Data analysis method and system for primary and secondary equipment of power distribution network based on SGAM model

Abstract

The embodiment of the invention provides a data analysis method and system for primary and secondary equipment of a power distribution network based on an SGAM model, and belongs to the technical field of intelligent power grids. The method comprises the following steps: acquiring and preprocessing data information of primary and secondary equipment and a communication system of the power distribution network; constructing an SGAM model; mapping the real-time operation data to the SGAM model so as to carry out panoramic state evaluation; calculating a power distribution network health index according to the panoramic state evaluation result; and generating a control instruction according to the health index to regulate and control the operation state of the power distribution network. According to the method, the SGAM model is constructed to reasonably divide each level and field of the power distribution network, and deep analysis can be carried out on the power distribution network from different dimensions and levels, so that panoramic state monitoring is realized.

Description

Technical Field

[0001] This invention relates to the field of smart grid technology, and more specifically to a data analysis method and system for primary and secondary equipment in a distribution network based on the SGAM model. Background Technology

[0002] With the deepening of smart grid construction, the number of primary equipment (such as circuit breakers and transformers) and secondary equipment (such as protection devices and measurement and control units) deployed in the distribution network has increased significantly, and the information interaction between equipment has become increasingly complex, placing higher demands on the comprehensive perception and intelligent control of the system status. However, traditional distribution network data management methods have obvious limitations: primary and secondary equipment and communication systems mostly use independent data acquisition and monitoring systems (SCADA), lacking a unified information model and integration architecture, resulting in scattered data sources, heterogeneous formats, and asynchronous time, making it difficult to achieve cross-level and cross-domain data fusion and collaborative analysis.

[0003] Currently, distribution network condition assessment relies heavily on electrical measurement data, failing to fully integrate information such as the status of primary and secondary equipment and the reliability of communication systems. This results in incomplete assessments that cannot accurately reflect the overall operating status of the system. Furthermore, existing methods typically use fixed weights for condition estimation, neglecting the impact of the reliability of the measuring equipment and the status of the communication links on data quality. This can easily lead to misjudgments when equipment malfunctions or communication is interrupted, affecting the accuracy of control strategies. Summary of the Invention

[0004] The purpose of this invention is to provide a data analysis method and system for primary and secondary equipment in a distribution network based on the SGAM model, which solves the reliability problem of multi-source data fusion and state assessment in the distribution network.

[0005] To achieve the above objectives, embodiments of the present invention provide a data analysis method for primary and secondary equipment in a distribution network based on the SGAM model, the method comprising: Acquire and preprocess data information from primary and secondary equipment and communication systems in the power distribution network; Construct the SGAM model; The real-time operational data is mapped to the SGAM model for panoramic state assessment. Calculate the power distribution network health index based on the panoramic status assessment results; Control commands are generated based on the health index to regulate the operation of the power distribution network.

[0006] Optionally, data information from the primary and secondary equipment and communication systems of the power distribution network is acquired and preprocessed, including: Configure all primary and secondary equipment and communication systems with BeiDou high-precision clock sources; Add a high-precision timestamp when collecting each piece of data; The data information is then time-aligned.

[0007] The data information is cleaned.

[0008] Optionally, constructing the SGAM model includes, According to formula (1), each interoperability layer of the SGAM model is divided into domains and partitions. (1) in, The SGAM model comprises components, communication, information, functionality, and operational layers. For the SGAM model, partition the market, enterprise, operation, plant, local and process. This applies to the SGAM model in the fields of power generation, transmission, distribution, and distributed energy.

[0009] Optionally, constructing an SGAM model includes: Determine the logical devices of the primary and secondary equipment and communication system at the information layer; The logical device is decomposed into multiple logical nodes according to its functions; Determine the data objects and data attributes under each logical node.

[0010] Optionally, mapping the real-time operational data to the SGAM model for panoramic state assessment includes: The credibility of each node is evaluated to calculate the electrical index; List the available status of all monitored functions to calculate the protection control index; Visually identify areas that have lost monitoring due to equipment failure in order to calculate the communication index.

[0011] Optionally, confidence is assessed for each node to calculate an electrical index, including: Obtain all relevant measurement points around the node; Determine the measurement vector based on the relevant measurement points; Find the secondary device identifier and communication channel identifier that provide the measurement vector from the information layer; Query the real-time status data of the secondary equipment and communication channel; Calculate the communication status score and the secondary equipment status score; The quality factor is calculated based on the communication status score and the secondary equipment status score to obtain the electrical index.

