A low-voltage area topology identification method based on big data and micro-current transmission

Abstract

The present application relates to the technical field of power distribution network automation, in particular to a low-voltage transformer area topology identification method based on big data and micro-current transmission. The low-voltage transformer area includes a master station, an energy controller, a transformer area test table, intelligent circuit breakers installed at the outgoing lines of the transformer area at all levels, and a transformer area user table. The intelligent circuit breakers and the transformer area user table are provided with HPLC modules capable of transmitting micro-current signals. The intelligent circuit breakers have the function of identifying micro-current signals. The present application combines big data with micro-current transmission. Big data calculation is the main method, and micro-current transmission is the auxiliary method. The inherent defects of single use of big data or micro-current transmission are overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network automation, and in particular to a low-voltage substation topology identification method based on big data and micro-current transmission. Background Art

[0002] The topology relationship refers to the relationship between all user meters within a substation and the circuit breakers at each node. Correct substation topology is essential for analyzing substation line loss rates, locating and rapidly repairing faults, and managing three-phase imbalance. Currently, topology analysis of 10kV distribution networks is relatively accurate. However, for substation low-voltage distribution networks, after power transfer or load adjustment of branch lines, the topology often cannot be updated promptly and accurately due to factors such as personnel commitment and technical expertise. Therefore, accurately obtaining low-voltage substation topology has long been a challenge for power grid companies.

[0003] To obtain the topological relationship of low-voltage substations, some regional power grid companies use a method of detecting the power supply status of the lines after manual power outages to perform topological identification. This method is highly accurate and easy to use, but it wastes manpower, is inefficient, and power outages can cause inconvenience to residents. Consequently, two new automated identification technologies have emerged. One uses big data methods to sort out topological relationships at all levels using the conservation of current, power, or electricity. For example, the low-voltage substation topological relationship identification method and device based on current optimization matching disclosed in Publication No. CN110389269A is very prone to misjudgment for user meters whose loads are not in use, i.e., the user meter is assigned to the wrong branch. The other method uses a microcurrent transmission method, which issues a command to cause all user meters in the substation and the HPLC modules of the intelligent circuit breakers to transmit microcurrent signals. The device that receives the strongest signal is used as the direct superior device of the transmitting device. This method is significantly affected by grid noise, and signal injection can have a certain impact on power quality and grid operation. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the shortcomings of the existing technology and provide a low-voltage substation topology identification method based on big data and micro-current transmission.

[0005] The technical solution adopted by the present invention to solve its technical problems is as follows: the low-voltage substation includes a master station, an energy controller, a substation assessment table, an intelligent circuit breaker installed at each level of the substation outlet, and a substation user meter. The intelligent circuit breaker and the substation user meter are provided with an HPLC module capable of sending microcurrent signals. The intelligent circuit breaker has the function of identifying microcurrent signals. The low-voltage substation topology identification method includes the following steps:

[0006] S1: The master station sends clock signals to calibrate the energy controller, substation assessment table, intelligent circuit breaker, and substation user meter;

[0007] S2: The energy controller collects the forward active power data of the intelligent circuit breakers at all levels in the substation, the substation assessment table, and the substation user table;

[0008] S3: Based on the power conservation relationship, the Lasso regression model is used to sort out the topological structure of the substation from top to bottom;

[0009] S4: Determine whether there is a user table or intelligent circuit breaker to which the user belongs that has not been found. If so, execute S5; otherwise, do nothing.

[0010] S5: The energy controller sends a characteristic micro-current signal sending command to the user meter or intelligent circuit breaker that has not been found to belong, and identifies the device with the largest characteristic signal strength as the direct superior device of the sending device.

[0011] After the S1 time calibration, the clock synchronization error of the intelligent circuit breaker and the user meter is less than 2s.

[0012] The collection type of the forward active power data in S2 is frozen data.

[0013] The S3 includes the following sub-steps:

[0014] S31: Apply the Lasso regression model to the substation assessment table and the intelligent circuit breaker. Based on the power conservation relationship, solve the coefficient matrix of the intelligent circuit breaker. The intelligent circuit breakers with a coefficient greater than α1 and an average power of no less than 0.1kW are selected as the direct subordinate devices of the assessment table, that is, to find the first-level branch devices.

[0015] S32: Refer to the method in S31 to find the directly subordinate devices of each first-level branch device, and continue to sort out the hierarchical relationship from top to bottom until the hierarchical relationship between the substation assessment table and all intelligent circuit breakers is clearly sorted out;

[0016] S33: Based on the power conservation relationship and Lasso regression model, the coefficient matrix of the user table of each terminal intelligent circuit breaker and all substation user tables is solved, and the user table with a coefficient greater than α2 and an average power of not less than 0.1kW is used as the user table served by the intelligent circuit breaker.

[0017] The calculation formula for the coefficient matrix based on the Lasso regression model is:

[0018]

[0019] Where y is the forward active power vector of the upper device, X is the forward active power matrix of all lower devices, and β is the coefficient vector. is the estimated value of β, and λ is the regularization coefficient.

[0020] The value range of α1 is 0.8~0.9.

