A distribution network non-electric quantity abnormality identification method and system

Abstract

This invention relates to the field of distribution network monitoring technology, specifically to a method and system for identifying non-electrical quantity anomalies in distribution networks. In this invention, a finite state machine is introduced to traverse and determine adjacent state code pairs, checking each code migration order and dwell time against preset intervals, and examining for any missing intermediate state codes. At the state migration level, normal migration is distinguished from various violations such as jumps, reversals, and gaps, transforming anomaly identification from single-point state deviations to complete migration trajectory determination. The AC-3 algorithm is used to perform consistency checks on node value ranges. By repeatedly correcting the constraint relationships between upstream and downstream nodes, conflicting nodes whose value ranges are compressed to empty are quickly identified, and the source of constraints is traced back along the power supply path, providing a definite propagation basis for conflict location. Only objects that simultaneously meet the conditions for state migration anomalies and power supply level conflicts are retained, narrowing the scope of associated nodes and relationships.

Description

Technical Field

[0001] This invention relates to the field of distribution network monitoring technology, and in particular to a method and system for identifying non-electrical quantity anomalies in distribution networks. Background Technology

[0002] The field of distribution network monitoring technology aims to systematically determine multiple operating and business states during the operation of the distribution network, thereby enabling continuous monitoring of the distribution network's operating status and timely identification of abnormal situations. The goal is to ensure the safe operation of the distribution network and improve the controllability of operation management. The focus is on judging the operating status of lines, transformer substations, metering units, and related business objects in the distribution network, establishing logical constraint relationships between states, and discovering abnormal situations that do not conform to preset operating rules.

[0003] The purpose of a method for identifying non-electrical quantity anomalies in distribution networks is to determine the operating states of multiple non-electrical quantities during the operation of the distribution network, identify abnormal situations that do not conform to preset operating rules and logical constraints, and thus discover distribution network operation anomalies without relying on electricity metering deviations. By constructing state correlation relationships between operating objects, abnormal states are clearly identified, improving the completeness and accuracy of distribution network operation anomaly identification, and providing a basis for judgment for subsequent operation and maintenance and operation management.

[0004] Existing methods for identifying non-electrical quantity anomalies in distribution networks largely rely on static comparisons of operating states based on rule-based conditions. The judgment process typically revolves around whether a single state meets preset rules, lacking formal constraints on the transition order and duration between states. Furthermore, state records are often discretely distributed, and the same object may undergo multiple state transitions within a short period. Current technologies do not model the state transition process, making it difficult to effectively identify phenomena such as skipping intermediate states and short-term reverse order. Anomalies are only exposed at the result level, lacking process evidence. Although existing technologies focus on the relationships between objects, they mostly remain at the level of logical associations and empirical rules, failing to incorporate power supply direction and hierarchical constraints into a unified judgment process. The impact of upstream anomalies on downstream objects lacks a systematic propagation mechanism, easily leading to situations where upstream power supply is lost while downstream is still judged as normal. Anomaly identification results are mostly output in the form of object lists, without deep alignment with business states such as maintenance, power outage / restoration, and freeze / change, requiring maintenance personnel to review across systems, increasing processing delays. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method and system for identifying non-electrical quantity anomalies in distribution networks.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for identifying non-electrical quantity anomalies in a distribution network, comprising the following steps: S1: Focusing on the operating objects of lines, transformer areas, and metering units in the distribution network monitoring, read the object number, status code, occurrence time, verify the time sequence, mark the status duration interval, write the allowed pre-sequence and allowed post-sequence coding relationship, form the object status mapping, and obtain the status relationship table. S2: Based on the state relationship table, a finite state machine is used to extract adjacent state code pairs, check the code order, compare the dwell time intervals, check the occurrence of intermediate states, mark jump records, reverse order records, and gap records, collect abnormal trajectories according to object number, and generate a set of violation trajectories. S3: Based on the set of violation trajectories, combined with the power supply direction of the feeder and the on / off status of the switch, the upstream non-conducting state is transmitted to the downstream node, the range of power supply values ​​that can be obtained for the downstream node is compressed, the node transmission order is recorded, and the constraint trajectory chain is obtained. S4: Based on the constrained trajectory chain, the AC-3 algorithm is used to trace the power supply path in reverse for nodes with empty value ranges, align the corresponding time records in the set of violations, filter out objects that simultaneously have abnormal states and hierarchical conflicts, compress the associated nodes and relationship ranges, and form a conflict subset. S5: Based on the conflict subset, read the status code of the conflict node, determine the category of power supply connection, maintenance stage, power outage / restoration mark, and freeze / change, extract the object number and target status code, and form a disposal list.

[0007] As a further embodiment of the present invention, the state relationship table includes a state code set, a preceding state code set, and a following state code set; the violation trajectory set includes a state transition trajectory, a state reversal trajectory, and a state missing trajectory; the constraint trajectory chain includes a constraint source node, a constraint target node, and a constraint transmission path; the conflict subset includes a conflict node set and a conflict association set; and the handling list includes an abnormal object number, a current state code, and a target state code.

[0008] As a further aspect of the present invention, the specific steps for generating the state relationship table are as follows: Focusing on the operating objects of lines, transformer substations, and metering units in the distribution network monitoring, the object number, status code, and occurrence time are read, grouped by object number, and the timestamps within the same group are sorted sequentially. Records with reversed time sequence are removed, and the status at the same time point is retained and marked to form a status record set. Based on the state record set, the time difference between adjacent states is calculated one by one, the state duration interval is marked, and a correspondence is established between each state and its previous state code. At the same time, the allowed preorder and allowed postorder code sets are written to generate an object state mapping and obtain a state relationship table.

