment resource load change data. Based on standardized equipment resource load change data, the resource occupancy of each core control equipment within a continuous time window is extracted sequentially according to the alternative self-healing control strategy, and the resource occupancy changes of each core control equipment are sorted out in chronological order to form equipment resource load change curves.

[0029] Understandably, after obtaining the third-node status data, based on the execution process corresponding to each alternative self-healing control strategy, the resource occupancy of the core control equipment involved in the regulation is recorded within each continuous time window. This resource occupancy characterizes the operational capacity consumed or occupied by the equipment during self-healing operations, including the equipment's output power level, number of switching actions, voltage regulation range changes, and operating parameters reflecting the equipment's load status, such as operating current or load rate. During data acquisition, real-time collection of the operating parameters of each core control device yields its status data within each time window, which is then correlated with the third-node status data in chronological order. This ensures that changes in node status within each time window match the resource occupancy information of the corresponding equipment. For example, when a switching operation is performed within a certain time window, the action status and number of actions of the corresponding switching equipment are recorded; when a voltage regulation operation is performed, the voltage regulation range change value and direction are recorded; when power regulation is performed, the charging and discharging power changes and duration of the energy storage equipment are recorded, thus forming raw resource occupancy data reflecting the equipment's regulation process.

[0030] After acquiring node status data and equipment resource occupancy data within each time window, the two types of data are aligned and organized according to a unified time order. Node status change information within the same time window is integrated with the resource occupancy data of the corresponding core control equipment, forming a combined data record containing time identifiers, node status change characteristics, and equipment resource occupancy information. This combined data record is then sequentially connected to the data records of each time window to form preliminary equipment resource load change data. Based on this, different types of resource occupancy in the preliminary data undergo unified formatting, including normalization or standardization mapping of operating parameters with different dimensions. This allows different types of data, such as power, number of actions, and gear changes, to be compared and analyzed on a unified scale, resulting in standardized equipment resource load change data. Subsequently, based on the standardized data, according to the execution order of each alternative self-healing control strategy, the resource occupancy changes of each core control equipment within continuous time windows are extracted sequentially. The resource occupancy changes of each device are then organized according to time order, ultimately forming an equipment resource load change curve that reflects the load change trend of the equipment during the self-healing process.

[0031] Furthermore, in the equipment resource load change curve, the resource occupancy sequence and corresponding maximum and average resource occupancy values ​​of each core control device during the self-healing operation sequence are extracted. The resource occupancy sequence is then corrected by threshold range constraints to obtain a constraint-corrected resource occupancy sequence. Subsequently, the resource occupancy change rate is obtained based on the constraint-corrected resource occupancy sequence, and combined with the maximum and average resource occupancy values ​​to obtain a resource occupancy fluctuation index. This is accumulated to obtain the cumulative resource consumption, thereby generating a resource consumption dataset corresponding to the self-healing operation, specifically including: Based on the equipment resource load change curve, identify the core control equipment that actually participates in the regulation during the execution of the alternative self-healing control strategy, and obtain a list of execution equipment. Based on the list of executing devices, extract the resource usage data of each core control device during the execution of the self-healing operation sequence to obtain the resource usage sequence of each device; Based on the resource occupancy sequence, the maximum and average resource occupancy values ​​of each core control device are obtained as statistical indicators of resource occupancy. Based on the maximum and average resource occupancy values ​​in the occupancy statistics, a resource occupancy threshold range is constructed, and the resource occupancy value at each time point in the resource occupancy sequence is compared with the resource occupancy threshold range to determine the resource occupancy status at each time point. When the resource occupancy value is within the resource occupancy threshold range, the original resource occupancy value is retained. When the resource occupancy value exceeds the resource occupancy threshold range, the resource occupancy value exceeding the upper threshold is adjusted to the upper limit of the threshold range, and the resource occupancy value below the lower threshold is adjusted to the lower limit of the threshold range, so as to achieve the limiting processing of the resource occupancy value and obtain the constrained and corrected resource occupancy sequence. The constrained resource occupancy sequence is divided into local time windows, and the resource occupancy fluctuation characteristics within each time window are extracted to characterize the intensity of resource occupancy changes. Based on the constrained and corrected resource occupancy sequence and resource occupancy fluctuation characteristics, the resource occupancy change rate of each core control device during the execution of the self-healing operation sequence is obtained; The maximum, average, and rate of change of resource usage are compiled to form a resource usage fluctuation index for each core control device. Based on the equipment resource load change curve corresponding to each alternative self-healing control strategy, the resource occupancy of each core control device during the execution of the self-healing operation sequence is accumulated to obtain the cumulative resource consumption of each core control device. By correlating resource usage fluctuation indicators with cumulative resource consumption, a resource consumption dataset corresponding to candidate self-healing operations is generated.

