Intelligent electrical topology generation method suitable for power load resource investigation

Abstract

The invention discloses an intelligent electrical topology generation method suitable for power load resource investigation. The intelligent electrical topology generation method comprises the following steps: creating a power utilization database of a power system to collect and store power utilization data in real time; determining a data accurate characterization coefficient according to the electricity consumption abnormity characteristics so as to judge the tendency category of electrical topology generation; selecting an electrical topology generation mode according to the tendency category of electrical topology generation: eliminating error data in the user data and replacing the error data with an average value, determining the current flowing direction and the power flowing direction of each coupling node, extracting the current and the power of each coupling node to determine the connection relationship and the branch distribution of each coupling node, and generating the electrical topology according to the current flowing direction and the power flowing direction of each coupling node; generating an electrical topological graph; alternatively, the elements and the connection relationships are identified to generate an electrical topological graph. According to the method, the accuracy of the electrical topological graph information is improved on the premise of ensuring the data reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical topology generation, and in particular to an intelligent electrical topology generation method suitable for power load resource investigation. Background Art

[0002] With the expansion of the power system, the number of equipment has increased dramatically. The traditional manual drawing method cannot efficiently handle such a huge amount of data. The automated topology map generation method can quickly process and analyze large amounts of data and improve work efficiency. At the same time, with the construction of smart grids, it is necessary to monitor and analyze the topology of the power system in real time in order to perform effective fault diagnosis, power dispatching and load resource management. The power topology map can provide accurate network topology information to support the operation and management of smart grids.

[0003] CN117639239A discloses a method for identifying and automatically generating low-voltage substation topology, and the steps are as follows: collecting data after substation equipment is synchronized; dividing substation electrical data into historical data and real-time data, and performing data mining on the electrical data; sorting out the energy conservation relationship of historical data, and generating a topological relationship database; screening effective real-time data; integrating, calculating, and analyzing effective real-time data; using effective real-time data to improve topological relationship data; dividing communication nodes into identifiable nodes and unidentifiable nodes; vectorizing each identifiable node, and constructing a substation topological relationship based on topological relationship data and real-time data; relying on equipment archive information and type parameters, combined with dynamic programming methods, automatically drawing the topology.

[0004] However, the following problems still exist in the prior art: when generating an electrical topology map, the frequency of faults in the power consumption data and the impact of abnormal frequency data on the generation results of the electrical topology map are not considered. Due to the presence of a large amount of abnormal data, the accuracy of the information presented by the electrical topology map may be reduced. Summary of the invention

[0005] To this end, the present invention provides an intelligent electrical topology generation method suitable for power load resource investigation, so as to overcome the problems existing in the prior art and improve the accuracy of electrical topology information while ensuring data reliability.

[0006] To achieve the above object, the present invention provides an intelligent electrical topology generation method suitable for power load resource investigation, which comprises: Creating a power consumption database of the power system to collect and store power consumption data in real time, wherein the power consumption data includes current, power and power consumption abnormality characteristics of several coupling nodes, wherein the power consumption abnormality characteristics include fault occurrence frequency and abnormal frequency; Determining a data accurate characterization coefficient according to the abnormal power consumption characteristics to determine a tendency category of electrical topology generation; According to the tendency category of the electrical topology generation, the electrical topology generation method is selected, including: Eliminate erroneous data in the user data and replace it with an average value, determine the current flow direction and power flow direction of each coupling node, extract the current and power of each coupling node to determine the connection relationship and branch distribution of each coupling node, and generate an electrical topology diagram; Or, identify components and connection relationships to generate an electrical topology diagram.

[0007] Furthermore, the process of determining the coefficients for accurately representing the data includes: Calculating the ratio of the abnormal frequency to the abnormal frequency threshold and assigning a corresponding weight coefficient as the first data feature; Assigning a corresponding weight coefficient to the fault occurrence frequency as the second data feature; The sum of the first data feature and the second data feature is determined as a data accuracy characterization coefficient.

[0008] Furthermore, the tendency category of electrical topology generation is determined, including: If the data accurate characterization coefficient is greater than or equal to the data accurate characterization coefficient threshold, the electrical topology generation is determined to be a weak tendency category; If the data accurate characterization coefficient is less than the data accurate characterization coefficient threshold, the electrical topology is determined to be generated as a strong tendency category.

[0009] Furthermore, a topology generation method is selected, including: If the electrical topology is generated as a weak tendency category, the erroneous data in the user data is removed and replaced with the average value, the current flow direction and the power flow direction of each coupling node are determined, and the current and power of each coupling node are extracted to determine the connection relationship and branch distribution of each coupling node to generate an electrical topology diagram; If the electrical topology is generated as a strong tendency category, the components and connection relationships are identified to generate an electrical topology diagram.

