Method and device for representing power system operation data

Abstract

This application discloses a method and apparatus for characterizing power system operation data, comprising: selecting key equipment categories from the power system as measurement points; constructing a data model reflecting the network topology of the measurement points based on the line topology connection relationship between the equipment corresponding to the measurement points; separating and standardizing the static parameters and dynamic measurement data corresponding to each measurement point from the specification parameters and real-time operation data of the equipment corresponding to each measurement point based on the data standardization definition; mapping the static parameters and dynamic measurement data to the corresponding measurement points in the data model; continuously monitoring the actual operating status of the power system; dynamically locking the state-changing measurement points in the data model based on the hierarchical association rules of the power system; and dynamically updating the parts of the static parameters and dynamic measurement data mapped to the state-changing measurement points that do not match the actual operating status of the power system based on the current specification parameters and real-time operation data of the equipment corresponding to the state-changing measurement points, so as to characterize the actual operating status of the power system.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method and apparatus for characterizing power system operation data. Background Technology

[0002] With the continuous deepening of the construction of new power systems and the ongoing improvement of the transparent grid technology framework, characteristics such as a high proportion of renewable energy grid integration are becoming increasingly prominent. This has significantly enhanced the dynamic volatility, operational complexity, and coupling correlation of power systems, placing higher demands on the real-time perception, accurate assessment, and rapid response of grid operating status. Against this backdrop, real-time grid operating data, as a true reflection of the power system's operating status and conditions, has become a core element supporting grid analysis. Furthermore, grid analysis, as a key means to achieve observable, measurable, and controllable grids, is a crucial foundation for ensuring the safe, stable, and economical operation of the power system.

[0003] In current power grid analysis, the data relied upon is typically obtained by processing real-time power grid operating data using data models such as bus-branch models and equipment-switch diagram models. However, because bus-branch models oversimplify the power system topology and lose a large amount of crucial information such as equipment connection relationships, the data provided by bus-branch models for power grid analysis is severely distorted and cannot accurately reflect the actual operating state of the power system. While equipment-switch diagram models can completely preserve the details of power grid equipment and connections, their complex structure and high data redundancy make it difficult to synchronize with the actual operating state of the power system in real time. Therefore, equipment-switch diagram models cannot quickly provide power grid analysis with data that accurately reflects the actual operating state of the power system.

[0004] Therefore, there is an urgent need to propose a representation scheme for power system operation data in order to quickly provide high-fidelity and accurate real-time data samples that represent the actual operating status and conditions of the power system. Summary of the Invention

[0005] This application proposes a method and apparatus for characterizing power system operation data. The main purpose is to quickly provide high-fidelity and accurate real-time data samples that characterize the actual operating status and conditions of the power system, thereby providing effective data support for power grid analysis.

[0006] To achieve the above objectives, this application mainly provides the following technical solutions: Firstly, this application provides a method for characterizing power system operation data. The method for characterizing power system operation data provided in this embodiment may include at least: Equipment belonging to the critical equipment category in the power system is selected as measurement points, and a data model is constructed to reflect the network topology of the measurement points based on the line topology connection relationship between the equipment corresponding to the measurement points. Based on the data standardization definition of the power system, the static parameters and dynamic measurement data corresponding to each measuring point are separated and standardized from the specification parameters and real-time operation data of the equipment corresponding to each measuring point, and the static parameters and dynamic measurement data are mapped to the corresponding measuring points in the data model. The system continuously monitors the actual operating status of the power system, dynamically locks the state change measurement points in the data model based on the hierarchical association rules of the power system, and dynamically updates the static parameters mapped by the state change measurement points and the parts of the dynamic measurement data that do not match the actual operating status of the power system according to the current specification parameters and real-time operating data of the equipment corresponding to the state change measurement points, so as to characterize the actual operating status of the power system. The hierarchical association rules are rules for locking the state change measurement points based on the multi-level structure of the measurement point network topology and the association relationship between measurement points at the same level and different levels.

[0007] Secondly, this application provides a power system operation data characterization device, which in this embodiment may include at least: The module is used to select equipment belonging to the critical equipment category from the power system as measurement points, and to build a data model to reflect the network topology of the measurement points based on the line topology connection relationship between the equipment corresponding to the measurement points. The processing module is used to separate and standardize the static parameters and dynamic measurement data corresponding to each measuring point from the specification parameters and real-time operation data of the equipment corresponding to each measuring point based on the data standardization definition of the power system, and map the static parameters and dynamic measurement data to the corresponding measuring points in the data model. The update module is used to continuously monitor the actual operating status of the power system, dynamically lock the state change measurement points in the data model based on the hierarchical association rules of the power system, and dynamically update the static parameters mapped by the state change measurement points and the parts of the dynamic measurement data that do not match the actual operating status of the power system according to the current specification parameters and real-time operating data of the equipment corresponding to the state change measurement points, so as to characterize the actual operating status of the power system. The hierarchical association rules are rules for locking the state change measurement points based on the multi-level structure of the measurement point network topology and the association relationship between measurement points at the same level and different levels.

[0008] Thirdly, this application provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the storage medium is located to execute the power system operation data characterization method described in the first aspect.

[0009] Fourthly, this application provides an electronic device comprising: a memory for storing a program; and a processor coupled to the memory for running the program to execute the power system operation data characterization method described in the first aspect.

[0010] Fifthly, this application provides a computer program product comprising: a computer program / computer-executable instructions, wherein the computer program / computer-executable method for characterizing power system operating data according to the first aspect is provided.

[0011] The method and apparatus for characterizing power system operation data provided in this application, when it is determined that power system operation data needs to be characterized, selects equipment belonging to the key equipment category in the power system as measurement points, and constructs a data model to represent the network topology of the measurement points based on the line topology connection relationship between the equipment corresponding to the measurement points. Subsequently, based on the data standardization definition of the power system, static parameters and dynamic measurement data corresponding to each measurement point are separated and standardized from the specification parameters and real-time operation data of the equipment corresponding to each measurement point, and the static parameters and dynamic measurement data are mapped to the corresponding measurement points in the data model. Finally, the actual operating status of the power system is continuously monitored, and the state change measurement points in the data model are dynamically locked based on the hierarchical association rules of the power system. Based on the current specification parameters and real-time operation data of the equipment corresponding to the state change measurement points, the parts of the static parameters and dynamic measurement data mapped to the state change measurement points that do not match the actual operating status of the power system are dynamically updated to characterize the actual operating status of the power system. Therefore, the solution provided in this embodiment only selects equipment belonging to the key equipment category as measurement points, constructs a data model to reflect the measurement point network topology, and dynamically maps static parameters and dynamic measurement data of each measurement point in the measurement point network topology in real time to be consistent with the actual operating state of the power system. This achieves accurate characterization of the actual operating state and conditions of the power system, thereby enabling the rapid output of high-fidelity real-time data samples that are accurately aligned with the actual operating state of the power system. This makes the actual operating state of the power grid intuitively discernible, and the real-time data samples can provide efficient and reliable data support for power grid analysis, helping the power grid analysis to be more in line with the actual power grid, improving the accuracy and efficiency of power grid analysis. In this way, it can accurately determine whether there are some risks in the power grid (e.g., whether there are risks such as voltage over-limit, line overload, insufficient stability margin, etc.) and accurately predict whether there are some faults (e.g., short circuit fault, cascading trip, etc.), thereby avoiding large-scale power outages.

[0012] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 A flowchart illustrating a method for characterizing power system operation data according to an embodiment of this application is shown; Figure 2 This illustration shows a schematic diagram of the structure of a power system operation data characterization device according to an embodiment of this application; Figure 3 A schematic diagram of the structure of a power system operation data characterization device provided in another embodiment of this application is shown. Detailed Implementation

[0015] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0016] Currently, power grid analysis is a crucial foundation for ensuring the safe, stable, and economical operation of power systems. Its application scenarios broadly cover the entire chain of power production and transmission, including long-term power grid planning analysis and grid structure optimization demonstrations during the planning phase, as well as real-time security and stability analysis, static / dynamic security verification, power flow distribution calculation, and short-circuit current simulation during the operation phase. It also involves power supply reliability verification and load transfer feasibility analysis in maintenance planning, and emergency simulations under extreme weather or fault scenarios, black-start scheme optimization, and post-accident cause tracing and recovery strategy formulation. Therefore, power grid analysis can provide a scientific basis for power grid dispatching decisions, equipment operation and maintenance, and resource allocation, ensuring that the power system achieves its core objectives of meeting power supply quality standards, high energy efficiency, and controllable safety risks under various operating conditions.