[0032] Specifically, when analyzing equipment resource load change curves, the resource occupancy of the equipment before and after the self-healing operation, as well as within continuous time windows, is compared to determine whether an actual change has occurred in the equipment's resource occupancy. An actual change here refers to a sustained or significant adjustment in the equipment's resource occupancy relative to its stable state before the self-healing operation, rather than minor fluctuations or measurement noise present during normal operation. For example, if a device's resource occupancy remains relatively stable over several continuous time windows before the self-healing operation, but after the operation begins, its resource occupancy exhibits a step change, a continuous increase, or a continuous decrease within one or more continuous time windows, and the magnitude of the change exceeds the preset normal operating fluctuation range, then the equipment's resource occupancy is considered to have undergone an actual change, and the device is considered to have participated in the self-healing adjustment process. Conversely, if a device's resource occupancy only exhibits small random fluctuations around its original level throughout the entire self-healing operation, and does not exceed the allowable fluctuation range for normal operation, then this change is considered not to constitute an actual change, and the device is not included in the list of executing equipment. Based on the list of executing devices, resource usage data for each device during the entire self-healing operation sequence is further extracted from the device resource load change curves and organized in chronological order to form a corresponding resource usage sequence, which is used to depict the dynamic changes in resource usage of the devices throughout the self-healing process.

[0033] After forming the resource usage sequence of each device, statistical analysis is performed on the resource usage sequence. The peak value in the sequence of each device during the execution of the self-healing operation sequence is extracted as the maximum resource usage value. The arithmetic mean of the resource usage is calculated by dividing the sum of all sampled values ​​by the number of sample points over a continuous time window. This average resource usage value is used to reflect the overall load level of the device. Furthermore, a resource occupancy threshold interval is constructed using the maximum and average resource occupancy values ​​as reference statistics. The maximum resource occupancy value characterizes the peak level of the resource occupancy sequence within the observation period, while the average resource occupancy value characterizes the overall baseline level of the resource occupancy sequence. Based on this, an upper and lower limit threshold for resource occupancy are determined. The upper limit threshold is obtained by proportionally mapping the difference between the maximum and average resource occupancy values, i.e., the difference between the maximum and average values ​​is used as an upper-side extended benchmark and superimposed on the average resource occupancy value. This proportional mapping relationship is determined based on the relative proportion of the difference magnitude on the average level. The lower limit threshold is obtained by proportionally mapping the difference between the average and minimum resource occupancy values, i.e., the difference between the average and minimum values ​​is used as a lower-side contraction benchmark and subtracted from the average resource occupancy value, thus forming a resource occupancy threshold interval for constraint analysis. After constructing the resource occupancy threshold range, the resource occupancy values ​​at each time point in the resource occupancy sequence are analyzed and compared point by point to determine the distribution relationship of the resource occupancy values ​​at each time point relative to the threshold range. When the resource occupancy value at a certain time point is within the resource occupancy threshold range, the resource occupancy value at that time point remains unchanged. When the resource occupancy value at a certain time point is higher than the upper limit threshold, the resource occupancy value at that time point is adjusted to the value corresponding to the upper limit threshold. When the resource occupancy value at a certain time point is lower than the lower limit threshold, the resource occupancy value at that time point is adjusted to the value corresponding to the lower limit threshold. In this way, the abnormal fluctuation points in the resource occupancy sequence are constrained and corrected to obtain the constrained and corrected resource occupancy sequence, so that the overall trend of the resource occupancy sequence tends to be smooth and the impact of abnormal fluctuations is reduced. After obtaining the constrained and corrected resource occupancy sequence, it is divided into local time windows. Each time window is used to characterize the resource usage change process of the device within a continuous time segment. Within each time window, the fluctuation characteristics of the resource occupancy value are calculated. The fluctuation characteristics include the maximum change amplitude of resource occupancy and the degree of deviation of the mean of resource occupancy. The maximum change amplitude of resource occupancy is used to characterize the fluctuation intensity of the resource occupancy value within the same time window, and the degree of deviation of the mean of resource occupancy is used to characterize the deviation of the resource occupancy value from the local average level within the time window, thereby comprehensively characterizing the stability of resource occupancy changes in the time dimension.After completing the fluctuation characteristic analysis, the difference between the maximum and average resource usage in the constrained and corrected resource usage sequence is combined and normalized to obtain the resource usage change rate. This rate characterizes the degree of resource fluctuation in each core control device during the execution of the self-healing operation sequence. Finally, the maximum, average, and change rates of resource usage are summarized to form a resource usage fluctuation index, comprehensively reflecting the intensity and fluctuation characteristics of equipment resource usage. Based on this, for each candidate self-healing control strategy, the resource usage of each device is cumulatively calculated throughout the entire self-healing operation execution process based on the corresponding equipment resource load change curve. This yields the cumulative resource consumption of each device under that operation sequence, reflecting the scale of resource usage during the entire self-healing process. Finally, the resource usage fluctuation index and cumulative resource consumption are correlated and integrated to form a resource consumption dataset corresponding to the candidate self-healing operations. This provides a unified data foundation for comparative analysis of resource consumption between different self-healing operation sequences and subsequent optimization decisions.

