Application-oriented power grid power data collection method and system

Abstract

The invention discloses an application-oriented power grid power data collection method and system, and the method comprises the steps: S101, carrying out the data collection of a plurality of data sources through a county-level power grid dispatching control center according to a unified data logic model; s102, the prefecture and city power grid dispatching control center firstly performs data preprocessing on the data uploaded by the county-level power grid dispatching control center, and then collects the preprocessed data according to a data collection model to form a collection file; and S103, the provincial power grid dispatching control center obtains the collection file uploaded by the prefectural and municipal power grid dispatching control center, inquires the mapping configuration file according to the collection file information in the data to obtain storage system information, and stores the uploaded data in a corresponding storage system. According to the method, power grid big data hierarchical collaborative treatment is realized, data integrity and accuracy are guaranteed, data quality is improved, efficient storage is provided according to data characteristics and application requirements, and data support is provided for realizing power grid intelligent management.

Description

Technical Field

[0001] The present invention relates to the technical field of big data collaborative governance, and in particular to an application-oriented power grid power data collection method and system. Background Art

[0002] In recent years, the level of informatization and intelligence in the power sector has developed rapidly, and various information systems have generated massive amounts of multi-source heterogeneous data, which provide strong data support for grid security status perception, equipment fault diagnosis, power production and load forecasting, and grid intelligent dispatching. However, how to break through the information barriers of various information systems, effectively manage the above massive data, and maximize the value of the data itself is a topic that needs to be continuously studied in the power sector.

[0003] In the power system, it is usually divided into multiple levels, including national dispatching, network dispatching, provincial dispatching (provincial power grid), local dispatching (municipal power grid), and county dispatching (county power grid). Among them, the local and county power grids are usually connected with the substation automation system data. The provincial power grid is the forefront of data analysis and application. At the same time, for the convenience of description and understanding, the present invention realizes the collaborative governance of power grid big data at the provincial, municipal and county levels. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the present invention provides an application-oriented power grid power data collection method and system. The collected data is standardized through a unified data logic model at the county dispatching center. The data collection model is used at the local dispatching center to associate and expand the dimensions of various types of information data. The data storage system and business application development are adapted according to data characteristics and business needs at the provincial dispatching center to achieve provincial, municipal and county three-level collaborative governance of power grid big data, ensure data integrity and accuracy, and improve data quality. The data collection model has the advantages of scalability and business orientation, and provides efficient storage according to data characteristics and application needs, providing data support for the realization of intelligent power grid management.

[0005] In a first aspect, the present invention discloses an application-oriented power grid power data collection method, comprising the following steps: Step S101: The county-level power grid dispatching and control center collects data from multiple data sources according to a unified data logic model; the unified data logic model includes a location information dimension, a device information dimension, a data description dimension, and a measurement information dimension; wherein the data description dimension includes a data collection timestamp and a data collection frequency; Step S102: The prefecture-level power grid dispatching and control center first pre-processes the data uploaded by the county-level power grid dispatching and control center, and then aggregates the pre-processed data according to the data aggregation model to form an aggregation file; the data pre-processing includes data integrity verification, missing data processing, and noise data processing; the data aggregation model includes a fault diagnosis data aggregation model, a power load forecasting data aggregation model, and a power grid safety status data aggregation model; Step S103: The provincial power grid dispatching and control center obtains the collection file uploaded by the municipal power grid dispatching and control center, queries the mapping configuration file to obtain the storage system information based on the collection file information in the data, and stores the uploaded data in the corresponding storage system; the mapping configuration file contains the correspondence between the collection file information and the storage system.

[0006] Preferably, the location information dimension of the unified data logic model is used to describe the geographical location information of the data source, and the location information dimension further includes regional information and plant information; the regional information includes the regional codes corresponding to provinces, cities, and counties; the plant information further includes power plant codes, substation codes, and transmission line codes. The equipment information dimension of the unified data logic model is used to describe the main information of the data source, and the equipment information dimension further includes equipment ID, equipment type, and equipment model; the data description dimension of the unified data logic model is used to define and explain the measurement information dimension, and the data description dimension further includes the measurement field name and the measurement field unit; the acquisition timestamp is defined in a standard time format, accurate to milliseconds; the measurement information dimension of the unified data logic model is used to record the measurement value corresponding to each measurement field name in the data description dimension; wherein the measurement value is a numerical value or a status code.

[0007] Preferably, the data preprocessing includes data integrity verification, missing data processing, and noise data processing, which are specifically as follows: the data integrity verification includes an information dimension integrity verification step and a data volume integrity verification step; the information dimension integrity verification step is used to verify whether the location information dimension, device information dimension, data description dimension and measurement information dimension are complete, and whether the measurement field name and measurement value in the data description dimension and the measurement information dimension correspond to each other; the data volume integrity verification step verifies whether the number of data collected per unit time is complete according to the data collection frequency; if the information dimension integrity verification results and the data volume integrity verification results both meet the preset conditions, the missing measurement values ​​are interpolated using the corresponding mean or mode; multiple measurement values ​​within a certain time range are obtained, and the multiple measurement values ​​are clustered using the K-means clustering algorithm to obtain a number of data clusters, and the outliers of each data cluster are obtained according to the set distance, and the outliers are replaced by the cluster center value of the data cluster to which the outliers belong.