[0012] Optionally, a quality factor is calculated based on the communication status score and the secondary device status score, including: The quality factor is calculated according to formula (1). (1) Calculate the dynamic weight matrix according to formula (2). (2) The objective function is established using the least squares method according to formula (3). (3) in, for The quality factor of a measurement vector for Communication status score of a measurement vector, for Secondary equipment status score for each measurement vector. for The dynamic weight matrix of each measurement vector. for A fixed weight matrix for each measurement vector. for The error variance of each measurement vector, Let be the objective function. For measurement vectors, For mapping functions, This is the state vector.

[0013] Optionally, the distribution network health index is calculated based on the panoramic status assessment results, including: Calculate the health index according to formulas (4) to (5). (4) (5) in, For health index, As the first weighting coefficient, This is the second weighting coefficient. The third weighting coefficient, For electrical index, To protect the control index, This is a communication index.

[0014] On the other hand, the present invention also provides a data analysis system for primary and secondary equipment in a power distribution network based on the SGAM model, the system including a processor for executing the data analysis method as described above.

[0015] In another aspect, the present invention also provides a computer-readable storage medium storing instructions for being read by a machine to cause the machine to perform any of the data analysis methods described above.

[0016] Through the above technical solution, this invention provides a method and system for analyzing data from primary and secondary equipment in a distribution network based on the SGAM model. Utilizing the SGAM model as a unified framework, it integrates relatively independent primary and secondary equipment and communication systems within the distribution network into a unified, hierarchical analysis framework. Then, based on this framework, cross-domain and cross-layer data calculations and analyses are performed, thereby achieving in-depth insight and collaborative optimization of the overall operating status of the distribution network, equipment health status, and communication system performance.

[0017] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a data analysis method according to an embodiment of the present invention; Figure 2 This is a flowchart of data acquisition according to one embodiment of the present invention; Figure 3 This is a flowchart of constructing an SGAM model according to one embodiment of the present invention; Figure 4 This is a flowchart illustrating the mapping of data to the SGAM model according to one embodiment of the present invention; Figure 5 This is a flowchart for measuring the reliability of a node according to one embodiment of the present invention; Figure 6 This is a flowchart of calculating the quality factor according to one embodiment of the present invention.

[0019] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0020] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0021] Figure 1 This is a flowchart of a data analysis method according to an embodiment of the present invention, in which the data analysis method includes: In step S1, data information from the primary and secondary equipment and communication system of the power distribution network is acquired and preprocessed.

[0022] In step S2, the SGAM model is constructed.

[0023] In step S3, the real-time running data is mapped to the SGAM model for panoramic state assessment.

[0024] In step S4, the distribution network health index is calculated based on the panoramic status assessment results.

[0025] In step S5, control commands are generated based on the health index to regulate the operating status of the power distribution network.

[0026] In steps S1 to S5, a SGAM model is constructed. Through information fusion and collaborative computing, the intrinsic connections between the physical power grid, control system, and information network are deeply revealed, ultimately enabling a safer, more reliable, and more efficient self-healing distribution network. By constructing the SGAM model and mapping real-time data to it, the operating status of the distribution network can be comprehensively and systematically evaluated. The SGAM model rationally divides the various levels and domains of the distribution network, facilitating in-depth analysis from different dimensions and levels, thereby achieving panoramic status monitoring. Based on the panoramic status assessment results, the distribution network health index can be calculated to quantify the operating status of the distribution network. Multi-dimensional assessments, such as electrical indices, protection and control indices, and communication indices, further improve the accuracy of monitoring. This quantified health index provides an intuitive basis for the maintenance and scheduling of the distribution network, helping to identify potential problems in advance and avoid major failures.

[0027] In this embodiment of the present invention, the step of acquiring data can be one of various methods known to those skilled in the art. In one example of the present invention, the specific acquisition method can be as follows: Figure 2 As shown. Specifically, in Figure 2 In addition, this data analysis method may also include: In step S11, a BeiDou high-precision clock source is configured for all primary and secondary equipment and communication systems.

[0028] In step S12, a high-precision timestamp is added when collecting each piece of data.

[0029] In step S13, the data information is time-aligned. This time-alignment process ensures that time information collected by different devices is aligned, avoiding data deviations or mismatches caused by device asynchrony.

[0030] In step S14, the data is cleaned. This cleaning includes deduplication, handling of missing values, and normalization.

[0031] In steps S11 to S14, high-precision time synchronization and data processing flow optimization can ensure data consistency, effectively improve data quality, and thus enhance the reliability of data analysis results.