[0034] Furthermore, based on the resource occupancy fluctuation index and cumulative resource consumption in the resource consumption dataset, the candidate self-healing control strategies are ranked. The ranking results are then filtered in conjunction with the constraints of the core control equipment to obtain an optimized self-healing operation sequence. Corresponding equipment resource optimization schemes are generated, and then self-healing operations are executed sequentially according to the equipment resource optimization schemes to optimize equipment resources in the active distribution network. Specifically, this includes: Based on the resource consumption dataset, we analyze the resource consumption of each alternative self-healing regulation strategy. Based on resource consumption, the candidate self-healing control strategies are prioritized to obtain an operation order list and generate the corresponding operation sequence. By combining the rated operating parameters, adjustable operating range and corresponding constraints of each core control equipment, the feasibility of the sorted operation sequence is verified, operation combinations that do not meet the equipment constraints are eliminated, and feasible operation sequences are obtained. Adjust and optimize the feasible operation sequence to generate an optimized self-healing operation sequence. Specifically, after obtaining the resource consumption datasets corresponding to candidate self-healing operations, a comparative analysis is conducted on the resource consumption generated during the execution of different candidate self-healing control strategies. By comprehensively examining the cumulative resource consumption and resource usage fluctuation indicators of core control equipment under each operation sequence, the intensity of resource consumption and the degree of operational impact of different operation sequences on equipment resources are assessed, thereby forming an overall understanding of the resource consumption level of each candidate self-healing control strategy. Based on this, the candidate self-healing control strategies are prioritized according to resource consumption, giving higher priority to operation sequences with lower resource consumption, smaller resource usage fluctuations, and more gradual impact on equipment operation, while giving relatively lower priority to operation sequences with larger resource consumption or more drastic resource usage fluctuations. Finally, an operation sequence list reflecting the execution order of each candidate self-healing control strategy is obtained to guide subsequent operation selection and execution. After prioritizing the operations, and considering the rated operating parameters, adjustable operating ranges, and corresponding constraints of each core control device, the feasibility of each self-healing operation is first verified individually to ensure that each operation complies with safety constraints, topology constraints, operational constraints, and time constraints under its own operating conditions. Then, a combined constraint verification is performed on the entire prioritized operation sequence, considering potential conflicts that may arise when multiple devices execute operations within the same time period, including device load conflicts, topology limitation conflicts, and operation sequence conflicts. Operation combinations that do not meet the combined constraints are eliminated. Operation sequences that satisfy both single-device constraints and combined constraints are retained as feasible sequences; operation combinations that violate any constraint or have execution conflicts are directly excluded. Subsequently, based on the feasible operation sequences, the operation order and operation combinations are further adjusted and optimized. For example, while ensuring that the constraints are not violated, some operations are rearranged or merged to reduce overall resource consumption or smooth changes in device resource usage. Finally, an optimized self-healing operation sequence is generated for subsequent device resource optimization execution.

