Current transformer reverse connection verification optimization method and system based on greedy algorithm

Abstract

The present invention relates to the field of power system monitoring and control technology, and is a current transformer reverse connection verification optimization method and system based on a greedy algorithm, comprising: building a distributed acquisition network for a regional distribution network, collecting the operating data of the current transformer in the regional distribution network through each distributed acquisition node, generating a multidimensional data set in combination with the unique numbers of the regional distribution network, ring network cabinet, and switch equipment, and uploading the multidimensional data set to an operation database; in the database, grouping and aggregating the multidimensional data set according to the ring network cabinet number to obtain multiple sub-level data sets; traversing the data in the multiple sub-level data sets respectively, obtaining multiple local optimal solutions of the sets through a greedy algorithm, and generating a current transformer reverse connection verification result. The present invention solves the technical problems that the current transformer reverse connection verification method in the prior art cannot perform batch discrimination on current transformers in a larger range, and has poor judgment efficiency and accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field related to power system monitoring and control, and in particular to a current transformer reverse connection verification optimization method and system based on a greedy algorithm. Background Art

[0002] Current transformers are devices used to measure the current in a circuit. When installing and maintaining these sensors, reverse connection judgment is often required. Reverse connection judgment refers to confirming whether the sensor wiring is correct, that is, whether the current flow direction is consistent with the direction expected by the sensor design.

[0003] At present, during the maintenance of current transformers, secondary protection professionals need to verify their polarity to ensure that the secondary circuit meets the protection and measurement requirements. Traditional testing methods such as AC and DC methods have limitations, especially the DC method, which is complicated to operate, requires the cooperation of professionals, and has low efficiency. Manual judgment of reverse connection is time-consuming and labor-intensive, and is prone to large errors due to human factors. Although the transformer comprehensive tester is intelligent and can intuitively display transformer information, it is expensive and can only test a single transformer.

[0004] In summary, the current transformer reverse connection verification method in the prior art has the technical problems of being unable to perform batch identification on a larger range of current transformers, and having low identification efficiency and poor accuracy. Summary of the invention

[0005] The present application provides a current transformer reverse connection verification optimization method and system based on a greedy algorithm, aiming to solve the technical problems that the current transformer reverse connection verification method in the prior art cannot perform batch identification of current transformers in a large range, and has low judgment efficiency and poor accuracy.

[0006] In view of the above problems, an embodiment of the present application provides a current transformer reverse connection verification optimization method and system based on a greedy algorithm.

[0007] The first aspect disclosed in the present application provides a current transformer reverse connection verification optimization method based on a greedy algorithm, the method comprising: determining a regional distribution network, building a distributed acquisition network for the regional distribution network, wherein the regional distribution network comprises a plurality of ring main units, each ring main unit has a plurality of switch devices, and a current transformer is installed on the inlet and outlet sides of each switch device, and the current transformer is used to obtain active power values, wherein the regional distribution network, the ring main unit, and the switch device all have unique numbers; through each distributed acquisition node in the distributed acquisition network, the operation data of the current transformer in the regional distribution network is collected, and a multidimensional data set is generated in combination with the unique numbers of the regional distribution network, the ring main unit, and the switch device, and the multidimensional data set is uploaded to an operation database; in the operation database, the multidimensional data set is grouped and aggregated according to the ring main unit number to obtain a plurality of sub-level data sets, and the plurality of sub-level data sets correspond one to one with the plurality of ring main units; the data in the plurality of sub-level data sets are traversed respectively, and a plurality of local optimal solutions of the sets are obtained through a greedy algorithm, and a current transformer reverse connection verification result is generated according to the plurality of local optimal solutions of the sets.

