CN117375231A - Statistical method and data processing system based on power grid data nodes - Google Patents
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Abstract
The invention discloses a statistical method and a data processing system based on power grid data nodes, and relates to the technical field of power systems. According to the statistical method based on the power grid data nodes, the power grid manager is allowed to monitor the power grid performance in real time by collecting the real-time power grid node data and the historical data, historical performance analysis is supported, and the data is screened, classified and summarized, so that deep knowledge of the overall load condition and the performance trend of the power grid is provided, the power grid manager can plan and optimize the power grid operation better, and the reliability and the efficiency of the power grid manager are improved; the data processing system based on the statistical method of the power grid data nodes integrates modules such as data acquisition, load statistics, load growth rate calculation, power supply capacity evaluation and storage, so that a power grid manager can monitor the power grid performance in real time, analyze historical performance trend, estimate load demand and evaluate power supply capacity to effectively manage and maintain the power grid, improve the stability and usability of the power grid and provide support for future planning.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a statistical method and a data processing system based on power grid data nodes.
Background
The quality of the power grid data is a prerequisite for the reliability of the power grid dispatching decision, and the power grid dispatching mainly improves the power grid data parameters by means of state estimation and parameter identification, but the power grid dispatching has some problems in reagent operation.
The Chinese patent publication No. CN114201649A discloses that the data of the topological structure of the power grid, element parameters, line voltage, line current, line power, monitoring point temperature and monitoring point humidity and the power grid monitoring video of each power grid point are collected and transmitted to a data management server through a data transmission network and a video transmission module, and the data are classified, managed and stored through a data classification module, so that the data are conveniently queried, and the data are conveniently and efficiently analyzed and checked by a data analysis and verification module.
However, the prior art has the problems that the performance of the power grid is difficult to monitor in real time, potential problems and load fluctuation are identified, and the overall load condition of the power grid is estimated.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a statistical method and a data processing system based on power grid data nodes, which solve the problems that the prior art is difficult to monitor the power grid performance in real time, identify potential problems and load fluctuation and evaluate the overall load condition of the power grid.
In order to achieve the above purpose, the invention is realized by the following technical scheme: a statistical method based on grid data nodes, comprising the steps of: determining grid nodes to be subjected to data statistics, and acquiring node data of each grid node based on a set sampling period; selecting power grid data to be summarized from the obtained node data, and preprocessing the power grid data; acquiring power grid historical data from a historical database, and preprocessing the power grid historical data; classifying and summarizing the preprocessed power grid data and the preprocessed power grid historical data according to a set classification standard; evaluating the performance of the power grid based on the classified and summarized power grid data and power grid historical data; an assessment report is generated.
Further, the power grid data to be summarized comprise node load data of each power grid node at the current stage and power supply quantity of the power grid at the current stage; the power grid historical data comprise historical load data of each power grid node and historical power supply quantity of the power grid.
Further, the evaluation of the power grid performance comprises evaluation of the current stage integral load index of the power grid, the historical stage integral load index of the power grid, the load increase index of each node, the load increase index of the power grid and the power supply capacity index of the power grid.
The data processing system for the statistical method based on the power grid data nodes comprises a data acquisition module, a node load statistical module, a load growth rate calculation module, a power grid power supply capacity assessment module and a storage module, wherein: the data acquisition module is used for acquiring node load data of each power grid node at the current stage, the power supply quantity of the power grid at the current stage, and historical load data and power grid historical power supply quantity of each power grid node stored in the storage module; the node load statistics module is used for calculating the whole load index of the current stage of the power grid according to the node load data of each power grid node acquired by the data acquisition module, and calculating the whole load index of the historical stage of the power grid according to the historical load data of each power grid node; the load increase rate calculation module is used for calculating a power grid load increase index according to the overall load index in the power grid history stage; the power supply capacity evaluation module is used for calculating a power supply capacity index of the power grid according to the power supply capacity of the power grid at the current stage, node load data of each power grid node at the current stage and the power supply growth index of the power grid; the storage module is used for storing historical load data of each power grid node and historical power supply quantity of the power grid.
Further, based on the dataThe process of calculating the whole load index of the current stage of the power grid by taking the node load data of each power grid node obtained by the module and calculating the whole load index of the historical stage of the power grid by the historical load data of each power grid node is as follows: deleting abnormal values in node load data of each power grid node; calculating node load indexes of all nodes according to the periodically acquired node load data of all power grid nodes, and summarizing the node load indexes to obtain the integral load index lambda of the power grid at the current stage d The method comprises the steps of carrying out a first treatment on the surface of the Grouping the historical load data of each power grid node according to the set historical data sampling period, calculating the historical load index of each node in the historical data sampling period based on the grouped historical load data, and summarizing the historical load indexes to obtain the overall load index of the power grid historical stage in each historical data sampling period.