[0017] The data relied upon for power grid analysis is typically obtained by processing power grid operation data using data models such as bus-branch models and device-switch diagrams. However, bus-branch models, due to their oversimplification of the power system topology, lose a significant amount of crucial information such as equipment connection relationships. This results in bus-branch models failing to accurately map dynamic details such as physical connections of equipment, making it difficult to accurately reflect the actual operating state of the power grid. Consequently, the data provided for power grid analysis using bus-branch models is severely distorted and cannot accurately reflect the actual operating state of the power system. While device-switch diagram models can more completely preserve the details of power grid equipment and connections, their complex structure and high data redundancy make it difficult to achieve real-time synchronization with the actual operating state of the power grid when dealing with tens of thousands of data points. Therefore, device-switch diagram models cannot quickly provide power grid analysis with data that accurately reflects the actual operating state of the power system.

[0018] Research has revealed that a power system operation data characterization technology can be designed to rapidly output high-fidelity real-time data samples that are precisely aligned with the actual operating state of the power system. This data can then serve as data support for power grid analysis, thereby helping to make power grid analysis more relevant to the actual power grid situation and improving the accuracy and efficiency of power grid analysis.

[0019] Based on the above findings, this embodiment specifically provides a technical solution for characterizing power system operation data. Specifically, it includes: selecting equipment belonging to the critical equipment category from the power system as measurement points; constructing a data model to represent the network topology of the measurement points based on the line topology connection relationships between the equipment corresponding to the measurement points; based on the power system's data standardization definition, separating and standardizing the static parameters and dynamic measurement data corresponding to each measurement point from the specifications and real-time operation data of the equipment corresponding to each measurement point, and mapping the static parameters and dynamic measurement data to the corresponding measurement points in the data model; continuously monitoring the actual operating state of the power system; dynamically locking the state-changing measurement points in the data model based on the hierarchical association rules of the power system; and dynamically updating the parts of the static parameters and dynamic measurement data mapped to the state-changing measurement points that do not match the actual operating state of the power system based on the current specifications and real-time operation data of the equipment corresponding to the state-changing measurement points, in order to characterize the actual operating state of the power system; the hierarchical association rules are rules for locking state-changing measurement points based on the multi-level structure of the measurement point network topology and the association relationships between measurement points at the same and different levels. Therefore, the solution provided in this embodiment only selects devices belonging to the key equipment category as measurement points, constructs a data model to reflect the measurement point network topology, and dynamically maps static parameters and dynamic measurement data of each measurement point in the measurement point network topology in real time to be consistent with the actual operating state of the power system. This achieves accurate characterization of the actual operating state and conditions of the power system, thereby enabling the rapid output of high-fidelity real-time data samples that are accurately aligned with the actual operating state of the power system. This makes the actual operating state of the power grid intuitively discernible, and the real-time data samples can provide efficient and reliable data support for power grid analysis, helping the power grid analysis to be more in line with the actual power grid and improving the accuracy and efficiency of power grid analysis.

[0020] Based on the above-mentioned power system operation data characterization technology, this embodiment specifically provides a power system operation data characterization method and apparatus. The power system operation data characterization method and apparatus provided in this embodiment will be described in detail below.

[0021] This application provides a method for characterizing power system operation data, such as... Figure 1 As shown, the power system operation data characterization method provided in this embodiment may include at least the following steps 101 to 103.

[0022] 101. Select equipment belonging to the critical equipment category from the power system as measurement points, and construct a data model to reflect the network topology of the measurement points based on the line topology connection relationship between the equipment corresponding to the measurement points.

[0023] In some embodiments, when initiating the power system operation data characterization process, a power system is first selected to facilitate subsequent, precise characterization of power system operation data for the selected power system. The method for selecting a power system may include: upon receiving a power system selection instruction, selecting the power system specified by the instruction as the power system to be characterized for power system operation data. This allows users to initiate power system operation data characterization at their own pace. It should be noted that the selected power system can be of any level, and this embodiment does not limit this. Here, "level" can be defined based on at least one of voltage level and management scope (e.g., administrative or geographical jurisdiction). For example, the selected power system is a prefecture-level city regional power system.

[0024] In some embodiments, power systems typically include a large number of devices. However, auxiliary devices and terminal branch devices contribute limitedly to power grid analysis. Including these devices in the data model would introduce a large amount of non-critical information, resulting in a complex network topology and significant data redundancy. Therefore, after selecting the power system, devices belonging to the critical equipment category are chosen as measurement points to construct the data model using only those belonging to this category. The critical equipment category is a hierarchical classification of devices that play a decisive role in the overall operation of the power system, based on their functional weight, failure impact, and security priority. Devices belonging to the critical equipment category play an irreplaceable role in the safe and stable operation of the power system, the realization of core functions, and the continuity of important services; their failures or malfunctions can lead to security risks or economic losses. The critical equipment category may include, but is not limited to, at least one of the following: busbar category, transformer category, and switch category. Busbars are critical nodes in a power system, connecting multiple devices (such as transformers and lines). Busbar voltage data (primarily measured via voltage transformers (TVs)) directly impacts grid stability and power quality, playing a central role in grid analysis operations such as power flow calculations, voltage control, and fault analysis. Therefore, busbars are considered a critical equipment category. Transformers are key devices in the grid that enable voltage level conversion. Their operating status (such as tap position and winding temperature) directly affects power flow distribution and voltage regulation. Therefore, transformers are also considered a critical equipment category. Switches (such as disconnectors and circuit breakers) are key devices in the grid used to control the connection status of other equipment. Their status (such as open / closed status) directly affects the grid's topology and operating mode. Therefore, switches are also considered a critical equipment category.

[0025] In some embodiments, this embodiment may also include at least one of the following schemes A1 and A2, so as to flexibly adjust the measurement points used to construct the data model through schemes A1 and A2.

[0026] Solution A1, in order to enable flexible inclusion of devices that do not belong to the critical equipment category as measurement points into the data model construction based on business needs, the power system operation data representation method provided in this embodiment may further include the following steps: displaying the first equipment identification information corresponding to each device in the power system that does not belong to the critical equipment category; in the case of selected first equipment identification information, adding the device corresponding to the selected first equipment identification information as a supplementary measurement point incrementally to the measurement points.

[0027] Specifically, the process of displaying the first equipment identification information for each piece of equipment in the power system that does not belong to the critical equipment category may include: obtaining the latest equipment data of the power system, which records the equipment identification information of all equipment currently included in the power system; retrieving the first equipment identification information for each piece of equipment that does not belong to the critical equipment category from the equipment data, and displaying it through a preset first interactive interface for user selection. It should be noted that the first equipment identification information is unique and may include, but is not limited to, at least one of the following: equipment name, equipment ID, equipment number, etc.

[0028] Specifically, if there is selected first device identification information, it means that the power grid analysis needs to involve the device corresponding to the selected first device identification information. Therefore, in order to meet the power grid analysis requirements, the device corresponding to the selected first device identification information is added incrementally as a supplementary measurement point to the already selected measurement points.

[0029] In Scheme A2, to avoid including unnecessary devices in the power grid analysis as measurement points in the data model construction, the power system operation data representation method provided in this embodiment may further include the following steps: displaying the second device identification information corresponding to each device as a measurement point; if there is a selected second device identification information, then removing the measurement point corresponding to the selected second device identification information from the measurement points.

[0030] Specifically, the second device identification information corresponding to each device used as a measurement point is obtained from the latest equipment data of the power system and displayed through a preset second interactive interface for user selection. When a second device identification information is selected, it indicates that the power grid analysis likely does not need to cover the device corresponding to the selected second device identification information. Therefore, to reduce the complexity of the data model and data redundancy, the measurement points corresponding to the selected second device identification information are removed from the measurement points, so that the device corresponding to the selected second device identification information no longer participates in the construction of the data model as a measurement point.

[0031] In some embodiments, the network topology of the measurement points is crucial for connecting isolated measurement points and reconstructing the overall true operating state of the power system. Without topological support, the results of power grid analysis will deviate from reality. Therefore, after selecting equipment belonging to key equipment categories as measurement points from the power system, the line topology connections between the corresponding equipment are obtained from the equipment ledger association database maintained by the power system's Energy Management System (EMS) or Distribution Management System (DMS). This information serves as the basis for constructing a data model that reflects the network topology of the measurement points. The line topology connections between the corresponding equipment refer to the physical layout and electrical connection structure formed by the interconnection of the corresponding equipment through transmission lines, distribution lines, and other electrical lines. After obtaining the line topology connections between the corresponding equipment, the step of constructing a data model based on these connections is performed. This data model provides real-time data samples that characterize the actual operating state and conditions of the power system for power grid analysis.

[0032] The process of constructing a data model to represent the network topology of the measurement points based on the line topology connection relationship between the corresponding devices of the measurement points may include the following steps 101A to 101C.

[0033] 101A. Obtain line connection relationship data to describe the line topology connection relationship between the devices corresponding to the measurement points.

