Self-supervised low-voltage transformer area topology identification method based on stacked ridge regression

Abstract

The invention provides a self-supervised low-voltage transformer area topology identification method based on stacked ridge regression, and relates to the technical field of power distribution network management. Comprising the following steps: S1, executing a networking command by an energy controller, and carrying out broadcast timing on transformer area measurement equipment; s2, judging whether the transformer area topology is changed or not, and if the transformer area topology is changed, executing the step S3; if not, calculating whether the line loss rate of each stage of the transformer area exceeds the limit, if so, executing the step S3, and otherwise, ending the execution of the current identification method; s3, the energy controller collects 96-point forward active power data of the transformer area measurement equipment; s4, establishing a stacked ridge regression model, and sorting topological relations step by step; and S5, judging whether the topological structure of the transformer area needs to be updated or not and performing corresponding execution. According to the method, topology identification of the low-voltage transformer area can be realized, and the problem that the existing topology identification method of the low-voltage transformer area has limitation is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network management, and in particular to a self-supervised low-voltage substation topology recognition method based on stacked ridge regression. Background Art

[0002] In recent years, with the significant improvement of stability and low loss requirements, loss reduction has become one of the key tasks of power supply companies; and there is a close relationship between loss reduction and topology identification. By improving the quality and efficiency of topology identification, the loss reduction goal can be achieved more effectively. At present, the topology combing method for 10kV distribution networks has been relatively clear, but at the low-voltage substation level, there are still a large number of situations where the substation topology is unclear and the household-to-substation relationship is very chaotic. Based on this, for high-loss substations, the chaos of the topological structure will greatly increase the workload of line loss investigation; for problems such as power outages at branch nodes and line failures, it is impossible to quickly and effectively locate them, which will affect the efficiency of emergency repairs. Therefore, the unclear topology problem in the low-voltage substation will seriously affect the promotion of lean line loss management and the efficiency of detection and emergency repair when faults occur in the substation.

[0003] In the prior art, a considerable number of power supply companies are still using traditional manual topology combing methods, such as power outage distinction, on-site manual combing of lines, etc., which have the problems of low efficiency and limited effect. With the development of technology, some topology identification methods using computer technology have also emerged. Among them, the mainstream solutions include correlation methods based on substation information and methods based on characteristic current signals; for example, the patent with publication number CN110727662A provides a low-voltage substation user phase identification method and system based on correlation analysis, and the patent with publication number CN114142464A provides a low-voltage substation topology identification method and device based on characteristic current signal detection. However, the correlation method based on substation information is more dependent on the efficiency and accuracy of information collection, and the identification cycle is long, and the identification effect for common zero, coupling and back-to-back substations is poor; the method based on characteristic current signals requires additional equipment, the overall operating cost is high, and high-frequency signals need to be injected into the power lines, which will have a certain impact on the stability of the power grid.

[0004] Therefore, it is necessary to study a safe, reliable, efficient and economical low-voltage area topology identification method. Summary of the invention

[0005] The purpose of the present invention is to provide a self-supervised low-voltage area topology identification method based on stacked ridge regression to solve the problem mentioned in the above background technology that the existing low-voltage area topology identification methods all have limitations.

[0006] The present invention is achieved by adopting the following technical solutions: A self-supervised low-voltage station area topology recognition method based on stacked ridge regression includes the following steps: Step S1: The energy controller in the low-voltage area executes the networking command and broadcasts the time calibration to the area measurement equipment; Step S2: judging whether the area topology has changed according to the networking result, if it has changed, directly executing step S3; if it has not changed, calculating whether the line loss rate of each level in the area exceeds the limit, if it exceeds the limit, executing step S3, if it does not exceed the limit, ending the execution of the current identification method; Step S3: The energy controller collects 96 points of forward active power data from the area measurement equipment; Step S4: Establish a stacked ridge regression model, calculate the topological results based on the model, and sort out the topological relationships step by step; Step S5: Calculate the topology results multiple times and compare them with the existing archive information of the substation to determine whether the substation topology structure needs to be updated and perform corresponding operations.

[0007] Furthermore, in the step S1: by performing broadcast time calibration, the clock synchronization error between the measuring devices in the station area is made less than 2s.

