Intelligent substation data storage method based on object model

Abstract

The application discloses a kind of intelligent substation data storage methods based on object model.The method includes the following steps: obtaining the CID file of the primary equipment of intelligent substation, obtaining the corresponding object model template according to the equipment type in the CID file;According to the object model template, create the corresponding JSON instance and generate JSON file, save the sampling data in the CID file to the JSON file;The sampling data in the JSON file includes first sampling data and second sampling data, store the first sampling data to time series database, and store the second sampling data to relational database;The first sampling data is the sampling data with time sequence characteristics, and the second sampling data is the remaining sampling data in the JSON file except the first sampling data.The technical scheme of the present application improves the storage efficiency of the sampling data of the primary equipment of intelligent substation in the power system.

Description

Technical Field

[0001] This invention relates to the field of equipment data storage technology, and in particular to a data storage method for intelligent substations based on a physical model. Background Technology

[0002] With the advent of the "Internet of Things" era and the continuous advancement of ubiquitous IoT construction, the degree of digitalization in power systems is increasing. To accurately describe power equipment, real-time data collection of equipment data and operating status is necessary, leading to a significant increase in data collection points and frequency. Statistics show that the amount of power system data will rise from GB to TB levels to TB to PB levels. Due to the large volume, diverse types, and low quality of power system data, the load on the power grid communication system is increased, severely impacting communication efficiency.

[0003] Substations are responsible for monitoring and managing primary equipment, and for transmitting data up and down, making them crucial data hubs in the power system. To address this, the International Electrotechnical Commission (IEC) proposed the IEC 61850 communication protocol to standardize data transmission in substations, enabling interoperability and information sharing between equipment. Currently, the IEC 61850 standard has become a vital support for smart substations, a standard for newly built substations in China, and is explicitly intended for widespread application in all public power sectors. However, the complexity and large volume of data in IEC 61850 data places significant pressure on power system data storage and application. Current data storage methods commonly used in power systems are insufficient for efficiently storing data from smart substations using the IEC 61850 standard. Existing technologies have the following problems:

[0004] (1) Most current power systems use relational databases for data storage. Relational databases are not good at handling massive amounts of data. As the amount of data increases, the data read and write speed will decrease significantly, making it difficult to meet the real-time requirements of the power system.

[0005] (2) The data in the power system mainly consists of electrical quantity sampling values, which are typical time-series data. Their data structure is inconsistent with that of relational databases, and the calculation and analysis of data during the operation and maintenance of the power system are also based on time series. This makes it difficult for relational databases to meet the requirements in terms of both data storage and data manipulation.

[0006] (3) The data model used in the current power system database differs significantly from the IEC61850 data model. The IEC61850 data model is difficult to directly map to the database for storage, affecting storage efficiency. For example, the monitoring database stores data in the form of a remote monitoring point table, which requires converting the IEC61850 data format to the remote monitoring point table format, making the storage process cumbersome.

[0007] (4) The creation of storage point tables in current relational databases often requires manual intervention, resulting in low automation and efficiency, and manual operation carries a significant risk of error. Summary of the Invention

[0008] This invention provides a data storage method for smart substations based on object models, which improves the storage efficiency of sampling data from primary equipment in smart substations within a power system.

[0009] An embodiment of the present invention provides a data storage method for intelligent substations based on a physical model, comprising the following steps:

[0010] Obtain the CID files of the primary equipment in the smart substation, and obtain the corresponding object model templates based on the equipment types in the CID files;

[0011] Create a corresponding JSON instance based on the object model template and generate a JSON file, and save the sampling data in the CID file to the JSON file;

[0012] The sampling data in the JSON file includes first sampling data and second sampling data. The first sampling data is stored in a time-series database, and the second sampling data is stored in a relational database. The first sampling data is sampling data with time-series characteristics, and the second sampling data is the remaining sampling data in the JSON file other than the first sampling data.

[0013] Furthermore, creating a corresponding JSON instance based on the object model template includes the following steps:

[0014] Create a corresponding JSON instance based on the object model template, and save the device information in the CID file to the static properties of the JSON instance;

[0015] Read the datasets, data objects, and data attributes under each logical node in the CID file. Based on the function of the logical node and the description of the dataset, find the service module corresponding to the logical node in the JSON instance, and store the data objects and data attributes under the logical node into the service module.

[0016] Furthermore, the data objects and data attributes are stored in the service module in the form of a structure.

[0017] Furthermore, storing the first sampled data in a time-series database includes the following steps:

[0018] Obtain the device type of the primary device from the static properties of the JSON file, and retrieve the corresponding super table based on the device type;

[0019] The super table is instantiated in the time-series database based on the static attributes of the JSON file to obtain the entity table corresponding to the primary device;

[0020] The first sampled data is stored in the entity table corresponding to the primary device.