[0009] As a further aspect of the present invention, the specific steps for generating the set of violation trajectories are as follows: Based on the state relationship table, a finite state machine is used to extract continuous state records according to the object number, read the previous state code and the next state code in sequence, and read the corresponding occurrence time and duration interval identifier at the same time. For each pair of adjacent states, a pair record is established, and the state code order position and time difference are retained to generate a state reference set. Based on the state comparison set, the sequential position of the previous and subsequent state codes in the relation table is checked one by one, the state duration is compared with the upper and lower limits of the allowed dwell interval, and the necessary states are checked to see if they occur within the time range. Records that do not conform to the order, duration, or passing requirements are written into the tag field to generate an abnormal tag set. Based on the anomaly marker set, the anomaly records are merged according to the object number, and the anomaly markers in adjacent time periods are continuously spliced ​​together. The start time, end time and anomaly type combination are recorded to form a complete state anomaly trajectory and generate a violation trajectory set.

[0010] As a further aspect of the present invention, the finite state machine first uses the object number as the instance identifier of the finite state machine, loads the corresponding state set and state transition relationship for each object, initializes the current state as the state code corresponding to the earliest timestamp record, and reads subsequent state records in chronological order. When reading the next state record, the read state code is used as a candidate target state and matched and verified with the current state in the state transition relationship. At the same time, the occurrence time corresponding to the two state records is read and the time difference is calculated. The calculated time difference is compared and verified with the allowable transition time interval of the current state. When the candidate target state satisfies the transition relationship constraint, the state switch is completed, and the state code order position and time difference are recorded synchronously. Then, the candidate target state is updated to the current state and the traversal process continues until the object state records are read, forming a state comparison set composed of adjacent state code pairs and corresponding time differences.

[0011] As a further aspect of the present invention, the specific steps for generating the constraint trajectory chain are as follows: Based on the set of violation trajectories, the associated lines and transformer area objects are extracted according to the feeder number, the power supply direction identifier and the switch on / off status are read, the disconnection identifier is mapped to the non-conducting mark, and the nodes are arranged in the order of power supply direction to form a transmission sequence set; Based on the transmission order set, non-conductive markers are applied to downstream nodes step by step. For each node, the power supply range is processed by retention and rejection. The node receiving order and source node are recorded simultaneously to form a continuous association record and obtain a constraint trajectory chain.

[0012] As a further aspect of the present invention, the specific steps for generating the conflict subset are as follows: Based on the constrained trajectory chain, the AC-3 algorithm is used to filter nodes whose power supply reach range is empty, read the node number, upstream node number and connection sequence identifier, search for related nodes level by level according to the power supply direction, record each level of backtracking node and corresponding transmission order, and form a backtracking path set. Based on the backtracking path set, the node numbers in each path are read one by one, and the time overlap of the node occurrence time segment with the abnormal time segment of the corresponding object in the violation trajectory set is compared. The nodes and trajectory records with overlapping relationships are retained, and the time mismatched entries are removed to generate a conflict candidate set. Based on the conflict candidate set, object numbers that simultaneously possess both state anomaly markers and power supply level conflict markers are selected, nodes with only one-sided anomalies are deleted, node combinations with direct connection relationships are merged, and the number of nodes and the range of associated paths are compressed to form a conflict subset.

[0013] As a further aspect of the present invention, the AC-3 algorithm first constructs a set of directed arcs by connecting each node in the constraint trajectory chain with its upstream nodes, and configures a power supply reachable value range for each node. The queue of arcs to be checked is initialized with all arc relationships in the constraint trajectory chain. Then, an arc is sequentially retrieved from the queue, and a consistency check is performed on the power supply reachable value range of the arc's starting node. The algorithm verifies whether the current value of the starting node satisfies the constraint relationship with an upstream node value. When the starting node has a value that does not satisfy the constraint relationship, the corresponding value is deleted from its value range. When a value deletion occurs, the upstream arc associated with the starting node is re-added to the queue of arcs to be checked. The arc checking and value deletion operations are continuously performed until the queue of arcs to be checked is empty and a node's power supply reachable value range is empty. Simultaneously, during the value deletion process, the source node number, target node number, and transmission order number of the constraint transmission are recorded. Finally, a set of nodes with empty power supply reachable value ranges and corresponding transmission order records are obtained, and a backtracking path set is formed accordingly.

[0014] As a further aspect of the present invention, the specific steps for generating the disposal list are as follows: Based on the aforementioned abnormal conflict association subset, the conflict node object number and current status code are read one by one, and the status code is mapped to four types of identifier intervals: power supply connection, maintenance stage, power outage / restoration mark, and freeze / change. The object number is used to complete the classification and collection, forming an abnormal classification set. Based on the aforementioned anomaly classification set, the target status code corresponding to each object is extracted. The object number, current status code, and target status code are combined and organized, duplicate entries are removed, and the results are summarized in order of object number to form a disposal list.