[0010] Furthermore, the erroneous data to be eliminated include missing or error data and abnormal fluctuation data.

[0011] Further, the process of determining the current flow direction of each coupling node includes: The path that the charges move in the wire is determined as the direction of current flow.

[0012] Further, the process of determining the power flow direction of each coupling node includes: A path direction in which power flows from the coupling node to the load node is determined as a power flow direction.

[0013] Further, determining the connection relationship of each of the coupling nodes includes: Determining the connection direction between the coupling nodes according to the current flow vector of each coupling node; If the current outflow amount of a coupling node is equal to the current inflow amount of any other coupling node, it is determined that a connection relationship exists.

[0014] Further, determining the branch distribution of each coupling node includes: Obtaining the power flow direction of each of the coupling nodes; If the difference between the power of a coupling node and the power of any other coupling node in the same power flow direction is within a predetermined range, it is determined that they are in the same branch.

[0015] Furthermore, the components to be identified include power supplies, loads, and wires.

[0016] Compared with the prior art, the present invention improves the accuracy of electrical topology information while ensuring data reliability.

[0017] In particular, the present invention determines the data accuracy characterization coefficient based on the abnormal characteristics of electricity consumption. In actual situations, the electricity consumption data may contain a lot of erroneous data and abnormal values, which will affect the accuracy of the generated electrical topology information, such as data missing, data jumps, and repeated values, as well as communication failures, line failures, system failures and other failures. Therefore, the present invention calculates the data preparation characterization coefficient through the frequency of fault occurrence and the abnormal frequency to characterize the accuracy of user data, provide data support for the subsequent determination of the category of electrical topology generation, and then adaptively select the electrical topology generation method.

[0018] In particular, the present invention uses electrical topology to generate weak categories to characterize the situation where the accuracy of user data is low and the accuracy of the generated electrical topology map information is low. Therefore, the present invention eliminates erroneous data in the user data and replaces it with an average value to determine the accuracy of the power consumption data, and then obtains the current and power of each coupling node and the flow direction corresponding to the current and power to determine the connection relationship between each coupling node, and generates an electrical topology generation map. The present invention can improve the accuracy of the generated electrical topology map information while ensuring data reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of the steps of a method for generating an intelligent electrical topology applicable to power load resource troubleshooting according to an embodiment of the present invention; Figure 2 A logic decision diagram for determining the tendency category of electrical topology generation according to an embodiment of the present invention; Figure 3A logic decision diagram for selecting an electrical topology generation method for an embodiment of the present invention; Figure 4 This is a logic determination diagram for determining whether there is a connection relationship between coupling nodes according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0022] The intelligent electrical topology generation method applicable to power load resource troubleshooting of the embodiment of the present invention is as follows: Figure 1 As shown, including: Step S1, creating a power consumption database of the power system to collect and store power consumption data in real time, wherein the power consumption data includes current, power and power consumption abnormality characteristics of several coupling nodes, wherein the power consumption abnormality characteristics include fault occurrence frequency and abnormal frequency; Step S2, determining a data accurate characterization coefficient according to the abnormal power consumption characteristics to determine a tendency category of electrical topology generation; Step S3, selecting an electrical topology generation method according to the tendency category of the electrical topology generation, including: Eliminate erroneous data in the user data and replace it with an average value, determine the current flow direction and power flow direction of each coupling node, extract the current and power of each coupling node to determine the connection relationship and branch distribution of each coupling node, and generate an electrical topology diagram; Or, identify components and connection relationships to generate an electrical topology diagram.

[0023] In this embodiment, the electrical topology diagram is generated according to the following method: Step 1, determine the information of each coupling node, including clarifying all the coupling nodes involved in the power system, numbering and marking each node, recording the location and type of the above coupling nodes, such as power nodes, load nodes, and intermediate transfer nodes, as well as related electrical parameters, such as voltage level, rated power, etc.; For example, in the power supply system of a factory, it is necessary to clearly identify the substation nodes that serve as power input, the power load nodes of each workshop, etc., and record in detail the power supply voltage of the substation, the power demand of the load of each workshop, etc.; Step 2, sort out the existing connection relationships between the coupling nodes, including the connection type between any two coupling nodes, the type of electrical components used for the connection, and the direction of the connection; The connection types include direct connection, connection through series electrical components, etc. For directional components, for example, the circuit part composed of diodes, the direction of current flow needs to be clear; Step 3, organize the branch information, including marking the specific electrical equipment and corresponding parameters contained in each branch and accurately recording the connection order between the equipment, and determining the starting and ending nodes of each branch. Among them, electrical equipment includes resistors, capacitors, inductors, transformers, switches, etc.; parameters include resistance, capacitance, inductance, transformation ratio, open and closed status, etc.; determine the starting and ending nodes of the branch by clarifying the two coupling nodes connected to each branch, which also helps to determine the location of the connection when accurately constructing the topology diagram later.