[0035] Based on the optimized self-healing operation sequence, a corresponding equipment resource optimization plan is generated, clarifying the execution order and parameter settings of each core control device in each operation; The equipment resource optimization scheme is input into the control system, and the self-healing operation is executed sequentially on each core control equipment in the active power distribution network to implement equipment resource regulation. During the self-healing operation, the status changes of each node and the resource occupancy of the core control equipment are monitored in real time, and dynamic adjustments are made for abnormal or excessive equipment operation. After completing all self-healing operations, record the final node status and resource usage of each device to generate the device resource optimization execution results.

[0036] Understandably, after obtaining the optimized self-healing operation sequence, a corresponding equipment resource optimization scheme is first generated based on this sequence. This configuration scheme clearly specifies the specific execution order and parameter settings for each core control device in each self-healing operation, such as the on / off state of switching equipment, the voltage regulation setting of voltage regulators, and the output power level of power control equipment, to ensure clear guidance and controllability in the use and adjustment of equipment resources during operation. The generated configuration scheme also includes the time sequence and duration of the operations, facilitating the sequential implementation of each self-healing operation according to the predetermined order in actual execution. Subsequently, the complete equipment resource optimization scheme is input into the distribution network control system, which then executes self-healing operations sequentially on each core control device in the active distribution network, achieving real-time adjustment of the grid's operating status. During execution, the control system issues instructions according to the configuration scheme and simultaneously collects data such as the operating status of core control devices, node voltage, and line current, monitoring equipment resource occupancy and node-line operating status in real time. For example, when a voltage regulator adjusts its voltage level or a power control device changes its output power, the system records the change in its resource occupancy and synchronously updates the voltage offset characteristics and line current fluctuation characteristics of the relevant nodes. During the self-healing operation, if equipment operating parameters exceed safe limits, load levels are abnormal, or other anomalies are detected, the control system can dynamically adjust the subsequent operation sequence, execution parameters, or action magnitude to ensure the safe operation of core control equipment, the rational use of equipment resources, and the overall stability of the distribution network. After all self-healing operations are completed, the final operating status of each node, the current of each line, and the resource usage of each core control device are recorded, forming a complete set of equipment resource optimization execution results. This provides reliable data support for subsequent operation effect evaluation, resource consumption analysis, and optimization strategies.

[0037] Furthermore, an active distribution network resource optimization system based on operational status awareness is proposed to implement any of the resource optimization methods mentioned above, including: The operation status perception module is used to acquire the voltage deviation characteristics of each node, the current fluctuation characteristics of each line, and the resource occupancy data of the core control equipment in the active distribution network, and generate a node-line status feature dataset. The topology state modeling module is used to construct a node-line association state matrix based on the node-line state feature dataset, forming a correspondence between the state features of nodes and connected lines. The core equipment identification module is used to filter core control equipment based on the correspondence between node and line status characteristics, obtain the rated operating parameters, historical operating boundaries and current operating status of each core control equipment, determine the adjustable operating range and constraints of each core control equipment, and generate alternative self-healing control strategies. The resource load analysis module is used to obtain the changes in resource occupancy and node status of core control equipment after the execution of alternative self-healing control strategies, form equipment resource load change curves, and perform standardized processing on them. The resource consumption assessment module is used to extract the maximum, average and rate of change of resource occupancy of core control equipment based on the standardized equipment resource load change curve, calculate the cumulative resource consumption, and generate the resource consumption dataset corresponding to the candidate self-healing operation. The optimization decision execution module is used to adjust and optimize the alternative self-healing control strategies based on the resource consumption dataset, generate the optimized self-healing operation sequence, and execute the operations in sequence to realize the dynamic optimization configuration of equipment resources, while recording the final node status and equipment resource usage.