[0008] Furthermore, in the step S2: comparing whether there is any discrepancy between the networking result and the existing archive information of the substation area, if there is any discrepancy, it means that the substation area topology has changed.

[0009] Furthermore, the evaluation index of whether there is a discrepancy includes: a. The network results contain additional meter records; b. The network result contains the deletion of meter records; c. The networking results contain a record of replacing the electric meter.

[0010] Furthermore, in step S2: if the absolute value of the line loss rate of each level in the substation exceeds 70%, it means that the limit is exceeded. The formula for calculating the line loss rate of each level in the substation is: , In the formula, represents the line loss rate of the b-th level node, Indicates b The parent node of each level node freezes the power data of the day. Indicates b The corresponding node in the level i The power consumption data of each child node is frozen on that day.

[0011] Furthermore, in step S4: a stacked ridge regression model is established based on the principle that the active power of parent and child nodes at all levels satisfies the power conservation principle, the regression coefficient is calculated and solved, and the child nodes whose coefficient values ​​exceed the preset value and whose average power is relatively significant are assigned to the corresponding parent nodes, so as to sort out the topological relationship step by step.

[0012] Furthermore, the topological relationship between the substation assessment table and the intelligent circuit breakers at all levels in the substation measurement equipment is sorted out, which specifically includes the following sub-steps: Step S4-A1: Initialize and set the layer data parameters b =1, parent node number parameter j =1, the child node set is initialized to empty ; Step S4-A2: Establish the b Level j The stacked ridge regression model of parent-child nodes is initialized with the number of stacking times. t =1; Initialize the number of child nodes is the maximum number that can be reached, that is, the number of all intelligent circuit breakers that are parent nodes are excluded; Step S4-A3: Calculate the b Level j Parent node t The coefficient vector of the sub-solution , the formula is, , In the formula, is the active power matrix composed of the current intelligent circuit breaker sub-nodes, N Indicates the number of data points; Indicates the current remaining active power data of the parent node, that is, the original active power of the parent node minus the sum of the active power of the determined child nodes; represents the identity matrix; Step S4-A4: Select coefficient vector The sub-node set corresponding to the coefficient exceeding 0.85 in the above formula is used to calculate the average active power of each sub-node; the sub-node set whose average active power is greater than the mean of the average active power of all sub-nodes is retained. To determine the child node, ; Update the parent-child node data, that is, the remaining active power data at the current parent node Subtract the child node set from The active power matrix composed of the current intelligent circuit breaker sub-nodes is Remove the child node set represents the data sequence; Step S4-A5: If the coefficient vector If the number of values ​​exceeding 0.85 does not exceed 1, then execute step S4-A4 and then execute step S4-A6; otherwise, the number of stacking times is t To do incremental operation , repeat steps S4-A3 to S4-A4; Step S4-A6: If the level data parameter b =1, then execute step S4-A7; otherwise, the parent node number parameter j To do incremental operation , the remaining active power data of the current parent node Updated to j Repeat steps S4-A2 to S4-A5 to sort out the data of the parent node. j The topology of each parent node is sorted out until all parent nodes of the current level are sorted out; Step S4-A7: Level data parameters b To do incremental operation , reset the number of parent nodes at the current level j =1, child node set , record the child nodes of the current level as the parent nodes of the next level, repeat steps S4-A2 to S4-A6 until all intelligent circuit breakers are divided, and output the topological relationship between the substation assessment table and the intelligent circuit breakers at all levels .

[0013] Furthermore, it includes combing the topological relationship between the terminal intelligent circuit breaker as the meter box layer in the area measurement equipment and all user meters, which specifically includes the following sub-steps: Step S4-B1: Initialize the child node set to empty, that is ; Step S4-B2: Establish a stacked ridge regression model for all parent and child nodes and initialize the number of stacking times t =1; Step S4-B3: Calculate the t The coefficient matrix of the solution , the calculation formula is, , In the formula, is the active power matrix composed of all user tables, U Indicates the number of user tables. Indicates t The residual active power matrix composed of all terminal intelligent circuit breakers during the second calculation is the original active power of each parent node minus the sum of the active power of its subordinate child nodes that have been determined; Step S4-B4: Determine the pre-assignment relationship between the meter box and the user according to the maximum coefficient principle, that is, the pre-assignment number of the terminal intelligent circuit breaker to which the user meter belongs is ; Moreover, in each parent node, a set of child nodes whose average active power is greater than the mean of the average active power of all corresponding child nodes is retained To determine the child node, update the child node set , update the parent-child node data, that is, the remaining active power matrix composed of all terminal intelligent circuit breakers Subtract the corresponding child node set from The active power matrix composed of all user tables is Remove the child node set represents the data sequence; Step S4-B5: Stacking times t To do incremental operation , repeat steps S4-B3 to S4-B4, if the active power matrix composed of all user tables The number of remaining columns does not exceed the number of terminal intelligent circuit breakers J , then keep the result and output the topological relationship between the terminal intelligent circuit breaker and all user tables.