[0034] The process of acquiring line connection relationship data may include: collecting the specifications and real-time operating data of the equipment corresponding to the measuring points and the line connection data between the equipment corresponding to the measuring points from the CIM diagram or substation wiring diagram currently used in the power system; after cleaning the collected data by removing duplicates and errors and unifying the measuring point coding and connection relationship format, the specifications and real-time operating data of the equipment corresponding to the measuring points and the line connection data between the equipment corresponding to the measuring points are obtained as line connection relationship data to describe the line topology connection relationship between the equipment corresponding to the measuring points.

[0035] 101B. Based on the line connection relationship data, the preset model building tool transforms the connection relationship between the measurement points and the lines into a structured data model that reflects the network topology of the measurement points, so as to clarify the position and association logic of each measurement point in the network topology.

[0036] The preset model building tool is a tool that automatically constructs a data model representing the network topology of the measurement points based on the connection rules between the devices corresponding to the measurement points (such as the interface type between the line and the transformer, the influence of the switch status on the topology, etc.) and the line connection relationship data. The preset model building tool can be flexibly selected based on business needs, and this embodiment does not limit it. For example, the preset model building tool can be a GIS system, power grid topology modeling software, etc.

[0037] 101C. Verify the accuracy of the data model by simulating actual power grid business scenarios (such as power flow calculation, topology analysis, etc.). If topology mapping deviation or logical loopholes are found, backtrack and adjust the model connection logic until the data model can accurately reflect the real topology relationship of the measurement point network and meet the power grid analysis requirements.

[0038] 102. Based on the data standardization definition of the power system, the static parameters and dynamic measurement data corresponding to each measuring point are separated and standardized from the specification parameters and real-time operation data of the equipment corresponding to each measuring point, and the static parameters and dynamic measurement data are mapped to the corresponding measuring points in the data model.

[0039] The measurement point network topology reflected in the data model is only a crucial foundation for constructing the power grid operation data system. It is also necessary to associate the measurement points with corresponding static parameters and dynamic measurement data. Only by combining these three elements can we comprehensively and accurately provide real-time data samples characterizing the actual operating status and conditions of the power system. Therefore, it is necessary to perform data standardization definitions based on the power system, separating and standardizing the static parameters and dynamic measurement data corresponding to each measurement point from the specifications and real-time operating data of the equipment corresponding to each measurement point, and mapping the static parameters and dynamic measurement data to the corresponding measurement points in the data model.

[0040] In some embodiments, to facilitate mapping static parameters and dynamic measurement data of measurement points to specifications, it is necessary to obtain the data standardization definition of the power system. The data standardization definition is used to standardize the static parameters and dynamic measurement data of the specifications. The methods for obtaining the power system data standardization definition can include the following two: One is to obtain the sample data standardization definition corresponding to the power system attribute information, based on a preset correspondence between power system attribute information and sample data standardization definitions, as the applicable data standardization definition for the power system. The other is to obtain the equipment description information of the measurement points in the power system, and customize the corresponding data standardization definition for the power system based on the equipment description information. The equipment description information may include, but is not limited to, equipment name, equipment function, and equipment category.

[0041] In some embodiments, the power system data standardization definition is at least one, and each data standardization definition has a corresponding equipment category. Each data standardization definition is applicable to standardizing the corresponding static parameters and dynamic measurement data for the measurement points of the corresponding equipment category. Based on this, the process of separating and standardizing the static parameters and dynamic measurement data corresponding to each measurement point from the specification parameters and real-time operation data of the equipment corresponding to each measurement point, and mapping the static parameters and dynamic measurement data to the corresponding measurement points in the data model, based on the power system data standardization definition, may include at least the following steps 102A to 102B.

[0042] 102A. Static parameters and dynamic measurement data are respectively used as targets to be constructed.

[0043] Static parameters describe the fundamental attributes of measurement points, possessing both long-term stability and the capacity to support these fundamental attributes, thus serving as crucial support for ensuring the integrity and consistency of the data model. Static parameters are related to the specific configuration of the equipment corresponding to each measurement point; for example, static parameters may include, but are not limited to, at least one of the following: equipment physical parameters (e.g., number, reference voltage, reference power, etc.), line impedance information, etc. Dynamic measurement data reflects the actual operating conditions of the equipment and changes in the operating state of the power system, providing key support for real-time monitoring, dispatching decisions, and fault analysis. Therefore, while static parameters exhibit long-term stability, dynamic measurement data exhibits high-frequency variability. Thus, static parameters and dynamic measurement data are treated as independent targets for construction, allowing for separate maintenance of the constructed static parameters and dynamic measurement data, thereby reducing the complexity of data maintenance.

[0044] 102B. For each target to be constructed at each measurement point, perform the following: Select the target data standardization definition corresponding to the equipment category of the current measurement point, separate the benchmark data for constructing the current target from the specifications and real-time operation data of the equipment corresponding to the current measurement point through the target data standardization definition, and perform standardization processing on the benchmark data to obtain the current target to be constructed corresponding to the current measurement point.

[0045] Specifically, to accurately adapt to the attribute characteristics and analysis needs of each type of equipment and avoid information redundancy or omissions caused by generalized data definitions, each equipment category has its own corresponding standardized data definition. The standardized data definition includes at least one data field corresponding to each target to be constructed under the corresponding equipment category, as well as usage information corresponding to each data field. Usage information indicates the purpose of the field value of the data field. The usage information corresponding to the data field may include, but is not limited to, at least one of the following: usage information describing the field use in the power grid, and usage information describing the purpose of the model.

[0046] For example, as shown in Table-1, Table-1 is a mapping table between measuring points and equipment categories, used to indicate the mapping relationship between each measuring point and its corresponding equipment category. Table-1 shows the standardized data definition corresponding to "bus measuring point" for the bus category, the standardized data definition corresponding to "transformer measuring point" for the transformer category, and the standardized data definition corresponding to "switch measuring point" for the switch category. In addition, Table-1 also shows the standardized data definition corresponding to the measuring point connection lines. The measuring point connection lines are the general term for conductors and auxiliary facilities in the power grid that undertake the core functions of power transmission and distribution. They are the basic elements constituting the physical skeleton of the power network, and their operating status directly determines the stability of the power grid.

[0047] Table 1

[0048] The models used in Table 1 may include, but are not limited to, large-scale power models.

[0049] Based on this, in order to construct the target to be constructed at the current measurement point in a targeted manner, and in order to unify the data standards of similar equipment, the target data standardization definition corresponding to the equipment category of the current measurement point is selected. The benchmark data for constructing the target to be constructed is separated from the specification parameters and real-time operation data of the corresponding equipment at the current measurement point through the target data standardization definition.

[0050] Specifically, the benchmark data for constructing the current target is separated from the specifications and real-time operating data of the equipment corresponding to the current measurement point through the standardization definition of target data. The benchmark data is then standardized to obtain the implementation process of the current target to be constructed corresponding to the measurement point, which includes the following steps 102B1 to 102B3.

[0051] 102B1. By defining the target data standardization, extract the field values ​​of each data field corresponding to the target to be constructed from the specification parameters and real-time operation data of the equipment corresponding to the current measurement point, and use them as the benchmark data for constructing the target to be constructed.

[0052] The specifications and real-time operating data of the equipment corresponding to the measuring point cover the current basic attribute data and measurement data of the equipment. Therefore, the specifications and real-time operating data of the equipment corresponding to the measuring point are used as the basis for constructing the static parameters and dynamic measurement data of the measuring point. The equipment corresponding to the measuring point has an associated measuring device used to collect the real-time operating data of the equipment. Therefore, the real-time operating data of the equipment corresponding to the current measuring point is obtained from the associated measuring device.

[0053] The specific implementation process of step 102B1 may include performing the following steps for each data field corresponding to the current target to be constructed: extracting the field value corresponding to the current data field from the specification parameters and real-time operating data of the device corresponding to the current measurement point using the regular expression corresponding to the current data field. For example, if the device corresponding to the current measurement point is bus 1, the current target to be constructed is static parameters, and the current data field is reference voltage, then obtain the regular expression for extracting the voltage value of the reference voltage of bus 1, and call the regular expression to extract the field value, i.e., the voltage value, corresponding to the reference voltage from the specification parameters and real-time operating data of the device corresponding to the current measurement point.

[0054] If all field values ​​for each data field corresponding to the target to be constructed are extracted, these field values ​​will be used as the baseline data for constructing the target. It should be noted that if some data fields have missing values, these fields can be marked, and their default values ​​will be set to these fields to avoid interrupting the power system operation data representation process. Simultaneously, the marked data fields will be highlighted so that business personnel can replace the default values ​​of the marked fields with the correct values.

[0055] 102B2. Based on the usage information of each data field corresponding to the target to be constructed, standardize the corresponding field values ​​respectively.

[0056] The field values ​​of the data fields will be applied to the uses indicated by the usage information. Therefore, based on the usage information of each data field corresponding to the target to be built, the corresponding field values ​​are standardized to ensure the usability of the field values ​​under the uses indicated by the usage information of the corresponding data field.

