Intelligent measurement system for topology identification of power distribution area

Abstract

The invention discloses an intelligent measurement system for topology identification of a power distribution area, relates to the technical field of power distribution areas, and solves the problems that the situation that newly-added nodes exist in a corresponding power node arrangement diagram is not fully considered, partial node data is shunted after the newly-added power nodes are added, and the reliability of the power distribution area is improved. In order to solve the problem that the data of some nodes are abnormal due to the fact that the data of the nodes are abnormal, the abnormal nodes can be accurately identified by taking a standard node as a measurement standard and accurately calculating the difference of measurement ratios of different nodes in different periods; moreover, the system not only can carry out calibration and signal display on abnormal nodes, but also can sensitively judge the possible node missing condition and generate a corresponding signal to remind external personnel when the undetermined node set does not find the abnormity but the data still has the abnormity; therefore, the system has high adaptability and flexibility when facing complex topological change of the power distribution area, and stable operation of the power distribution area can be effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution area, and in particular to an intelligent measurement system for topological identification of power distribution area. Background Art

[0002] In the power system, the distribution substation is a key link in power transmission. The accurate identification of its topological structure is crucial to ensure the stability and reliability of power supply. With the continuous development of the power industry, the scale of the power grid is expanding, and the structure of the distribution substation is becoming more complex and diverse.

[0003] In the early days, the topology identification of distribution substations mainly relied on manual inspections and simple measurement tools. This method was inefficient and easily affected by human factors, making it difficult to accurately and real-timely grasp the topology of distribution substations. With the advancement of technology, some monitoring technologies based on traditional sensors have gradually been applied.

[0004] The application with announcement number CN116436152B discloses a method for identifying the topology of a low-voltage distribution station area based on the correlation of characteristic information. Based on the intelligent fusion terminal and combined with high-speed power carrier communication technology, a low-voltage distribution station area topology identification architecture system is constructed; the characteristic data sets collected by the intelligent monitoring unit at each branch box, the intelligent meter box and the user side are extracted and analyzed, and the correlation between the characteristic information of each branch incoming and outgoing line in the station area is calculated; combined with the signal-to-noise ratio of each node in the station area and the zero-crossing phase offset, and then based on the correlation of the characteristic information of the incoming and outgoing lines, the intelligent fusion terminal is used to conduct a comprehensive analysis of the household transformer relationship, phase sequence relationship and low-voltage topology relationship in the station area, and the low-voltage topology map of the station area is updated in real time in combination with edge computing. The present invention improves the accuracy and efficiency of automatic identification of the low-voltage distribution station area topology, and realizes the dynamic management of the low-voltage station area topology identification, providing an effective intelligent monitoring means for fault diagnosis of low-voltage side lines, and reducing manual inspection and operation and maintenance cycles.

[0005] During the identification and measurement process, the circuit topology diagram is generally used to assess whether there is a specific fault in the corresponding node based on the numerical display associated with the corresponding node. However, in the actual assessment process, only the numerical changes between the nodes are considered, and the existence of new nodes in the corresponding power node layout diagram is not fully considered. After the power node is added, the data of some nodes will be diverted, which will cause abnormal data in some nodes. However, such abnormality is not a fault, but a result of current diversion. The original fault assessment method is rather one-sided. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides an intelligent measurement system for distribution station area topology identification, which solves the problem that the existence of new nodes in the corresponding power node layout diagram is not fully considered. After the power nodes are added, some node data will be diverted, resulting in abnormal data in some nodes.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent measurement system for topology identification of distribution station areas, comprising:

[0008] The node parameter monitoring terminal monitors the current parameters associated with different nodes in the distribution station area in real time, and transmits the real-time monitored current parameters belonging to different nodes to the node parameter analysis terminal;

[0009] The cloud database internally stores a circuit topology diagram associated with the distribution substation, wherein the circuit topology diagram is a preset power node connection diagram, and the node parameter analysis end extracts the power node connection diagram from the cloud database;

[0010] The node parameter analysis end performs a comprehensive analysis of the current parameters between different nodes based on the different current parameters monitored in real time at different nodes and in combination with the preset circuit topology diagram. Based on the specific results of the comprehensive analysis, it is confirmed whether there are abnormal parameters between the corresponding nodes. If so, multiple groups of nodes are integrated to determine the set of pending nodes. If not, continuous monitoring is sufficient. The specific method is as follows:

[0011] Based on the preset circuit topology, a group of nodes is randomly selected as the main node, and other nodes downstream of the main node are determined and recorded as the secondary nodes of the main node. The current of the main node is transmitted to the secondary node, and each group of different main nodes is associated with a single group or multiple groups of secondary nodes;

[0012] The current parameter exported by the corresponding main node is calibrated as D i , where i represents different master nodes, and the sink current parameter of the secondary node associated with this master node is calibrated as C k , where k represents different secondary nodes, and the confirmed groups C k Perform summation to lock the sum parameter ZH and identify D i And whether ZH satisfies: (D i -ZH)∈set interval, where the set interval is a preset interval. If it is not satisfied, the primary node and the associated multiple groups of secondary nodes are integrated to determine the set of pending nodes; if D i And ZH satisfies: (D i -ZH)∈set interval, then continuous monitoring;

[0013] The standard node determination end, based on the determined pending node set, identifies the change trends of different secondary nodes in different periodic periods from the multiple groups of secondary nodes included in the pending node set, and then selects the standard node based on the specific trend changes, specifically in the following manner:

[0014] Based on the current moment, a set of tracing cycles and monitoring cycles are confirmed. The tracing cycles and monitoring cycles are both preset cycles, and the duration of the two cycles is the same. The tracing cycle is the time period generated by the past period, and the monitoring cycle is the time period generated by the subsequent period of the current moment.

[0015] From the confirmed traceability cycle, confirm the specific time period when each sub-node has current inflow. From the confirmed specific time period, identify the current inflow value generated by each sub-node in the unit time period, where the unit time period is a preset time period. Perform average processing on several groups of current inflow values ​​generated by the corresponding sub-node in the specific time period, and confirm the current inflow characteristics generated by the corresponding sub-node in the specific time period, which is recorded as TZ1 k , where k represents different secondary nodes;

[0016] Then, from the confirmed monitoring cycle, confirm the specific time period when the current inflow exists in each sub-node, and then use the same processing method as the current inflow characteristics of the corresponding sub-node confirmed in the tracing cycle to confirm the current inflow characteristics generated by the corresponding sub-node in the monitoring cycle, and record it as TZ2 k ;

[0017] Use: PD k =|TZ1 k -TZ2 k |Confirm the rating value PD associated with the corresponding secondary node k , select PD k The secondary node associated with min is used as the standard node of the pending node set, and the confirmed standard node is transmitted to the verification processing end;

[0018] The verification processing end uses the selected standard node as a specific measurement standard to identify the measurement ratios associated with several groups of sub-nodes in different periods, and then identifies the relevant differences in the measurement ratios based on the different measurement ratios generated by different sub-nodes in different periods. Based on the identification results, it is determined whether there are abnormal nodes in the set of pending nodes. The specific method is as follows:

[0019] Based on the determined standard node, the current associated with the standard node is fed into the characteristic TZ1 k As the main standard value, confirm the current sink characteristics TZ1 generated by different secondary nodes during the traceability cycle k , the currents associated with different subnodes are merged into the characteristic TZ1 kCompare the value with the primary standard value to confirm the traceability ratio sequence. Each different secondary node corresponds to a different traceability ratio sequence.

[0020] Then the current associated with the standard node is fed into the characteristic TZ2 k As the main standard value, identify the current sink characteristics TZ2 generated by different nodes during the monitoring period k , the currents associated with different subnodes are merged into the characteristic TZ2 k Perform ratio processing with the main standard value to confirm the monitoring ratio sequence. Each different secondary node corresponds to a different monitoring ratio sequence;

[0021] The traceability ratio sequence and monitoring ratio sequence associated with the same group of sub-nodes are identified by differences: the ratios of the standard nodes in the traceability ratio sequence and the monitoring ratio sequence are adjusted to the same value. After the ratios of the standard nodes are adjusted, the traceability ratio sequence and other ratios in the monitoring ratio sequence are adjusted synchronously. After the ratio adjustment process, the specific difference of the ratios associated with the same sub-node is identified, and the specific difference is ≥ 0. The specific difference confirmed by different sub-nodes is calibrated as CZ k ;

[0022] The specific difference CZ associated with the corresponding secondary node k Check with the preset value Y1: If CZ k ≤Y1, no calibration is performed, where Y1 is the preset value;

[0023] If there is no abnormal node in the pending node set, a node missing signal is generated and displayed.