[0015] A distribution network non-electrical quantity anomaly identification system is provided, the system being used to execute the above-described distribution network non-electrical quantity anomaly identification method, the system comprising: State modeling module: Focusing on the operating objects of lines, transformer substations, and metering units in distribution network monitoring, it reads the object number, state code, occurrence time, verifies the time sequence, marks the state duration interval, writes the allowed preceding and allowed following sequence code relationship, forms the object state mapping, and obtains the state relationship table; State evolution verification module: Based on the state relationship table, using a finite state machine, it extracts adjacent state code pairs, checks the code order, compares the dwell time intervals, checks the occurrence of intermediate states, marks jump records, reverse order records, and gap records, collects abnormal trajectories by object number, and generates a set of violation trajectories; Power supply constraint transmission module: Based on the set of violation trajectories, combined with the power supply direction of the feeder and the switch on / off indicators, the upstream non-conducting state is transmitted to the downstream node, the range of power supply values ​​that can be obtained for the downstream node is compressed, the node transmission order is recorded, and the constraint trajectory chain is obtained. Conflict backtracking and localization module: Based on the constrained trajectory chain, the AC-3 algorithm is used to trace the power supply path in reverse for nodes with empty value ranges, align the corresponding time records in the set of violation trajectories, filter objects that simultaneously have abnormal states and hierarchical conflicts, compress the associated nodes and relationship ranges, and form a conflict subset; Anomaly handling generation module: Based on the aforementioned conflict subset, read the status codes of conflict nodes, determine their category (power supply connection, maintenance phase, power outage / restoration marker, freeze / change category), extract the object number and target status code, and generate a handling list.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, the object number, status code and occurrence time are read around the operating objects such as lines, transformer areas and metering units. Through time sequence verification and status duration interval labeling, the discrete state records are organized into time sequence segments with clear start and end boundaries. On this basis, the relationship between allowed preceding codes and allowed following codes is written, so that the object state evolution path is formalized into a set of decidable state transitions. In this invention, a finite state machine is introduced to traverse and determine adjacent state code pairs, check whether the code migration order and dwell time fall within the preset range, and check whether there are any missing intermediate state codes. At the state migration level, normal migration is distinguished from multiple violations such as jumps, reversals, and gaps, so that the anomaly identification is transformed from single-point state deviation to complete migration trajectory determination. In this invention, the AC-3 algorithm is used to perform consistency verification on the value range of nodes. By repeatedly correcting the constraint relationship between upstream and downstream nodes, conflicting nodes whose value range is compressed to empty are quickly identified, and the source of constraints is traced back along the power supply path, so that the conflict location has a definite propagation basis. The conflicting nodes are aligned with the corresponding time records in the violation trajectory set, and only objects that simultaneously meet the conditions of state transition anomaly and power supply level conflict are retained, thus shrinking the scope of associated nodes and relationships. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the workflow of the present invention; Figure 2 This is a system flowchart of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] Example 1 Please see Figure 1 This invention provides a technical solution: a method for identifying non-electrical quantity anomalies in a distribution network, comprising the following steps: S1: Focusing on the operating objects of lines, transformer areas, and metering units in the distribution network monitoring, read the object number, status code, occurrence time, verify the time sequence, mark the status duration interval, write the allowed pre-sequence and allowed post-sequence coding relationship, form the object status mapping, and obtain the status relationship table. S2: Based on the state relationship table, a finite state machine is used to extract adjacent state code pairs, check the code order, compare the dwell time intervals, check the occurrence of intermediate states, mark jump records, reverse records, and gap records, collect abnormal trajectories by object number, and generate a set of violation trajectories. S3: Based on the set of violation trajectories, combined with the power supply direction of the feeder and the on / off status of the switch, the upstream non-conducting state is transmitted to the downstream node, the range of power supply values ​​that can be obtained for the downstream node is compressed, the node transmission order is recorded, and the constraint trajectory chain is obtained. S4: Based on the constraint trajectory chain, the AC-3 algorithm is used to trace the power supply path in reverse for nodes with empty value ranges, align the corresponding time records in the violation trajectory set, filter objects that simultaneously have abnormal states and hierarchical conflicts, compress the range of associated nodes and relationships, and form a conflict subset. S5: Based on the conflict subset, read the status code of the conflict node, determine the category of power supply connection, maintenance stage, power outage / restoration mark, and freeze / change, extract the object number and target status code, and form a disposal list.

[0020] The state relationship table includes a set of state codes, a set of preceding state codes, and a set of following state codes. The violation trajectory set includes state transition trajectories, state reversal trajectories, and state missing trajectories. The constraint trajectory chain includes constraint source nodes, constraint target nodes, and constraint propagation paths. The conflict subset includes a set of conflict nodes and a set of conflict associations. The handling list includes the abnormal object number, the current state code, and the target state code.

[0021] The specific steps for generating the state relationship table are as follows: Focusing on the operating objects of lines, transformer substations, and metering units in the distribution network monitoring, the object number, status code, and occurrence time are read, grouped by object number, and the timestamps within the same group are sorted sequentially. Records with reversed time sequence are removed, and the status at the same time point is retained and marked to form a status record set. Based on the state record set, calculate the time difference between adjacent states one by one, mark the state duration interval, establish a correspondence between each state and its previous state code, and write it into the allowed preorder and allowed postorder code set to generate the object state mapping and obtain the state relationship table. Focusing on the operational objects of lines, transformer substations, and metering units in distribution network monitoring, this study employs a timestamp-based stable sorting method to group the object number field based on the original object status record table. For the same object number, the status codes and occurrence times are arranged in ascending order of timestamp values. During the sorting process, the occurrence times are uniformly converted to millisecond-level integer timestamps for comparison. In the sorting traversal phase, the timestamp of the current record is compared with that of the previous record. When the current timestamp is less than the previous timestamp, the corresponding record is added to the reverse record set and removed from the main sequence. When the current timestamp equals the previous timestamp, the corresponding status code is written to the same time point marker field and the record is retained. After sorting, a monotonically increasing sequence of status records is formed. Subsequently, based on this sequence, adjacent records are used for further sorting. The recording time difference calculation method scans adjacent state records one by one. By performing a numerical subtraction operation between the current record's time identifier and the previous record's time identifier, the state duration value is obtained and written into the state duration interval field. At the same time, the interval start time and interval end time are written for each state record. The interval start time is taken from the current record's time identifier, and the interval end time is taken from the next record's time identifier and the upper limit value of the object's end time. Then, a key-value mapping construction method is used with the state code as the unique key. The previous state code is written into the allowed preceding code set, and the next state code is written into the allowed following code set. Both the allowed preceding code set and the allowed following code set are stored in the form of integer arrays. The array elements are written one by one without merging or simplification. Finally, the object state mapping is generated and the state relationship table is output. Based on the state relation table, a finite state machine method is used to sequentially traverse the state sequence of each object. In the finite state machine, the complete set of state codes is used as the state set, and the allowed preorder code set and allowed postorder code set are used as state transition constraints. During the traversal, adjacent state code records are read one by one. A set inclusion check is performed to determine whether the current state code exists in the allowed postorder code set of the previous state. If the inclusion relationship is not satisfied, a transition flag is written and an exception type value is recorded. The timestamp of the current state record is compared with the timestamp of the previous state record. If a time reversal occurs, a reversal flag is written and an exception type value is recorded. At the same time, the state persistence region is also monitored. The interval value is compared with the preset dwell time upper and lower limits. When the value exceeds the upper and lower limits, a timeout flag is written. Then, according to the predefined intermediate necessary state code set in the finite state machine, the necessary state is compared item by item between adjacent state codes. When any necessary state code does not appear in the corresponding time interval, a gap flag is written and the abnormality type value is recorded. After traversal, all abnormal flag records under the same object number are collected in chronological order. The sequential list aggregation method is used to write the state code, occurrence time, and abnormality type item by item into the trajectory list. The list elements maintain the original chronological order without reordering. Finally, the set of violation trajectories is output.