[0024] For example, a branch line from a substation to a workshop may include a section of cable, several disconnectors, a step-down transformer, etc.

[0025] Step 4, determine the software for drawing the electrical topology diagram. In this embodiment, AutoCAD Electrical professional electrical drawing software is selected. AutoCAD Electrical has a rich electrical symbol library, which can easily draw various electrical components, and can connect according to the set rules, accurately present the electrical connection relationship, and can perform electrical rule checks on the drawn diagram to ensure the accuracy of the topology diagram.

[0026] Step 5, drawing an electrical topology diagram, including placing graphic symbols representing each coupling node in the corresponding area of ​​the drawing tool in sequence according to the position and number of the coupling node determined above; starting from the starting coupling node according to the sorted branch information, a straight line or curve is selected to connect to the ending coupling node according to the direction of the actual line to indicate the direction of the branch; on the branch, according to the recorded electrical component conditions, the corresponding component symbols are added to the connection line in sequence, and placed in the actual connection order, and the key parameters of the electrical components are marked at appropriate positions near the component symbols; For example, on a branch circuit that contains a resistor and a capacitor in series, after drawing the connection line, place the resistor symbol and capacitor symbol in order on the connection line, and mark the resistance and capacitance values ​​respectively.

[0027] Step 6, complete the details of the electrical topology diagram, including adding necessary text annotations, such as the title of the electrical topology diagram, drawing date, version number and other basic information, to facilitate subsequent viewing and management; color-coding the electrical topology diagram, for example, branches of different voltage levels can be represented by different colors, or parts of the same functional area can be marked with the same color to enhance the readability of the topology diagram and obtain key information from the diagram more quickly; check whether all connection relationships in the topology diagram are consistent with the collected information to ensure that there are no missing nodes, branches, and incorrect connections, and to ensure that the topology diagram accurately reflects the actual electrical coupling node connection relationship and branch distribution.

[0028] Specifically, there is no limitation on the type of electricity consumption database, which only needs to be able to collect and store electricity consumption data in real time, such as MySQl, ObjectDB, which will not be elaborated here.

[0029] Specifically, the process of determining the coefficients that accurately represent the data includes: Calculating the ratio of the abnormal frequency to the abnormal frequency threshold and assigning a corresponding weight coefficient as the first data feature; Assigning a corresponding weight coefficient to the fault occurrence frequency as the second data feature; The sum of the first data feature and the second data feature is determined as a data accuracy characterization coefficient.

[0030] In this embodiment, the data accuracy characterization coefficient is calculated according to the following formula:

[0031] Where D represents the data accuracy characterization coefficient, P represents the abnormal frequency, P0 represents the abnormal frequency threshold, C represents the fault frequency, α represents the abnormal frequency weight coefficient, and β represents the fault frequency weight coefficient.

[0032] In this embodiment, the abnormal frequency threshold P0 and the interaction frequency threshold D0 are obtained in advance, and the power consumption data for generating the electrical topology diagram is obtained several times, the abnormal frequency data is extracted, and the abnormal frequency mean is solved. , set P0 = r × , where r represents the coefficient of accuracy, 1.05<r<1.1, α is 0.45, and β is 0.55.

[0033] In this embodiment, when a power system fails, it is more intuitively manifested as a problem with the load in the system or the operation of the system itself, that is, the frequency of the failure. As for the abnormal frequency, it is the number of abnormalities in the power system within a certain period of time. The data representation of the fault frequency is stronger than that of the abnormal frequency. At the same time, the occurrence of a fault will affect the level of the abnormal frequency, so the weights of the two will not differ too much. Therefore, the abnormal frequency weight coefficient α is taken as 0.45, and the fault frequency weight coefficient β is taken as 0.55.

[0034] It can be understood that the communication failures that occur during data transmission, the system failures of the power system, the line failures of the power lines, and the failures caused by weather conditions that affect the operation of power equipment, and the frequency of the above failures occurring in the power consumption data are determined as the failure occurrence frequency, which will not be elaborated here.