[0024] Preferably, if CZ k >Y1, the corresponding secondary node is marked as an abnormal node, and an abnormal node signal is directly generated for display.

[0025] The present invention provides an intelligent measurement system for topology identification of distribution station areas. Compared with the prior art, it has the following beneficial effects:

[0026] The present invention uses a node parameter monitoring terminal to monitor the current parameters of different nodes in the distribution station area in real time, and the node parameter analysis terminal can quickly combine the preset circuit topology diagram to conduct a comprehensive analysis of the current parameters; once a parameter abnormality occurs, the set of pending nodes can be accurately determined. Compared with the traditional monitoring method, the timeliness of abnormality discovery and the accuracy of positioning are greatly improved, and the problem area can be quickly locked, saving a lot of time for subsequent troubleshooting and repair.

[0027] The standard node determination end is based on the set of pending nodes. By comparing the current inflow characteristics of the secondary nodes in different cycle periods, the change trend is identified, and the standard node is selected. This method fully considers the characteristics of the node under different operating conditions, and determines a relatively stable standard reference in a scientific way, which provides a reliable basis for the subsequent judgment of abnormal nodes, making the abnormal judgment more scientific and reliable.

[0028] The verification and validation processing end uses standard nodes as the measurement standard, and can accurately identify abnormal nodes by accurately calculating the difference in measurement ratios of different sub-nodes in different cycles. In addition, the system can not only calibrate abnormal nodes and display signals, but also can keenly judge possible node missing situations when no abnormalities are found in the pending node set but the data still has abnormalities, and generate corresponding signals to alert external personnel. This makes the system highly adaptable and flexible when facing complex topology changes in distribution substations, and can effectively ensure the stable operation of distribution substations, reduce power outages caused by topology changes or node abnormalities, and improve power supply reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] See also Figure 1 , the present application provides an intelligent measurement system for topology identification of a distribution station area, including a node parameter monitoring terminal, a cloud database, a node parameter analysis terminal, a standard node determination terminal, and a verification and checking processing terminal, wherein the cloud database and the node parameter monitoring terminal are both electrically connected to the input node of the node parameter analysis terminal, and the node parameter analysis terminal, the standard node determination terminal, and the verification and checking processing terminal are all electrically connected from the output node to the input node;

[0032] The node parameter monitoring terminal monitors the current parameters associated with different nodes in the distribution station area in real time, and transmits the real-time monitored current parameters belonging to different nodes to the node parameter analysis terminal;

[0033] The cloud database internally stores the circuit topology diagram associated with the distribution substation. The circuit topology diagram is a preset power node connection diagram, which is prepared in advance by relevant operators. The node parameter analysis terminal extracts the power node connection diagram from the cloud database.

[0034] The node parameter analysis end performs a comprehensive analysis of the current parameters between different nodes based on the different current parameters monitored in real time at different nodes and in combination with the preset circuit topology diagram. Based on the specific results of the comprehensive analysis, it is confirmed whether there are abnormal parameters between the corresponding nodes. If so, multiple groups of nodes are integrated to determine the set of pending nodes. If not, continuous monitoring is sufficient. The specific method for confirmation is as follows:

[0035] Based on the preset circuit topology diagram, a group of nodes is randomly selected as the main node, and other nodes downstream of the main node are determined and recorded as the secondary nodes of the main node. The current of the main node is transmitted to the secondary node. Each group of different main nodes is associated with a single group or multiple groups of secondary nodes, which can be directly confirmed from the circuit topology diagram. The circuit topology diagram is a preset diagram, and the connection relationship and power relationship between different nodes can be clearly obtained. Therefore, the corresponding main node and the associated secondary node can be determined based on the specific flow direction of the corresponding power. The secondary node belongs to the downstream node of the corresponding main node, and the main node will transmit power to the downstream secondary node;

[0036] The current parameter exported by the corresponding main node is calibrated as D i , where i represents different master nodes, and the sink current parameter of the secondary node associated with this master node is calibrated as C k , where k represents different secondary nodes, and the confirmed groups C k Perform summation to lock the sum parameter ZH and identify D i And whether ZH satisfies: (D i -ZH)∈set interval, where the set interval is a preset interval, which is prepared in advance by relevant operators. If it is satisfied (representing that there is no abnormality in the parameter), continuous monitoring is performed; if it is not satisfied (representing that there is an abnormality in the parameter), the main node and the multiple groups of associated secondary nodes are integrated to determine the set of pending nodes.