[0022] The specific steps for generating the set of violation trajectories are as follows: Based on the state relationship table, a finite state machine is used to extract continuous state records according to the object number. The previous state code and the next state code are read in sequence, and the corresponding occurrence time and duration interval identifier are read at the same time. A pair of records is established for each pair of adjacent states, and the state code order position and time difference are retained to generate a state reference set. Based on the state comparison set, the sequential position of the previous and subsequent state codes in the relation table is checked one by one, the state duration is compared with the upper and lower limits of the allowed dwell interval, and the necessary states are checked to see if they appear within the time range. Records that do not conform to the order, duration, or dwell requirements are written into the tag field to generate an abnormal tag set. Based on the anomaly marker set, the anomaly records are merged according to the object number, and the anomaly markers in adjacent time periods are continuously spliced ​​together. The start time, end time and anomaly type combination are recorded to form a complete state anomaly trajectory and generate a violation trajectory set. Based on the state relationship table, a finite state machine method is used to perform group traversal processing on the object number field, reading the state record sequence arranged in chronological order under the same object number one by one. During the traversal, the current state of the state machine is initialized as the first state code, and the subsequent state codes are introduced into the processing flow as candidate transition states in turn. At the same time, the occurrence time identifiers corresponding to the previous state and the next state are read synchronously, and the state duration interval identifiers written to the previous state are read. A unique sequential index value is assigned to each pair of adjacent states inside the finite state machine. The index value is incremented in the order of reading and written to the sequential position field. In the same processing flow, the time identifiers of two adjacent state records are calculated, and the obtained time difference value is written to the time difference field. Then, the previous state code and the next state code are combined in order and written to the paired record structure. The paired record contains the object number, the previous state code, the next state code, the sequential position identifier, the time difference identifier, and the duration interval identifier. Multiple paired records are written to the set structure in the original order, and finally a state reference set is generated. Based on the state reference set, a sequential consistency check method is used. The preceding and following state codes in each pair of records are read one by one, and the corresponding state code entries are located in the state relation table. The sequential position identifiers in the paired records are compared with the sequential index values ​​in the relation table, using the allowed preceding and following sequence indexes. When the sequential position does not meet the relation table constraints, the paired records are written to the sequence exception flag field. Simultaneously, the persistence interval identifiers in the paired records are read, and the lower and upper limits of the allowed dwell interval for the corresponding state code are retrieved from the state relation table. The system performs numerical comparisons item by item. When the value of the continuous interval identifier is less than the lower limit or greater than the upper limit, the paired records are written to the duration anomaly flag field. Then, the system reads the list of necessary state codes from the state relationship table and scans all state code records under the associated object number in the state comparison set within the time range corresponding to the paired records. The system checks whether the necessary state codes have appeared item by item. When any necessary state code has not been scanned, the corresponding paired records are written to the passage anomaly flag field. Finally, the records containing the sequence anomaly flag, duration anomaly flag, and passage anomaly flag are summarized to generate an anomaly flag set. Based on the anomaly marker set, a time adjacency merging method is adopted. Anomaly marker records are grouped by object number. Within the same object number group, anomaly marker records are traversed sequentially according to their occurrence time. Adjacency judgment is performed on the occurrence times of adjacent anomaly records. When the occurrence time of the next anomaly record does not exceed the end time threshold of the previous anomaly record, the two anomaly records are merged into the same anomaly segment, and an append operation is performed on the anomaly type field. When the occurrence time of the next anomaly record exceeds the end time threshold of the previous anomaly record, the current anomaly segment is closed and a new anomaly segment is opened. The start time identifier and end time identifier are recorded in each anomaly segment, and the corresponding type identifiers of sequential anomaly, duration anomaly, and passage anomaly are combined and written. The combination method adopts a string encoding form concatenated in the order of occurrence. All anomaly segments are sequentially written into the trajectory set structure, and finally a set of violation trajectories is generated.

[0023] The finite state machine first uses the object number as the instance identifier. For each object, a corresponding set of states and state transition relationships are loaded. The current state is initialized as the state code corresponding to the earliest timestamp. Subsequent state records are read sequentially according to time order. When reading the next state record, the read state code is used as a candidate target state and matched and verified with the current state in the state transition relationship. At the same time, the occurrence time of the two state records is read and the time difference is calculated. The calculated time difference is compared and verified with the allowable transition time interval of the current state. When the candidate target state meets the transition relationship constraints, the state switch is completed, and the state code order position and time difference are recorded synchronously. Then, the candidate target state is updated to the current state and the traversal process continues until the object state records are read, forming a state comparison set composed of adjacent state code pairs and corresponding time differences. Finite state machines, according to the formula:

[0024] in: Indicates the first The result of the access determination for whether a load is permitted to connect to the busbar power supply. This represents the unit step function used to map continuous decision variables to discrete admission states. This represents the available power margin of the busbar calculated based on the busbar power boundary value. This represents the power reserve factor used to reserve a safety power band within the releasable power. Indicates the relationship with the first Startup impact factor associated with each load type, Indicates the first The startup power required by each load during the startup phase. This represents the bus power protection margin used to compensate for bus power measurement errors and prevent power overruns. This indicates the cumulative power occupancy of the busbar caused by multiple registered but not yet started loads. This indicates the scheduling scan time when the admission control algorithm is executed. Indicates the first The access time identifier recorded when each load initiates an access request. Represented as the first Minimum access wait time for each load configuration This represents the first value calculated based on the service level starvation state and combined with power coupling risk. Each load admission weight score This represents the minimum admission weight threshold used to limit loads from entering the busbar power supply registration set; Execution process: The formula execution process is as follows: at the system scheduling scan time, the bus power boundary value is read and the bus release power margin is calculated. And based on the configured power reserve factor The releasable power is deducted to form a safe available power range, and the cumulative power occupancy of registered but incomplete start-up loads is read. And read the bus power protection margin. Subsequently, regarding the first Each load reads the startup power requirement. And combined with the starting impact coefficient corresponding to the load Calculate the equivalent starting peak power, compare the deducted bus available power with the equivalent starting peak power, protection margin, and occupied power, and complete the power access determination using a unit step function, while simultaneously reading the scheduling scan time. and load access time identifier And combined with minimum access waiting time The time-based admission decision is completed, followed by the calculation of the load admission weight score based on the load service level's starvation status and power coupling risk. and weight threshold After comparing and determining the weighted admission criteria, the power admission criteria result, the time admission criteria result, and the weighted admission criteria result are logically multiplied to generate the load admission flag. and with Based on the criteria, loads that meet the conditions are registered as candidate loads for busbar power supply access.