[0008] Another aspect disclosed in the present application provides a current transformer reverse connection verification optimization system based on a greedy algorithm, the system comprising: a distributed acquisition network building module: used to determine a regional distribution network, and to build a distributed acquisition network for the regional distribution network, wherein the regional distribution network comprises a plurality of ring network cabinets, each of which has a plurality of switch devices, and each switch device is equipped with a current transformer on the inlet and outlet sides, and the current transformer is used to obtain active power values, wherein the regional distribution network, the ring network cabinet, and the switch device all have unique numbers; a multidimensional data set generation module: used to collect the power in the regional distribution network through each distributed acquisition node in the distributed acquisition network. The operation data of the current transformer is combined with the unique numbers of the regional distribution network, ring main unit, and switchgear to generate a multidimensional data set, and the multidimensional data set is uploaded to the operation database; a sub-level data set acquisition module: used in the operation database, the multidimensional data set is grouped and aggregated according to the ring main unit number, and multiple sub-level data sets are acquired, and the multiple sub-level data sets correspond one to one with multiple ring main units; a reverse connection verification result generation module: used to traverse the data in multiple sub-level data sets respectively, obtain multiple local optimal solutions of the sets through a greedy algorithm, and generate the current transformer reverse connection verification result according to the multiple local optimal solutions of the sets.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] By building a distributed data collection network for the regional distribution network, the operating data of the current transformers in the regional distribution network are collected through each distributed data collection node, and a multidimensional data set is generated in combination with the unique numbers of the regional distribution network, ring network cabinet, and switch equipment; in the operating database, the multidimensional data set is grouped and aggregated according to the ring network cabinet number; the data in multiple sub-level data sets are traversed respectively, and multiple local optimal solutions of the sets are obtained through the greedy algorithm, and the current transformer reverse connection verification results are generated according to the multiple local optimal solutions of the sets. The technical effect of batch identification of current transformers in a large range and high judgment efficiency and accuracy is achieved.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A flow chart of a current transformer reverse connection verification optimization method based on a greedy algorithm is provided for an embodiment of the present application;

[0013] Figure 2 A schematic flow chart of obtaining multiple sets of local optimal solutions in a current transformer reverse connection verification optimization method based on a greedy algorithm is provided for an embodiment of the present application;

[0014] Figure 3 A possible structural schematic diagram of a current transformer reverse connection verification optimization system based on a greedy algorithm is provided for an embodiment of the present application.

[0015] Explanation of the reference numerals: module for building a distributed acquisition network 11, module for generating a multidimensional data set 12, module for obtaining a sub-level data set 13, module for generating a reverse connection verification result 14. DETAILED DESCRIPTION

[0016] The overall idea of ​​the technical solution provided by this application is as follows:

[0017] The embodiment of the present application provides a current transformer reverse connection verification optimization method and system based on a greedy algorithm, and proposes an effective solution to the current transformer reverse connection problem in the regional distribution network. The method first constructs a distributed acquisition network to collect current transformer operation data, and combines the unique number to form a multidimensional data set. Subsequently, the data is grouped and aggregated by the ring network cabinet number in the database to form a sub-level data set. Finally, the greedy algorithm is used to traverse the subset data to find the local optimal solution, thereby generating an accurate current transformer reverse connection verification result.

[0018] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0019] Embodiment 1

[0020] like Figure 1 As shown, an embodiment of the present application provides a current transformer reverse connection verification optimization method based on a greedy algorithm, the method comprising:

[0021] Step S100: determining a regional distribution network, and building a distributed data acquisition network for the regional distribution network, wherein the regional distribution network includes a plurality of ring network cabinets, each of which has a plurality of switch devices, and each switch device is equipped with a current transformer on the inlet and outlet sides, and the current transformer is used to obtain active power values, wherein the regional distribution network, the ring network cabinet, and the switch device all have unique numbers;

[0022] Specifically: First, it is necessary to conduct on-site surveys in the target areas to understand the existing distribution network structure, equipment layout and operating conditions. Based on the survey results, the specific scope of the regional distribution network is determined, including the covered geographical area, power supply load and major users, etc. A unique numbering system is compiled for the regional distribution network, ring main units and switchgear to facilitate subsequent data management and maintenance.

[0023] Furthermore, current transformers are installed on the incoming and outgoing line sides of the switchgear of each ring network cabinet, and distributed acquisition nodes are set up in each ring network cabinet or key locations to form a distributed acquisition network. Including, equipment inspection: confirm that the switchgear in each ring network cabinet is in good condition and has the conditions for installing current transformers. Install current transformers: Install current transformers on the incoming and outgoing line sides of each switchgear to ensure that the installation position is accurate and the connection is firm. Select equipment: Select a suitable data acquisition terminal according to actual needs, such as RTU (remote terminal unit) or DTU (data acquisition transmission unit). Installation location: Install the data acquisition terminal in a location that is easy to maintain and does not affect the normal operation of the equipment. Select communication technology: Select a suitable communication technology according to site conditions and communication needs, such as fiber optic communication, wireless communication, etc. Build a network: Establish a stable and reliable communication network to ensure unimpeded data transmission between the data acquisition terminal and the master station.

[0024] Step S200: collecting the operating data of the current transformers in the regional distribution network through each distributed collection node in the distributed collection network, generating a multidimensional data set in combination with the unique numbers of the regional distribution network, the ring main unit, and the switchgear, and uploading the multidimensional data set to the operation database;

[0025] Specifically, the distributed acquisition nodes are used to collect the operating data of the current transformer in real time and accurately, and integrate these data with the unique numbers of the corresponding regional distribution network, ring network cabinet, and switchgear to form a multi-dimensional data set. This data set is then uploaded to the operation database for subsequent data analysis, processing, and verification.