[0008] Preferably, the fault diagnosis data collection model is used to collect electrical signals required for the diagnosis object, and the diagnosis object includes equipment and lines; the electrical signals include voltage phasors, current phasors, line power, angular mutations, switch telesignal displacements, protection action signals, fault recording signals, and traveling wave signals; the power load forecasting data collection model is used to collect meteorological data and power market data required for the forecasting object, and the forecasting object includes two dimensions: geographical area and interval range, and the interval range includes hourly, daily, and monthly levels; meteorological data include temperature, humidity, wind speed, and rainfall; power market data include electricity prices and transaction volume information; the power grid safety status data collection model is used to collect parameter information of static power grid safety, dynamic power grid safety, and equipment safety; the static safety parameter information includes bus voltage, voltage margin, and active power margin; the dynamic safety parameter information includes power grid load rate, power grid overload rate, node voltage change rate, and transformer load rate; the equipment safety parameter information includes operating parameter information of generator sets, transformers, circuit breakers, and transmission lines; the collection file includes the name of the data collection model, collection time information, and collection area information.

[0009] Preferably, the collection file information is parsed to obtain the collection file name, and the mapping configuration file is queried according to the collection file name to obtain the storage system information; if the collection file name is a fault diagnosis data collection model, the mapping configuration file is queried to obtain the storage system information as the fault diagnosis subject database information, and the collection file is saved to the fault diagnosis subject database according to the fault diagnosis subject database information, and the data is partitioned using the collection area information as the partition key, and the collection time information is used as the data sorting key within the partition; the fault diagnosis subject database is a non-relational database; if the collection file name is a power load forecasting data collection model, the mapping configuration file is queried to obtain the storage system information as the load forecasting subject database information, and the fault diagnosis subject database is stored in the fault diagnosis subject database according to the fault diagnosis subject database information. The collection file is saved to the subject database according to the subject database information, and each attribute in the setting information dimension, equipment information dimension, data description dimension and measurement information dimension of each record in the collection file is stored independently as a column; the subject database is a time series database; if the collection file name is the power grid security status data collection model, the mapping configuration file is queried to obtain the storage system information as the power grid security status subject database information, the collection file is saved to the power grid security status subject database according to the power grid security status subject database information, and the data survival time is set according to the collection time information, and the power grid security status subject database automatically clears expired data according to the data survival time; the power grid security status subject database is a memory database.

[0010] Preferably, power grid equipment fault diagnosis applications and power grid equipment reliability analysis applications are developed based on the fault diagnosis subject database information; power peak warning applications and power load anomaly mining applications are developed based on the load forecasting subject database information; and power grid operation safety status intelligent perception and warning applications are developed based on the power grid safety status subject database.

[0011] In the second aspect, the present invention discloses an application-oriented power grid power data collection system, including a data acquisition module, a data preprocessing module, a data collection module, a data storage module, and an application development module: Data acquisition module: The county-level power grid dispatching and control center collects data from multiple data sources based on a unified data logic model; the unified data logic model includes a location information dimension, a device information dimension, a data description dimension, and a measurement information dimension; wherein the data description dimension includes a data collection timestamp and a data collection frequency; Data preprocessing module: The prefecture-level power grid dispatching and control center first preprocesses the data uploaded by the county-level power grid dispatching and control center, and the data preprocessing includes data integrity verification, missing data processing, and noise data processing; Data collection module: The prefecture-level power grid dispatching and control center collects data from multiple data sources based on a unified data logic model; the unified data logic model includes a location information dimension, a device information dimension, a data description dimension, and a measurement information dimension; wherein the data description dimension includes a data collection timestamp and a data collection frequency; Data preprocessing module: The prefecture-level power grid dispatching and control center preprocesses the data uploaded by the county-level power grid dispatching and control center, and the data preprocessing includes data integrity verification, missing data processing, and noise data processing; The pre-processed data is aggregated according to the aggregation model to form an aggregation file; the data aggregation model includes a fault diagnosis data aggregation model, a power load forecasting data aggregation model, and a power grid safety status data aggregation model; data storage module: the provincial power grid dispatching and control center obtains the aggregation file uploaded by the municipal power grid dispatching and control center, queries the mapping configuration file to obtain the storage system information according to the aggregation file information in the data, and stores the uploaded data in the corresponding storage system; the mapping configuration file contains the correspondence between the aggregation file information and the storage system; application development module: the provincial power grid dispatching and control center develops power grid equipment fault diagnosis applications, power grid equipment reliability analysis applications, power peak warning applications, power load anomaly mining applications, and power grid operation safety status intelligent perception and warning applications based on the storage system.