[0057] The specific implementation process of step 102B2 can be as follows: For each data field corresponding to the current target to be constructed, perform the following steps: Determine the standardization rules of the current data field based on the purpose information of the current data field; Standardize the field values ​​corresponding to the current data field based on the standardization rules; Verify the processed field values ​​to confirm whether they fully meet the purpose requirements of the corresponding field, and ensure that the standardized field values ​​can directly serve the purpose indicated by the purpose information in terms of format and meaning.

[0058] For example, the current data field is "upper and lower limits of line voltage amplitude," and its corresponding usage information indicates that its purpose is "the basis for judging whether the calculated value exceeds the limit." For instance, the field value corresponding to the current data field is 37 kV. Based on the usage information of the current data field, the standardization rule for the current data field is determined to be: "retain two decimal places, unit uniformly in kV, effective range 31.5kV-36.75kV." Subsequently, standardization processing is performed according to the standardization rule. First, the "kilovolt" in the field value "37 kV" is converted to "kV." Then, because the value exceeds the upper limit range, "37 kV" is corrected to "36.75kV" based on the business scenario. Finally, the field value is verified, and it is confirmed that the corrected "36.75kV" fully complies with the format, unit, and effective range requirements. Therefore, "36.75kV" is used as the standardized field value.

[0059] For example, the current data field is "upper and lower limits of line voltage amplitude," and its corresponding usage information indicates that its purpose is "the basis for judging whether the calculated value exceeds the limit." For instance, the field value corresponding to the current data field is 35 kV. Based on the usage information of the current data field, the standardization rule for the current data field is determined as: "retain two decimal places, unit uniformly in kV, effective range 31.5kV-36.75kV." Subsequently, standardization processing is performed according to the standardization rule. First, the "kilovolt" in the field value "35 kV" is converted to "kV." Then, since the value does not exceed the range, the value of 35 is kept unchanged. Therefore, "35.00kV" is finally used as the standardized field value.

[0060] In some embodiments, to further ensure the availability of field values ​​for their respective uses, the power system operation data characterization method provided in this embodiment may further include the following steps: For each data field, the standardized field value is processed as follows: verifying whether the standardized field value of the current data field conforms to the intended use indicated by the intended use information of the current data field; if it conforms, then step 102B3 is executed to organize the standardized field value in the data format matched by the current measuring point to obtain the current target to be constructed corresponding to the current measuring point; if it does not conform, then a standardization processing anomaly is issued for the current data field so that business personnel can intervene and troubleshoot the anomaly.

[0061] 102B3. Organize and standardize the field values ​​according to the data format matched by the current measurement point to obtain the current target to be constructed corresponding to the current measurement point.

[0062] The standardized field values ​​corresponding to each data field form the foundational data for the target to be constructed. Therefore, to ensure the standardization of the target, the standardized field values ​​are organized according to the data format matched to the current measuring point (e.g., including uniform specifications for field value order, numerical precision, and unit identifiers) to obtain the target to be constructed corresponding to the current measuring point. The data format can be one that meets the requirements of subsequent power grid analysis, thus eliminating the need for additional format handling during subsequent power grid analysis and improving the efficiency of the analysis.

[0063] In some embodiments, after the static parameters and dynamic measurement data of the current measurement point have been constructed, the static parameters and dynamic measurement data are mapped to the current measurement point in the data model. It should be noted that static parameters and dynamic measurement data need to be stored separately, and a differentiated storage strategy is adopted. The purpose of this is that static parameters remain stable over a long period and do not require high-frequency read / write operations; the core requirements are low maintenance costs and long-term secure retention, thus a low-cost, high-reliability storage strategy is suitable. Dynamic measurement data requires continuous writing and rapid reading, placing extremely high demands on storage IO performance, response speed, and scalability; therefore, high-performance storage media and a flexible scheduling storage strategy are matched. This approach can both prevent the high-frequency operation of dynamic measurement data from interfering with the stability of static parameters through separate storage and allocate resources specifically through a differentiated strategy, thus eliminating performance waste and ensuring the storage security and access efficiency of both types of data, achieving precise adaptation and overall optimization of data management.

[0064] 103. Continuously monitor the actual operating status of the power system, dynamically lock the state change measurement points in the data model based on the hierarchical association rules of the power system, and dynamically update the static parameters mapped by the state change measurement points and the parts of the dynamic measurement data that do not match the actual operating status of the power system according to the current specification parameters and real-time operating data of the equipment corresponding to the state change measurement points, so as to characterize the actual operating status of the power system; the hierarchical association rules are the rules for locking the state change measurement points based on the multi-level structure of the measurement point network topology and the association relationship between measurement points at the same level and different levels.

[0065] The operating status of a power system changes in real time, and the static parameters and dynamic measurement data at the measuring points change as the operating status of the power system changes. Therefore, in order to provide high-fidelity and accurate real-time data samples that reflect the actual operating status and conditions of the power system, it is necessary to continuously monitor the actual operating status of the power system. ， The static parameters and dynamic measurement data of the measuring points are dynamically updated to keep them consistent with the actual operating status of the power system.

[0066] In some embodiments, the various measuring points in a power system are closely interconnected through topological connections and load transfer. When a power system state change event triggers an operational change in one measuring point, it can trigger a chain reaction through mechanisms such as electrical coupling and power flow adjustment, causing other measuring points associated with that point to also change. Therefore, it is necessary to conduct equipment correlation analysis in conjunction with the operational state change event, identify all affected measuring points based on the grid topology, and dynamically update the static parameters mapped to these measuring points and the parts of the dynamic measurement data that do not match the actual operating state of the power system. Based on this, the hierarchical correlation rules include correlation logic corresponding to at least one equipment category under different operational state change events. The correlation logic is the logic for locating the measuring point with the changed state based on the correlation relationship between the equipment of the corresponding equipment category and measuring points at the same and different levels under the corresponding operational state change event.

[0067] Specifically, the process of continuously monitoring the actual operating status of the power system and dynamically locking the state change measurement points in the data model based on the hierarchical association rules of the power system can include the following steps 103A to 103C.

[0068] 103A. Continuously monitor whether state change events occur in the power system.

[0069] The process of continuously monitoring whether state change events occur in the power system can include the following steps for each measuring point: Real-time acquisition of the current specifications and real-time operating data of the corresponding equipment using the measurement devices associated with the measuring point; preliminary cleaning of the acquired current specifications and real-time operating data using a preset power grid data cleaning method or technology (e.g., filtering noise, completing missing values); storage of the cleaned data in a time-series database, and comparison and analysis of the current specifications and real-time operating data of the corresponding equipment at the measuring point with the current specifications and real-time operating data of the corresponding equipment at the previous moment, based on the state change judgment conditions preset by the rule engine (e.g., voltage over-limit threshold, switch on / off state transition, equipment fault code triggering, etc.), and identification of state change events based on the comparison analysis results; when a state change event is identified, the event details (state before and after change, occurrence time, associated equipment ID, etc.) are written to the event log database. It should be noted that artificial intelligence methods can also be introduced to identify irregular state change events (e.g., gradual parameter shifts caused by equipment aging) to ensure the comprehensiveness of change event identification. Status change events may include, but are not limited to, at least one of the following: line maintenance events, equipment failure events, and operation mode adjustment events.

[0070] 103B. If a target operating state change event is detected, determine the first measurement point involved in the target operating state change event.

[0071] The first measurement point is the measurement point that can be directly identified through the event details as a result of changes in the target's operating status.

[0072] 103C. Based on the association logic corresponding to the target operating state change event and the device category of the first measurement point, determine the second measurement point associated with the first measurement point, and lock the first and second measurement points as state change measurement points.

[0073] Changes in the operating state of a power system can trigger a chain reaction. Therefore, it is necessary to determine the second measuring point associated with the first measuring point based on the association logic corresponding to the target operating state change event and the equipment category of the first measuring point, and then lock both the first and second measuring points as state change measuring points. This way, it is not necessary to update all measuring points fully; only the locked state change measuring points need to be updated locally.

[0074] Specifically, the association logic limits the total value of the recursive hierarchy. When determining the second measurement point associated with the first measurement point, firstly, the connecting measurement points directly connected to the first measurement point in the measurement point network topology are determined. This process continues recursively layer by layer from the connecting measurement points until the layer reached and the layer where the first measurement point is located are considered to be the total number of layer intervals. Then, the connecting measurement points and the recursively derived measurement points are determined as the second measurement points associated with the first measurement point. For example, in the measurement point network topology, the first measurement point is a two-winding transformer located in the first layer of the topology, with a preset total value (number of layer intervals) of 2. First, the connection points directly connected to the first measurement point are determined, that is, the measurement points at both ends corresponding to the switches (both are in the second layer of the topology). Then, starting from these two measurement points corresponding to the switches, the process is repeated layer by layer. Each measurement point corresponding to the switch is further connected to the measurement point corresponding to the bus (in the third layer of the topology). At this time, the number of layer intervals between the third layer and the first layer where the first measurement point is located is 2, reaching the preset total value. Finally, the measurement points directly connected to the switches (connection points) and the measurement points corresponding to the buses (repeated measurement points) are jointly determined as the second measurement point associated with the first measurement point.