[0026] Among them, the multi-dimensional data includes regions, ring network cabinets, switchgear and active power values, and the active power values ​​are obtained through current transformers; the operating data of current transformers in the regional distribution network are sorted according to the data collection dimension to obtain a multi-dimensional data set.

[0027] Furthermore, uploading the multidimensional data set to the operation database includes obtaining upload hardware parameters of the distributed acquisition network, obtaining the optimal number of threads N for data upload according to the upload hardware parameters, and dividing the multidimensional data set into N data subsets; creating N data upload threads in the distributed acquisition network, each data upload thread corresponding to a data subset; starting the N data upload threads, and uploading the N data subsets to the operation database in parallel.

[0028] Furthermore, in step S200 of the present application, the operating data of the current transformer in the regional distribution network is collected, and a multidimensional data set is generated in combination with the unique numbers of the regional distribution network, the ring main unit, and the switchgear, including:

[0029] Setting data collection dimensions, wherein the data collection dimensions include regions, ring main units, switchgear, and active power values, wherein the active power values ​​are obtained through current transformers;

[0030] The operating data of the current transformers in the regional distribution network are sorted according to the data collection dimensions to obtain a multi-dimensional data set.

[0031] Specifically: the regional distribution network refers to a regional distribution network that usually covers a specific geographical area, such as a city, a district, and the like. In this region, there are multiple ring main units distributed in different locations, which together constitute the power distribution network of the region. The ring main unit is an independent functional unit, and its function is to connect and switch cables to achieve reliable distribution of electricity. There are usually multiple ring main units in a regional distribution network, and each ring main unit has its own unique name or number. Multiple switch devices, such as load switches and circuit breakers, are installed inside each ring main unit. These switch devices can be operated manually or remotely to connect, disconnect and switch cables. Each switch device also has its own unique name or number. The current transformer is for measurement and protection, and a current transformer is installed on the incoming and outgoing line sides of each switch device. The function of the current transformer is to convert high current into low current for easy measurement and collection. Each current transformer has a one-to-one correspondence with its corresponding switch device.

[0032] In the multidimensional data set (region, ring main unit name, switch name, active value), "region" indicates the regional distribution network to which the ring main unit belongs. "Ring main unit name" indicates the specific ring main unit corresponding to the data. "Switch name" indicates the specific switch device corresponding to the data. "Active value" indicates the active power data collected by the current transformer corresponding to the switch device. The operating data of the current transformer in the regional distribution network is sorted according to the data collection dimension to obtain a multidimensional data set.

[0033] Based on this step, the operating data of current transformers in the regional distribution network can be effectively collected, sorted and analyzed, providing strong support for the monitoring, control and management of the power system, thereby improving the efficiency of reverse connection judgment of current transformers.

[0034] Furthermore, in step S200 of the present application, uploading the multidimensional data set to the operation database includes:

[0035] Obtaining upload hardware parameters of the distributed acquisition network, wherein the upload hardware parameters include the number of CPU cores, memory capacity, network bandwidth, and storage performance;

[0036] According to the upload hardware parameters, the optimal number of threads N for data upload is obtained, and the multidimensional data set is evenly divided into N data subsets;

[0037] Creating N data upload threads in the distributed acquisition network according to the optimal number of threads N, each data upload thread corresponding to a data subset;

[0038] The N data upload threads are started to upload the N data subsets to the operation database in parallel.

[0039] Specifically: obtain the upload hardware parameters of the distributed acquisition network, which include the number of CPU cores, memory capacity, network bandwidth and storage performance; the number of CPU cores: represents the computing power of the server. Memory capacity: determines the amount of data that the server can process simultaneously. Network bandwidth: limits the rate of data transmission. Storage performance: determines the speed of writing to the database.

[0040] Based on the obtained upload hardware parameters, calculate the optimal number of threads N for data upload. The optimal number of threads should take into account the number of CPU cores, memory capacity, network bandwidth, and storage performance to ensure that resources are fully utilized while avoiding resource contention and overload. The calculation method for obtaining the optimal number of threads N for data upload includes:

[0041] Based on the number of CPU cores: Match the number of threads to the number of CPU cores. For example, if there is a quad-core CPU, theoretically four threads can be run simultaneously without causing too many context switches. However, this does not mean that it is the best choice in all cases. Based on hyperthreading technology: The CPU supports hyperthreading technology (such as Intel's Hyper-Threading), and each of its physical cores can handle two threads. In this case, the number of threads can be set to twice the number of physical cores. Based on memory and storage performance: The number of threads is also limited by memory and storage performance. Insufficient system memory or low storage performance will cause performance degradation in the process of increasing the number of threads, and threads will compete for limited resources. Testing and adjustment: Determine the optimal number of threads through actual testing. Use different numbers of threads to perform the same task and measure the performance under each configuration. By comparing the performance results under different configurations, find the optimal number of threads for the application and hardware environment. It should be noted that the optimal number of threads is a dynamic concept that will change with hardware upgrades, software updates, or changes in workloads.