[0014] Furthermore, in step S5: if the topology result is consistent with the existing archival information of the substation, then the user table corresponding to the regression coefficient exceeding the preset value in the hierarchical node where the line loss rate exceeds the warning value is recorded, and it is reported as an abnormal event; otherwise, the line loss rate of each level with the intelligent circuit breaker in the substation measuring equipment as the parent node is calculated, and the result with the lowest average line loss rate is reported.

[0015] Furthermore, the warning value is 70% and the preset value is 1.5.

[0016] The beneficial effects achieved by the present invention are: By executing steps S1 to S5, the topology identification of the low-voltage substation can be realized, wherein: by comparing the networking results of the energy actuators with the existing archival information of the substation, and according to the changes in the line loss rates at various levels of the substation, it is possible to self-supervise and judge whether to execute the topology identification command, and it has strong adaptability; and, the calculation is based on the active power data, the calculation amount is small, there is no need to install signal injection equipment, and the measurement signal does not need to be uploaded to the main station, and can be analyzed on the local device, which is friendly to the safety of the power grid and has good engineering practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a schematic diagram of a flow chart of a topology identification method according to an embodiment of the present invention; Figure 2 It is a schematic diagram of the low-voltage area topology used as an example in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0019] Example 1 Please refer to Figure 1 to Figure 2The present invention provides a self-supervised low-voltage area topology identification method based on stacked ridge regression, comprising the following steps: Step S1: The energy controller in the low-voltage area executes the networking command and broadcasts the time calibration to the area measurement equipment. Specifically: In this step, broadcast time calibration is performed to make the clock synchronization error between the measurement equipment in the substation less than 2s, thereby ensuring the clock synchronization of the measurement equipment in the substation; the measurement equipment in the substation includes intelligent circuit breakers and intelligent meters.

[0020] Step S2: Determine whether the area topology has changed according to the networking result. If it has changed, directly execute step S3; if it has not changed, calculate whether the line loss rate of each level in the area exceeds the limit. If it exceeds the limit, execute step S3; if it does not exceed the limit, end the execution of the current identification method. Specifically: In this step, the networking result is compared with the existing archive information of the substation to see if there is any discrepancy. If there is any discrepancy, it means that the substation topology has changed. The evaluation indicators for whether there is any discrepancy include: a. The network results contain additional meter records; b. The network result contains the deletion of meter records; c. The networking results contain a record of replacing the electric meter.

[0021] If the absolute value of the line loss rate at each level in the substation exceeds 70%, it means that the limit is exceeded. The formula for calculating the line loss rate at each level in the substation is: , In the formula, represents the line loss rate of the b-th level node, Indicates b The parent node of each level node freezes the power data of the day. Indicates b The corresponding node in the level i The power consumption data of each child node is frozen on that day.

[0022] Step S3: The energy controller collects 96 points of forward active power data of the area measurement equipment through HPLC on a daily basis.

[0023] Step S4: Establish a stacked ridge regression model, calculate the topological results based on the model, and sort out the topological relationships step by step. Specifically: In this step, a stacked ridge regression model is established based on the principle that the active power of parent and child nodes at all levels satisfies the power conservation principle, the regression coefficient is calculated and solved, and the child nodes with coefficient values ​​exceeding the preset value and relatively significant average power are assigned to the corresponding parent nodes, so as to sort out the topological relationship step by step; including sorting out the topological relationship between the substation assessment total table and the intelligent circuit breakers at all levels in the substation measurement equipment, and sorting out the topological relationship between the terminal intelligent circuit breaker as the meter box layer in the substation measurement equipment and all user meters.