[0075] In some embodiments, after the second measuring point is determined, since the second measuring point may also change its operating state due to the target operating state change event, the first measuring point and the second measuring point are locked as state change measuring points, and the steps of dynamically updating the static parameters of the state change measuring point mapping and the parts of the dynamic measurement data that do not match the actual operating state of the power system based on the current specification parameters and real-time operating data of the equipment corresponding to the state change measuring point are continued to be executed, so as to achieve consistency with the actual operating state of the power system. The implementation process of this step may include the following steps 103D and 103E.

[0076] 103D. In the case of a status change event being a specification parameter change event, the first data in the static parameters and dynamic measurement data of the status change measurement point that needs to be updated synchronously due to the specification parameter change event will be regarded as the part that does not match the actual operating status of the power system, and will be dynamically updated based on the current specification parameters and real-time operating data of the equipment corresponding to the status change measurement point.

[0077] Specification parameter change events refer to events in which static parameters such as the model, rated parameters, and topology of power grid equipment are adjusted or modified. These events trigger changes in static parameters, which inevitably lead to changes in the dynamic measurement data at the measuring points. Therefore, to ensure that the static parameters and dynamic measurement data mapped from state-change measuring points are consistent with the current actual operating state of the power grid, the first data in the static parameters and dynamic measurement data mapped from state-change measuring points that needs to be updated synchronously due to the specification parameter change event is considered the part that does not match the actual operating state of the power system. Compared to a full update, this approach only updates the part that does not match the actual operating state of the power grid, thus improving update efficiency.

[0078] 103E. In the case of a power system operating status change event, the second data in the dynamic measurement data mapped by the state change measurement point that needs to be updated due to the power system operating status change event is regarded as the part that does not match the actual operating status of the power system, and is dynamically updated according to the current specification parameters and real-time operating data of the equipment corresponding to the state change measurement point.

[0079] A power system operating state change event refers to an event in which the dynamic operating parameters or topological connections of the power grid, such as voltage, current, and power, change, causing the overall or partial operating conditions to deviate from the original state. Since power system operating state change events do not cause changes in static parameters, only the second data point in the dynamic measurement data mapped to the state change measurement point that needs updating due to the power system operating state change event is considered the part that does not match the actual operating state of the power system. This part is then dynamically updated based on the current specification parameters and real-time operating data corresponding to the state change measurement point.

[0080] In some embodiments, further, to continuously maintain the standardization of data representation, before dynamically updating the static parameters and dynamic measurement data of the state change measurement point mapping that do not match the actual operating state of the power system based on the current specification parameters and real-time operating data of the equipment corresponding to the state change measurement point, the power system operating data representation method provided in this embodiment further includes: determining target data in the current specification parameters and real-time operating data of the equipment corresponding to the state change measurement point, wherein the target data matches the portion of the static parameters and dynamic measurement data of the state change measurement point mapping that does not match the actual operating state of the power system; and standardizing the target data based on the power system's data standardization definition. The specific process of standardizing the target data based on the power system's data standardization definition can be found in the standardization process in step 102 above, and will not be repeated here.

[0081] In some embodiments, further, after updating the static parameters and dynamic measurement data of the state change measurement point mapping that do not match the actual operating state of the power system, the power system operation data characterization method provided in this embodiment further includes: labeling the state change measurement points with tags corresponding to the operating state change events. For example, in the case of a line maintenance event: when a line needs maintenance, this condition can be represented by disconnecting the measurement point corresponding to the switch connected to that line and marking the line status as "out of service". The connection relationship of the measurement points in the measurement point network topology of the data model remains unchanged; only the status of the measurement point corresponding to the switch changes from "closed" to "open", and the line status changes from "in operation" to "out of service". This adjustment method reflects the actual operating state while maintaining the topology of the power grid. For example, in the case of an equipment failure event: when a device (such as a transformer) corresponding to a measurement point fails, this condition can be represented by marking the measurement point status as "faulty" and disconnecting the switch measurement point connected to that measurement point. In the measurement point network topology of the data model, the connection relationship of the measurement points remains unchanged; only the state of the measurement point changes from "normal" to "fault," and the state of the switch measurement point changes from "closed" to "open." For example, an operational state change event is a case of operational mode adjustment: When the power grid operational mode changes, such as adjusting the tap position of a transformer or changing the state of a switch, the specifications and operational data of the corresponding equipment at the measurement point may be updated in real time to reflect the new operational state. For instance, when adjusting the tap position of a transformer, the "tap position (taper)" attribute of the transformer measurement point will be updated. Simultaneously, after the transformer tap position is adjusted, the change in its electrical characteristics will affect the connected switch measurement points, thereby causing the voltage amplitude and phase angle of the bus measurement points connected to that switch measurement point to also adjust according to the new tap position.

[0082] The power system operation data characterization method provided in this application, when it is determined that power system operation data needs to be characterized, selects equipment belonging to the key equipment category in the power system as measurement points, and constructs a data model to represent the network topology of the measurement points based on the line topology connection relationship between the equipment corresponding to the measurement points. Subsequently, based on the data standardization definition of the power system, static parameters and dynamic measurement data corresponding to each measurement point are separated and standardized from the specification parameters and real-time operation data of the equipment corresponding to each measurement point, and the static parameters and dynamic measurement data are mapped to the corresponding measurement points in the data model. Finally, the actual operating status of the power system is continuously monitored, and the state change measurement points in the data model are dynamically locked based on the hierarchical association rules of the power system. Based on the current specification parameters and real-time operation data of the equipment corresponding to the state change measurement points, the parts of the static parameters and dynamic measurement data mapped to the state change measurement points that do not match the actual operating status of the power system are dynamically updated to characterize the actual operating status of the power system. Therefore, the solution provided in this embodiment only selects equipment belonging to the key equipment category as measurement points, constructs a data model to reflect the measurement point network topology, and dynamically maps static parameters and dynamic measurement data of each measurement point in the measurement point network topology in real time to be consistent with the actual operating state of the power system. This achieves accurate characterization of the actual operating state and conditions of the power system, thereby enabling the rapid output of high-fidelity real-time data samples that are accurately aligned with the actual operating state of the power system. This makes the actual operating state of the power grid intuitively discernible, and the real-time data samples can provide efficient and reliable data support for power grid analysis, helping the power grid analysis to be more in line with the actual power grid, improving the accuracy and efficiency of power grid analysis. In this way, it can accurately determine whether there are some risks in the power grid (e.g., whether there are risks such as voltage over-limit, line overload, insufficient stability margin, etc.) and accurately predict whether there are some faults (e.g., short circuit fault, cascading trip, etc.), thereby avoiding large-scale power outages.

[0083] In some embodiments of this application, during the operation of a power system, some new equipment may be deployed based on new business needs, and some equipment may be decommissioned according to business requirements. Therefore, in order to ensure that the representation of power system operation data remains consistent with the actual operating state of the power system, the power system operation data representation method provided in this embodiment may further include at least one of the following schemes B1 and B2.

[0084] Option B1 involves continuously monitoring whether any new critical equipment has been deployed in the power system. If such deployment is detected, the newly deployed equipment is designated as a target measurement point, and the target association between the target measurement point and existing measurement points in the power system is determined. Based on this association, the target measurement point is added to the measurement point network topology represented in the data model. Static parameters and dynamic measurement data corresponding to the target measurement point are mapped to the target measurement point based on the current specifications and real-time operating data of the equipment corresponding to the target measurement point. Measurement points with target associations are designated as first-state-change measurement points. The static parameters and dynamic measurement data mapped to the first-state-change measurement points are dynamically updated based on the current specifications and real-time operating data of the equipment corresponding to the first-state-change measurement points to avoid mismatches with the actual operating state of the power system. This allows for timely expansion of the measurement point network topology of the data model based on the actual operating state of the power system, ensuring that the representation of power system operating data remains consistent with the actual operating state of the power system.

[0085] Option B2 involves continuously monitoring the power system for any officially decommissioned equipment. If decommissioned equipment is detected, the corresponding decommissioned measurement points are removed from the measurement point network topology represented in the data model, along with the static parameters and dynamic measurement data mapped to these points. Measurement points in the power system that are associated with the decommissioned measurement points are designated as second-state-change measurement points. Based on the current specifications and real-time operating data of the equipment corresponding to these second-state-change measurement points, the static parameters and dynamic measurement data mapped to these second-state-change measurement points are dynamically updated to reflect any discrepancies with the actual operating state of the power system. This approach allows for the timely removal of unnecessary measurement points based on the actual operating state of the power system, reducing data redundancy and ensuring that the representation of power system operating data remains consistent with the actual operating state of the power system.