[0025] The specific steps for generating the constrained trajectory chain are as follows: Based on the set of violation trajectories, the associated lines and transformer area objects are extracted by feeder number, the power supply direction identifier and switch on / off status are read, the disconnection identifier is mapped to a non-conducting mark, and the nodes are arranged in the order of power supply direction to form a transmission sequence set; Based on the transmission order set, the non-conductive marker is applied to the downstream nodes step by step. For each node, the power supply range is processed by retention and rejection. The node receiving order and source node are recorded simultaneously to form a continuous associated record and obtain the constraint trajectory chain. Based on the set of violation trajectories, a topology traversal method based on a directed graph is adopted. For each record in the trajectory set, the feeder number field is read, and the line object and transformer area object with the same feeder number are retrieved in the feeder topology table. The retrieved object number is written into the list of nodes to be processed. For each node, the power supply direction identifier field is read and converted into a fixed enumeration value, where the upstream direction is mapped to the integer zero and the downstream direction is mapped to the integer one. At the same time, the switch open / closed status field is read and the open state is written to the non-conducting mark value one according to the predefined mapping table, and the closed state is written to the non-conducting mark value zero. Then, a sorting method based on the power supply direction is adopted to sort the node list in ascending order according to the power supply direction enumeration value. During the sorting process, the node sequence index field is written, and the node sequence index is incremented from the beginning. At the same time, the node number, power supply direction enumeration value, non-conducting mark value, and node sequence index are written into the sequence record structure. All sequence records are written into the set structure in order according to the sorting result, and finally the transmission sequence set is generated. Based on the transmission order set, a step-by-step constraint propagation method is adopted. Starting from the node with the smallest sequence index, the system traverses and reads the non-conductive flag value of the current node. When the non-conductive flag value is one, the flag is written to the power supply reachable value range field of the current node, and the current node number is written to the source node field. Then, the downstream nodes with sequence indices greater than the current node's sequence index and consistent power supply direction are located. For each downstream node, the original power supply reachable value range list is read. The list is stored in the form of an integer array. All elements in the array that represent conductable values ​​are scanned and deleted one by one, retaining only the non-conductive values. The receiving sequence index and the source node number are written to the downstream node record. When the non-conductive flag value is zero, only the node traversal sequence index is written without modifying the downstream node's value range. During the traversal, a node processing record is generated for each node. The record content includes the node number, receiving sequence index, source node number, and the current power supply reachable value range array. All node processing records are continuously written into a chain structure in traversal order, ultimately forming a constraint trajectory chain.

[0026] The specific steps for generating conflict subsets are as follows: Based on the constrained trajectory chain, the AC-3 algorithm is used to filter nodes whose power supply reach range is empty, read the node number, upstream node number and connection sequence identifier, and search for related nodes level by level according to the power supply direction. Each level of backtracking node and its corresponding transmission order are recorded to form a backtracking path set. Based on the backtracking path set, the node numbers in each path are read one by one. The time intervals in which the nodes occurred are compared with the abnormal time intervals of the corresponding objects in the violation trajectory set. The nodes and trajectory records with overlapping relationships are retained, and the time mismatched items are removed to generate a conflict candidate set. Based on the conflict candidate set, the object numbers that simultaneously have both state abnormality markers and power supply level conflict markers are filtered out, nodes with only one-sided abnormalities are deleted, node combinations with direct connection relationships are merged, and the number of nodes and the range of associated paths are compressed to form a conflict subset. Based on the constrained trajectory chain, the AC-3 algorithm is adopted. Each node in the trajectory chain is treated as a variable node, the reachable power supply range of the node is treated as a variable domain, and the power supply connection relationship between nodes is treated as a binary constraint set. During constraint queue initialization, each pair of nodes with upstream and downstream connections in the trajectory chain is read sequentially, and the node numbers of each pair are written into the constraint queue in connection order. During algorithm execution, a set of node pairs is retrieved from the head of the queue. The reachable power supply range arrays of the upstream and downstream nodes are read. Each value in the upstream node array is scanned item by item, and the downstream node array is searched for a value that satisfies the power supply connection constraint. If a certain upstream value does not have any value in the downstream node array that satisfies the connection constraint, the upstream value is removed. The value is deleted from the upstream node array, and the deletion operation record is written to the node constraint revision log. When the upstream node array changes, all node pairs that are connected to the upstream node are rewritten to the constraint queue. The above process is repeated until the constraint queue is empty. After completion, the power supply reachable value range array of all nodes is checked, and nodes with a value range array length of zero are filtered. The node number field, upstream node number field, and connection order identifier field are read one by one. The connection order-based reverse traversal method is used to search for the associated upstream node in reverse order according to the power supply direction. During each level of backtracking, the current node number, source node number, and transmission order identifier are recorded. All backtracking records are written to the path record structure in the search order, and finally a backtracking path set is generated. Based on the backtracking path set, a time segment overlap comparison method is adopted. The node number field in each path record is read one by one, and the start and end time identifiers of the corresponding node occurrence time are located in the node status time table. At the same time, records with the same object number are searched in the violation trajectory set, and the start and end time identifiers of the abnormal time are read. The two sets of time segments are cross-judged item by item. When the node time start identifier is less than the abnormal time end identifier and the node time end identifier is greater than the abnormal time start identifier, the node number and the corresponding trajectory number are written into the retention list. When the cross-judgment condition is not met, the corresponding record is written into the elimination list and removed from the candidate processing. After all path records have been compared, the node records with time overlap relationship and the corresponding violation trajectory records are written into the set structure in a one-to-one relationship, and finally a conflict candidate set is generated. Based on the conflict candidate set, a multi-condition intersection filtering method is adopted. The object number records in the candidate set are read one by one, and the object status abnormality flag field and the power supply level conflict flag field are read simultaneously. The object number is written into the retention set only when both types of flag fields have valid identifier values. When only a single flag field exists, the corresponding object number is deleted. Then, the object number in the retention set is processed by the connection relationship merging method. The direct connection relationship records between objects are read one by one. The object numbers with direct connection relationships are combined and merged. The combined object number is written into the same node group identifier field, and duplicate node number records are deleted. Only one combined record is retained for each node group. At the same time, the node connection node records that do not participate in the combined connection are deleted. After all the combined processing is completed, the node group records and associated path records are written into the compressed result structure, and finally the conflict subset is formed.