[0021] Furthermore, a super table is pre-established for primary equipment of the same type, and the table header of the super table is constructed according to the data attributes corresponding to all data sampling points of primary equipment of the same type. The data attributes include a static attribute used as a unique identifier for primary equipment.

[0022] Furthermore, the entity tables of primary equipment of the same type are aggregated to obtain the master table of the primary equipment of the same type.

[0023] Furthermore, the sampling time and the unique identifier are used as the composite primary key of the master table.

[0024] Furthermore, the time series database uses the TDengine time series database.

[0025] The embodiments of the present invention have the following beneficial effects:

[0026] This invention provides a data storage method for smart substations based on a physical model. The method creates a corresponding JSON instance and generates a JSON file based on a physical model template. Sampling data from the CID file is saved to the JSON file. Then, the first and second sampling data from the JSON file are stored in a time-series database and a relational database, respectively. The principle is that by using the JSON file created based on the physical model template as a medium, a channel for quickly extracting and storing sampling data from the CID file into the database is established, improving the storage efficiency of sampling data from primary equipment in smart substations in power systems. Simultaneously, by storing sampling data with different data characteristics separately in the time-series database and the relational database, the method fully considers the compatibility between sampling data with different characteristics and different data types, further improving the storage efficiency of sampling data from primary equipment in smart substations in power systems. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a smart substation data storage method based on a physical model, according to an embodiment of the present invention.

[0028] Figure 2 This is a schematic diagram of the engineering configuration process of an intelligent substation based on the IEC61850 standard, which is a data storage method for intelligent substations based on a physical model provided in an embodiment of the present invention.

[0029] Figure 3 This is a schematic diagram comparing the storage methods of relational databases and time-series databases in a data storage method for intelligent substations based on a physical model, provided in an embodiment of the present invention.

[0030] Figure 4 This is a schematic diagram illustrating the automatic mapping between the device information of the IED and the static attributes of the object model template in the CID file of the intelligent substation data storage method based on the object model provided in an embodiment of the present invention.

[0031] Figure 5 This is a schematic diagram of the object model template structure of a smart substation data storage method based on an object model provided in an embodiment of the present invention. Detailed Implementation

[0032] The technical solutions of this invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0033] like Figure 1 As shown, an embodiment of the present invention provides a data storage method for intelligent substations based on a physical model, comprising the following steps:

[0034] Step S101: Obtain the CID file of the primary equipment in the smart substation, and obtain the corresponding object model template according to the equipment type in the CID file. The object model template is pre-established based on the data transmission format of the CID file of the same type of primary equipment. The CID file is a configuration file for the primary equipment in the smart substation obtained according to the IEC61850 standard. Specifically, the corresponding object model template is pre-established based on the CID file of the primary equipment, and the object model template is uploaded to the equipment access platform in advance. When it is necessary to save the sampled data to the database, the object model template is obtained from the equipment access platform.

[0035] Step S102: Create a corresponding JSON instance and generate a JSON file based on the object model template, and save the sampling data from the CID file to the JSON file. Specifically, when creating the corresponding JSON instance based on the object model template, the JSON instance with the same data structure as the object model template is created.

[0036] The object model of this invention is a digital model defined for primary equipment, used to describe the physical properties, functions, and services that the primary equipment can provide, thereby realizing the abstraction and virtualization of the primary equipment.

[0037] To store sampled data from primary equipment in a substation, the database first needs to locate the configuration file for that primary equipment within the substation's configuration file system. The IEC 61850 standard uses the SCL language to create configuration files that describe the communication structure of the entire substation, as well as the functions and parameters of each device. Version 1 of the IEC 61850 standard defines four types of configuration files: ICD, SSD, SCD, and CID files.

[0038] (1) ICD file, also known as IED capability description file; provided to the integrator by the manufacturers of each device. The ICD file describes the functions and basic information of each device.

[0039] (2) SSD file, also known as system specification file; used to describe the main wiring diagram of the smart substation and the logical function nodes that match the main wiring diagram, sometimes appearing as part of the SCD file.

[0040] (3) SCD file, also known as the whole system configuration file; this file is unique across the whole site and is used to describe the power configuration, communication parameters, connections between IEDs, and system structure of all IEDs across the whole site.

[0041] (4) CID file, also known as IED instance configuration file, is used to describe the function, parameters and basic information of each IED in the site.