[0037] Among them, the standard node determination end, based on the determined pending node set, identifies the change trends of different secondary nodes in different periodic periods from multiple groups of secondary nodes included in the pending node set, and then selects the standard node based on the specific trend change. The specific method of selection is:

[0038] Based on the current moment, a set of tracing cycles and monitoring cycles are confirmed. The tracing cycles and monitoring cycles are both preset cycles, and the duration of the two cycles is the same. The tracing cycle is the time period generated by the past period, and the monitoring cycle is the time period generated by the subsequent period of the current moment. It can be understood that the tracing cycle belongs to the period in the normal operating state without abnormal state, and the monitoring cycle belongs to the period in the abnormal operating state after the abnormal state occurs;

[0039] From the confirmed traceability cycle, confirm the specific time period when each sub-node has current inflow. From the confirmed specific time period, identify the current inflow value generated by each sub-node in the unit time period (each different unit time period has a corresponding current inflow value). The unit time period is a preset time period, generally 1 second. Perform average processing on several groups of current inflow values ​​generated by the corresponding sub-node in the specific time period, and confirm the current inflow characteristics generated by the corresponding sub-node in the specific time period (that is, the specific time period is 5 seconds, and a group of current inflow values ​​is generated every one second. The current inflow values ​​generated within 5 seconds are averaged to confirm the current inflow characteristics associated with this node), recorded as TZ1 k , where k represents different secondary nodes;

[0040] Then, from the confirmed monitoring cycle, confirm the specific time period when the current inflow exists in each sub-node, and then use the same processing method as the current inflow characteristics of the corresponding sub-node confirmed in the tracing cycle to confirm the current inflow characteristics generated by the corresponding sub-node in the monitoring cycle, and record it as TZ2 k ;

[0041] Use: PD k =|TZ1 k -TZ2 k |Confirm the rating value PD associated with the corresponding secondary node k (The smaller the evaluation value, the smaller the change range of the associated sub-node in different cycles, so the standard node can be selected from the corresponding sub-nodes). k The secondary node associated with min is used as the standard node of the pending node set, and the confirmed standard node is transmitted to the verification processing end;

[0042] Specifically, in the corresponding periodic change process, there are related changes in the change trends associated with different sub-nodes. The related sub-nodes with smaller change trends belong to the specific nodes that have not undergone major changes. Then, compared with other sub-nodes, such nodes can be used as corresponding association standards to facilitate the subsequent specific confirmation of abnormal nodes.

[0043] Among them, the verification and checking processing end uses the selected standard node as a specific measurement standard, identifies the measurement ratios associated with several groups of sub-nodes in different periods, and then identifies the relevant differences in the measurement ratios based on the different measurement ratios generated by different sub-nodes in different periods. Based on the identification results, it is locked whether there are abnormal nodes in the pending node set, wherein the specific method of identification is:

[0044] Based on the determined standard node, the current associated with the standard node is fed into the characteristic TZ1 kAs the main standard value, confirm the current sink characteristics TZ1 generated by different secondary nodes during the traceability cycle k , the currents associated with different subnodes are merged into the characteristic TZ1 k Compare the value with the primary standard value to confirm the traceability ratio sequence. Each different secondary node corresponds to a different traceability ratio sequence.