[0042] Secondly, the multidimensional data set is divided into N data subsets (N≧2), each of which should contain a similar number of data items to ensure that the workload of each thread is relatively balanced, thereby improving the operating speed of the system.

[0043] Furthermore, N data upload threads are created in the distributed acquisition network according to the optimal number of threads N, wherein the N data upload threads refer to N independent execution units created in the distributed acquisition network for uploading multiple data subsets to the operation database in parallel. Each thread is responsible for processing a data subset, thereby achieving efficient data upload and optimized operation of the system.

[0044] Finally, N data upload threads are started, and N data subsets are uploaded to the running database in parallel. During the data upload process, the system is monitored and tuned. Monitoring can provide real-time information on the performance and status of the system, including CPU utilization, memory usage, network bandwidth usage, etc. If a thread is found to have a performance bottleneck or resource contention, the number of threads can be adjusted or the code logic can be optimized in a timely manner. In addition, load balancing technology can be used to balance the load between different servers to further improve the overall performance and stability of the system. The technical effect of batch identification of current transformers in a larger range is achieved. The efficiency and accuracy of batch identification of current transformers in a larger range can be guaranteed.

[0045] Step S300: In the operation database, grouping and aggregating the multidimensional data set according to the ring main unit number to obtain multiple sub-level data sets, where the multiple sub-level data sets correspond to multiple ring main units one by one;

[0046] Specifically: in the distributed data collection network, after the multidimensional data set is successfully uploaded to the operation database, the multidimensional data set is grouped and data aggregated according to the ring main unit number to obtain multiple sub-level data sets corresponding to multiple ring main units. This step includes: data preparation, data grouping, data aggregation, creation of sub-level data sets, etc.

[0047] Among them, in data preparation, it is necessary to ensure that the multidimensional data set uploaded to the running database contains the field of the ring network cabinet number, and the data in this field is accurate and complete. In the database, SQL queries or other data processing tools are used to group the data according to the ring network cabinet number field. Data items with the same ring network cabinet number are classified into the same data group. For each data group (that is, the data of each ring network cabinet), data aggregation is performed according to. The data aggregation refers to grouping the collected multidimensional data set according to a specific dimension (such as the ring network cabinet number), and performing some form of aggregation or calculation operation on the data within each group to generate a higher level of summary data or statistical information. Its operation may include calculating statistical information such as average, sum, maximum, minimum, etc., and further, the data group of each ring network cabinet (including aggregated data) is stored in the database as a sub-level data set. These sub-level data sets correspond one-to-one to multiple ring network cabinets, which facilitates subsequent data query and analysis.

[0048] Through data aggregation, useful information can be extracted from a large amount of raw data, which helps to understand the overall situation of the data, identify trends, discover anomalies or conduct more in-depth analysis, thereby improving the efficiency of batch identification of current transformers in a larger range.

[0049] Step S400: traverse the data in multiple sub-level data sets respectively, obtain multiple local optimal solutions of the sets through a greedy algorithm, and generate current transformer reverse connection verification results according to the multiple local optimal solutions of the sets.

[0050] Specifically: traverse multiple sub-level data sets, where each sub-level data set contains the region, ring network cabinet name, switch name, and active value, so as to obtain the first sub-level data set; the greedy algorithm is an algorithm that takes the best or optimal (i.e., most favorable) choice in the current state in each step of selection, so as to hope that the result will be the best or optimal. A "local optimal" standard needs to be defined. For example, define a rule that if the current reading is abnormally low (or negative), and the voltage reading is normal, and the phase angle does not conform to the normal current and voltage phase relationship, it is determined that the current transformer may be reversely connected.

[0051] Apply the greedy algorithm to each sub-level data set. Traverse each data record in the set and check whether the current transformer reverse connection condition is met according to the verification criteria and greedy strategy. For each sub-level data set, record the local optimal solution found by the greedy algorithm. Integrate the local optimal solutions of all sub-level data sets to generate an overall current transformer reverse connection verification result.