[0086] In some embodiments of this application, the data model is used to provide power system operation data for power grid analysis. Based on this, the power system operation data characterization method provided in this embodiment may further include the following steps: upon receiving a data sample generation instruction, based on the current measurement point network topology represented by the data model and the static parameters and dynamic measurement data currently mapped to each measurement point in the current measurement point network topology, a real-time data sample is generated to characterize the actual operating state and conditions of the power system.

[0087] The generated power system operation data integrates the structural information of the power grid equipment network topology, the inherent static specifications of the equipment, and the real-time dynamic operating status. It not only clarifies the connection logic and basic characteristics between measuring points, but also accurately reflects the real-time operating conditions of each measuring point and the overall power grid, providing reliable data support for power grid analysis work such as operation monitoring, status assessment, fault diagnosis, load forecasting, and optimized scheduling.

[0088] For example, taking a 110kV regional power system as an example, the final generated real-time data sample includes: First, the structural information of the measurement point network topology, namely the equipment connection logic of "2 substations - 5 transmission lines - 8 distribution substations" and the positional relationship of each device in the topology (such as the connection relationship between the No. 1 main transformer and a certain busbar, and the No. 3 transmission line); Second, the static parameters of the measurement points, covering the inherent attribute parameters of all topology devices, such as the rated capacity of the No. 1 main transformer (50MVA, rated voltage 110 / 10kV), the conductor type of the No. 3 transmission line (LGJ-240, length 12km); Third, the real-time dynamic measurement data of the measurement points, including the real-time load of the No. 1 main transformer (28MW, high-voltage side voltage 112kV), the real-time current of the No. 3 transmission line (180A, power factor 0.92), and other current operating status data of each device.

[0089] If the generated power system operation data needs to be provided to the power big data model for power grid analysis work such as training and inference, real-time status monitoring and intelligent dispatch decision-making, then after generating the power system operation data, the real-time data samples can be converted into the data format required by the power big data model for its use.

[0090] In some embodiments of this application, the power system operation data characterization method provided in this embodiment will be specifically described below in conjunction with a power grid analysis business scenario. Specifically, in a power grid analysis business scenario, the power system operation data characterization method provided in this embodiment may include at least the following steps one to six.

[0091] Step 1: Upon receiving a special request for customized power grid analysis for a power system, access the target data system of that power system.

[0092] The target data system aggregates the specifications and real-time operating data of all equipment in the power system and continuously updates the data to ensure consistency with the actual data on site. The target data system may include, but is not limited to: (1) at least one of the Equipment Asset Management System (EAM) and Production Management System (PMS), which are used to aggregate the specifications and parameters of all equipment in the power system; (2) at least one of the Substation Automation System (SAS), Distribution Automation System (DAS), Dispatch Automation System (SCADA / EMS), and Online Monitoring System, which are used to aggregate the real-time operating data of all equipment in the power system.

[0093] Step two involves entering the power system operation data representation process and executing steps 101 to 102 above. During the execution of steps 101 to 102, corresponding data is retrieved from the target data system according to the data usage requirements of each step, so as to support the execution of the corresponding steps.

[0094] For example, during step 101, the line topology connection relationship between the devices corresponding to the measurement points is obtained from the target data system. For example, during step 102, the specifications and real-time operating data of the devices corresponding to each measurement point are obtained from the target data system.

[0095] Step 3: The data model obtained by mapping static parameters and dynamic measurement data in Step 102 is customized and deployed as the data model for the power system.

[0096] Step four: Continue the power system operation data characterization process, executing step 103 above. During the execution of step 103, acquire relevant data from the target data system according to the data usage requirements of each step, so as to support the execution of the corresponding steps.

[0097] For example, when step 103 is executed, the current specification parameters and real-time operating data of the equipment corresponding to the state change measurement point are obtained from the target data system.

[0098] Step 5: Upon receiving a data sample generation instruction (which is issued when there is a power grid analysis requirement for the power system), based on the current measurement point network topology reflected in the data model and the static parameters and dynamic measurement data currently mapped to each measurement point in the current measurement point network topology, generate real-time data samples to characterize the actual operating status and conditions of the power system.

[0099] Step 6: Based on the generated real-time data samples used to characterize the actual operating status and conditions of the power system, perform power grid analysis operations that meet the current power grid analysis requirements for the power system.

[0100] For example, if the current power grid analysis requirement is to identify potential safety hazards in power grid operation, a power big data model adapted to this requirement can be used to analyze whether the power system has risks such as voltage overruns, line overloads, and insufficient stability margins based on the generated real-time data samples. It can also predict the probability of short-circuit faults, cascading trips, and other accidents to avoid large-scale power outages.

[0101] For example, when the current power grid analysis requirement is to assess the operational stability of the power grid, a power large model adapted to this requirement can be used to determine the transient and static stability levels of the power system under conditions such as load fluctuations and power source start-up and shutdown based on the generated real-time data samples. This clarifies the boundary of the power system's ability to withstand disturbances and guides the formulation of stability control strategies.

[0102] For example, given the current need for power grid analysis to support power grid dispatching decisions and emergency response, a power big data model adapted to this need can be used to conduct online power flow calculations and fault simulation analyses based on the generated real-time data samples. This can clarify the impact range and recovery path after a fault occurs, and guide dispatchers to quickly take measures such as load shedding and adjusting unit output to shorten fault handling time.

[0103] As can be seen from steps one through six above, the solution provided in this embodiment can customize and deploy a data model to represent the network topology of measurement points within the power system. Furthermore, it dynamically maps static parameters and dynamic measurement data of each measurement point in the network topology to the actual operating state of the power system in real time, thereby achieving an accurate representation of the actual operating state and conditions of the power system. Thus, when performing power grid analysis operations that meet the current power grid analysis requirements, high-fidelity real-time data samples that are precisely aligned with the actual operating state of the power system can be quickly obtained to support the power grid analysis. This enables the power grid analysis to be more closely aligned with the actual power grid situation, improving the accuracy and efficiency of the power grid analysis.

[0104] Furthermore, one embodiment of this application also provides a device for characterizing power system operating data, such as... Figure 2 As shown, the power system operation data characterization device provided in this embodiment may include at least: Module 21 is used to select equipment belonging to the critical equipment category from the power system as measurement points, and to construct a data model to reflect the network topology of the measurement points based on the line topology connection relationship between the equipment corresponding to the measurement points. Processing module 22 is used to separate and standardize the static parameters and dynamic measurement data corresponding to each measuring point from the specification parameters and real-time operation data of the equipment corresponding to each measuring point based on the data standardization definition of the power system, and map the static parameters and dynamic measurement data to the corresponding measuring points in the data model; The update module 23 is used to continuously monitor the actual operating status of the power system, dynamically lock the state change measurement points in the data model based on the hierarchical association rules of the power system, and dynamically update the static parameters mapped by the state change measurement points and the parts of the dynamic measurement data that do not match the actual operating status of the power system according to the current specification parameters and real-time operating data of the equipment corresponding to the state change measurement points, so as to characterize the actual operating status of the power system. The hierarchical association rules are rules for locking the state change measurement points based on the multi-level structure of the measurement point network topology and the association relationship between measurement points at the same level and different levels.

[0105] The power system operation data characterization device provided in this application only selects equipment belonging to the key equipment category as measurement points, constructs a data model to reflect the measurement point network topology, and dynamically maps static parameters and dynamic measurement data of each measurement point in the measurement point network topology in real time to be consistent with the actual operating state of the power system. This achieves accurate characterization of the actual operating state and conditions of the power system, thereby enabling the rapid output of high-fidelity real-time data samples that are accurately aligned with the actual operating state of the power system. This makes the actual operating state of the power grid intuitively discernible, and the real-time data samples can provide efficient and reliable data support for power grid analysis, helping the power grid analysis to be more in line with the actual power grid, improving the accuracy and efficiency of power grid analysis. In this way, it can accurately determine whether there are some risks in the power grid (e.g., whether there are risks such as voltage over-limit, line overload, insufficient stability margin, etc.) and accurately predict whether there are some faults (e.g., short circuit fault, cascading trip, etc.), thereby avoiding large-scale power outage events.

[0106] In some embodiments of this application, such as Figure 3 As shown, the data standardization definition of the power system is at least one, and each data standardization definition has a corresponding equipment category. Therefore, the processing module 22 may include: The first determining unit 221 is used to take static parameters and dynamic measurement data as targets to be constructed, respectively. The first processing unit 222 is used to perform the following for each target to be constructed at each measurement point: select the target data standardization definition corresponding to the equipment category of the current measurement point, separate the reference data for constructing the current target from the specification parameters and real-time operation data of the equipment corresponding to the current measurement point through the target data standardization definition, and perform standardization processing on the reference data to obtain the current target to be constructed corresponding to the current measurement point.