[0038] Furthermore, the topology state modeling module includes: The node status acquisition unit is used to acquire the voltage offset characteristics and operating status data of each node within a continuous time window. The line status acquisition unit is used to acquire the current fluctuation characteristics of each line within a continuous time window. The state association generation unit is used to match the state features of a node with the state features of its directly connected lines to form a node-line state feature correspondence. The correlation matrix generation unit is used to construct a node-line correlation state matrix based on the correspondence between node-line state characteristics.

[0039] Furthermore, the core device identification module includes: The candidate node-line filtering unit is used to filter nodes and lines that meet the preset anomaly judgment conditions based on the correspondence between node-line status characteristics, forming a candidate node-line set. The core control equipment determination unit is used to identify controllable equipment associated with nodes and lines based on the candidate node-line set, and form core control equipment. The adjustable operating range determination unit is used to obtain the rated operating parameters, historical operating boundaries and current operating status of each core control device, and to determine the adjustable operating range and constraints. The candidate operation generation unit is used to combine the self-healing operations that the core control equipment can execute under different operating conditions to generate alternative self-healing control strategies.

[0040] Furthermore, the resource consumption assessment module includes: The resource indicator extraction unit is used to extract the maximum, average, and rate of change of resource usage for each core control device based on the resource usage of each core control device during the execution of the alternative self-healing control strategy. The resource consumption calculation unit is used to accumulate and calculate the cumulative resource consumption of each core control device during the execution of the alternative self-healing control strategy. The resource consumption dataset generation unit is used to associate the resource occupancy indicators of each core control device with the cumulative resource consumption to form the resource consumption dataset corresponding to the candidate self-healing operation.

[0041] In summary, the advantages of this invention are as follows: By acquiring the voltage offset characteristics of nodes and the current fluctuation characteristics of lines in an active distribution network, a node-line state characteristic correspondence is constructed, core control equipment and its adjustable operating range are identified, and alternative self-healing control strategies are optimized and adjusted based on resource consumption data, thereby achieving efficient allocation of equipment resources and dynamic optimization of self-healing operations. This method can improve the execution efficiency of self-healing operations, balance the resource utilization rate of core control equipment, reduce the risk of resource waste, and enhance the stability and reliability of active distribution networks under complex operating conditions, providing technical support for the safe and efficient operation of distribution networks.

[0042] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for optimizing active distribution network resources based on operational status awareness, characterized in that, include: The set of nodes in the active distribution network that are under operation monitoring is determined, and the voltage deviation characteristics of each node and the current fluctuation characteristics of the lines connected to the node are obtained within a continuous time window. The nodes are the operating nodes in the distribution network where monitoring devices have been deployed. Based on voltage offset characteristics and current fluctuation characteristics, a node-line association state matrix is ​​generated to record the correspondence between the state characteristics of each node and the connected line within a continuous time window. Based on the correspondence of state characteristics, the core control equipment is determined, and the adjustable operating range and constraints of each core control equipment are obtained, generating alternative self-healing control strategies corresponding to the resources of the core control equipment. For each alternative self-healing control strategy, obtain the node state changes, self-healing voltage offset characteristics, self-healing current fluctuation characteristics, and equipment resource occupancy after its triggering, and organize them in time sequence to form an equipment resource load change curve. In the equipment resource load change curve, the resource occupancy sequence and the corresponding maximum and average resource occupancy of each core control equipment during the execution of the self-healing operation sequence are extracted. The resource occupancy sequence is then corrected by threshold interval constraints to obtain the constrained resource occupancy sequence. The resource occupancy change rate is then obtained based on the constrained resource occupancy sequence and combined with the maximum and average resource occupancy to obtain the resource occupancy fluctuation index. The cumulative resource consumption is then obtained, and the resource consumption dataset corresponding to the self-healing operation is generated. Based on the resource occupancy fluctuation index and cumulative resource consumption in the resource consumption dataset, the candidate self-healing control strategies are ranked. The ranking results are then filtered in conjunction with the constraints of the core control equipment to obtain an optimized self-healing operation sequence. A corresponding equipment resource optimization scheme is generated, and then the self-healing operation is executed sequentially according to the equipment resource optimization scheme to optimize the equipment resources of the active distribution network.