[0052] Further, such as Figure 2 As shown, in step S400 of the present application, the data in multiple sub-level data sets are traversed respectively, and multiple local optimal solutions of the sets are obtained through a greedy algorithm, including:

[0053] Traversing multiple sub-level data sets to determine a first sub-level data set;

[0054] Extracting active values ​​from the first sub-level data set to form a first active value set, and calculating the algebraic sum of the active values ​​in the first active value set to obtain a first algebraic sum;

[0055] Comparing the absolute value of the first algebraic sum with a preset threshold, and when the absolute value of the first algebraic sum is less than the preset threshold, marking the first sub-level data set as abnormal, and using the first active value set as an abnormal active value set;

[0056] Traverse the active values ​​in the abnormal active value set, select an active value each time, and invert it, calculate the abnormal algebraic sum of the abnormal active value set, and obtain the abnormal algebraic sum set;

[0057] Extracting the minimum abnormal algebraic sum and the corresponding negated active value in the abnormal algebraic sum set as the first set local optimal solution of the first sub-level data set;

[0058] The first set of local optimal solutions is added to the multiple sets of local optimal solutions.

[0059] Specifically, multiple sub-level data sets are traversed to determine a first sub-level data set, where the first sub-level data set is taken out from the sub-level data set list as the set currently being processed. Active values ​​in the first sub-level data set are extracted, and all active values ​​in the first active value set are summed to obtain a first algebraic sum.

[0060] Comparing the absolute value of the first algebraic sum with a preset threshold, when the absolute value of the first algebraic sum is less than the preset threshold, marking the first sub-level data set as abnormal, and taking the first active value set as an abnormal active value set;

[0061] Furthermore, the active values ​​in the abnormal active value set are traversed, one active value is selected each time, and it is inverted, that is, a positive number is changed to a negative number, and a negative number is changed to a positive number, and then the abnormal algebraic sum of the abnormal active value set is calculated to obtain an abnormal algebraic sum set.

[0062] The minimum abnormal algebraic sum and the corresponding negated active value are extracted from the abnormal algebraic sum set as the first set local optimal solution of the first sub-level data set, wherein the minimum abnormal algebra refers to the minimum value of all calculated abnormal algebraic sums.

[0063] Finally, the local optimal solution of the first set is added to multiple sets of local optimal solutions, and the multiple sets of local optimal solutions are used to store the local optimal solutions of all sub-level data sets. By repeating the above steps, multiple local optimal solutions can be obtained and added to the list of multiple sets of local optimal solutions.

[0064] The above steps help to timely discover potential problems or errors in the data. Automated processing can be achieved through traversal and calculation, which can efficiently process large data sets. The efficiency and accuracy of the current transformer reverse connection verification optimization are significantly improved.

[0065] Furthermore, in step S400 of the present application, a current transformer reverse connection verification result is generated according to multiple sets of local optimal solutions, including:

[0066] Extract the inverted active values ​​from the local optimal solutions of multiple sets, mark them as reverse data, and obtain a reverse data list;

[0067] Determine a reverse connection current transformer list and list-related information according to the reverse connection data list and the multi-dimensional data set, wherein the list-related information includes the region, ring network cabinet, and switchgear to which the reverse connection current transformer belongs;

[0068] The reverse connection current transformer list and the list-related information are integrated to generate a reverse connection verification result.

[0069] Specifically: extract all the inverted active values ​​from multiple sets of local optimal solutions, and these inverted active values ​​are identified as reverse data. Arrange these reverse data into a list, namely the reverse data list, which should clearly list each reverse data, including its specific value and possible other relevant information (such as the source sub-level data set, original value, etc.). After constructing the reverse data list, verify and proofread it to ensure that each data point in the list is correct and there is no omission or duplication. Provide strong support for subsequent analysis and processing.

[0070] Then, the reverse connection data list is analyzed in detail. The reverse connection data is associated with the data collected by the current transformer, and the data related to which current transformers are marked as reverse connection is identified. Each data point in the reverse connection data list is matched with the multidimensional data set.

[0071] For each determined reverse current transformer, its associated information is extracted from the multidimensional data set. This information includes the region to which the current transformer belongs, the ring network cabinet number, the switchgear number, etc. After building the reverse current transformer list, verification and proofreading are performed to ensure that each current transformer in the list is correct and the associated information is accurate.

[0072] Furthermore, the present application also includes the following steps:

[0073] When the absolute value of the first algebraic sum is greater than or equal to a preset threshold, the first sub-level data set is marked normally.

[0074] Specifically: when the absolute value of the algebraic sum of the active values ​​in the first sub-level data set is greater than or equal to the preset threshold, it means that the sub-level data set is in a normal state and does not show abnormal characteristics. Then, the first sub-level data set is marked as normal. The normal mark is a simple mark, status code or label used to distinguish normal and abnormal data sets in subsequent processing. After completing the processing of the first sub-level data set, continue to traverse other sub-level data sets and repeat the above steps to mark them as normal or abnormal.