[0024] When sorting out the topological relationship between the substation assessment summary table and the intelligent circuit breakers at all levels in the substation measurement equipment, the following sub-steps are specifically included: Step S4-A1: Initialize and set the layer data parameters b =1, parent node number parameter j =1, the child node set is initialized to empty ; Step S4-A2: Establish the b Level j The stacked ridge regression model of parent-child nodes is initialized with the number of stacking times. t =1; Initialize the number of child nodes is the maximum number that can be reached, that is, the number of all intelligent circuit breakers that are parent nodes are excluded; Step S4-A3: Calculate the b Level j Parent node t The coefficient vector of the sub-solution , the formula is, , In the formula, is the active power matrix composed of the current intelligent circuit breaker sub-nodes, N Indicates the number of data points; Indicates the current remaining active power data of the parent node, that is, the original active power of the parent node minus the sum of the active power of the determined child nodes; represents the identity matrix; Step S4-A4: Select coefficient vector The sub-node set corresponding to the coefficient exceeding 0.85 in the above formula is used to calculate the average active power of each sub-node; the sub-node set whose average active power is greater than the mean of the average active power of all sub-nodes is retained. To determine the child node, ; Update the parent-child node data, that is, the remaining active power data at the current parent node Subtract the child node set from The active power matrix composed of the current intelligent circuit breaker sub-nodes is Remove the child node set represents the data sequence; Step S4-A5: If the coefficient vector If the number of values ​​exceeding 0.85 does not exceed 1, then execute step S4-A4 and then execute step S4-A6; otherwise, the number of stacking times is t To do incremental operation , repeat steps S4-A3 to S4-A4; Step S4-A6: If the level data parameter b =1, then execute step S4-A7; otherwise, the parent node number parameter j To do incremental operation , the remaining active power data of the current parent node Updated to j Repeat steps S4-A2 to S4-A5 to sort out the data of the parent node. j The topology of each parent node is sorted out until all parent nodes of the current level are sorted out; Step S4-A7: Level data parameters b To do incremental operation , reset the number of parent nodes at the current level j =1, child node set , record the child nodes of the current level as the parent nodes of the next level, repeat steps S4-A2 to S4-A6 until all intelligent circuit breakers are divided, and output the topological relationship between the substation assessment table and the intelligent circuit breakers at all levels .

[0025] When sorting out the topological relationship between the terminal intelligent circuit breaker as the meter box layer in the substation measurement equipment and all user meters, the specific sub-steps include the following: Step S4-B1: Initialize the child node set to empty, that is ; Step S4-B2: Establish a stacked ridge regression model for all parent and child nodes and initialize the number of stacking times t =1; Step S4-B3: Calculate the t The coefficient matrix of the solution , the calculation formula is, , In the formula, is the active power matrix composed of all user tables, U Indicates the number of user tables. Indicates t The residual active power matrix composed of all terminal intelligent circuit breakers during the second calculation is the original active power of each parent node minus the sum of the active power of its subordinate child nodes that have been determined; Step S4-B4: Determine the pre-assignment relationship between the meter box and the user according to the maximum coefficient principle, that is, the pre-assignment number of the terminal intelligent circuit breaker to which the user meter belongs is ; Moreover, in each parent node, a set of child nodes whose average active power is greater than the mean of the average active power of all corresponding child nodes is retained To determine the child node, update the child node set , update the parent-child node data, that is, the remaining active power matrix composed of all terminal intelligent circuit breakers Subtract the corresponding child node set from The active power matrix composed of all user tables is Remove the child node set represents the data sequence; Step S4-B5: Stacking times t To do incremental operation , repeat steps S4-B3 to S4-B4, if the active power matrix composed of all user tables The number of remaining columns does not exceed the number of terminal intelligent circuit breakers J , then keep the result and output the topological relationship between the terminal intelligent circuit breaker and all user tables.

[0026] Step S5: Calculate the topology results multiple times and compare them with the existing archive information of the substation to determine whether the substation topology structure needs to be updated and perform corresponding operations. Specifically: In this step, if the topology result is consistent with the existing archive information of the substation, the user table corresponding to the regression coefficient exceeding the preset value in the hierarchical node where the line loss rate exceeds the warning value is recorded and reported as an abnormal event, indicating that the corresponding user table is an abnormal power user or a suspected power theft user; otherwise, the intelligent circuit breaker in the substation measurement equipment is calculated as the parent node of the line loss rate at each level, and the result with the lowest average line loss rate is reported. Among them, the warning value is 70% and the preset value is 1.5.