[0032] In this embodiment of the invention, after obtaining the multidimensional historical data, it is also necessary to construct an SGAM model based on the multidimensional historical data. Specifically, the construction method can be as follows: Figure 3 As shown. Specifically, in Figure 3 In addition, this data analysis method may also include: In step S21, each interoperability layer of the SGAM model is divided according to domain and partition according to formula (1). (1) in, The SGAM model comprises components, communication, information, functionality, and operational layers. For the SGAM model, partition the market, enterprise, operation, plant, local and process. This applies to the SGAM model in the areas of power generation, transmission, distribution, and distributed energy. Among these, In step S22, the logical devices of the primary and secondary equipment and communication system at the information layer are determined. All devices have a globally unique identifier at the information layer, representing a physical device as a logical device in the information world.

[0033] In step S23, the logic device is decomposed into multiple logic nodes according to their functions.

[0034] In step S24, the data objects and data attributes under each logical node are determined. International standards are used to define each data object in the information layer.

[0035] In steps S21 to S24, by mapping physical devices to logical devices in the information layer and assigning a globally unique identifier to each device, the uniqueness and clarity of the devices are ensured. This allows managers to accurately track the status and behavior of each device, enhancing their control over the devices. Simultaneously, by decomposing logical devices into multiple logical nodes according to their functions, more flexible and modular management can be achieved. Each node represents a functional unit of the device, capable of independent operation or fault isolation, improving the system's flexibility and fault tolerance. Finally, through the explicit definition of each logical node and data object, data analysis becomes more accurate and systematic, making device management and data application more efficient, flexible, and intelligent.

[0036] In this implementation, after constructing the SGAM model, it is necessary to map the data to the SGAM model. The specific steps can be as follows: Figure 4 As shown. Specifically, in Figure 4 In addition, this data analysis method may also include: In step S31, the confidence level is evaluated for each node to calculate the electrical index.

[0037] In step S32, the available status of all monitored functions is listed to calculate the protection control index.

[0038] In step S33, the areas that have lost monitoring due to the failure of the monitoring equipment are visually identified in order to calculate the communication index.

[0039] In steps S31 to S33, the operating status of each important link of the distribution network is quantified and monitored through three dimensions: electrical index, protection and control index, and communication index. Finally, the distribution network health index is obtained, providing a comprehensive and real-time health management tool. This monitoring and evaluation mode not only helps to improve the stability and security of the system, but also optimizes resource allocation, improves operation and maintenance efficiency, and enhances decision support.

[0040] In this embodiment, the methods for measuring whether a node is reliable can be various and known to those skilled in the art. In one example of the present invention, the specific steps for measuring whether a node is reliable can be as follows: Figure 5 As shown. Specifically, in Figure 5 In addition, this data analysis method may also include: In step S311, all relevant measurement points around the node are obtained.

[0041] In step S312, the measurement vector is determined based on the relevant measuring points.

[0042] In step S313, the secondary device identifier and communication channel identifier that provide the measurement vector are found from the information layer.

[0043] In step S314, the real-time status data of the secondary equipment and communication channel are queried.

[0044] In step S315, the communication status score and the secondary device status score are calculated.

[0045] In step S316, a quality factor is calculated based on the communication status score and the secondary equipment status score to obtain the electrical index.

[0046] In steps S311 to S316, when calculating the electrical index, a quality factor based on the primary and secondary equipment and communication status is introduced to dynamically adjust the weights. This enables the state estimation to automatically reduce the impact of unreliable data, thereby outputting results that are closer to the actual power grid status, providing a solid and reliable data foundation for subsequent control decisions.

[0047] In this embodiment, the specific steps for calculating the quality factor can be various, as known to those skilled in the art. In one example of the present invention, the specific steps for calculating the quality factor can be as follows: Figure 6 As shown. Specifically, in Figure 6 In addition, this data analysis method may also include: In step S3161, the quality factor is calculated according to formula (1). (1) In step S3162, the dynamic weight matrix is ​​calculated according to formula (2). (2) In step S3163, the objective function is established using the least squares method according to formula (3). (3) in, for The quality factor of a measurement vector for Communication status score of a measurement vector, for Secondary equipment status score for each measurement vector. for The dynamic weight matrix of each measurement vector. for A fixed weight matrix for each measurement vector. for The error variance of each measurement vector, Let be the objective function. For measurement vectors, For mapping functions, This is the state vector.

[0048] In steps S3161 to S3163, the quality factor represents the overall quality of each measurement vector, combining the scores of communication status and device status. The dynamic weight matrix reflects the contribution of each measurement vector to the state estimation, and the state vector estimation is optimized by minimizing the weighted error using the least squares objective function, which significantly improves the robustness of electrical index calculation.

[0049] In this embodiment, the method for calculating the distribution network health index can be various, as known to those skilled in the art. In one example of the present invention, it can be calculated according to formulas (4) to (5). Specifically, it includes: Calculate the health index according to formulas (4) to (5). (4) (5) in, For health index, As the first weighting coefficient, This is the second weighting coefficient. The third weighting coefficient, For electrical index, To protect the control index, This is a communication index. When During a descent, maintenance personnel can decompose... , , The system can quickly pinpoint which system (primary, secondary, or communication) is experiencing problems, and then take precise action based on deeper analysis (such as whether a function is unavailable due to a communication interruption), thus achieving closed-loop management from macro-level situation to micro-level root cause.