[0075] By performing such processing on each sub-level data set, the state of the entire data set can be comprehensively checked, and which sub-level data sets are in a normal state and which may be abnormal can be accurately identified, thereby improving the accuracy of batch identification of current transformers.

[0076] Furthermore, the present application also includes the following steps:

[0077] Setting a timer, wherein the timer has a preset time interval;

[0078] Performing cyclic reverse connection verification on the multi-dimensional data set at a preset time interval, and in each round of reverse connection verification, comparing the latest reverse connection verification result with the historical reverse connection verification result to determine the changed current transformer;

[0079] The variable current transformer is multi-marked and pushed with priority.

[0080] Specifically: First, determine the preset time interval (for example, every minute, every hour, or every day). Use the timer function in the programming language (such as JavaScript's setInterval, Python's threading.Timer or schedule library, etc.) to set the timer. Then perform a cyclic reverse connection verification on the multidimensional data set at the preset time interval. In each round of reverse connection verification, compare the latest reverse connection verification result with the historical reverse connection verification result, that is, in the timer's callback function, call the reverse connection verification logic. Ensure that the latest data is accessed each time it is called. In each round of reverse connection verification, compare the latest result with the previously stored historical results. If the data of a current transformer is found to have changed (for example, exceeding the preset threshold or showing an abnormal pattern), it is identified as a "changed current transformer."

[0081] Multiple tagging is performed on the identified changed current transformers. That is, one or more additional identifiers, tags, or status codes are added to the current transformer in the data set. For the identified changed current transformers, push them to relevant personnel or systems using appropriate communication mechanisms (such as API calls, message queues, emails, or SMS notifications). Ensure that these pushes are prioritized so that relevant personnel can respond quickly and take necessary actions.

[0082] The above steps realize real-time monitoring and effective management of the current transformer status in a multi-dimensional data set through technical means such as timed monitoring, automated verification, timely identification of changes, multiple markings and priority push, thereby improving the stability and reliability of the system, optimizing resource allocation, reducing the occurrence of human errors, and improving processing efficiency and accuracy.

[0083] In summary, the current transformer reverse connection verification optimization method and system based on the greedy algorithm provided in the embodiment of the present application have the following technical effects:

[0084] 1. By building a distributed data collection network for the regional distribution network, the operating data of the current transformers in the regional distribution network are collected through each distributed data collection node, and a multidimensional data set is generated in combination with the unique numbers of the regional distribution network, ring network cabinet, and switchgear; in the operating database, the multidimensional data set is grouped and aggregated according to the ring network cabinet number; the data in multiple sub-level data sets are traversed respectively, and multiple local optimal solutions of the sets are obtained through the greedy algorithm, and the current transformer reverse connection verification results are generated according to the multiple local optimal solutions of the sets. The technical effect of batch identification of current transformers in a large range and high judgment efficiency and accuracy is achieved.

[0085] 2. By setting the data collection dimensions, the data collection dimensions include regions, ring network cabinets, switchgear and active values, among which the active values ​​are obtained through current transformers; the operation data of current transformers in the regional distribution network are sorted according to the data collection dimensions to obtain a multi-dimensional data set. The operation data of current transformers in the regional distribution network can be effectively collected, sorted and analyzed, providing strong support for the monitoring, control and management of the power system, and achieving the goal of improving the efficiency of the reverse connection judgment of the current transformer.

[0086] 3. By traversing the data in multiple sub-level data sets separately and using the greedy algorithm to obtain the local optimal solutions of multiple sets, it is helpful to timely discover potential problems or errors in the data. Automated processing can be achieved through traversal and calculation, and a large number of data sets can be processed efficiently. The efficiency and accuracy of the reverse connection verification optimization of the current transformer are significantly improved.

[0087] Embodiment 2

[0088] Based on the same inventive concept as the current transformer reverse connection verification optimization method based on the greedy algorithm in the aforementioned embodiment, Figure 3 As shown, the embodiment of the present application provides a current transformer reverse connection verification optimization system based on a greedy algorithm, and the system includes:

[0089] Building a distributed data acquisition network module 11: used to determine a regional distribution network and build a distributed data acquisition network for the regional distribution network, wherein the regional distribution network includes multiple ring network cabinets, each of which has multiple switch devices, and each switch device is equipped with a current transformer on the inlet and outlet sides, and the current transformer is used to obtain active power values, wherein the regional distribution network, the ring network cabinet, and the switch device all have unique numbers;

[0090] A module 12 for generating a multidimensional data set is used to collect the operation data of the current transformer in the regional distribution network through each distributed collection node in the distributed collection network, generate a multidimensional data set in combination with the unique numbers of the regional distribution network, the ring network cabinet, and the switchgear, and upload the multidimensional data set to the operation database;

[0091] A sub-level data set acquisition module 13 is used to group and aggregate the multi-dimensional data set in the operation database according to the ring main unit number to acquire multiple sub-level data sets, wherein the multiple sub-level data sets correspond to multiple ring main units one by one;

[0092] The reverse connection verification result generating module 14 is used to traverse the data in multiple sub-level data sets respectively, obtain the local optimal solutions of multiple sets through the greedy algorithm, and generate the current transformer reverse connection verification result according to the local optimal solutions of the multiple sets.