[0027] In summary, by executing steps S1 to S5, the topology identification of the low-voltage substation can be realized. The topology identification method provided in this embodiment can self-supervise and judge whether to execute the topology identification command by comparing the networking results of the energy actuator with the existing archive information of the substation, and according to the changes in the line loss rate at each level of the substation, and has strong adaptability; and, based on the active power data, the calculation amount is small, and there is no need to install signal injection equipment. The measurement signal does not need to be uploaded to the main station, and can be analyzed on the local device, which is friendly to the safety of the power grid and has good engineering practicality.

[0028] It should be noted that the parts not described in detail or expanded in the above scheme are all existing technologies, which do not belong to the improvements made by the present invention on the existing technologies, nor do they belong to the protection scope of the technical scheme of the present invention. Therefore, they will not be described in detail in this article.

[0029] Of course, the above contents are only preferred embodiments of the present invention and cannot be considered to limit the scope of the embodiments of the present invention. The present invention is not limited to the above examples, and equal changes and improvements made by ordinary technicians in the technical field within the essential scope of the present invention should all fall within the scope of the patent coverage of the present invention.

Claims

1. A self-supervised low-voltage area topology identification method based on stacked ridge regression, characterized in that: The steps include: Step S1: The energy controller in the low-voltage area executes the networking command and broadcasts the time calibration to the area measurement equipment; Step S2: judging whether the area topology has changed according to the networking result, if it has changed, directly executing step S3; if it has not changed, calculating whether the line loss rate of each level in the area exceeds the limit, if it exceeds the limit, executing step S3, if it does not exceed the limit, ending the execution of the current identification method; Step S3: The energy controller collects 96 points of forward active power data from the area measurement equipment; Step S4: Establish a stacked ridge regression model, calculate the topological results based on the model, and sort out the topological relationships step by step; Step S5: Calculate the topology results multiple times and compare them with the existing archive information of the substation to determine whether the substation topology structure needs to be updated and perform corresponding operations.

2. The self-supervised low-voltage station area topology identification method based on stacked ridge regression according to claim 1 is characterized in that: In the step S1: by performing broadcast time calibration, the clock synchronization error between the measuring devices in the station area is made less than 2s.

3. The self-supervised low-voltage station area topology identification method based on stacked ridge regression according to claim 1 is characterized in that: In the step S2: compare whether there is any discrepancy between the networking result and the existing archive information of the substation area. If there is any discrepancy, it means that the substation area topology has changed.

4. The self-supervised low-voltage area topology identification method based on stacked ridge regression according to claim 3 is characterized in that: The evaluation indicators of whether there is discrepancy include: a. The network results contain additional meter records; b. The network result contains the deletion of meter records; c. The networking results contain a record of replacing the electric meter.

5. The self-supervised low-voltage station area topology identification method based on stacked ridge regression according to claim 1 is characterized in that: In step S2: if the absolute value of the line loss rate of each level in the substation exceeds 70%, it means that the limit is exceeded. The formula for calculating the line loss rate of each level in the substation is: ， In the formula, represents the line loss rate of the b-th level node, Indicates b The parent node of each level node freezes the power data of the day. Indicates b The corresponding node in the level i The power consumption data of each child node is frozen on that day.

6. The self-supervised low-voltage station area topology identification method based on stacked ridge regression according to claim 1 is characterized in that: In step S4: a stacked ridge regression model is established based on the principle that the active power of parent and child nodes at all levels satisfies the power conservation principle, the regression coefficient is calculated and solved, and the child nodes whose coefficient values ​​exceed the preset value and whose average power is relatively significant are assigned to the corresponding parent nodes, so as to sort out the topological relationship step by step.