Further, the current-stage integral load index Λ of the power grid d The calculation formula of (2) is as follows:i=1, 2, 3..n is the number of grid nodes, j=1, 2, 3..m is the number of node load data per grid node, where fh ij Calculating compensation factors for the j-th node load data of the i-th power grid node, wherein gamma is the load of a single power grid node, and beta is the load of the power grid; the whole load index of the historical stage of the power gridThe calculation formula of (2) is as follows: />Where i=1, 2, 3..n is the number of grid nodes, j=1, 2, 3..m is the number of historical load data per grid node, where fh ij The j historical load data of the ith power grid node, gamma l Calculating compensation factors, beta, for individual grid node historical load differences l Calculating compensation parameters, fh, for historical loads of the power grid p And (5) a mean value of historical load data of each power grid node.
Further, calculating a grid load increase index Γ according to the grid history stage overall load index zzl The calculation formula of (2) is as follows:wherein C is the group number of grouping the historical load data of each power grid node according to the set historical data sampling period, delta is the growth index to calculate the modulation parameter, and e is the natural constant.
Further, the process of calculating the power supply capacity index of the power grid according to the power supply quantity of the power grid at the current stage, node load data of each power grid node at the current stage and the power supply growth index of the power grid is as follows: obtaining the highest load quantity of each power grid node in the current stage; integral load index lambda based on highest load and current stage of power grid d Calculating the analog load FH required by the current power grid mn The method comprises the steps of carrying out a first treatment on the surface of the Calculating power supply growth index gamma of power grid based on historical power supply quantity of power grid dw The method comprises the steps of carrying out a first treatment on the surface of the Based on required analog load capacity lambda of current stage electric network mn Current stage power supply DG w And grid power supply growth index Γ dw Calculating the power supply capacity index ψ of a power grid dw The calculation formula is as follows:
further, the calculation formula of the analog load quantity required by the current-stage power grid is as follows:
wherein->Calculating supplementary parameters for the load difference, ζ is the calculated modulation factor,>is the highest load of the ith grid node.
Further, the power grid supply is calculated based on the historical power supply quantity of the power gridThe electrical growth index is calculated by the following formula:wherein C is the group number of grouping the historical load data of each power grid node according to the set historical data sampling period, χ is the power supply quantity increase index, and a modulation factor is calculated,/->And->The historical power supply quantity of the power grid is the c and the c+1 respectively.
The invention has the following beneficial effects:
(1) According to the statistical method based on the grid data nodes, by collecting the real-time grid node data and the historical data, not only is the grid manager allowed to monitor the grid performance in real time, but also the historical performance analysis is supported, and by screening, classifying and summarizing the data, the deep knowledge of the overall load condition and the performance trend of the grid is provided, so that the grid manager can plan and optimize the grid operation better, the reliability and the efficiency of the grid operation are improved, and detailed evaluation reports are generated to support decision making and maintenance work.
(2) The data processing system based on the statistical method of the power grid data nodes integrates modules such as data acquisition, load statistics, load growth rate calculation, power supply capacity evaluation and storage, so that a power grid manager can monitor the power grid performance in real time, analyze historical performance trend, estimate load demand and evaluate power supply capacity to effectively manage and maintain the power grid, improve the stability and usability of the power grid and provide support for future planning.
Of course, it is not necessary for any one product to practice the invention to achieve all of the advantages set forth above at the same time.
Drawings
Fig. 1 is a flow chart of a statistical method based on grid data nodes.
FIG. 2 is a flow chart of a data processing system for a statistical method based on grid data nodes of the present invention.
FIG. 3 is a flow chart of the overall load index calculation of the historical stage of the power grid of the data processing system based on the statistical method of the power grid data nodes.
Fig. 4 is a flowchart of calculating a power supply capacity index of a power grid of a data processing system based on a statistical method of power grid data nodes.
Detailed Description
According to the method and the system for statistics based on the power grid data nodes, the method for statistics based on the power grid data nodes can help a power grid operator to manage and maintain the power grid better, improve stability, availability and efficiency of the power grid, reduce outage time, provide powerful support for future power grid planning, monitor power grid performance in real time, identify potential problems and load fluctuation, and evaluate overall load conditions of the power grid.