[0050] On the other hand, the present invention also provides a data analysis system for primary and secondary equipment in a power distribution network based on the SGAM model, the system including a processor for executing the data analysis method as described above.

[0051] In another aspect, the present invention also provides a computer-readable storage medium storing instructions for being read by a machine to cause the machine to perform any of the data analysis methods described above.

[0052] Through the above technical solution, this invention provides a method and system for analyzing data from primary and secondary equipment in a distribution network based on the SGAM model. Utilizing the SGAM model as a unified framework, it integrates relatively independent primary and secondary equipment and communication systems within the distribution network into a unified, hierarchical analysis framework. Then, based on this framework, cross-domain and cross-layer data calculations and analyses are performed, thereby achieving in-depth insight and collaborative optimization of the overall operating status of the distribution network, equipment health status, and communication system performance.

[0053] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0054] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0055] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0056] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0057] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0058] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0059] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0060] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0061] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A data analysis method for power distribution network primary and secondary equipment based on an SGAM model, characterized in that, The method comprises: obtaining data information of primary and secondary equipment and communication systems of a power distribution network and preprocessing the data information; constructing an SGAM model; mapping the real-time operation data to the SGAM model for panoramic state evaluation; calculating a health index of the power distribution network according to the panoramic state evaluation result; generating a control instruction according to the health index to regulate the operation state of the power distribution network.

2. The data analysis method of claim 1, wherein, The method of obtaining data information of primary and secondary equipment and communication systems of a power distribution network and preprocessing the data information comprises: configuring a Beidou high-precision clock source for all primary and secondary equipment and communication systems; stamping a high-precision time stamp on each data information when collecting the data information; performing time alignment processing on the data information; cleaning the data information.

3. The data analysis method of claim 1, wherein, The method of constructing an SGAM model comprises: dividing each interoperable layer of the SGAM model according to fields and partitions according to formula (1), ，（1） wherein, are components, communications, information, functions, and business operations layers of the SGAM model, are market, enterprise, operations, plant, on-site, and process zones of the SGAM model, are generation, transmission, distribution, and distributed energy fields of the SGAM model.

4. The data analysis method of claim 3, wherein, The method of constructing an SGAM model comprises: determining logical devices of the primary and secondary equipment and communication systems in the information layer; decomposing the logical devices into a plurality of logical nodes according to functions; determining data objects and data attributes under each logical node.

5. The data analysis method of claim 1, wherein, The method of mapping the real-time operation data to the SGAM model for panoramic state evaluation comprises: evaluating the credibility of each node to calculate an electrical index; listing the available states of all monitored functions to calculate a protection and control index; visually identifying the areas that lose monitoring due to the failure of monitoring equipment to calculate a communication index.

6. The data analysis method of claim 5, wherein, The method of evaluating the credibility of each node to calculate an electrical index comprises: obtaining all related measuring points around the node; determining a measurement vector according to the related measuring points; finding secondary equipment identifiers and communication channel identifiers that provide the measurement vector from the information layer; inquiring real-time state data of the secondary equipment and communication channels; calculating a communication state score and a secondary equipment state score; calculating a quality factor according to the communication state score and the secondary equipment state score to obtain an electrical index.

7. The data analysis method of claim 6, wherein, The method of calculating a quality factor according to the communication state score and the secondary equipment state score comprises: calculating a quality factor according to formula (1), ，（1） calculating a dynamic weight matrix according to formula (2), ，（2） establishing an objective function by using a least square method according to formula (3), ，（3） wherein is a quality factor of the measurement vector, is a communication status score of the measurement vector, is a secondary device status score of the measurement vector, is a dynamic weight matrix of the measurement vector, is a fixed weight matrix of the measurement vector, is an error variance of the measurement vector, is an objective function, is a measurement vector, is a mapping function, is a state vector.

8. The data analysis method of claim 5, wherein, The method of calculating a health index of the power distribution network according to the panoramic state evaluation result comprises: calculating a health index according to formulas (4) to (5), ，（4） ，（5） wherein, is a health index, is a first weight coefficient, is a second weight coefficient, is a third weight coefficient, is an electrical index, is a protection control index, is a communication index.

9. A data analysis system for power distribution network primary and secondary equipment based on an SGAM model, characterized in that, The system comprises a processor for executing the data analysis method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores instructions for being read by a machine to cause the machine to execute the data analysis method according to any one of claims 1 to 8.

Images