[0107] In some embodiments of this application, such as Figure 3As shown, the data standardization definition includes at least one data field corresponding to each target to be constructed under the corresponding equipment category and the usage information corresponding to each data field. The first processing unit 222 includes a processing subunit 2221, which separates the reference data for constructing the current target to be constructed from the specification parameters and real-time operation data of the equipment corresponding to the current measurement point through the target data standardization definition, performs standardization processing on the reference data, and obtains the current target to be constructed corresponding to the measurement point.

[0108] The processing subunit 2221 is specifically used to extract the field values ​​of each data field corresponding to the target to be constructed from the specification parameters and real-time operation data of the equipment corresponding to the current measurement point through the target data standardization definition, and use them as the reference data for constructing the target to be constructed; based on the usage information of each data field corresponding to the target to be constructed, the corresponding field values ​​are standardized respectively; and the standardized field values ​​are organized in the data format matched by the current measurement point to obtain the target to be constructed corresponding to the current measurement point.

[0109] In some embodiments of this application, such as Figure 3 As shown, the first processing unit 222 may further include: Verification subunit 2222 is used to perform the following steps for each data field: verifying whether the standardized field value of the current data field conforms to the purpose indicated by the purpose information of the current data field; if it conforms, then the processing subunit 2221 is triggered to perform the step of organizing the standardized field value according to the data format matched by the current measurement point to obtain the current target to be constructed corresponding to the measurement point; if it does not conform, then the prompting subunit 2223 is triggered to issue a prompt of standardization processing error for the current data field.

[0110] In some embodiments of this application, the usage information corresponding to the data field includes at least one of the following: usage information for describing the on-site use of the power grid, and usage information for describing the use of the model.

[0111] In some embodiments of this application, the hierarchical association rules include association logic corresponding to at least one device category under different operating state change events. The association logic is the logic for locating the state-change measurement point based on the association relationship between the device of the corresponding device category and measurement points at the same and different levels under the corresponding operating state change event; for example... Figure 3 As shown, update module 23 may include: Monitoring unit 231 is used to continuously monitor whether a state change event occurs in the power system; The second determining unit 232 is used to determine the first measuring point involved in the target operating state change event when a target operating state change event is detected. The locking unit 233 is used to determine the second measurement point associated with the first measurement point based on the association logic corresponding to the target operating state change event and the device category of the first measurement point, and lock the first measurement point and the second measurement point as state change measurement points.

[0112] In some embodiments of this application, such as Figure 3 As shown, update module 23 may include: The first update unit 234 is used to, when the state change event is a specification parameter change event, take the first data that needs to be updated synchronously in the static parameters and dynamic measurement data of the state change measurement point as the part that does not match the actual operating state of the power system, and dynamically update it according to the current specification parameters and real-time operating data of the equipment corresponding to the state change measurement point. The second update unit 235 is used to, when the state change event is a power system operation state change event, take the second data in the dynamic measurement data mapped by the state change measurement point that needs to be updated due to the power system operation state change event as the part that does not match the actual operation state of the power system, and dynamically update it according to the current specification parameters and real-time operation data of the equipment corresponding to the state change measurement point.

[0113] In some embodiments of this application, such as Figure 3 As shown, update module 23 may also include: The second processing unit 236 is used to determine target data in the current specification parameters and real-time operating data of the device corresponding to the state change measurement point before dynamically updating the parts of the static parameters and dynamic measurement data of the state change measurement point mapping that do not match the actual operating state of the power system based on the current specification parameters and real-time operating data of the device corresponding to the state change measurement point. The target data matches the parts of the static parameters and dynamic measurement data of the state change measurement point mapping that do not match the actual operating state of the power system. Based on the data standardization definition of the power system, the target data is standardized.

[0114] In some embodiments of this application, such as Figure 3 As shown, the power system operation data characterization device provided in this embodiment may further include: The first modification module 24 is used to continuously monitor whether any new equipment belonging to the critical equipment category has been deployed in the power system; when a new equipment belonging to the critical equipment category is detected in the power system, the newly deployed equipment is designated as a target measurement point, and the target association relationship between the target measurement point and the existing measurement points in the power system is determined; based on the target association relationship, the target measurement point is added to the measurement point network topology represented by the data model, and the static parameters and dynamic measurement data corresponding to the target measurement point are mapped to the target measurement point according to the current specification parameters and real-time operating data of the equipment corresponding to the target measurement point; the measurement points that have a target association relationship with the target measurement point are designated as first state change measurement points, and the static parameters and dynamic measurement data mapped to the first state change measurement points that do not match the actual operating state of the power system are dynamically updated according to the current specification parameters and real-time operating data of the equipment corresponding to the first state change measurement points.

[0115] In some embodiments of this application, such as Figure 3 As shown, the power system operation data characterization device provided in this embodiment may further include: The second modification module 25 is used to continuously monitor whether there are any decommissioned devices that have been officially taken out of operation in the power system; when decommissioned devices are detected in the power system, the decommissioned measurement points corresponding to the decommissioned devices are deleted from the measurement point network topology reflected in the data model, and the static parameters and dynamic measurement data mapped to the decommissioned measurement points are deleted; the measurement points in the power system that are associated with the decommissioned measurement points are designated as second state change measurement points, and the static parameters and dynamic measurement data mapped to the second state change measurement points that do not match the actual operating state of the power system are dynamically updated according to the current specification parameters and real-time operating data of the devices corresponding to the second state change measurement points.

[0116] In some embodiments of this application, such as Figure 3 As shown, the power system operation data characterization device provided in this embodiment may further include: a storage module 26, used for separate storage of static parameters and dynamic measurement data, and adopting a differentiated storage strategy.

[0117] In some embodiments of this application, such as Figure 3 As shown, the power system operation data characterization device provided in this embodiment may further include: a generation module 27, which, upon receiving a data sample generation instruction, generates real-time data samples to characterize the actual operating status and conditions of the power system based on the current measurement point network topology reflected in the data model and the static parameters and dynamic measurement data currently mapped to each measurement point in the current measurement point network topology.

[0118] In some embodiments of this application, such as Figure 3As shown, the power system operation data characterization device provided in this embodiment may further include: an addition module 28, used to display the first equipment identification information corresponding to each equipment in the power system that does not belong to the critical equipment category; in the case of selected first equipment identification information, the equipment corresponding to the selected first equipment identification information is added incrementally to the measurement points as a supplementary measurement point.

[0119] In some embodiments of this application, such as Figure 3 As shown, the power system operation data characterization device provided in this embodiment may further include: a deletion module 29, used to display the second device identification information corresponding to each device as a measurement point, and when there is a selected second device identification information, the measurement point corresponding to the selected second device identification information is removed from the measurement points.

[0120] In some embodiments of this application, the key equipment category includes at least one of the following: busbar category, transformer category, and switch category.

[0121] For a detailed explanation of the power system operation data characterization device provided in this application embodiment, please refer to the corresponding detailed explanation of the power system operation data characterization method embodiment above, and it will not be repeated here.

[0122] Furthermore, one embodiment of this application also provides a computer-readable storage medium, the storage medium including a stored program, wherein, when the program is executed, it controls the device where the storage medium is located to execute the above-described method for representing power system operating data.

[0123] Furthermore, one embodiment of this application also provides an electronic device, the electronic device comprising: a memory for storing a program; and a processor coupled to the memory for running the program to execute the above-described method for characterizing power system operating data.

[0124] Furthermore, one embodiment of this application also provides a computer program product, the computer program product comprising: a computer program / computer executable instructions, the computer program / computer executable to perform the above-described method for representing power system operating data.

[0125] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0126] It is understood that the relevant features in the above methods and apparatus can be referenced interchangeably. Furthermore, the terms "first," "second," etc., in the above embodiments are used to distinguish between embodiments and do not represent the superiority or inferiority of any particular embodiment.

[0127] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0128] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this application is not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing preferred embodiments of this application.

[0129] In addition, the memory may include non-permanent 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, and the memory includes at least one memory chip.

[0130] 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.

[0131] 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 cutover device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data cutover device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0132] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data cutover device to operate 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 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data cutover device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes. In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0134] 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.

[0135] 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 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.

[0136] 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.

[0137] 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.