The problems in the embodiments of the present application are as follows:
the node load data and the power supply quantity of each power grid node are obtained in real time, the historical load data and the historical power supply quantity of the power grid node are stored, the timeliness and the accuracy of the data are ensured through the sensor and the monitoring equipment, and the obtained data are preprocessed, including data cleaning, abnormal value detection, data format conversion and the like, so that the data quality is ensured.
And calculating the whole load index of the current stage of the power grid by using the node load statistics module, and simultaneously calculating the whole load index of the historical stage of the power grid by using the historical data. And then, calculating a load increase index of the power grid by using a load increase rate calculation module to know the increase trend of the power grid load, calculating the power supply capacity index of the power grid by using a power supply capacity evaluation module according to the current power supply capacity of the power grid, node load data and the load increase index, periodically acquiring data, and carrying out load statistics, load increase rate calculation and power supply capacity evaluation to monitor the performance of the power grid in real time. A real-time report is generated to provide current status and trend analysis of the grid.
Referring to fig. 1, the embodiment of the invention provides a technical scheme: a statistical method based on grid data nodes, comprising the steps of: determining grid nodes to be subjected to data statistics, and acquiring node data of each grid node based on a set sampling period; selecting power grid data to be summarized from the obtained node data, and preprocessing the power grid data; acquiring power grid historical data from a historical database, and preprocessing the power grid historical data; classifying and summarizing the preprocessed power grid data and the preprocessed power grid historical data according to a set classification standard; evaluating the performance of the power grid based on the classified and summarized power grid data and power grid historical data; an assessment report is generated.
Specifically, the power grid data to be summarized comprises node load data of each power grid node at the current stage and power supply quantity of the power grid at the current stage; the grid history data comprises the history load data of each grid node and the grid history power supply quantity.
Specifically, evaluating the grid performance includes evaluating a grid current stage overall load index, a grid historical stage overall load index, a load growth index of each node, a grid load growth index, and a grid power supply capacity index.
In this embodiment, firstly, reliable data acquisition is ensured, including real-time node load data, grid supply and historical data. Preprocessing the data, including data cleaning, outlier detection and format conversion, and classifying and summarizing the preprocessed data according to a set classification standard. The current stage and the historical data are separated and classified according to grid nodes so as to further analyze, and according to the data collected by classification, the performance indexes of the grid, such as the overall load index, the historical load index, the load growth index and the power supply capacity index, are calculated to generate an evaluation report, wherein the evaluation report comprises an evaluation result and trend analysis. Reports may be used to support decision making, helping grid managers to take necessary measures to improve the performance and reliability of the grid.
The method allows a power grid manager to monitor load data and power supply quantity of power grid nodes in real time, is helpful for rapidly identifying potential problems in the power grid, such as overload or insufficient power supply, and can be compared with the past performance by preprocessing and classifying and summarizing the historical data of the power grid. This helps identify long-term trends and changes in grid performance, as well as seasonal or annual fluctuations.
The load growth index and the power grid load growth index of each node are calculated, so that a power grid manager can be helped to know the growth trend of the power grid load, whether the power grid is enough to meet the current and future energy demands can be determined by calculating the power grid power supply capacity index, the power supply deficiency can be avoided, the availability and the reliability of the power grid are improved, and based on the evaluation result, the power grid manager can identify problems and formulate improvement measures to optimize the performance of the power grid.
A data processing system for a statistical method based on grid data nodes, as shown in fig. 2, comprises a data acquisition module, a node load statistical module, a load growth rate calculation module, a grid power supply capacity assessment module and a storage module, wherein: the data acquisition module is used for acquiring node load data of each power grid node at the current stage, the power supply quantity of the power grid at the current stage, and historical load data and power grid historical power supply quantity of each power grid node stored in the storage module; the node load statistics module is used for calculating the whole load index of the current stage of the power grid according to the node load data of each power grid node acquired by the data acquisition module, and calculating the whole load index of the historical stage of the power grid according to the historical load data of each power grid node; the load increase rate calculation module is used for calculating a power grid load increase index according to the overall load index in the power grid history stage; the power supply capacity evaluation module is used for calculating a power supply capacity index of the power grid according to the power supply capacity of the power grid at the current stage, node load data of each power grid node at the current stage and the power supply growth index of the power grid; the storage module is used for storing historical load data of each power grid node and historical power supply quantity of the power grid.