[0012] Preferably, the location information dimension of the unified data logic model is used to describe the geographical location information of the data source, and the location information dimension further includes regional information and plant information; the regional information includes the regional codes corresponding to provinces, cities and counties; the plant information further includes power plant codes, substation codes and transmission line codes; the equipment information dimension of the unified data logic model is used to describe the main information of the data source, and the equipment information dimension further includes equipment ID, equipment type and equipment model; the data description dimension of the unified data logic model is used to define and explain the measurement information dimension, and the data description dimension further includes the measurement field name and the measurement field unit; the acquisition timestamp is defined in a standard time format, accurate to milliseconds; the measurement information dimension of the unified data logic model is used to record the measurement value corresponding to each measurement field name in the data description dimension; wherein the measurement value is a numerical value or a status code; The fault diagnosis data collection model is used to collect electrical signals required for the diagnosis object, and the diagnosis object includes equipment and lines; the electrical signals include voltage phasors, current phasors, line power, angular mutations, switch telesignal displacements, protection action signals, fault recording signals, and traveling wave signals; the power load forecasting data collection model is used to collect meteorological data and power market data required for the forecasting object, and the forecasting object includes two dimensions: geographical area and interval range, and the interval range includes hourly, daily, and monthly levels; meteorological data includes temperature, humidity, wind speed, and rainfall; power market data includes electricity prices and transaction volume information; the power grid safety status data collection model is used to collect parameter information on static security, dynamic security, and equipment security of the power grid; the static safety parameter information includes bus voltage, voltage margin, and active power margin; dynamic safety parameter information includes power grid load rate, power grid overload rate, node voltage change rate, and transformer load rate; equipment safety parameter information includes operating parameter information of generator sets, transformers, circuit breakers, and transmission lines; the collection file includes the name of the data collection model, collection time information, and collection area information.

[0013] Preferably, the data preprocessing module includes a data integrity verification unit, a missing data processing unit, and a noise data processing unit; the data integrity verification unit: the data integrity verification unit further includes an information dimension integrity verification subunit and a data volume integrity verification subunit; the information dimension integrity verification subunit is used to verify whether the location information dimension, the device information dimension, the data description dimension, and the measurement information dimension are complete, and whether the measurement field name and the measurement value in the data description dimension and the measurement information dimension correspond; the data volume integrity verification subunit verifies whether the number of data collected per unit time is complete according to the data collection frequency. The missing data processing unit further includes a judgment subunit and a missing data interpolation subunit; the judgment subunit is used to judge whether the information dimension integrity check result and the data volume integrity check result meet the preset conditions; the missing data interpolation subunit uses the corresponding mean or mode to interpolate the missing measurement value; the noise data processing unit: used for data denoising; obtain multiple measurement values ​​within a certain time range, cluster the multiple measurement values ​​using the K-means clustering algorithm to obtain several data clusters, obtain the outliers of each data cluster according to the set distance, and replace the outliers with the cluster center value of the data cluster to which the outliers belong.

[0014] Preferably, the data storage module includes a query system mapping unit and a data classification storage unit; the query system mapping unit: parses the collection file information to obtain the collection file name, and queries the mapping configuration file according to the collection file name to obtain the storage system information; the data classification storage unit: if the collection file name is a fault diagnosis data collection model, queries the mapping configuration file to obtain the storage system information as the fault diagnosis subject database information, saves the collection file to the fault diagnosis subject database according to the fault diagnosis subject database information, partitions the data with the collection area information as the partition key, and uses the collection time information as the data sorting key within the partition; the fault diagnosis subject database is a non-relational database; If the collection file is named as a power load forecast data collection model, query the mapping configuration file to obtain the storage system information as load forecast subject database information, save the collection file to the subject database according to the subject database information, and use each attribute in the location information dimension, device information dimension, data description dimension and measurement information dimension of each record in the collection file as a column for independent storage; the subject database is a time series database; If the collection file name is a power grid security status data collection model, the mapping configuration file is queried to obtain storage system information as power grid security status subject database information, the collection file is saved to the power grid security status subject database according to the power grid security status subject database information, and the data survival time is set according to the collection time information, and the power grid security status subject database automatically clears expired data according to the data survival time; the power grid security status subject database is a memory database.

[0015] The present invention realizes the hierarchical and collaborative governance of power grid big data. The collected data is standardized through a unified data logic model at the county dispatching center. The data aggregation model is used at the local dispatching center to associate and expand the dimensions of various types of information data. The data storage system is adapted and related application systems are developed according to data characteristics and business needs at the provincial dispatching center. This ensures data integrity and accuracy, improves data quality, and provides classification and efficient storage according to data characteristics and application needs, which is convenient for power big data mining, fault diagnosis, and power grid safety status analysis, providing data support for realizing intelligent power grid management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0017] Figure 1 It is a flow chart of an application-oriented power grid power data collection method of the present invention; Figure 2 is a flow chart of the noise processing method of the present invention; Figure 3 It is a structural schematic diagram of an application-oriented power grid power data collection system of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention. Example 1

[0019] See also Figure 1 As shown, the present invention discloses an application-oriented power grid power data collection method, comprising the following steps: Step S101: The county-level power grid dispatching and control center collects data from multiple data sources based on a unified data logic model.

[0020] In the process of electric power informatization and automation construction, power grid dispatching and control centers at all levels have deployed or developed information systems such as SCADA dispatching automation systems, production management information systems, and marketing management systems. The business data generated by the above information systems have formed power grid big data. Power grid big data includes real-time operation data, power generation, power load, unit status, power consumption and load changes in the power grid, etc. The above information systems are developed and deployed by different departments, resulting in data barriers between different information systems, which is not conducive to data sharing, data mining and analysis. In order to facilitate the use of data generated by various information systems, the present invention adopts a unified data logic model to collect data from multiple data sources, normalizes, standardizes and generalizes the collected data, can effectively integrate the data generated by various business systems, and effectively improve the utilization level of power grid big data.