[0021] The value range of α2 is 0.85~0.95.

[0022] The S5 comprises the following sub-steps:

[0023] S51: The energy controller sends a characteristic micro-current signal to the user meter or smart circuit breaker for which no assigned user is found.

[0024] S52: Generate a micro-current signal with a characteristic code bit through the built-in resistor switching device of the HPLC module and inject it into the power line, and record the injection time;

[0025] S53: The energy controller with cross-collection function and the intelligent circuit breaker simultaneously perform continuous characteristic current detection on the power line, recording whether a characteristic micro-current signal is detected and the strength of the detected characteristic signal;

[0026] S54: Compare the strengths of the characteristic signals detected by the various identification devices. The device with the largest detection strength is the direct superior device of the sending device.

[0027] Compared with the prior art, the present invention has the following beneficial effects: the present invention only needs to obtain the 9-point and 6-point forward active power data of the substation assessment table, the intelligent circuit breaker and the user table, and uses the power conservation relationship and the Lasso regression model to sort out the substation topology relationship from top to bottom. For some user tables or intelligent circuit breakers that cannot be found because the load they carry does not use electricity or the power consumption is very small, a micro-current transmission method is further adopted to detect the intelligent circuit breaker with the largest characteristic signal intensity as its direct superior device. The present invention combines big data with micro-current transmission, with big data calculation as the main method and micro-current transmission as the auxiliary method, overcoming the inherent defects of using only big data or micro-current transmission. It can solve the problem of misjudgment of measuring equipment due to the load not using electricity, and will not affect the operating status of the power grid due to the long-term injection of micro-current. The obtained low-voltage substation topology identification result is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flow chart of the present invention.

[0029] Figure 2 This is a flowchart of the present invention for sorting out topological structures based on the big data method.

[0030] Figure 3 It is a flow chart of further sorting out the topological structure based on the micro-current sending method described in the present invention.

[0031] Figure 4 It is a schematic diagram of further sorting out the topological structure based on the micro-current sending method described in the present invention. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0033] Referring to Figures 1-4 , the low-voltage transformer area includes a master station, an energy controller, a transformer area test table, intelligent circuit breakers installed at each level of outgoing lines of the transformer area, and a transformer area user table, the intelligent circuit breakers and the transformer area user table are provided with HPLC modules capable of sending micro-current signals, the intelligent circuit breakers have a micro-current signal identification function, and a low-voltage transformer area topology identification method includes the following steps:

[0034] S1: the master station issues a clock signal to calibrate the energy controller, and the energy controller broadcasts a calibration command to calibrate the transformer area test table, all intelligent circuit breakers of the transformer area, and the transformer area user table, so as to ensure the unity of time; after calibration in S1, it is necessary to ensure that the clock synchronization error of all intelligent circuit breakers and the user table is less than 2s.

[0035] S2: the energy controller collects 9-point and 6-point forward active power data of the intelligent circuit breakers at each level of the transformer area, the transformer area test table, and the transformer area user table through the HPLC module; the collection type of the forward active power data in S2 is frozen data.

[0036] S3: according to the power conservation relationship, the topology structure of the transformer area is combed from top to bottom by using a Lasso regression model; S3 includes the following sub-steps:

[0037] S31: the Lasso regression model is applied to the transformer area test table and all intelligent circuit breakers, the coefficient matrix of the intelligent circuit breakers is solved from the power conservation relationship, the intelligent circuit breakers with a solved coefficient greater than α1 and an average power not lower than 0.1kW are taken as subordinate subordinate devices of the test table, that is, the first-level branch device is found; the value range of α1 is 0.8-0.9.

[0038] S32: referring to the method of S31, the subordinate subordinate devices of each first-level branch device are found, and the combing is continued from top to bottom until the hierarchical relationship between the transformer area test table and all intelligent circuit breakers is combed clearly;

[0039] S33: according to the power conservation relationship and the Lasso regression model, the coefficient matrix of the user table is solved for each terminal intelligent circuit breaker and all transformer area user tables combed out, and the user table with a solved coefficient greater than α2 and an average power not lower than 0.1kW is taken as the user table carried by the intelligent circuit breaker. The value range of α2 is 0.85-0.95.

[0040] According to the Lasso regression model, the regression coefficients of the superior device and the subordinate device are solved, and the optimization target of the Lasso regression model is:

[0041]

[0042] Wherein, y is the positive active power vector of the superior device, X is the positive active power matrix of all subordinate devices, β is the coefficient vector, is the estimated value of β, and λ is the regularization coefficient.

[0043] S4: According to the station area file, it is judged whether there is a user table or intelligent circuit breaker which is not found to belong to, if there is, S5 is executed, otherwise no operation is performed;

[0044] S5: The energy controller issues a characteristic micro-current signal sending command to the user table or intelligent circuit breaker which is not found to belong to, and the device with the maximum characteristic signal strength is taken as the subordinate superior device of the sending device.