7. The self-supervised low-voltage area topology identification method based on stacked ridge regression according to claim 6 is characterized in that: It includes sorting out the topological relationship between the substation assessment table and the intelligent circuit breakers at all levels in the substation measurement equipment, which specifically includes the following sub-steps: Step S4-A1: Initialize and set the layer data parameters b =1, parent node number parameter j =1, the child node set is initialized to empty ; Step S4-A2: Establish the b Level j The stacked ridge regression model of parent-child nodes is initialized with the number of stacking times. t =1; Initialize the number of child nodes is the maximum number that can be reached, that is, the number of all intelligent circuit breakers that are parent nodes are excluded; Step S4-A3: Calculate the b Level j Parent node t The coefficient vector of the sub-solution , the formula is, ， In the formula, is the active power matrix composed of the current intelligent circuit breaker sub-nodes, N Indicates the number of data points; Indicates the current remaining active power data of the parent node, that is, the original active power of the parent node minus the sum of the active power of the determined child nodes; represents the identity matrix; Step S4-A4: Select coefficient vector The sub-node set corresponding to the coefficient exceeding 0.85 in the above formula is used to calculate the average active power of each sub-node; the sub-node set whose average active power is greater than the mean of the average active power of all sub-nodes is retained. To determine the child node, ; Update the parent-child node data, that is, the remaining active power data at the current parent node Subtract the child node set from The active power matrix composed of the current intelligent circuit breaker sub-nodes is Remove the child node set represents the data sequence; Step S4-A5: If the coefficient vector If the number of values ​​exceeding 0.85 does not exceed 1, then execute step S4-A4 and then execute step S4-A6; otherwise, the number of stacking times is t To do incremental operation , repeat steps S4-A3 to S4-A4; Step S4-A6: If the level data parameter b =1, then execute step S4-A7; Otherwise, for the parent node number parameter j To do incremental operation , the remaining active power data of the current parent node Updated to j Repeat steps S4-A2 to S4-A5 to sort out the data of the parent node. j The topology of each parent node is sorted out until all parent nodes of the current level are sorted out; Step S4-A7: Level data parameters b To do incremental operation , reset the number of parent nodes at the current level j =1, child node set , record the child nodes of the current level as the parent nodes of the next level, repeat steps S4-A2 to S4-A6 until all intelligent circuit breakers are divided, and output the topological relationship between the substation assessment table and the intelligent circuit breakers at all levels .

8. The self-supervised low-voltage station area topology identification method based on stacked ridge regression according to claim 6 is characterized in that: It includes sorting out the topological relationship between the terminal intelligent circuit breaker as the meter box layer in the area measurement equipment and all user meters, which specifically includes the following sub-steps: Step S4-B1: Initialize the child node set to empty, that is ; Step S4-B2: Establish a stacked ridge regression model for all parent and child nodes and initialize the number of stacking times t =1; Step S4-B3: Calculate the t The coefficient matrix of the solution , the calculation formula is, ， In the formula, is the active power matrix composed of all user tables, U Indicates the number of user tables. Indicates t The residual active power matrix composed of all terminal intelligent circuit breakers during the second calculation is the original active power of each parent node minus the sum of the active power of its subordinate child nodes that have been determined; Step S4-B4: Determine the pre-assignment relationship between the meter box and the user according to the maximum coefficient principle, that is, the pre-assignment number of the terminal intelligent circuit breaker to which the user meter belongs is ; Moreover, in each parent node, a set of child nodes whose average active power is greater than the mean of the average active power of all corresponding child nodes is retained To determine the child node, update the child node set , update the parent-child node data, that is, the remaining active power matrix composed of all terminal intelligent circuit breakers Subtract the corresponding child node set from The active power matrix composed of all user tables is Remove the child node set represents the data sequence; Step S4-B5: Stacking times t To do incremental operation , repeat steps S4-B3 to S4-B4, if the active power matrix composed of all user tables The number of remaining columns does not exceed the number of terminal intelligent circuit breakers J , then keep the result and output the topological relationship between the terminal intelligent circuit breaker and all user tables.

9. The self-supervised low-voltage area topology identification method based on stacked ridge regression according to claim 1, characterized in that: In step S5: if the topology result is consistent with the existing archival information of the substation, then the user table corresponding to the regression coefficient exceeding the preset value in the hierarchical node where the line loss rate exceeds the warning value is recorded, and it is reported as an abnormal event; otherwise, the line loss rate of each level with the intelligent circuit breaker in the substation measurement equipment as the parent node is calculated, and the result with the lowest average line loss rate is reported.

10. The self-supervised low-voltage area topology identification method based on stacked ridge regression according to claim 9 is characterized in that: The warning value is 70% and the preset value is 1.5.

Images