[0021] The unified data logic model includes location information dimension, equipment information dimension, data description dimension and measurement information dimension. The location information dimension of the unified data logic model is used to describe the geographical location information of the data source, and the location information dimension further includes regional information and plant information. The regional information includes the regional codes corresponding to provinces (autonomous regions, municipalities directly under the central government), cities, and counties (districts). Regional information is used to define the administrative division of the data source. Of course, it can be further divided in detail according to actual needs, and location information dimensions such as streets and management grids can be added. Plant information further includes power plant codes, substation codes, and transmission line codes. ‌Power plants‌ are the places where electricity is produced, and ‌substations‌ are power facilities in the power system that transform voltage, receive and distribute electric energy, control the flow of electricity, and adjust voltage. The power grids of various voltage levels are connected through transformers to achieve the step-up and step-down of electric energy, so as to meet the needs of different equipment and users. The main equipment of substations includes switches and transformers. ‌Lines‌ are physical paths connecting power plants and substations, and between substations and users, to achieve the transmission of electric energy.

[0022] Different power equipment has different equipment attribute fields and operating parameters (i.e., measurement information). The present invention adopts equipment information dimension and measurement information dimension to correspond to equipment attribute fields and operating parameters respectively. The data description dimension of the unified data logic model is used to define and interpret the measurement information dimension, which is equivalent to the metadata of the measurement information dimension. The equipment information dimension of the unified data logic model is used to describe the main information of the data source, and the equipment information dimension further includes equipment ID, equipment type, and equipment model. Among them, the data description dimension includes data collection timestamp, data collection frequency, measurement field name, and measurement field unit. The collection timestamp is defined in a standard time format, accurate to milliseconds. The measurement information dimension of the unified data logic model is used to record the measurement value corresponding to each measurement field name in the data description dimension. Among them, the measurement value is a numerical value or a state code.

[0023] Step S102: The prefecture-level power grid dispatching and control center first pre-processes the data uploaded by the county-level power grid dispatching and control center, and then aggregates the pre-processed data according to the data aggregation model to form an aggregation file.

[0024] Data preprocessing includes data integrity verification, missing data processing, and noise data processing. Specifically, the data integrity verification includes an information dimension integrity verification step and a data volume integrity verification step. The information dimension integrity verification step is used to verify whether the location information dimension, device information dimension, data description dimension, and measurement information dimension are complete, and whether the measurement field name and measurement value in the data description dimension and the measurement information dimension correspond. The data volume integrity verification step verifies whether the number of data collected per unit time is complete according to the data collection frequency. If both the information dimension integrity verification result and the data volume integrity verification result meet the preset conditions, the missing measurement values ​​are interpolated using the corresponding mean or mode. For example, if both the information dimension integrity verification result and the data volume integrity verification result exceed 80%, it means that the data integrity meets the preset conditions, and the next step of missing data processing and noise data processing can be executed; if at least one of the information dimension integrity verification result and the data volume integrity verification result is not greater than 80%, it means that the data integrity does not meet the preset conditions, and the county-level power grid dispatching and control center is notified to check and optimize the corresponding data collection.

[0025] Acquire multiple measurement values ​​within a certain time range, perform clustering processing on the multiple measurement values ​​using the K-means clustering algorithm to obtain several data clusters, obtain the outliers of each data cluster according to the set distance, and use the cluster center value of the data cluster to which the outliers belong to replace the outliers. i1 , x i2 ,…, x in ), the specific noise processing steps are as follows Figure 2 As shown, S1: Randomly select K data objects as the initial cluster centers. S2: Calculate the Euclidean distance from each data object to each cluster center, and assign each data object to the cluster where the nearest cluster center is located. S3: Recalculate the cluster center of each cluster (that is, the average value of the data objects in the cluster). S4: Repeat S2-S3 until the cluster center no longer changes or the preset number of iterations is reached, and K data clusters are obtained. S5: In each data cluster, obtain the outliers of each data cluster according to the set distance. S6: Use the cluster center value of the data cluster to which the outlier belongs to replace the outlier. In a data cluster, if the distance of a data object from the cluster center is significantly higher than the average distance, the data object is determined to be an outlier.

[0026] Through data integrity verification, missing data processing, and noise data processing, the quality of data collection is guaranteed, which is conducive to providing correct and reasonable data for data mining and analysis. The data aggregation model is used to aggregate and associate the data that has passed the data preprocessing to facilitate the subsequent storage and utilization of the data. The data aggregation model has the following characteristics: the data aggregation model is business application-oriented, and the data required by the business application is aggregated and associated to meet the needs of different business applications. The data aggregation model is dynamically expandable, and new data aggregation models are developed according to new business needs. The present invention takes the common fault diagnosis, prediction, and safety status perception business applications in the power grid as examples to describe the operating principle of the data aggregation model.

[0027] The data collection model includes a fault diagnosis data collection model, a power load forecasting data collection model, and a power grid safety status data collection model. The fault diagnosis data collection model is used to collect electrical signals required for the diagnosis object, and the diagnosis object includes equipment and lines. Electrical signals include voltage phasors, current phasors, line power, angular mutations, switch telesignaling displacement, protection action signals, fault recording signals, and traveling wave signals. The power load forecasting data collection model is used to collect meteorological data and power market data required for the forecast object, and the forecast object includes two dimensions: geographical area and interval range, and the interval range includes hourly level, daily level, and monthly level. Meteorological data includes temperature, humidity, wind speed, and rainfall. Power market data includes electricity price and transaction power information. The power grid safety status data collection model is used to collect parameter information of static security, dynamic security, and equipment security of the power grid. The static security parameter information includes bus voltage, voltage margin, and active power margin. Dynamic security parameter information includes power grid load rate, power grid overload rate, node voltage change rate, and transformer load rate. Equipment safety parameter information includes operating parameter information of generator sets, transformers, circuit breakers, and transmission lines. The collection file includes the name of the data collection model, collection time information, and collection area information. According to actual needs, the collection file can be compressed first and then uploaded to the provincial power grid dispatching and control center to reduce the amount of data transmission.