[0042] The specific configuration process of an IEC61850 smart substation is as follows: Figure 2 As shown, all ICD files within the intelligent station are aggregated into an SCD file. Through on-site instantiation—that is, modifying the IEDNAME, communication address, and configuring virtual terminals—a CID file is exported using the SCD tool. The CID file provides the most comprehensive description of the intelligent station's on-site operating status; one CID file corresponds to one device, realizing a digital description of the physical equipment. Through the CID file, the transmission methods of parameters such as the device's parameters, functions, operating status, and communication status can be accurately obtained. Therefore, this invention selects the CID file as the source file for storing data objects in the database.

[0043] In one embodiment, the object model template includes multiple static attributes and multiple services. The services describe the business capabilities of the device, and the business capabilities of the primary device are described through multiple services. Each service includes multiple data objects (such as service attributes, commands, input parameters, and output parameters of the commands). The static attributes correspond to the device information in the CID file, including but not limited to device name, device type, device serial number, device model, production date, software version, and communication protocol.

[0044] Using a physical model template to save the sampling data from the CID file to the corresponding JSON file has the following advantages: it unifies and annotates the sampling data, simplifies user understanding of the data, enables linkage between different devices, and facilitates the sharing of sampling data between different devices; it greatly improves the efficiency of data parsing; it decouples storage devices from primary devices, simplifying the development process; and JSON files are highly readable, lightweight text formats and data exchange formats that can convert data structures into formats recognizable by other languages ​​and platforms, making them very suitable for storing temporary data.

[0045] A primary device includes its own static attributes and multiple services. Services are divided into multiple types according to their functions. Different types of services contain their own service attributes. If a service needs to complete some operations through commands, then the service also contains commands, command input parameters, and command output parameters.

[0046] As one embodiment, creating a corresponding JSON instance based on the object model template includes the following steps:

[0047] Step S1021: Create a corresponding JSON instance based on the object model template, and save the device information of the IED in the CID file to the static attributes of the JSON instance; specifically, name the JSON instance DevType_DevID_DevModel.json according to a certain naming rule, such as the naming rule: Device Type_DevID_DevModel.json. Figure 4 As shown, the device information of the IED in the CID file and the model template (i.e., the corresponding model template) are implemented according to the communication specifications of IEC 61850 and the modeling standards of the object model template. Figure 4 Automatic mapping between the static attributes of the object model in the CID file and the static attributes of the JSON instance can be achieved, thereby saving the device information of the IED in the CID file to the static attributes of the JSON instance.

[0048] Step S1022: Read the datasets, data objects, and data attributes under each logical node in the CID file. Based on the function of the logical node and the description of the dataset, find the service module corresponding to the logical node in the JSON instance, and save the data objects and data attributes under the logical node to the service module.

[0049] As one specific embodiment, the primary device is a relay protection device, specifically the PL2201A test protection device manufactured by NARI Group Corporation. The CID file of the PL2201A test protection device includes several logical nodes, including but not limited to internal status monitoring logical nodes and protection setting logical nodes. The dataset under each logical node contains several data objects and data attributes.

[0050] Based on the data transfer format of the CID file of the PL2201A test protection device, a physical model template of the PL2201A test protection device (i.e., ...) is established. Figure 5 (The object model in the middle) yields the following results: Figure 5 The diagram shows the physical model template structure.

[0051] According to steps S1021-S1022, a JSON instance is created and a JSON file (e.g., protection_2341_ISA-753A.json) is generated. Specifically, the static properties of the protection_2341_ISA-753A.json file are as follows:

[0052] "DevName":"PL2201A",

[0053] "DevDesc":"Test protection equipment",

[0054] "DevType":"protect",

[0055] "DevModel":"ISA-753A",

[0056] "DevMf":"NARI Group",

[0057] "DevSN":"NXM59CN013411009EA7600",

[0058] "DevID":"2341",

[0059] "MfDate:":"2021-07-26",

[0060] "SoftVer":"V1.01",

[0061] "HardVer":"V1.00",

[0062] "ProtocolType":"CMS",

[0063] "DevAddr":"192.168.0.26",

[0064] The JSON file service module includes an internal status monitoring service module and a protection setting service module.

[0065] The internal status monitoring service module for protecting the _2341_ISA-753A.json file is shown below:

[0066] The protection setting service module for the protected _2341_ISA-753A.json file is shown below:

[0067]

[0068]

[0069] The internal state monitoring service module in the physical model template of the PL2201A test protection device is used to save monitoring data of the internal state. Specifically, it needs to save the data attributes of two data objects: the measured internal voltage and temperature values. These data attributes include, but are not limited to, numerical values, data quality, and timestamps. Therefore, saving the sampled data from the CID file to a JSON file according to the physical model template offers advantages such as clear structure, easy correspondence with the data format described in the CID, and automatic mapping between the two sets of data.