[0093] Furthermore, the module 12 for generating a multidimensional data set is also used for:

[0094] Setting data collection dimensions, wherein the data collection dimensions include regions, ring main units, switchgear, and active power values, wherein the active power values ​​are obtained through current transformers;

[0095] The operating data of the current transformers in the regional distribution network are sorted according to the data collection dimensions to obtain a multi-dimensional data set.

[0096] Furthermore, the module 12 for generating a multidimensional data set is also used for:

[0097] Obtaining upload hardware parameters of the distributed acquisition network, wherein the upload hardware parameters include the number of CPU cores, memory capacity, network bandwidth, and storage performance;

[0098] According to the upload hardware parameters, the optimal number of threads N for data upload is obtained, and the multidimensional data set is evenly divided into N data subsets;

[0099] Creating N data upload threads in the distributed acquisition network according to the optimal number of threads N, each data upload thread corresponding to a data subset;

[0100] The N data upload threads are started to upload the N data subsets to the operation database in parallel.

[0101] Furthermore, the reverse connection verification result generating module 14 is also used for:

[0102] Traversing multiple sub-level data sets to determine a first sub-level data set;

[0103] Extracting active values ​​from the first sub-level data set to form a first active value set, and calculating the algebraic sum of the active values ​​in the first active value set to obtain a first algebraic sum;

[0104] Comparing the absolute value of the first algebraic sum with a preset threshold, and when the absolute value of the first algebraic sum is less than the preset threshold, marking the first sub-level data set as abnormal, and using the first active value set as an abnormal active value set;

[0105] Traverse the active values ​​in the abnormal active value set, select an active value each time, and invert it, calculate the abnormal algebraic sum of the abnormal active value set, and obtain the abnormal algebraic sum set;

[0106] Extracting the minimum abnormal algebraic sum and the corresponding negated active value in the abnormal algebraic sum set as the first set local optimal solution of the first sub-level data set;

[0107] The first set of local optimal solutions is added to the multiple sets of local optimal solutions.

[0108] Furthermore, the reverse connection verification result generating module 14 is also used for:

[0109] Extract the inverted active values ​​from the local optimal solutions of multiple sets, mark them as reverse data, and obtain a reverse data list;

[0110] Determine a reverse connection current transformer list and list-related information according to the reverse connection data list and the multi-dimensional data set, wherein the list-related information includes the region, ring network cabinet, and switchgear to which the reverse connection current transformer belongs;

[0111] The reverse connection current transformer list and the list-related information are integrated to generate a reverse connection verification result.

[0112] Furthermore, the system is also used for:

[0113] When the absolute value of the first algebraic sum is greater than or equal to a preset threshold, the first sub-level data set is marked normally.

[0114] Furthermore, the system is also used for:

[0115] Setting a timer, wherein the timer has a preset time interval;

[0116] Performing cyclic reverse connection verification on the multi-dimensional data set at a preset time interval, and in each round of reverse connection verification, comparing the latest reverse connection verification result with the historical reverse connection verification result to determine the changed current transformer;

[0117] The variable current transformer is multi-marked and pushed with priority.