[0028] Step S103: The provincial power grid dispatching and control center obtains the collection file uploaded by the municipal power grid dispatching and control center, queries the mapping configuration file to obtain the storage system information according to the collection file information in the data, and stores the uploaded data in the corresponding storage system. The mapping configuration file contains the corresponding relationship between the collection file information and the storage system.

[0029] The present invention is guided by business needs, and by considering the data structure characteristics of the aggregated file information and the needs of business applications for data writing efficiency and data query efficiency, different storage methods are used for different aggregated files to store data with the database.

[0030] First, parse the collection file information to obtain the collection file name, and query the mapping configuration file according to the collection file name to obtain the storage system information. If the collection file name is a fault diagnosis data collection model, query the mapping configuration file to obtain the storage system information as the fault diagnosis subject database information, save the collection file to the fault diagnosis subject database according to the fault diagnosis subject database information, partition the data using the collection area information as the partition key, and use the collection time information as the data sorting key within the partition. The fault diagnosis subject database is a non-relational database. If the collection file name is a power load forecasting data collection model, query the mapping configuration file to obtain the storage system information as the load forecasting subject database information, save the collection file to the subject database according to the subject database information, and use each attribute in the setting information dimension, equipment information dimension, data description dimension, and measurement information dimension of each record in the collection file as a column for independent storage. The subject database is a time series database. If the collection file name is the power grid security status data collection model, the mapping configuration file is queried to obtain the storage system information as the power grid security status subject database information, the collection file is saved to the power grid security status subject database according to the power grid security status subject database information, and the data survival time is set according to the collection time information, and the power grid security status subject database automatically clears expired data according to the data survival time. The power grid security status subject database is a memory database, and the memory database is preferably Redis.

[0031] The provincial power grid dispatching and control center develops various applications based on the data of each subject database in the storage system. For example, based on the information of the fault diagnosis subject database, it develops power grid equipment fault diagnosis applications and power grid equipment reliability analysis applications; based on the information of the load forecast subject database, it develops power peak warning applications and power load anomaly mining applications; based on the power grid safety status subject database, it develops power grid operation safety status intelligent perception and warning applications. Example 2

[0032] The present invention also discloses an application-oriented power grid power data collection system, such as Figure 3As shown, it includes a data acquisition module, a data preprocessing module, a data collection module, a data storage module, and an application development module. Data acquisition module: The county-level power grid dispatching and control center collects data from multiple data sources based on a unified data logic model. The unified data logic model includes location information dimension, equipment information dimension, data description dimension and measurement information dimension, wherein the data description dimension includes data acquisition timestamp and data acquisition frequency. Data preprocessing module: The prefecture-level power grid dispatching and control center first preprocesses the data uploaded by the county-level power grid dispatching and control center, and the data preprocessing includes data integrity verification, missing data processing, and noise data processing. Data collection module: The prefecture-level power grid dispatching and control center collects the preprocessed data according to the data collection model to form a collection file. The data collection model includes a fault diagnosis data collection model, a power load forecasting data collection model, and a power grid safety status data collection model. Data storage module: The provincial power grid dispatching and control center obtains the collection file uploaded by the prefecture-level power grid dispatching and control center, queries the mapping configuration file according to the collection file information in the data to obtain the storage system information, and stores the uploaded data in the corresponding storage system, and the mapping configuration file contains the corresponding relationship between the collection file information and the storage system. Application development module: The provincial power grid dispatching and control center develops power grid equipment fault diagnosis applications, power grid equipment reliability analysis applications, power peak warning applications, power load anomaly mining applications, and intelligent perception and warning applications of power grid operation safety status based on the storage system.

[0033] The location information dimension of the unified data logic model is used to describe the geographic location information of the data source. The location information dimension further includes regional information and plant information. The regional information includes the regional codes corresponding to provinces, cities, and counties. The plant information further includes power plant codes, substation codes, and transmission line codes. The equipment information dimension of the unified data logic model is used to describe the main information of the data source. The equipment information dimension further includes equipment ID, equipment type, and equipment model. The data description dimension of the unified data logic model is used to define and interpret the measurement information dimension. The data description dimension further includes the measurement field name and the measurement field unit. The acquisition timestamp is defined in a standard time format, accurate to milliseconds. The measurement information dimension of the unified data logic model is used to record the measurement value corresponding to each measurement field name in the data description dimension; wherein the measurement value is a numerical value or a status code.