[0045] Then the current associated with the standard node is fed into the characteristic TZ2 k As the main standard value, identify the current sink characteristics TZ2 generated by different nodes during the monitoring period k , the currents associated with different subnodes are merged into the characteristic TZ2 k Perform ratio processing with the main standard value to confirm the monitoring ratio sequence. Each different secondary node corresponds to a different monitoring ratio sequence;

[0046] The traceability ratio sequence and monitoring ratio sequence associated with the same group of sub-nodes are identified by differences: the ratios of the standard nodes in the traceability ratio sequence and the monitoring ratio sequence are adjusted to the same value. After the ratios of the standard nodes are adjusted, the traceability ratio sequence and other ratios in the monitoring ratio sequence are adjusted synchronously (for example, the traceability ratio sequence is: A: B = 1: 2, and A: B = 2: 5 in the monitoring ratio sequence. The corresponding B is the standard node, then the ratio associated with the standard node B is adjusted to the same value, assuming it is adjusted to 10, then A: B = 1: 2 = 5: 10, and it is adjusted to: A: B = 2: 5 = 4: 10 in the monitoring ratio sequence). After the ratio adjustment process, the specific difference of the ratios associated with the same sub-node is identified (the confirmed specific difference = | 5-4 | = 1), and the specific difference ≥ 0, and the specific difference confirmed by different sub-nodes is calibrated as CZ k ;

[0047] The specific difference CZ associated with the corresponding secondary node k Check with the preset value Y1: If CZ k ≤Y1, no calibration is performed. If CZ k >Y1, the corresponding secondary node is marked as an abnormal node, where Y1 is a preset value, and its specific value is determined by the operator based on experience, and an abnormal node signal is directly generated for display for external personnel to view;

[0048] If there are no abnormal nodes in the pending node set, a node missing signal is generated and displayed. When external relevant personnel receive a node missing signal in the pending node set, they need to analyze other nodes that may be connected around the pending node set and update the circuit topology map in a timely manner.

[0049] Specifically, when the data associated with the corresponding pending node set is abnormal, either a node is involved in this set, but the circuit topology diagram is not updated in time, resulting in current loss during data settlement:

[0050] Then, in the case where it is unclear whether there are other nodes involved in this set, by analyzing the specific numerical changes of the known nodes, that is, when all the secondary nodes are in normal operation, the changing trend of their current operation will not change, but the total current that the corresponding node can receive will be partially lost. Then, by analyzing the changing trend of the corresponding secondary node, it can be determined whether there is an abnormality in the current value change of the corresponding secondary node. If there is a specific abnormality, it means that there is a node abnormality in the corresponding node set. If there is no specific abnormality, but the current numerical characteristics of the overall node set are abnormal, then it is very likely that a circuit node is involved, but is in an unknown state, resulting in part of the current divided by the corresponding main node being diverted to this newly added node, thereby causing the analyzed data to be abnormal.

[0051] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0052] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An intelligent measurement system for topology identification of distribution station areas, characterized in that: include: The node parameter monitoring terminal monitors the current parameters associated with different nodes in the distribution station area in real time, and transmits the real-time monitored current parameters belonging to different nodes to the node parameter analysis terminal; The cloud database internally stores a circuit topology diagram associated with the distribution substation, wherein the circuit topology diagram is a preset power node connection diagram, and the node parameter analysis end extracts the power node connection diagram from the cloud database; The node parameter analysis end performs a comprehensive analysis of the current parameters between different nodes based on the different current parameters monitored in real time at different nodes and in combination with the preset circuit topology diagram. Based on the specific results of the comprehensive analysis, it is confirmed whether there are any parameter abnormalities between the corresponding nodes. If so, multiple groups of nodes are integrated to determine the set of pending nodes. If not, continuous monitoring is sufficient. The standard node determination end, based on the determined pending node set, identifies the change trends of different sub-nodes in different periodic periods from the multiple groups of sub-nodes included in the pending node set, and then selects the standard node based on the specific trend changes; The verification and validation processing end uses the selected standard node as a specific measurement standard to identify the measurement ratios associated with several groups of sub-nodes in different periods, and then identifies the relevant differences in the measurement ratios based on the different measurement ratios generated by different sub-nodes in different periods. Based on the identification results, it is determined whether there are abnormal nodes in the set of pending nodes.