[0027] The AC-3 algorithm first constructs a set of directed arcs by connecting each node in the constraint trajectory chain with its upstream nodes, and assigns a power reachable value range to each node. It initializes the arc queue to be checked with all arc relationships in the constraint trajectory chain. Then, it sequentially takes an arc from the queue and performs a consistency check on the power reachable value range of the arc's starting node. It verifies one by one whether the current value of the starting node has an upstream node value that satisfies the constraint relationship. When the starting node has a value that does not satisfy the constraint relationship, it deletes the corresponding value from the value range. When a value deletion occurs, the upstream arc associated with the starting node is added back to the queue to be checked. The arc checking and value deletion operations are continuously performed until the arc queue to be checked is empty and a node's power reachable value range is empty. At the same time, during the value deletion process, the source node number, target node number, and transmission order number of the constraint transmission are recorded. Finally, the set of nodes with empty power reachable value ranges and the corresponding transmission order records are obtained, and a backtracking path set is formed accordingly. The AC-3 algorithm, according to the formula:

[0028] in: Indicates the first The state identifier of the next stage after a load completes one stage determination in the power stage finite state machine. Indicates the first The power stage status identifier of each load within the current scheduling period. This represents the step decision function used for stage determination, which outputs 1 when the decision condition is met and 0 when it is not met. This indicates the current calculation moment when the system performs evaluation and judgment on the load phase state. Indicates the first The load enters the current power stage. The time record enters the time marker. Indicates the first The load is in the power phase The corresponding standard stage duration parameter, Indicates the first The stage elasticity adjustment coefficient of each load, Indicates the first The load is in the power phase Permissible energy and power cap limits, Indicates the first The actual energy consumption and equivalent power consumption of each load at the current evaluation moment. Indicates the bus branch to which the load belongs. Configured branch protection margin constraints, Indicates the first The load is in the power phase The upper limit of the permissible rate of change of energy and power. Indicates the first The energy and power usage recorded by each load during the previous evaluation period. Indicates the time interval between two adjacent stage evaluation times. Indicates the first The overall weight score for phase switching is calculated for each load in the current phase. This represents the minimum stage switching weight threshold used to limit the load from entering the next power stage; Execution process: When performing unified scheduling evaluation on the load power phase, for the access phase... Each load reads its current power stage status flag. And read the corresponding entry time identifier. Compared with standard stage duration parameters Simultaneously read the stage elastic adjustment coefficient corresponding to the load. The duration of each stage is then scaled to create an actual stage determination timescale, which is then applied at the evaluation time. Calculate the time decision quantity and use the step decision function. The generation phase time satisfies the flag, and based on this, the permitted upper limit constraint value corresponding to the current power phase is read. And read the actual energy and equivalent power utilization of the load at the current evaluation moment. Simultaneously read the protection margin constraint of the bus branch to which the load belongs. It then completes the stage power and energy margin determination and generates a margin satisfaction flag, and subsequently reads the load's energy and power occupancy in the previous evaluation cycle. And combined with the time interval between adjacent evaluation times Calculate the actual rate of change and compare it with the upper limit of the allowed rate of change for the stage. A comparison is made to generate a rate-of-change indicator, and then the phase switching comprehensive weight score of the load in the current phase is calculated. and switch weight threshold with minimum stage A comparison is made to generate a weighted satisfaction flag. Finally, the time satisfaction flag, power margin satisfaction flag, rate of change satisfaction flag, and weighted satisfaction flag are logically multiplied together and used as the stage switching trigger condition. When the trigger condition is met, the current power stage state is marked. Update to the status indicator for the next power stage. The corresponding power limit parameter, allowable change rate parameter, and stage duration parameter are loaded using the updated stage status identifier as an index, and then aggregated by load identifier to form stage parameter records and summarize to establish a stage parameter set.