[0070] Step S103: The sampling data in the JSON file includes first sampling data and second sampling data. The first sampling data is stored in a time-series database, and the second sampling data is stored in a relational database. The first sampling data is sampling data with time-series characteristics, and the second sampling data is the remaining sampling data in the JSON file excluding the first sampling data. This embodiment of the invention improves the storage efficiency of sampling data from primary equipment in smart substations in power systems by selecting appropriate database types based on the different characteristics of the sampling data.

[0071] The fundamental difference between different types of databases lies in their table structures; for example, the storage methods of relational databases and time-series databases differ. Figure 3 As shown. The second sampled data is sampled data without temporal characteristics and is not suitable for storage in a time-series database. Therefore, the second sampled data is stored in a relational database. Since the process of storing the second sampled data in a relational database uses conventional techniques from the prior art without corresponding improvements, and is not the focus of this invention, the process of storing the second sampled data in a relational database will not be described further in this embodiment.

[0072] As one embodiment, a super table is pre-established, specifically: a super table is pre-established for primary devices of the same type, and the table header of the super table is constructed according to the data attributes corresponding to all data sampling points of the primary devices of the same type and the unique identifier of the primary devices. The unique identifier of the primary devices is a static attribute in the JSON file.

[0073] As one embodiment, storing the first sampled data in a time-series database includes the following steps:

[0074] The device type of the primary device is obtained from the static attributes of the JSON file, and the corresponding super table is obtained according to the device type; the super table refers to the set of data sampling points of a specific type of primary device.

[0075] The super table is instantiated in the time-series database based on the static attributes of the JSON file to obtain the entity table corresponding to the primary device; the time-series database is the TDengine time-series database.

[0076] The first sampled data is stored in the entity table corresponding to the primary device.

[0077] The entity tables of primary equipment of the same type are aggregated to obtain a master table of the same type of primary equipment. The sampling time and the unique identifier are used as the composite primary key of the master table.

[0078] When it is necessary to perform operation and maintenance on a certain device in a substation, the master table of that primary device can be found in the time series database, and the required sampling data can be retrieved based on the timestamp and static attributes used as unique identifiers, so that the working status of the device can be analyzed and calculated.

[0079] As one embodiment, the super table of the PL2201A test protection device is established according to the following statements:

[0080] Create stable PL2201A

[0081] (Ts timestamp,

[0082] Vol_value float,

[0083] Vol_quality int,

[0084] Tmp_value float,

[0085] Tmp_quality int)

[0086] tags(DevAddrvarbinary(4))

[0087] In the above statements, "stable" indicates the creation of a super table, PL2201A is the name of the super table, meaning a super table is created for this type of measurement and protection equipment; "Vol" and "Tmp" represent the sampled values ​​of voltage and temperature; "tags" are the static attribute tags of the protection equipment. This invention uses the device address as the tag. Using the super table, a corresponding entity table can be quickly created for each similar device of the PL2201A test protection equipment, such as a Creat table. <2341> using PL2201A tags("192.168.0.26"), table <2341> This is a sub-table of the stable PL2201A, and also the entity table corresponding to the PL2201A test protection device (i.e., the table obtained after instantiation from the super table). Different data objects of the same device are aggregated and saved to the same row of the sub-table using the same timestamp. Since the data attribute names of the entity table are consistent with the corresponding data attribute names in the JSON file, they can be stored through direct mapping. That is, sampled data at the same time are stored in the same row. Sampled data at different times are stored in a separate row of the entity table.

[0088] In smart grids, primary equipment is described using digital and analog signals. Digital signals record events (SOEs) of the primary equipment. Analog signals, on the other hand, possess typical time-series data characteristics and require storage in a time-series database, such as current and voltage measurements. By utilizing SOE event timestamps and retrieving time-series data for that time period from the time-series database, analysis and calculations can be performed on the primary equipment. Digital and analog signals define the data being described. Digital signals, represented by 0 and 1 variables, describe the equipment's state; analog signals, represented by measured values, describe the actual values ​​of the data collected by the equipment. A service describes the function of the equipment. A service consists of numerous data sets. For example, a protection service consists of current measurements (analog signals) and circuit breaker status (digital signals). If the analog current exceeds its rated value and the circuit breaker is in a closed state, a trip command (digital signal) is issued to implement the trip service.

[0089] This invention uses the TDengine time-series database to store the first sampled data in the JSON file. TDengine is a high-efficiency platform for storing, querying, and analyzing time-series databases. Compared with commonly used time-series databases such as InfluxDB, it has advantages such as faster read and write speeds and larger storage space.