[0034] The fault diagnosis data collection model is used to collect electrical signals required for the diagnosis object, and the diagnosis object includes equipment and lines; the electrical signals include voltage phasor, current phasor, line power, angular mutation, switch telesignal displacement, protection action signal, fault recording signal, and traveling wave signal. The power load forecasting data collection model is used to collect meteorological data and power market data required for the forecast object, and the forecast object includes two dimensions: geographical area and interval range, and the interval range includes hourly level, daily level, and monthly level; meteorological data includes temperature, humidity, wind speed, and rainfall. Power market data includes electricity price and transaction power information. The power grid security status data collection model is used to collect parameter information of static security, dynamic security, and equipment security of the power grid. The static security parameter information includes bus voltage, voltage margin, and active power margin; the dynamic security parameter information includes power grid load rate, power grid overload rate, node voltage change rate, and transformer load rate; the equipment safety parameter information includes the operating parameter information of generator sets, transformers, circuit breakers, and transmission lines. The collection file includes the name of the data collection model, collection time information, and collection area information.

[0035] Preferably, the data collection module also includes a collection model expansion unit, which is used to expand the data collection model according to the requirements of the provincial power grid dispatching control center application development module. For example, an intelligent dispatching data collection model is developed according to the requirements of the power grid intelligent dispatching business to provide data support for the power grid intelligent dispatching business application.

[0036] The data preprocessing module includes a data integrity verification unit, a missing data processing unit, and a noise data processing unit; Data integrity verification unit: The data integrity verification unit further includes an information dimension integrity verification subunit and a data quantity integrity verification subunit; The information dimension integrity verification subunit is used to verify whether the location information dimension, the device information dimension, the data description dimension, and the measurement information dimension are complete, and whether the measurement field names and measurement values ​​in the data description dimension and the measurement information dimension correspond. The data quantity integrity verification subunit verifies whether the number of data collected per unit time is complete based on the data collection frequency. Missing data processing unit: The missing data processing unit further includes a judgment subunit and a missing data interpolation subunit; The judgment subunit is used to judge whether the information dimension integrity verification results and the data quantity integrity verification results meet the preset conditions; The missing data interpolation subunit uses the corresponding mean or mode to interpolate the missing measurement values. Noise data processing unit: used for data denoising; obtain multiple measurement values ​​within a certain time range, cluster the multiple measurement values ​​using the K-means clustering algorithm to obtain several data clusters, obtain the outliers of each data cluster according to the set distance, and replace the outliers with the cluster center value of the data cluster to which the outliers belong.

[0037] The data storage module includes a query system mapping unit and a data classification storage unit; the query system mapping unit: parses the collection file information to obtain the collection file name, and queries the mapping configuration file according to the collection file name to obtain the storage system information. The data classification storage unit: if the collection file name is a fault diagnosis data collection model, the storage system information obtained by querying the mapping configuration file is fault diagnosis subject database information, and the collection file is saved to the fault diagnosis subject database according to the fault diagnosis subject database information, and the data is partitioned using the collection area information as the partition key, and the collection time information is used as the data sorting key in the partition, and the fault diagnosis subject database is a non-relational database.

[0038] If the name of the aggregation file is the power load forecast data aggregation model, the mapping configuration file is queried to obtain the storage system information as the load forecast subject database information, the aggregation file is saved to the subject database according to the subject database information, and each attribute in the setting information dimension, equipment information dimension, data description dimension and measurement information dimension of each record in the aggregation file is stored independently as a column, and the subject database is a time series database.

[0039] If the collection file name is the power grid security status data collection model, the mapping configuration file is queried to obtain the storage system information as the power grid security status subject database information, the collection file is saved to the power grid security status subject database according to the power grid security status subject database information, and the data survival time is set according to the collection time information, and the power grid security status subject database automatically clears expired data according to the data survival time. The power grid security status subject database is a memory database, and the memory database is preferably Redis.

[0040] Preferably, the data storage module further includes a mapping configuration file unit for collecting the correspondence between file information and storage system. After the data collection model is expanded, the expanded data collection model needs to be configured with corresponding storage system information in the mapping configuration file unit so that the collected data can be stored in the provincial power grid dispatching control center.

[0041] It should be pointed out that, according to the needs of implementation, the various components / steps described in the embodiments of the present application can be split into more components / steps, or two or more components / steps or partial operations of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present application.

[0042] The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or implemented as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or implemented as a computer code originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded through a network and to be stored in a local recording medium, so that the device described herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor or programmable or dedicated hardware (such as an ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, the device described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the device shown here, the execution of the code converts the general-purpose computer into a dedicated computer for executing the device shown here.

[0043] Those of ordinary skill in the art will appreciate that the units and device steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different devices to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present application.

[0044] The above implementation methods are only used to illustrate the embodiments of the present application, and are not limitations on the embodiments of the present application. Ordinary technicians in the relevant technical field can make various changes and modifications without departing from the spirit and scope of the embodiments of the present application. Therefore, all equivalent technical solutions also belong to the scope of the embodiments of the present application. The scope of patent protection of the embodiments of the present application should be limited by the claims.

Claims

1. An application-oriented power grid power data collection method, characterized in that: The following steps are involved: Step S101: The county-level power grid dispatching and control center collects data from multiple data sources based on a unified data logic model; The unified data logic model includes a location information dimension, a device information dimension, a data description dimension, and a measurement information dimension; wherein the data description dimension includes a data collection timestamp and a data collection frequency; Step S102: the prefecture-level power grid dispatching and control center first pre-processes the data uploaded by the county-level power grid dispatching and control center, and then aggregates the pre-processed data according to the data aggregation model to form an aggregation file; The data preprocessing includes data integrity verification, missing data processing, and noise data processing; the data collection model includes a fault diagnosis data collection model, a power load forecasting data collection model, and a power grid safety status data collection model; Step S103: The provincial power grid dispatching and control center obtains the collection file uploaded by the municipal power grid dispatching and control center, queries the mapping configuration file to obtain the storage system information according to the collection file information in the data, and stores the uploaded data in the corresponding storage system; The mapping configuration file includes a correspondence between the aggregated file information and the storage system.