2. The active distribution network resource optimization method based on operational status awareness according to claim 1, characterized in that, The process involves generating a node-line association state matrix based on voltage offset and current fluctuation characteristics. This matrix records the correspondence between the state characteristics of each node and its connected lines within a continuous time window. Specifically, this includes: Obtain network structure data of the power distribution network, the network structure data including node identification information, line identification information, and association configuration data between nodes and lines; The associated configuration data is matched with the corresponding node identification information and line identification information to obtain the matching results; Based on the matching results, determine the nodes connected to each line, and for each node, count all the lines it is connected to to form a set of corresponding nodes to lines. Based on the set of corresponding nodes to lines, traverse all nodes and lines to obtain the connection relationship between each node and line; Based on the connection relationship between nodes and lines, a node-line topology model is constructed. The node-line topology model uses distribution network nodes as model nodes and the lines connecting each node in the distribution network as model lines. The voltage offset characteristics of each node are mapped to the corresponding node in the topology model, and the current fluctuation characteristics of each line are mapped to the corresponding line in the topology model to obtain preliminary mapping data. The initial mapping data is standardized to obtain standardized mapping data. Based on the standardized mapping data, the state characteristics of each node and its directly connected lines within a continuous time window are paired to obtain the pairing results. The pairing results are recorded one by one to form a node-line association state matrix. The rows of the matrix correspond to each node in the node set, and the columns correspond to the set of lines directly connected to each node. Each element of the matrix contains the voltage offset characteristics of the node in a continuous time window and the current fluctuation characteristics of the line directly connected to the node in a continuous time window. Based on the row and column elements of the matrix, the voltage offset characteristics of each node are combined with the current fluctuation characteristics of its connected lines to generate a node-line state characteristic correspondence.

3. The active distribution network resource optimization method based on operational status awareness according to claim 1, characterized in that, The process of determining core control equipment based on state feature correspondence, obtaining the adjustable operating range and constraints of each core control equipment, and generating alternative self-healing control strategies corresponding to the resources of the core control equipment specifically includes: Obtain all nodes in the operational monitoring state and their connected lines; Based on the correspondence between node and line status characteristics, the voltage offset characteristics of each node and the current fluctuation characteristics of its connected lines within a continuous time window are determined. Nodes and lines whose voltage offset characteristics and current fluctuation characteristics of their connected lines simultaneously meet the preset anomaly judgment conditions are selected to obtain a set of candidate nodes and lines for self-healing analysis. Based on the candidate node-line set, determine the controllable equipment associated with the nodes and lines to form the core control equipment; Obtain the rated operating parameters, historical operating boundaries, and current operating status of each core control device; Based on the rated operating parameters, historical operating boundaries and current operating status of each core control device, the adjustable operating range of each core control device in self-healing operation is determined, forming an adjustable operating range; Based on the adjustable operating range, constraints that limit the operation of the equipment are set, including safety constraints, topology constraints, operational constraints, and time constraints, forming corresponding constraint conditions; Under the adjustable operating range and constraints, each core control device is sequentially generated with its allowed controllable actions, and the feasibility of each action under constraints is verified to obtain a self-healing operation that meets the constraints. The self-healing operations of each core control device are combined to generate a set of self-healing operations for self-healing analysis. By combining the self-healing operations that the core control equipment can perform under different states, alternative self-healing control strategies can be generated.