[0035] The present invention determines the data accuracy characterization coefficient according to the abnormal characteristics of electricity consumption. In actual situations, the electricity consumption data may contain a lot of erroneous data and abnormal values, which will affect the accuracy of the generated electrical topology information, such as data missing, data jump, and repeated values, as well as communication failures, line failures, system failures and other failures. Therefore, the present invention calculates the data preparation characterization coefficient through the frequency of fault occurrence and the abnormal frequency to characterize the accuracy of user data, provide data support for the subsequent determination of the category of electrical topology generation, and then adaptively select the electrical topology generation method.

[0036] Specifically, determine the tendency category of electrical topology generation, such as Figure 2 As shown, including: If the data accurate characterization coefficient is greater than or equal to the data accurate characterization coefficient threshold, the electrical topology generation is determined to be a weak tendency category; If the data accurate characterization coefficient is less than the data accurate characterization coefficient threshold, the electrical topology is determined to be generated as a strong tendency category.

[0037] The data accuracy characterization coefficient threshold D0 is selected within the interval [0.56, 0.81]. It can be understood that the purpose of setting the data accuracy characterization coefficient is to characterize the tendency of erroneous data to appear in actual situations. The first data feature characterizes the specific situation of the abnormal frequency under normal conditions, and usually fluctuates in the interval of [0.85, 1.25]. The second data feature usually fluctuates in the interval of [0.35, 0.45]. In order to make the data accuracy characterization coefficient threshold characterize the tendency of data abnormalities under normal conditions, the interval lower limit of the data accuracy characterization coefficient threshold is calculated based on the interval lower limit of the usual values ​​of the first data feature and the interval lower limit of the usual values ​​of the second data feature, and the interval upper limit of the data accuracy characterization coefficient threshold is calculated based on the interval upper limit of the usual values ​​of the first data feature and the interval upper limit of the usual values ​​of the second data feature.

[0038] Specifically, select the topology generation method, such as Figure 3 As shown, including: If the electrical topology is generated as a weak tendency category, the erroneous data in the user data is removed and replaced with the average value, the current flow direction and the power flow direction of each coupling node are determined, and the current and power of each coupling node are extracted to determine the connection relationship and branch distribution of each coupling node to generate an electrical topology diagram; If the electrical topology is generated as a strong tendency category, the components and connection relationships are identified to generate an electrical topology diagram.

[0039] The present invention uses electrical topology to generate weak categories to characterize the situation where the accuracy of user data is low and the accuracy of the generated electrical topology map information is low. Therefore, the present invention eliminates erroneous data in the user data and replaces it with an average value to determine the accuracy of the power consumption data. Furthermore, by obtaining the current and power of each coupling node and the flow direction corresponding to the current and power, the connection relationship between each coupling node is determined to generate an electrical topology generation map. The present invention can improve the accuracy of the generated electrical topology map information while ensuring data reliability.

[0040] Specifically, the erroneous data that need to be eliminated include missing or error data and abnormal fluctuation data.

[0041] Specifically, the process of determining the current flow direction of each coupling node includes: The path that the charges move in the wire is determined as the direction of current flow.

[0042] Specifically, the process of determining the power flow direction of each coupling node includes: A path direction in which power flows from the coupling node to the load node is determined as a power flow direction.

[0043] Specifically, the connection relationship of each coupling node is determined, such as Figure 4 As shown, including: Determining the connection direction between the coupling nodes according to the current flow vector of each coupling node; If the current outflow of a coupling node is equal to the current inflow of any other coupling node, it is determined that there is a connection relationship; If the current outflow amount of a coupling node is not equal to the current inflow amount of any other coupling node, it is determined that there is no connection relationship.

[0044] Specifically, determining the branch distribution of each coupling node includes: Obtaining the power flow direction of each of the coupling nodes; If the difference between the power of a coupling node and the power of any other coupling node in the same power flow direction is within a predetermined range, it is determined that they are in the same branch; If the difference between the power of a coupling node and the power of any other coupling node in the same power flow direction is not within a predetermined range, it is determined that they are not on the same branch.

[0045] It is understandable that the power interpolation range between coupling nodes in the same power flow direction can be determined according to the scale of the power system, load characteristics and topological structure of the power grid, which will not be elaborated here.

[0046] Specifically, the components that need to be identified include power supply, load, and wires.

[0047] Application Examples The coupling nodes are locally marked in the power system. A total of 14 coupling nodes are marked. A total of 60 monitoring cycles are set. The duration of a single monitoring cycle is 24 hours. The abnormal frequency of the coupling nodes in each monitoring cycle is recorded. The maintenance records of the local power grid where each coupling node is located are called to determine the fault frequency of the local power grid. The records are shown in Table 1.