2. The application-oriented power grid power data collection method according to claim 1, characterized in that: The location information dimension of the unified data logic model is used to describe the geographical location information of the data source, and the location information dimension further includes regional information and plant information; the regional information includes the regional codes corresponding to the province, city, and county; the plant information further includes the power plant code, substation code, and transmission line code; The device information dimension of the unified data logic model is used to describe the main information of the data source, and the device information dimension further includes device ID, device type, and device model; The data description dimension of the unified data logic model is used to define and explain the measurement information dimension, and the data description dimension further includes the measurement field name and the measurement field measurement unit; the acquisition timestamp is defined in a standard time format, accurate to milliseconds; The measurement information dimension of the unified data logic model is used to record the measurement value corresponding to each measurement field name in the data description dimension; wherein the measurement value is a numerical value or a status code.

3. The application-oriented power grid power data collection method according to claim 2, characterized in that: The data preprocessing includes data integrity check, missing data processing, and noise data processing, which are as follows: The data integrity check includes an information dimension integrity check step and a data volume integrity check step; the information dimension integrity check step is used to check whether the location information dimension, device information dimension, data description dimension and measurement information dimension are complete, and whether the measurement field names and measurement values ​​in the data description dimension and the measurement information dimension correspond to each other; the data volume integrity check step is used to check whether the number of data collected per unit time is complete according to the data collection frequency; If the information dimension integrity check results and the data volume integrity check results both meet the preset conditions, the missing measurement values ​​are interpolated using the corresponding mean or mode; A plurality of measurement values ​​within a certain time range are obtained, and the plurality of measurement values ​​are clustered using a K-means clustering algorithm to obtain a plurality of data clusters. Outliers of each data cluster are obtained according to a set distance, and the outliers are replaced by the cluster center value of the data cluster to which the outliers belong.

4. The application-oriented power grid power data collection method according to claim 3, characterized in that: The fault diagnosis data collection model is used to collect electrical signals required for the diagnosis object, the diagnosis object includes equipment and lines; the electrical signals include voltage phasor, current phasor, line power, angular mutation, switch telesignal displacement, protection action signal, fault recording signal, and traveling wave signal; The power load forecasting data collection model is used to collect meteorological data and power market data required for the forecast object, the forecast object includes two dimensions: geographical area and interval range, the interval range includes hourly level, daily level and monthly level; meteorological data includes temperature, humidity, wind speed and rainfall; power market data includes electricity price and transaction power information; The grid safety status data collection model is used to collect parameter information of the static safety, dynamic safety and equipment safety of the grid; the static safety parameter information includes bus voltage, voltage margin and active power margin; the dynamic safety parameter information includes grid load rate, grid overload rate, node voltage change rate and transformer load rate; the equipment safety parameter information includes operating parameter information of generator sets, transformers, circuit breakers and transmission lines; The collection file includes the name of the data collection model, collection time information, and collection area information.

5. The application-oriented power grid power data collection method according to claim 4, characterized in that: Parse the collection file information to obtain the collection file name, and query the mapping configuration file according to the collection file name to obtain the storage system information; If the collection file is named as a fault diagnosis data collection model, query the mapping configuration file to obtain the storage system information as fault diagnosis subject database information, save the collection file to the fault diagnosis subject database according to the fault diagnosis subject database information, partition the data using the collection area information as the partition key, and use the collection time information as the data sorting key within the partition; The fault diagnosis subject database is a non-relational database; If the collection file is named as a power load forecast data collection model, query the mapping configuration file to obtain the storage system information as load forecast subject database information, save the collection file to the subject database according to the subject database information, and use each attribute in the location information dimension, device information dimension, data description dimension and measurement information dimension of each record in the collection file as a column for independent storage; the subject database is a time series database; If the collection file name is a power grid security status data collection model, the mapping configuration file is queried to obtain storage system information as power grid security status subject database information, the collection file is saved to the power grid security status subject database according to the power grid security status subject database information, and the data survival time is set according to the collection time information, and the power grid security status subject database automatically clears expired data according to the data survival time; the power grid security status subject database is a memory database.

6. The application-oriented power grid power data collection method according to claim 5, characterized in that: Based on the information in the fault diagnosis subject database, develop power grid equipment fault diagnosis applications and power grid equipment reliability analysis applications; based on the information in the load forecasting subject database, develop power peak warning applications and power load anomaly mining applications; based on the power grid safety status subject database, develop intelligent perception and warning applications for power grid operation safety status.