2. According to claim 1, the intelligent measurement system for topology identification of distribution station area is characterized in that: The node parameter analysis end confirms whether there is a parameter anomaly between corresponding nodes in the following specific manner: Based on the preset circuit topology, a group of nodes is randomly selected as the main node, and other nodes downstream of the main node are determined and recorded as the secondary nodes of the main node. The current of the main node is transmitted to the secondary node, and each group of different main nodes is associated with a single group or multiple groups of secondary nodes; The current parameter exported by the corresponding main node is calibrated as D i , where i represents different master nodes, and the sink current parameter of the secondary node associated with this master node is calibrated as C k , where k represents different secondary nodes, and the confirmed groups C k Perform summation to lock the sum parameter ZH and identify D i And whether ZH satisfies: (D i -ZH)∈set interval, where the set interval is a preset interval. If it is not satisfied, the main node and the multiple groups of associated secondary nodes are integrated to determine the set of pending nodes.

3. The intelligent measurement system for topology identification of a distribution station area according to claim 2 is characterized in that: If D i And ZH satisfies: (D i -ZH)∈set interval, then continuous monitoring.

4. The intelligent measurement system for topology identification of a distribution station area according to claim 2 is characterized in that: The specific method of selecting the standard node at the standard node determination end is as follows: Based on the current moment, a set of tracing cycles and monitoring cycles are confirmed. The tracing cycles and monitoring cycles are both preset cycles, and the duration of the two cycles is the same. The tracing cycle is the time period generated by the past period, and the monitoring cycle is the time period generated by the subsequent period of the current moment. From the confirmed traceability cycle, confirm the specific time period when each sub-node has current inflow. From the confirmed specific time period, identify the current inflow value generated by each sub-node in the unit time period, where the unit time period is a preset time period. Perform average processing on several groups of current inflow values ​​generated by the corresponding sub-node in the specific time period, and confirm the current inflow characteristics generated by the corresponding sub-node in the specific time period, which is recorded as TZ1 k , where k represents different secondary nodes; Then, from the confirmed monitoring cycle, confirm the specific time period when the current inflow exists in each sub-node, and then use the same processing method as the current inflow characteristics of the corresponding sub-node confirmed in the tracing cycle to confirm the current inflow characteristics generated by the corresponding sub-node in the monitoring cycle, and record it as TZ2 k ; Use: PD k =|TZ1 k -TZ2 k |Confirm the rating value PD associated with the corresponding secondary node k , select PD k The secondary node associated with min is used as the standard node of the pending node set, and the confirmed standard node is transmitted to the verification processing end.

5. The intelligent measurement system for topology identification of distribution station area according to claim 4 is characterized in that: The specific method of the verification processing end to identify whether there is an abnormal node in the pending node set is: Based on the determined standard node, the current associated with the standard node is fed into the characteristic TZ1 k As the main standard value, confirm the current sink characteristics TZ1 generated by different secondary nodes during the traceability cycle k , the currents associated with different subnodes are merged into the characteristic TZ1 k Compare the value with the primary standard value to confirm the traceability ratio sequence. Each different secondary node corresponds to a different traceability ratio sequence. Then the current associated with the standard node is fed into the characteristic TZ2 k As the main standard value, identify the current sink characteristics TZ2 generated by different nodes during the monitoring period k , the currents associated with different subnodes are merged into the characteristic TZ2 k Perform ratio processing with the main standard value to confirm the monitoring ratio sequence. Each different secondary node corresponds to a different monitoring ratio sequence; The traceability ratio sequence and monitoring ratio sequence associated with the same group of sub-nodes are identified by differences: the ratios of the standard nodes in the traceability ratio sequence and the monitoring ratio sequence are adjusted to the same value. After the ratios of the standard nodes are adjusted, the traceability ratio sequence and other ratios in the monitoring ratio sequence are adjusted synchronously. After the ratio adjustment process, the specific difference of the ratios associated with the same sub-node is identified, and the specific difference is ≥ 0. The specific difference confirmed by different sub-nodes is calibrated as CZ k ; The specific difference CZ associated with the corresponding secondary node k Check with the preset value Y1: If CZ k ≤Y1, no calibration is performed, where Y1 is the preset value; If there is no abnormal node in the pending node set, a node missing signal is generated and displayed.

6. The intelligent measurement system for topology identification of a distribution station area according to claim 5, characterized in that: If CZ k >Y1, the corresponding secondary node is marked as an abnormal node, and an abnormal node signal is directly generated for display.

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