[0048] Table 1. Abnormal power consumption characteristics of coupling nodes

[0049] In Table 1, the fault occurrence frequency of the local power grid where the coupling node is located is recorded as the fault occurrence frequency of the corresponding coupling node.

[0050] Furthermore, the average abnormal frequency of the coupled nodes is 1.64, and the average fault occurrence frequency is 0.57.

[0051] The abnormal frequency threshold is 1.25, and the data accuracy coefficient is determined based on the data in Table 1

[0052] The data accuracy characterization coefficient threshold is set to 0.8 in the application; It can be seen that the data accurate characterization coefficient is greater than the data accurate characterization coefficient threshold, and the electrical topology generation is determined to be a weak tendency category, and the subsequent topology generation method is executed.

[0053] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. An intelligent electrical topology generation method suitable for power load resource investigation, characterized in that: include: Creating a power consumption database of the power system to collect and store power consumption data in real time, wherein the power consumption data includes current, power and power consumption abnormality characteristics of several coupling nodes, wherein the power consumption abnormality characteristics include fault occurrence frequency and abnormal frequency; Determining a data accurate characterization coefficient according to the abnormal power consumption characteristics to determine a tendency category of electrical topology generation; According to the tendency category of the electrical topology generation, the electrical topology generation method is selected, including: Eliminate erroneous data in the user data and replace it with an average value, determine the current flow direction and power flow direction of each coupling node, extract the current and power of each coupling node to determine the connection relationship and branch distribution of each coupling node, and generate an electrical topology diagram; Or, identify components and connection relationships to generate an electrical topology diagram.

2. The intelligent electrical topology generation method suitable for power load resource investigation according to claim 1 is characterized in that: The process of determining coefficients that accurately represent the data includes: Calculating the ratio of the abnormal frequency to the abnormal frequency threshold and assigning a corresponding weight coefficient as the first data feature; Assigning a corresponding weight coefficient to the fault occurrence frequency as the second data feature; The sum of the first data feature and the second data feature is determined as a data accuracy characterization coefficient.

3. The intelligent electrical topology generation method suitable for power load resource investigation according to claim 1 is characterized in that: Determine the tendency category of electrical topology generation, including: If the data accurate characterization coefficient is greater than or equal to the data accurate characterization coefficient threshold, the electrical topology generation is determined to be a weak tendency category; If the data accurate characterization coefficient is less than the data accurate characterization coefficient threshold, the electrical topology is determined to be generated as a strong tendency category.

4. The intelligent electrical topology generation method suitable for power load resource investigation according to claim 1 is characterized in that: Select the topology generation method, including: If the electrical topology is generated as a weak tendency category, the erroneous data in the user data is removed and replaced with the average value, the current flow direction and the power flow direction of each coupling node are determined, and the current and power of each coupling node are extracted to determine the connection relationship and branch distribution of each coupling node to generate an electrical topology diagram; If the electrical topology is generated as a strong tendency category, the components and connection relationships are identified to generate an electrical topology diagram.

5. The intelligent electrical topology generation method suitable for power load resource investigation according to claim 1 is characterized in that: The erroneous data that need to be eliminated include missing or error data and abnormal fluctuation data.

6. The intelligent electrical topology generation method suitable for power load resource investigation according to claim 1 is characterized in that: The process of determining the current flow direction of each coupling node includes: The path that the charges move in the wire is determined as the direction of current flow.

7. The intelligent electrical topology generation method suitable for power load resource investigation according to claim 1 is characterized in that: The process of determining the power flow direction of each coupling node includes: A path direction in which power flows from the coupling node to the load node is determined as a power flow direction.

8. The intelligent electrical topology generation method suitable for power load resource investigation according to claim 1 is characterized in that: Determining the connection relationship of each of the coupling nodes includes: Determining the connection direction between the coupling nodes according to the current flow vector of each coupling node; If the current outflow amount of a coupling node is equal to the current inflow amount of any other coupling node, it is determined that a connection relationship exists.

9. The intelligent electrical topology generation method suitable for power load resource investigation according to claim 1, characterized in that: Determining the branch distribution of each coupling node includes: Obtaining the power flow direction of each of the coupling nodes; If the difference between the power of a coupling node and the power of any other coupling node in the same power flow direction is within a predetermined range, it is determined that they are in the same branch.

10. The intelligent electrical topology generation method suitable for power load resource investigation according to claim 1, characterized in that: The components that need to be identified include the power supply, the load, and the wires.

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