7. An application-oriented power grid power data collection system, characterized in that: Including data acquisition module, data preprocessing module, data collection module, data storage module, application development module: Data acquisition module: The county-level power grid dispatching and control center collects data from multiple data sources based on a unified data logic model; the unified data logic model includes location information dimension, equipment information dimension, data description dimension and measurement information dimension; wherein the data description dimension includes data acquisition timestamp and data acquisition frequency; Data preprocessing module: The prefecture-level power grid dispatching and control center first preprocesses the data uploaded by the county-level power grid dispatching and control center, and the data preprocessing includes data integrity verification, missing data processing, and noise data processing; Data collection module: The prefecture-level power grid dispatching and control center collects the pre-processed data according to the data collection model to form a collection file; the data collection model includes a fault diagnosis data collection model, a power load forecasting data collection model, and a power grid safety status data collection model; Data storage module: The provincial power grid dispatching and control center obtains the collection file uploaded by the municipal power grid dispatching and control center, queries the mapping configuration file to obtain the storage system information according to the collection file information in the data, and stores the uploaded data in the corresponding storage system; the mapping configuration file contains the corresponding relationship between the collection file information and the storage system; Application development module: The provincial power grid dispatching and control center develops power grid equipment fault diagnosis applications, power grid equipment reliability analysis applications, power peak warning applications, power load anomaly mining applications, and intelligent perception and warning applications of power grid operation safety status based on the storage system.

8. The application-oriented power grid power data collection system according to claim 7, characterized in that: The location information dimension of the unified data logic model is used to describe the geographical location information of the data source, and the location information dimension further includes regional information and plant information; the regional information includes the regional codes corresponding to the province, city, and county; the plant information further includes the power plant code, substation code, and transmission line code; The device information dimension of the unified data logic model is used to describe the main information of the data source, and the device information dimension further includes device ID, device type, and device model; The data description dimension of the unified data logic model is used to define and explain the measurement information dimension, and the data description dimension further includes the measurement field name and the measurement field measurement unit; the acquisition timestamp is defined in a standard time format, accurate to milliseconds; The measurement information dimension of the unified data logic model is used to record the measurement value corresponding to each measurement field name of the data description dimension; wherein the measurement value is a numerical value or a status code; The fault diagnosis data collection model is used to collect electrical signals required for the diagnosis object, the diagnosis object includes equipment and lines; the electrical signals include voltage phasor, current phasor, line power, angular mutation, switch telesignal displacement, protection action signal, fault recording signal, and traveling wave signal; The power load forecasting data collection model is used to collect meteorological data and power market data required for the forecast object, the forecast object includes two dimensions: geographical area and interval range, the interval range includes hourly level, daily level and monthly level; meteorological data includes temperature, humidity, wind speed and rainfall; power market data includes electricity price and transaction power information; The grid safety status data collection model is used to collect parameter information of the static safety, dynamic safety and equipment safety of the grid; the static safety parameter information includes bus voltage, voltage margin and active power margin; the dynamic safety parameter information includes grid load rate, grid overload rate, node voltage change rate and transformer load rate; the equipment safety parameter information includes operating parameter information of generator sets, transformers, circuit breakers and transmission lines; The collection file includes the name of the data collection model, collection time information, and collection area information.

9. The application-oriented power grid power data collection system according to claim 8, characterized in that: The data preprocessing module includes a data integrity checking unit, a missing data processing unit, and a noise data processing unit; Data integrity verification unit: The data integrity verification unit further includes an information dimension integrity verification subunit and a data volume integrity verification subunit; the information dimension integrity verification subunit is used to verify whether the location information dimension, device information dimension, data description dimension and measurement information dimension are complete, and whether the measurement field name and measurement value in the data description dimension and the measurement information dimension correspond; the data volume integrity verification subunit verifies whether the number of data collected per unit time is complete according to the data collection frequency Missing data processing unit: The missing data processing unit further includes a judgment subunit and a missing data interpolation subunit; the judgment subunit is used to judge whether the information dimension integrity check result and the data volume integrity check result both meet the preset conditions; the missing data interpolation subunit uses the corresponding mean or mode to interpolate the missing measurement value; Noise data processing unit: used for data denoising; obtain multiple measurement values ​​within a certain time range, cluster the multiple measurement values ​​using the K-means clustering algorithm to obtain several data clusters, obtain the outliers of each data cluster according to the set distance, and replace the outliers with the cluster center value of the data cluster to which the outliers belong.

10. The application-oriented power grid power data collection system according to claim 9, characterized in that: The data storage module includes a query system mapping unit and a data classification storage unit; Query system mapping unit: parse the collection file information, obtain the collection file name, and query the mapping configuration file according to the collection file name to obtain the storage system information; Data classification storage unit: if the collection file name is a fault diagnosis data collection model, query the mapping configuration file to obtain storage system information as fault diagnosis subject database information, save the collection file to the fault diagnosis subject database according to the fault diagnosis subject database information, partition the data using the collection area information as the partition key, and use the collection time information as the data sorting key within the partition; The fault diagnosis subject database is a non-relational database; If the collection file is named as a power load forecast data collection model, query the mapping configuration file to obtain the storage system information as load forecast subject database information, save the collection file to the subject database according to the subject database information, and use each attribute in the location information dimension, device information dimension, data description dimension and measurement information dimension of each record in the collection file as a column for independent storage; the subject database is a time series database; If the collection file name is a power grid security status data collection model, the mapping configuration file is queried to obtain storage system information as power grid security status subject database information, the collection file is saved to the power grid security status subject database according to the power grid security status subject database information, and the data survival time is set according to the collection time information, and the power grid security status subject database automatically clears expired data according to the data survival time; the power grid security status subject database is a memory database.

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