[0045] The S5 includes the following sub-steps:

[0046] S51: The energy controller issues a characteristic micro-current signal sending command to the user table or intelligent circuit breaker which is not found to belong to according to the file;

[0047] S52: The micro-current signal of the characteristic code bit is injected into the power line by the resistance switching device built in the HPLC module, and the injection time is recorded;

[0048] S53: The energy controller and the intelligent circuit breaker with the function of power supply and load are simultaneously used to detect the characteristic current of the power line continuously, and whether the characteristic micro-current signal is detected and the strength of the detected characteristic signal is recorded;

[0049] S54: The characteristic signal strength detected by each identification device is compared, and the device with the maximum detection strength is the subordinate superior device of the sending device.

[0050] In S5, the subordinate superior device of the user table or intelligent circuit breaker which sends the micro-current is determined according to the maximum principle of the detected signal strength.

[0051] The present application mainly uses big data calculation and is assisted by micro-current sending, which overcomes the inherent defects of single use of big data or micro-current sending, can solve the problem of misjudgment of the user table due to the non-use of the load, and will not affect the operation state of the power grid due to long-time injection of micro-current, so that the low-voltage transformer area topology identification result is more accurate.

[0052] The above descriptions are merely optional embodiments of the present invention and do not limit the patent scope of the present invention. All equivalent structural transformations made using the contents of the present description and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included in the patent protection scope of the present invention.

Claims

1. A low-voltage area topology identification method based on big data and micro-current transmission, characterized in that: The low-voltage substation includes a master station, an energy controller, a substation assessment table, an intelligent circuit breaker installed at each level of the substation outgoing line, and a substation user table. The intelligent circuit breaker and substation user table are equipped with an HPLC module capable of sending microcurrent signals. The intelligent circuit breaker has the function of identifying microcurrent signals. The low-voltage substation topology identification method includes the following steps: S1: The master station sends clock signals to calibrate the energy controller, substation assessment table, intelligent circuit breaker, and substation user meter; S2: The energy controller collects the forward active power data of the intelligent circuit breakers at all levels in the substation, the substation assessment table, and the substation user table; S3: Based on the power conservation relationship, the Lasso regression model is used to sort out the topological structure of the substation from top to bottom. S3 includes the following sub-steps: S31: Apply the Lasso regression model to the substation assessment table and the intelligent circuit breaker. Based on the power conservation relationship, solve the coefficient matrix of the intelligent circuit breaker. The intelligent circuit breakers with a coefficient greater than α1 and an average power of no less than 0.1kW are selected as the direct subordinate devices of the assessment table, that is, to find the first-level branch devices. S32: Refer to the method in S31 to find the directly subordinate devices of each first-level branch device, and continue to sort out the hierarchical relationship from top to bottom until the hierarchical relationship between the substation assessment table and all intelligent circuit breakers is clearly sorted out; S33: Based on the power conservation relationship and Lasso regression model, the coefficient matrix of the user table is solved for each terminal intelligent circuit breaker and all substation user tables. The user table with a coefficient greater than α2 and an average power of not less than 0.1kW is used as the user table served by the intelligent circuit breaker; S4: Determine whether there is a user table or intelligent circuit breaker to which the user belongs that has not been found. If so, execute S5; otherwise, do nothing. S5: The energy controller sends a characteristic micro-current signal sending command to the user meter or intelligent circuit breaker that has not been found to belong, and identifies the device with the largest characteristic signal strength as the direct superior device of the sending device.

2. The method for identifying low-voltage substation topology based on big data and micro-current transmission according to claim 1, characterized in that: After the S1 time calibration, the clock synchronization error of the intelligent circuit breaker and the user meter is less than 2s.

3. The method for identifying low-voltage substation topology based on big data and micro-current transmission according to claim 1, characterized in that: The collection type of the forward active power data in S2 is frozen data.

4. The method for identifying low-voltage substation topology based on big data and micro-current transmission according to claim 1, characterized in that: The calculation formula for the coefficient matrix based on the Lasso regression model is: Where y is the forward active power vector of the upper device, X is the forward active power matrix of all lower devices, and β is the coefficient vector. is the estimated value of β, and λ is the regularization coefficient.

5. The method for identifying low-voltage substation topology based on big data and micro-current transmission according to claim 1, characterized in that: The value range of α1 is 0.8 to 0.

9.

6. The method for identifying low-voltage substation topology based on big data and micro-current transmission according to claim 1, characterized in that: The value range of α2 is 0.85 to 0.

95.

7. The method for identifying low-voltage substation topology based on big data and micro-current transmission according to claim 1, characterized in that: The S5 comprises the following sub-steps: S51: The energy controller sends a characteristic micro-current signal to the user meter or smart circuit breaker for which no assigned user is found. S52: Generate a micro-current signal with a characteristic code bit through the built-in resistor switching device of the HPLC module and inject it into the power line, and record the injection time; S53: The energy controller with cross-collection function and the intelligent circuit breaker simultaneously perform continuous characteristic current detection on the power line, recording whether a characteristic micro-current signal is detected and the strength of the detected characteristic signal; S54: Compare the strengths of the characteristic signals detected by the various identification devices. The device with the largest detection strength is the direct superior device of the sending device.

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