[0138] 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 method of characterizing power system operating data, the method comprising: The method includes: Equipment belonging to the critical equipment category in the power system is selected as measurement points, and a data model is constructed to reflect the network topology of the measurement points based on the line topology connection relationship between the equipment corresponding to the measurement points. Based on the data standardization definition of the power system, the static parameters and dynamic measurement data corresponding to each measuring point are separated and standardized from the specification parameters and real-time operation data of the equipment corresponding to each measuring point, and the static parameters and dynamic measurement data are mapped to the corresponding measuring points in the data model. The system continuously monitors the actual operating status of the power system, dynamically locks the state change measurement points in the data model based on the hierarchical association rules of the power system, and dynamically updates the static parameters mapped by the state change measurement points and the parts of the dynamic measurement data that do not match the actual operating status of the power system according to the current specification parameters and real-time operating data of the equipment corresponding to the state change measurement points, so as to characterize the actual operating status of the power system. The hierarchical association rules are rules for locking the state change measurement points based on the multi-level structure of the measurement point network topology and the association relationship between measurement points at the same level and different levels.

2. The method according to claim 1, characterized in that, The power system data standardization definition is at least one, and each data standardization definition has a corresponding equipment category. Based on the power system data standardization definition, the static parameters and dynamic measurement data corresponding to each measuring point are separated and standardized from the specification parameters and real-time operating data of the equipment corresponding to each measuring point, including: Static parameters and dynamic measurement data are respectively used as the targets to be constructed; For each target to be constructed at each measurement point, the following steps are performed: Select the target data standardization definition corresponding to the equipment category of the current measurement point; use the target data standardization definition to separate the baseline data for constructing the current target from the specifications and real-time operating data of the equipment corresponding to the current measurement point; perform standardization processing on the baseline data to obtain the current target to be constructed corresponding to the current measurement point.

3. The method according to claim 2, characterized in that, The data standardization definition includes at least one data field corresponding to each target to be constructed under the corresponding equipment category, and the usage information corresponding to each data field. Then, by using the target data standardization definition, benchmark data for constructing the current target to be constructed is separated from the specifications and real-time operating data of the corresponding equipment. The benchmark data is then standardized to obtain the current target to be constructed corresponding to the measurement point, including: By defining the target data standardization, the field values ​​corresponding to each data field of the target to be constructed are extracted from the specifications and real-time operating data of the equipment corresponding to the current measuring point, and used as the benchmark data for constructing the target to be constructed. Based on the purpose information of each data field corresponding to the current target to be constructed, the corresponding field values ​​are standardized and processed respectively. The field values ​​are standardized and processed according to the data format matched by the current measurement point to obtain the current target to be constructed corresponding to the current measurement point.

4. The method according to claim 3, characterized in that, The method further includes: for each data field, the following steps are performed for the standardized field value: verifying whether the standardized field value of the current data field conforms to the purpose indicated by the purpose information of the current data field; if it conforms, then the standardized field value is organized according to the data format matched by the current measurement point to obtain the current target to be constructed corresponding to the measurement point; if it does not conform, then a standardization processing error prompt is issued for the current data field. And / or, The usage information corresponding to the data field includes at least one of the following: usage information for describing the field use of the power grid, and usage information for describing the use of the model.

5. The method according to claim 1, characterized in that, The hierarchical association rules include association logic corresponding to at least one device category under different operating state change events. The association logic is the logic of locating the state change measurement point based on the association relationship between the device of the corresponding device category and the measurement points at the same and different levels under the corresponding operating state change event. The system continuously monitors the actual operating status of the power system and dynamically locks the state change measurement points in the data model based on the hierarchical association rules of the power system, including: Continuously monitor the power system for state change events; If a target operating state change event is detected, determine the first measurement point involved in the target operating state change event; Based on the association logic corresponding to the target operating state change event and the device category of the first measurement point, a second measurement point associated with the first measurement point is determined, and the first measurement point and the second measurement point are locked as state change measurement points.

6. The method according to claim 5, characterized in that, Based on the current specification parameters and real-time operating data corresponding to the state change measurement points, dynamically update the static parameters mapped to the state change measurement points and the portions of the dynamic measurement data that do not match the actual operating state of the power system, including: If the state change event is a specification parameter change event, then the first data in the static parameters and dynamic measurement data mapped by the state change measurement point that needs to be updated synchronously due to the specification parameter change event is regarded as the part that does not match the actual operating state of the power system, and is dynamically updated according to the current specification parameters and real-time operating data of the equipment corresponding to the state change measurement point. If the state change event is a power system operating state change event, then the second data in the dynamic measurement data mapped by the state change measurement point that needs to be updated due to the power system operating state change event is regarded as the part that does not match the actual operating state of the power system, and is dynamically updated according to the current specification parameters and real-time operating data of the equipment corresponding to the state change measurement point.

7. The method according to claim 1, characterized in that, Before dynamically updating the static parameters and dynamic measurement data mapped to the state change measurement points that do not match the actual operating state of the power system based on the current specifications and real-time operating data of the equipment corresponding to the state change measurement points, the method further includes: Determine the target data in the current specification parameters and real-time operating data of the equipment corresponding to the state change measurement point. The target data is matched with the part of the static parameters and dynamic measurement data mapped by the state change measurement point that does not match the actual operating state of the power system. Based on the data standardization definition of the power system, the target data is standardized.

8. The method according to any one of claims 1-7, characterized in that, The method further includes: continuously monitoring whether new equipment belonging to the critical equipment category has been deployed in the power system; when new equipment belonging to the critical equipment category has been deployed in the power system, the newly deployed equipment is taken as a target measurement point, and the target association relationship between the target measurement point and the original measurement points in the power system is determined; based on the target association relationship, the target measurement point is added to the measurement point network topology represented by the data model, and the static parameters and dynamic measurement data corresponding to the target measurement point are mapped to the target measurement point according to the current specification parameters and real-time operating data of the equipment corresponding to the target measurement point; the measurement points that have a target association relationship with the target measurement point are taken as first state change measurement points, and the static parameters and dynamic measurement data mapped to the first state change measurement points that do not match the actual operating state of the power system are dynamically updated according to the current specification parameters and real-time operating data of the equipment corresponding to the first state change measurement points. And / or, The method further includes: continuously monitoring whether there are decommissioned equipment that has been officially taken out of operation in the power system; when decommissioned equipment is detected in the power system, deleting the decommissioned measuring point corresponding to the decommissioned equipment from the measuring point network topology reflected in the data model, and deleting the static parameters and dynamic measurement data mapped by the decommissioned measuring point; taking the measuring points in the power system that are associated with the decommissioned measuring points as second state change measuring points, and dynamically updating the parts of the static parameters and dynamic measurement data mapped by the second state change measuring points that do not match the actual operating state of the power system according to the current specification parameters and real-time operating data of the equipment corresponding to the second state change measuring points.

9. The method according to any one of claims 1-7, characterized in that, The method further includes: storing static parameters and dynamic measurement data separately, and adopting a differentiated storage strategy; And / or, The method further includes: upon receiving a data sample generation instruction, generating a real-time data sample to characterize the actual operating status and conditions of the power system based on the current measurement point network topology reflected in the data model and the static parameters and dynamic measurement data currently mapped to each measurement point in the current measurement point network topology. And / or, The method further includes: displaying first equipment identification information corresponding to each piece of equipment in the power system that does not belong to the critical equipment category; and, if there is selected first equipment identification information, adding the equipment corresponding to the selected first equipment identification information as a supplementary measurement point incrementally to the measurement points. And / or, The method further includes: displaying the second device identification information corresponding to each device as a measurement point; if there is a selected second device identification information, then removing the measurement point corresponding to the selected second device identification information from the measurement points. And / or, The key equipment categories include at least one of the following: busbar category, transformer category, and switch category.

10. A device for characterizing power system operation data, characterized in that, The device includes: The module is used to select equipment belonging to the critical equipment category from the power system as measurement points, and to build a data model to reflect the network topology of the measurement points based on the line topology connection relationship between the equipment corresponding to the measurement points. The processing module is used to separate and standardize the static parameters and dynamic measurement data corresponding to each measuring point from the specification parameters and real-time operation data of the equipment corresponding to each measuring point based on the data standardization definition of the power system, and map the static parameters and dynamic measurement data to the corresponding measuring points in the data model. The update module is used to continuously monitor the actual operating status of the power system, dynamically lock the state change measurement points in the data model based on the hierarchical association rules of the power system, and dynamically update the static parameters mapped by the state change measurement points and the parts of the dynamic measurement data that do not match the actual operating status of the power system according to the current specification parameters and real-time operating data of the equipment corresponding to the state change measurement points, so as to characterize the actual operating status of the power system. The hierarchical association rules are rules for locking the state change measurement points based on the multi-level structure of the measurement point network topology and the association relationship between measurement points at the same level and different levels.

11. A computer-readable storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the storage medium to perform the power system operation data characterization method according to any one of claims 1 to 9.

12. An electronic device, characterized in that, The electronic device includes: a memory for storing a program; and a processor coupled to the memory for running the program to perform the power system operation data characterization method according to any one of claims 1 to 9.

13. A computer program product, characterized in that, The computer program product includes: a computer program / computer-executable instructions, wherein the computer program / computer-executable method for characterizing power system operation data according to any one of claims 1 to 9.

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