4. The active distribution network resource optimization method based on operational status awareness according to claim 1, characterized in that, For each candidate self-healing control strategy, the node state changes, self-healing voltage offset characteristics, self-healing current fluctuation characteristics, and equipment resource occupancy after triggering are obtained, and then organized into an equipment resource load change curve in chronological order, specifically including: Based on each alternative self-healing control strategy, the state changes of each node after its triggering are obtained sequentially and recorded as the first node state data. The node state changes include the voltage value, switching state and operating parameter changes of the node before and after the operation. Based on the first node state data, the self-healing voltage offset feature of each node within a continuous time window is obtained, and this feature is associated with the corresponding first node state data to obtain second node state data containing node state changes and self-healing voltage offset features. The connected lines corresponding to each node are determined by the node-line association state matrix before the self-healing operation. After implementing the alternative self-healing control strategy, the self-healing current fluctuation characteristics of each line within a continuous time window are obtained and correlated with the second node status data to form the third node status data. Based on the status data of the third node, the resource consumption of the core control equipment involved in the alternative self-healing control strategy is recorded during the execution process; Organize the status data of the third node and the resource occupancy of the core control equipment in chronological order to form preliminary equipment resource load change data; The initial load change data is formatted in a unified manner to obtain standardized equipment resource load change data. Based on standardized equipment resource load change data, the resource occupancy of each core control equipment within a continuous time window is extracted sequentially according to the alternative self-healing control strategy, and the resource occupancy changes of each core control equipment are sorted out in chronological order to form equipment resource load change curves.

5. The active distribution network resource optimization method based on operational status awareness according to claim 1, characterized in that, The process involves extracting the resource occupancy sequence and corresponding maximum and average resource occupancy values ​​of each core control device during the self-healing operation sequence execution from the equipment resource load change curve. The resource occupancy sequence is then corrected by threshold range constraints to obtain a constraint-corrected resource occupancy sequence. Subsequently, the resource occupancy change rate is obtained based on the constraint-corrected resource occupancy sequence, and combined with the maximum and average resource occupancy values ​​to obtain a resource occupancy fluctuation index. This is accumulated to obtain the cumulative resource consumption, thereby generating a resource consumption dataset corresponding to the self-healing operation. Specifically, this includes: Based on the equipment resource load change curve, identify the core control equipment that actually participates in the regulation during the execution of the alternative self-healing control strategy, and obtain a list of execution equipment. Based on the list of executing devices, extract the resource usage data of each core control device during the execution of the self-healing operation sequence to obtain the resource usage sequence of each device; Based on the resource occupancy sequence, the maximum and average resource occupancy values ​​of each core control device are obtained as statistical indicators of resource occupancy. Based on the maximum and average resource occupancy values ​​in the occupancy statistics, a resource occupancy threshold range is constructed, and the resource occupancy value at each time point in the resource occupancy sequence is compared with the resource occupancy threshold range to determine the resource occupancy status at each time point. When the resource occupancy value is within the resource occupancy threshold range, the original resource occupancy value is retained. When the resource occupancy value exceeds the resource occupancy threshold range, the resource occupancy value exceeding the upper threshold is adjusted to the upper limit of the threshold range, and the resource occupancy value below the lower threshold is adjusted to the lower limit of the threshold range, so as to achieve the limiting processing of the resource occupancy value and obtain the constrained and corrected resource occupancy sequence. The constrained resource occupancy sequence is divided into local time windows, and the resource occupancy fluctuation characteristics within each time window are extracted to characterize the intensity of resource occupancy changes. Based on the constrained and corrected resource occupancy sequence and resource occupancy fluctuation characteristics, the resource occupancy change rate of each core control device during the execution of the self-healing operation sequence is obtained; The maximum, average, and rate of change of resource usage are compiled to form a resource usage fluctuation index for each core control device. Based on the equipment resource load change curve corresponding to each alternative self-healing control strategy, the resource occupancy of each core control device during the execution of the self-healing operation sequence is accumulated to obtain the cumulative resource consumption of each core control device. By correlating resource usage fluctuation indicators with cumulative resource consumption, a resource consumption dataset corresponding to candidate self-healing operations is generated.