[0029] The specific steps for generating the disposal list are as follows: Based on the abnormal conflict association subset, the conflict node object number and current status code are read one by one, and the status code is mapped to four types of identifier ranges: power supply connection, maintenance stage, power outage / restoration mark, and freeze / change. The object number is used to complete the classification and collection to form an abnormal classification set. Based on the anomaly classification set, the target status code corresponding to each object is extracted. The object number, current status code and target status code are combined and sorted, duplicate entries are removed, and the results are summarized in order of object number to form a disposal list. Based on the abnormal conflict association subset, an interval determination method based on a rule mapping table is adopted. The conflict node object number field and the current status code field of each record in the subset are read one by one, and an equal value matching query is performed in the pre-constructed status code mapping table. The mapping table uses the status code as the unique index key and pre-sets four types of enumeration fields for each status code: power supply connection identifier interval value, maintenance stage identifier interval value, power outage and restoration marker interval value, and freeze change marker interval value. Each enumeration field takes an integer code value. During the query process, the current status code is compared with the index key of the mapping table item by item. When a match is successful, the corresponding four types of identifier interval codes are read in sequence and written into the classification field of the current record. Then, using the object number as the grouping key, all records written into the classification field are grouped and aggregated. During the aggregation process, multiple records under the same object number are written into the classification list structure according to the identifier interval code order. The classification list records the object number, status code, and four types of identifier interval codes, and the original reading order is maintained without reordering. After all object numbers are aggregated, the aggregation result is written into the set structure, and finally an abnormal classification set is generated. Based on the anomaly classification set, a target code matching method based on the state transition rule table is adopted. Each record corresponding to the object number in the classification set is read one by one, and the current state code field is read. An exact match query is performed in the state transition rule table using the current state code as the search key. A unique target state code value is pre-set for each state code in the state transition rule table. After a successful match, the target state code is written to the target field. Then, the object number, current state code, and target state code are combined and written into a combined record structure. The combined record structure is scanned one by one. When a duplicate record is detected where the object number, current state code, and target state code are completely identical, a deletion operation is performed, retaining only the first occurrence. After deduplication, a sequential aggregation method based on the object number is used to arrange the combined records in ascending order of object number value. The arrangement results are then written into a list structure. Each record in the list contains three fields: object number, current state code, and target state code, ultimately forming a disposal list.

[0030] Please see Figure 2 A distribution network non-electrical quantity anomaly identification system is provided. This system is used to execute the aforementioned distribution network non-electrical quantity anomaly identification method. The system includes: State modeling module: Focusing on the operating objects of lines, transformer substations, and metering units in distribution network monitoring, it reads the object number, state code, occurrence time, verifies the time sequence, marks the state duration interval, writes the allowed preceding and allowed following sequence code relationship, forms the object state mapping, and obtains the state relationship table; State evolution verification module: Based on the state relationship table, using a finite state machine, it extracts adjacent state code pairs, checks the code order, compares the dwell time intervals, checks the occurrence of intermediate states, marks jump records, reverse order records, and gap records, collects abnormal trajectories by object number, and generates a set of violation trajectories; Power supply constraint transmission module: Based on the set of violation trajectories, combined with the power supply direction of the feeder and the on / off status of the switch, the upstream non-conducting state is transmitted to the downstream node, the range of power supply values ​​that can be obtained for the downstream node is compressed, the node transmission order is recorded, and the constraint trajectory chain is obtained. Conflict backtracking and localization module: Based on the constraint trajectory chain, the AC-3 algorithm is used to trace the power supply path in reverse for nodes with empty value ranges, align the corresponding time records in the violation trajectory set, filter objects that simultaneously have abnormal status and hierarchical conflicts, compress the range of associated nodes and relationships, and form a conflict subset; Anomaly handling generation module: Based on the conflict subset, read the status code of the conflict node, determine the category of power supply connection, maintenance stage, power outage / restoration mark, and freeze / change, extract the object number and target status code, and form a handling list.

[0031] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for identifying non-electrical quantity anomalies in a distribution network, characterized in that, Includes the following steps: S1: Focusing on the operating objects of lines, transformer areas, and metering units in the distribution network monitoring, read the object number, status code, occurrence time, verify the time sequence, mark the status duration interval, write the allowed pre-sequence and allowed post-sequence coding relationship, form the object status mapping, and obtain the status relationship table. S2: Based on the state relationship table, a finite state machine is used to extract adjacent state code pairs, check the code order, compare the dwell time intervals, check the occurrence of intermediate states, mark jump records, reverse order records, and gap records, collect abnormal trajectories according to object number, and generate a set of violation trajectories. S3: Based on the set of violation trajectories, combined with the power supply direction of the feeder and the on / off status of the switch, the upstream non-conducting state is transmitted to the downstream node, the range of power supply values ​​that can be obtained for the downstream node is compressed, the node transmission order is recorded, and the constraint trajectory chain is obtained. S4: Based on the constrained trajectory chain, the AC-3 algorithm is used to trace the power supply path in reverse for nodes with empty value ranges, align the corresponding time records in the set of violations, filter out objects that simultaneously have abnormal states and hierarchical conflicts, compress the associated nodes and relationship ranges, and form a conflict subset. S5: Based on the conflict subset, read the status code of the conflict node, determine the category of power supply connection, maintenance stage, power outage / restoration mark, and freeze / change, extract the object number and target status code, and form a disposal list.

2. The method for identifying non-electrical quantity anomalies in distribution networks according to claim 1, characterized in that, The state relationship table includes a state code set, a preceding state code set, and a following state code set. The violation trajectory set includes state transition trajectories, state reversal trajectories, and state missing trajectories. The constraint trajectory chain includes constraint source nodes, constraint target nodes, and constraint propagation paths. The conflict subset includes a conflict node set and a conflict association set. The handling list includes an abnormal object number, current state code, and target state code.

3. The method for identifying non-electrical quantity anomalies in distribution networks according to claim 1, characterized in that, The specific steps for generating the state relationship table are as follows: Focusing on the operating objects of lines, transformer substations, and metering units in the distribution network monitoring, the object number, status code, and occurrence time are read, grouped by object number, and the timestamps within the same group are sorted sequentially. Records with reversed time sequence are removed, and the status at the same time point is retained and marked to form a status record set. Based on the state record set, the time difference between adjacent states is calculated one by one, the state duration interval is marked, and a correspondence is established between each state and its previous state code. At the same time, the allowed preorder and allowed postorder code sets are written to generate an object state mapping and obtain a state relationship table.

4. The method for identifying non-electrical quantity anomalies in distribution networks according to claim 1, characterized in that, The specific steps for generating the set of violation trajectories are as follows: Based on the state relationship table, a finite state machine is used to extract continuous state records according to the object number, read the previous state code and the next state code in sequence, and read the corresponding occurrence time and duration interval identifier at the same time. For each pair of adjacent states, a pair record is established, and the state code order position and time difference are retained to generate a state reference set. Based on the state comparison set, the sequential position of the previous and subsequent state codes in the relation table is checked one by one, the state duration is compared with the upper and lower limits of the allowed dwell interval, and the necessary states are checked to see if they occur within the time range. Records that do not conform to the order, duration, or passing requirements are written into the tag field to generate an abnormal tag set. Based on the anomaly marker set, the anomaly records are merged according to the object number, and the anomaly markers in adjacent time periods are continuously spliced ​​together. The start time, end time and anomaly type combination are recorded to form a complete state anomaly trajectory and generate a violation trajectory set.