[0118] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0119] Furthermore, the first or second mentioned above may not only represent an order relationship, but may also represent a specific concept, and / or refer to multiple elements that can be selected individually or in full. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A current transformer reverse connection verification optimization method based on a greedy algorithm, characterized in that: The method comprises: Determine a regional distribution network, and build a distributed data acquisition network for the regional distribution network, wherein the regional distribution network includes multiple ring network cabinets, each ring network cabinet has multiple switch devices, and each switch device is installed with a current transformer on the inlet and outlet sides, and the current transformer is used to obtain active power values, wherein the regional distribution network, the ring network cabinet, and the switch device all have unique numbers; Collecting the operating data of the current transformers in the regional distribution network through each distributed collection node in the distributed collection network, generating a multidimensional data set in combination with the unique numbers of the regional distribution network, the ring main unit, and the switchgear, and uploading the multidimensional data set to the operation database; In the operation database, the multidimensional data set is grouped and aggregated according to the ring main unit number to obtain multiple sub-level data sets, where the multiple sub-level data sets correspond one-to-one to the multiple ring main units; The data in multiple sub-level data sets are traversed respectively, and the local optimal solutions of multiple sets are obtained through the greedy algorithm, and the current transformer reverse connection verification results are generated according to the local optimal solutions of multiple sets; Traverse the data in multiple sub-level data sets respectively, and obtain the local optimal solutions of multiple sets through the greedy algorithm, including: Traversing multiple sub-level data sets to determine a first sub-level data set; Extracting active values ​​from the first sub-level data set to form a first active value set, and calculating the algebraic sum of the active values ​​in the first active value set to obtain a first algebraic sum; Comparing the absolute value of the first algebraic sum with a preset threshold, and when the absolute value of the first algebraic sum is less than the preset threshold, marking the first sub-level data set as abnormal, and using the first active value set as an abnormal active value set; Traverse the active values ​​in the abnormal active value set, select an active value each time, and invert it, calculate the abnormal algebraic sum of the abnormal active value set, and obtain the abnormal algebraic sum set; Extracting the minimum abnormal algebraic sum and the corresponding negated active value in the abnormal algebraic sum set as the first set local optimal solution of the first sub-level data set; Adding the first set of local optimal solutions to multiple sets of local optimal solutions; Generate current transformer reverse connection verification results based on multiple sets of local optimal solutions, including: Extract the inverted active values ​​from the local optimal solutions of multiple sets, mark them as reverse data, and obtain a reverse data list; Determine a reverse connection current transformer list and list-related information according to the reverse connection data list and the multi-dimensional data set, wherein the list-related information includes the region, ring network cabinet, and switchgear to which the reverse connection current transformer belongs; The reverse connection current transformer list and the list-related information are integrated to generate a reverse connection verification result.

2. The method according to claim 1, characterized in that Collect the operating data of current transformers in the regional distribution network, and generate a multi-dimensional data set based on the unique numbers of the regional distribution network, ring main unit, and switchgear, including: Setting data collection dimensions, wherein the data collection dimensions include regions, ring main units, switchgear, and active power values, wherein the active power values ​​are obtained through current transformers; The operating data of the current transformers in the regional distribution network are sorted according to the data collection dimensions to obtain a multi-dimensional data set.

3. The method according to claim 1, characterized in that Uploading the multidimensional data set to an operation database includes: Obtaining upload hardware parameters of the distributed acquisition network, wherein the upload hardware parameters include the number of CPU cores, memory capacity, network bandwidth, and storage performance; According to the upload hardware parameters, the optimal number of threads N for data upload is obtained, and the multidimensional data set is evenly divided into N data subsets; Creating N data upload threads in the distributed acquisition network according to the optimal number of threads N, each data upload thread corresponding to a data subset; The N data upload threads are started to upload the N data subsets to the operation database in parallel.

4. The method according to claim 1, characterized in that When the absolute value of the first algebraic sum is greater than or equal to a preset threshold, the first sub-level data set is marked normally.

5. The method according to claim 1, characterized in that The method further comprises: Setting a timer, wherein the timer has a preset time interval; Performing cyclic reverse connection verification on the multi-dimensional data set at a preset time interval, and in each round of reverse connection verification, comparing the latest reverse connection verification result with the historical reverse connection verification result to determine the changed current transformer; The variable current transformer is multi-marked and pushed with priority.

6. A current transformer reverse connection verification optimization system based on a greedy algorithm, characterized in that: For executing the method according to any one of claims 1 to 5, the system comprises: Building a distributed data acquisition network module: used to determine a regional distribution network and build a distributed data acquisition network for the regional distribution network, wherein the regional distribution network includes multiple ring network cabinets, each of which has multiple switchgears, and each switchgear is equipped with a current transformer on the inlet and outlet sides, and the current transformer is used to obtain active power values, wherein the regional distribution network, the ring network cabinet, and the switchgear all have unique numbers; A module for generating a multidimensional data set: used to collect the operating data of the current transformer in the regional distribution network through each distributed collection node in the distributed collection network, generate a multidimensional data set in combination with the unique numbers of the regional distribution network, the ring network cabinet, and the switchgear, and upload the multidimensional data set to the operation database; A module for obtaining a sub-level data set: used to group and aggregate the multi-dimensional data set according to the ring main unit number in the operation database to obtain a plurality of sub-level data sets, wherein the plurality of sub-level data sets correspond to a plurality of ring main units one by one; Generate reverse connection verification result module: used to traverse the data in multiple sub-level data sets respectively, obtain the local optimal solutions of multiple sets through the greedy algorithm, and generate the current transformer reverse connection verification result according to the local optimal solutions of multiple sets.

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