6. The active distribution network resource optimization method based on operational status awareness according to claim 1, characterized in that, Based on the resource consumption fluctuation index and cumulative resource consumption in the resource consumption dataset, the candidate self-healing control strategies are ranked. The ranking results are then filtered based on the constraints of the core control equipment to obtain an optimized self-healing operation sequence. Corresponding equipment resource optimization schemes are generated, and then self-healing operations are executed sequentially according to the equipment resource optimization schemes to optimize equipment resources in the active power distribution network. Specifically, this includes: Based on the resource consumption dataset, we analyze the resource consumption of each alternative self-healing regulation strategy. Based on resource consumption, the candidate self-healing control strategies are prioritized to obtain an operation order list and generate the corresponding operation sequence. By combining the rated operating parameters, adjustable operating range and corresponding constraints of each core control equipment, the feasibility of the sorted operation sequence is verified, operation combinations that do not meet the equipment constraints are eliminated, and feasible operation sequences are obtained. Adjust and optimize the feasible operation sequence to generate an optimized self-healing operation sequence. Based on the optimized self-healing operation sequence, a corresponding equipment resource optimization plan is generated, clarifying the execution order and parameter settings of each core control device in each operation; The equipment resource optimization scheme is input into the control system, and the self-healing operation is executed sequentially on each core control equipment in the active power distribution network to implement equipment resource regulation. During the self-healing operation, the status changes of each node and the resource occupancy of the core control equipment are monitored in real time, and dynamic adjustments are made for abnormal or excessive equipment operation. After completing all self-healing operations, record the final node status and resource usage of each device to generate the device resource optimization execution results.

7. An active distribution network resource optimization system based on operational status awareness, used to implement the resource optimization method as described in any one of claims 1-6, characterized in that, include: The operation status sensing module is used to acquire the voltage deviation characteristics of each node, the current fluctuation characteristics of each line, and the resource occupancy data of the core control equipment in the active distribution network, and generate a node-line status feature dataset. The topology state modeling module is used to construct a node-line association state matrix based on the node-line state feature dataset, forming a correspondence between the state features of nodes and connected lines. The core equipment identification module is used to filter core control equipment according to the correspondence between node and line status characteristics, obtain the rated operating parameters, historical operating boundaries and current operating status of each core control equipment, determine the adjustable operating range and constraints of each core control equipment, and generate alternative self-healing control strategies. The resource load analysis module is used to obtain the changes in resource occupancy and node status of core control equipment after the execution of the alternative self-healing control strategy, form the equipment resource load change curve, and perform standardized processing on it. The resource consumption assessment module is used to extract the maximum, average and change rate of resource occupancy of core control equipment based on the standardized equipment resource load change curve, accumulate and calculate the cumulative resource consumption, and generate a resource consumption dataset corresponding to the candidate self-healing operation. The optimization decision execution module is used to adjust and optimize the alternative self-healing control strategies based on the resource consumption dataset, generate an optimized self-healing operation sequence, and execute the operations in sequence to realize the dynamic optimization configuration of equipment resources, while recording the final node status and equipment resource usage.

8. The active distribution network resource optimization system based on operational status awareness according to claim 7, characterized in that, The topology state modeling module includes: A node status acquisition unit is used to acquire the voltage offset characteristics and operating status data of each node within a continuous time window. A line status acquisition unit is used to acquire the current fluctuation characteristics of each line within a continuous time window. A state association generation unit is used to pair the state features of a node with the state features of its directly connected lines to form a node-line state feature correspondence. An association matrix generation unit is used to construct a node-line association state matrix based on the correspondence between node-line state features.

9. The active distribution network resource optimization system based on operational status awareness according to claim 7, characterized in that, The core device identification module includes: The candidate node-line filtering unit is used to filter nodes and lines that meet preset anomaly judgment conditions based on the correspondence between node-line status features, forming a candidate node-line set. The core control equipment determination unit is used to identify controllable equipment associated with nodes and lines based on the candidate node-line set, thereby forming core control equipment. An adjustable operating range determination unit is used to obtain the rated operating parameters, historical operating boundaries and current operating status of each core control device, and to determine the adjustable operating range and constraints. A candidate operation generation unit is used to combine the self-healing operations that the core control equipment can execute under different operating states to generate alternative self-healing control strategies.

10. The active distribution network resource optimization system based on operational status awareness according to claim 7, characterized in that, The resource consumption assessment module includes: The resource indicator extraction unit is used to extract the maximum value, average value and rate of change of resource occupancy of each core control device based on the resource occupancy of each core control device during the execution of the alternative self-healing control strategy. A resource consumption calculation unit is used to cumulatively calculate the cumulative resource consumption of each core control device during the execution of alternative self-healing control strategies. The resource consumption dataset generation unit is used to associate the resource occupancy indicators of each core control device with the cumulative resource consumption to form a resource consumption dataset corresponding to the candidate self-healing operation.

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