5. The method for identifying non-electrical quantity anomalies in distribution networks according to claim 4, characterized in that, The finite state machine first uses the object number as the instance identifier, loads the corresponding state set and state transition relationship for each object, initializes the current state as the state code corresponding to the earliest timestamp record, and reads subsequent state records in chronological order. When reading the next state record, the read state code is used as a candidate target state and matched and verified with the current state in the state transition relationship. At the same time, the occurrence time of the two state records is read and the time difference is calculated. The calculated time difference is compared and verified with the allowed transition time interval of the current state. When the candidate target state meets the transition relationship constraint, the state switch is completed, and the state code order position and time difference are recorded synchronously. Then, the candidate target state is updated to the current state and the traversal process continues until the object state records are read, forming a state comparison set composed of adjacent state code pairs and corresponding time differences.

6. The method for identifying non-electrical quantity anomalies in distribution networks according to claim 1, characterized in that, The specific steps for generating the constrained trajectory chain are as follows: Based on the set of violation trajectories, the associated lines and transformer area objects are extracted according to the feeder number, the power supply direction identifier and the switch on / off status are read, the disconnection identifier is mapped to the non-conducting mark, and the nodes are arranged in the order of power supply direction to form a transmission sequence set; Based on the transmission order set, non-conductive markers are applied to downstream nodes step by step. For each node, the power supply range is processed by retention and rejection. The node receiving order and source node are recorded simultaneously to form a continuous association record and obtain a constraint trajectory chain.

7. The method for identifying non-electrical quantity anomalies in distribution networks according to claim 1, characterized in that, The specific steps for generating the conflict subset are as follows: Based on the constrained trajectory chain, the AC-3 algorithm is used to filter nodes whose power supply reach range is empty, read the node number, upstream node number and connection sequence identifier, and search for related nodes level by level according to the power supply direction. Each level of backtracking node and its corresponding transmission order are recorded to form a backtracking path set. Based on the backtracking path set, the node numbers in each path are read one by one, and the time overlap of the node occurrence time segment with the abnormal time segment of the corresponding object in the violation trajectory set is compared. The nodes and trajectory records with overlapping relationships are retained, and the time mismatched entries are removed to generate a conflict candidate set. Based on the conflict candidate set, object numbers that simultaneously possess both state anomaly markers and power supply level conflict markers are selected, nodes with only one-sided anomalies are deleted, node combinations with direct connection relationships are merged, and the number of nodes and the range of associated paths are compressed to form a conflict subset.

8. The method for identifying non-electrical quantity anomalies in a distribution network according to claim 7, characterized in that, The AC-3 algorithm first constructs a set of directed arcs by connecting each node in the constraint trajectory chain with its upstream nodes, and configures a power supply reachable value range for each node. It initializes the arc queue to be checked with all arc relationships in the constraint trajectory chain. Then, it sequentially retrieves an arc from the queue and performs a consistency check on the power supply reachable value range of the arc's starting node. It verifies whether the current value of the starting node satisfies the constraint relationship with an upstream node value. When a starting node has a value that does not satisfy the constraint relationship, it deletes the corresponding value from its value range. When a value deletion occurs, the upstream arc associated with the starting node is added back to the queue to be checked. This process of arc checking and value deletion continues until the arc queue is empty and a node's power supply reachable value range is empty. Simultaneously, during the value deletion process, the source node number, target node number, and transmission order number of the constraint transmission are recorded. Finally, a set of nodes with empty power supply reachable value ranges and corresponding transmission order records are obtained, forming a backtracking path set.

9. The method for identifying non-electrical quantity anomalies in a distribution network according to claim 1, characterized in that, The specific steps for generating the disposal list are as follows: Based on the aforementioned abnormal conflict association subset, the conflict node object number and current status code are read one by one, and the status code is mapped to four types of identifier intervals: power supply connection, maintenance stage, power outage / restoration mark, and freeze / change. The object number is used to complete the classification and collection, forming an abnormal classification set. Based on the aforementioned anomaly classification set, the target status code corresponding to each object is extracted. The object number, current status code, and target status code are combined and organized, duplicate entries are removed, and the results are summarized in order of object number to form a disposal list.

10. A distribution network non-electrical quantity anomaly identification system, characterized in that, The method for identifying non-electrical quantity anomalies in a distribution network according to any one of claims 1-9, wherein the system comprises: State modeling module: Focusing on the operating objects of lines, transformer substations, and metering units in distribution network monitoring, it reads the object number, state code, occurrence time, verifies the time sequence, marks the state duration interval, writes the allowed preceding and allowed following sequence code relationship, forms the object state mapping, and obtains the state relationship table; State evolution verification module: Based on the state relationship table, using a finite state machine, it extracts adjacent state code pairs, checks the code order, compares the dwell time intervals, checks the occurrence of intermediate states, marks jump records, reverse order records, and gap records, collects abnormal trajectories by object number, and generates a set of violation trajectories; Power supply constraint transmission module: Based on the set of violation trajectories, combined with the power supply direction of the feeder and the switch on / off indicators, the upstream non-conducting state is transmitted to the downstream node, the range of power supply values ​​that can be obtained for the downstream node is compressed, the node transmission order is recorded, and the constraint trajectory chain is obtained. Conflict backtracking and localization module: Based on the constrained trajectory chain, the AC-3 algorithm is used to trace the power supply path in reverse for nodes with empty value ranges, align the corresponding time records in the set of violation trajectories, filter objects that simultaneously have abnormal states and hierarchical conflicts, compress the associated nodes and relationship ranges, and form a conflict subset; Anomaly handling generation module: Based on the aforementioned conflict subset, read the status codes of conflict nodes, determine their category (power supply connection, maintenance phase, power outage / restoration marker, freeze / change category), extract the object number and target status code, and generate a handling list.

Images