[0090] This invention has the following innovative aspects when saving the first sampled data from the JSON file to the TDengine time series database:

[0091] 1) Single-point write: A separate entity table is created for each primary device. This eliminates the need to waste resources on locking mechanisms, significantly improving write speed. Furthermore, data can be appended at any time.

[0092] 2) A multi-column model was used when creating the tables. For the same primary device, with consistent timestamps, multiple measurement values ​​can be stored in separate columns. This improves data correlation and retrieval capabilities. Furthermore, column compression technology was employed, using different compression algorithms for different data types in each column, saving space.

[0093] 3) A Super Table (STable) is introduced. A Super Table refers to a collection of data sampling points of a specific type. Data sampling points of the same type of primary equipment can be integrated together and stored using the same table structure. By creating a Super Table, the number of sub-tables can be reduced, and table aggregation operations can be simplified. Static labels can be used to describe specific sampling points, which is beneficial for table reuse.

[0094] The main innovation of this invention lies in its ability to efficiently store sampled data from CID files into a time-series database using a physical model template. For the tree-like data structure of services, data objects, and data attributes in the physical model template, a multi-structure approach is adopted when creating corresponding JSON instances. This results in a clear hierarchy, forming a file that is easy to store in the database and ensuring interoperability between the two data formats (i.e., the data format of the CID file and the data format of the time-series database). Secondly, by analyzing the characteristics of power system data, the TDengine time-series database is used to store the sampled data. A super table is used to implement class modeling of the data storage tables for primary equipment in substations, effectively reducing the number of tables. Furthermore, the fast read / write capabilities of the TDengine database enable rapid storage and retrieval of equipment data, meeting the requirements of power system operation and maintenance and reducing the workload and difficulty for maintenance personnel.

[0095] This invention enables automatic storage of sampled data, reducing human intervention in the storage and retrieval process and minimizing human error. Furthermore, due to the strong temporal correlation of the sampled data, the time-series storage method allows for better monitoring and analysis of substations. Therefore, this invention offers significant advantages to the increasingly digitized power grid, improving the data storage efficiency of smart substations in power systems.

[0096] Those skilled in the art can understand and implement this invention without any creative effort. The above description represents preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the invention, and these improvements and modifications are also considered within the scope of protection of the present invention. Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the above embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

Claims

1. A method for storing data of a smart substation based on a physical model, characterized in that, Includes the following steps: Obtain the CID files of the primary equipment in the smart substation, and obtain the corresponding object model templates based on the equipment types in the CID files; Create a corresponding JSON instance and generate a JSON file based on the object model template, and save the sampling data in the CID file to the JSON file; wherein, creating a corresponding JSON instance based on the object model template includes the following steps: creating a corresponding JSON instance based on the object model template, saving the device information in the CID file to the static attributes of the JSON instance; reading the datasets under each logical node in the CID file, the data objects in the datasets, and the data attributes under the data objects, finding the service module corresponding to the logical node in the JSON instance according to the function of the logical node and the description of the dataset, and storing the data objects and data attributes under the logical node to the service module; The sampling data in the JSON file includes first sampling data and second sampling data. The first sampling data is stored in a time-series database, and the second sampling data is stored in a relational database. The first sampling data is sampling data with time-series characteristics, and the second sampling data is the remaining sampling data in the JSON file excluding the first sampling data. Storing the first sampling data in the time-series database includes the following steps: obtaining the device type of the primary device from the static attributes of the JSON file; obtaining the corresponding super table based on the device type; instantiating the super table in the time-series database based on the static attributes of the JSON file to obtain the entity table corresponding to the primary device; and storing the first sampling data in the entity table corresponding to the primary device.

2. The data storage method for intelligent substations based on a physical model according to claim 1, characterized in that, The data object and data attributes are stored in the service module in the form of a structure.

3. The data storage method for intelligent substations based on a physical model according to claim 1, characterized in that, A super table is pre-established for primary equipment of the same type, and the table header of the super table is constructed according to the data attributes corresponding to all data sampling points of primary equipment of the same type. The data attributes include a static attribute used as a unique identifier for primary equipment.

4. The data storage method for intelligent substations based on a physical model according to claim 3, characterized in that, Aggregate the entity tables of primary equipment of the same type to obtain the master table of primary equipment of the same type.

5. The data storage method for intelligent substations based on a physical model according to claim 4, characterized in that, The sampling time and the unique identifier are used as the composite primary key of the master table.

6. The data storage method for intelligent substations based on a physical model according to any one of claims 1 to 5, characterized in that, The time series database used is the TDengine time series database.

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