In this embodiment, the node load statistics module is used to calculate the current stage overall load index of the power grid and the historical stage overall load index of the power grid, and process the historical data for subsequent analysis and comparison. And calculating a load increase index of the power grid by using a load increase rate calculation module so as to know the increase trend of the load of the power grid, and calculating the power supply capacity index of the power grid according to the power supply quantity, the node load data and the load increase index by using a power supply capacity evaluation module of the power grid.
Grid performance is monitored and improved by real-time data acquisition, historical data analysis, and performance assessment. Through the data driving method, a power grid manager can better manage and maintain the power grid, improve the stability, availability and efficiency of the power grid, reduce the power failure time and provide support for future power grid planning.
Specifically, as shown in fig. 3, the process of calculating the current stage integral load index of the power grid according to the node load data of each power grid node acquired by the data acquisition module, and calculating the historical stage integral load index of the power grid according to the historical load data of each power grid node is as follows: deleting abnormal values in node load data of each power grid node; calculating node load indexes of all nodes according to the periodically acquired node load data of all power grid nodes, and summarizing the node load indexes to obtain the integral load index lambda of the power grid at the current stage d The method comprises the steps of carrying out a first treatment on the surface of the Grouping the historical load data of each power grid node according to the set historical data sampling period, calculating the historical load index of each node in the historical data sampling period based on the grouped historical load data, and summarizing the historical load indexes to obtain the overall load index of the power grid historical stage in each historical data sampling period.
In this embodiment, deleting outliers in the node load data of each grid node is an important step in data preprocessing. Outliers may affect accurate assessment of grid load, so removing these outliers helps to improve data quality and reliability of the results. The node load indexes of all the power grid nodes are calculated and summarized, so that the overall load index of the power grid at the current stage can be obtained, the index reflects the current load condition of the power grid, and the index is a key element for monitoring the performance of the power grid in real time.
By grouping historical load data and calculating historical load indexes, the overall load trend of the power grid in a historical data sampling period can be known, the current-stage overall load index and the historical-stage overall load index of the power grid are important bases for evaluating the performance and trend analysis of the power grid, and the method can be used for detecting potential problems, predicting future demands, formulating power grid planning strategies and identifying seasonal and annual changes of the power grid.
Specifically, the current stage overall load index Λ of the power grid d The calculation formula of (2) is as follows:i=1, 2, 3..n is the number of grid nodes, j=1, 2, 3..m is the number of node load data per grid node, where fh ij Calculating compensation factors for the j-th node load data of the i-th power grid node, wherein gamma is the load of a single power grid node, and beta is the load of the power grid; grid history stage overall load indexThe calculation formula of (2) is as follows: />Where i=1, 2, 3..n is the number of grid nodes, j=1, 2, 3..m is the number of historical load data per grid node, where fh ij The j historical load data of the ith power grid node, gamma l Calculating compensation factors, beta, for individual grid node historical load differences l Calculating compensation parameters, fh, for historical loads of the power grid p And (5) a mean value of historical load data of each power grid node.
In this embodiment, the calculation of the overall load index of the current stage of the power grid deletes the abnormal value from the node load data of each power grid node, ensures the accuracy and stability of the data, calculates the node load index for each power grid node, i.e. sums all the node load data of the power grid node, multiplies the node load calculation compensation factor and the load calculation compensation parameter, sums the node load indexes of all the power grid nodes, and then weights the load calculation compensation parameter to obtain the overall load index of the current stage of the power grid.
Calculating the whole load index of the power grid in the historical stage, grouping the historical load data of each power grid node according to the set historical data sampling period, and executing the following steps for each historical data sampling period: comparing the historical load data of each power grid node with the average load data in the sampling period of the historical load data, calculating a historical load difference value, multiplying the historical load difference value by the historical load difference value to calculate a compensation factor, adding the average load data to the result, multiplying the result by the historical load calculation compensation parameter, and summing the historical load indexes calculated in each historical data sampling period to obtain the overall load index of the power grid in the historical stage.
Load data, historical load data, compensation factors and parameters of the grid nodes are comprehensively considered to obtain overall load indexes of the current stage and the historical stage of the grid, and the indexes can be used for evaluating the performance of the grid, detecting potential problems, analyzing load trends and supporting decision making.
The compensation factors and parameters in the calculation formula take the uncertainty and fluctuation of the load data and the difference of the historical data into consideration, and are helpful for adjusting the index calculation, so that the method is more suitable for load analysis and performance evaluation under different conditions.
Specifically, the grid load increase index Γ is calculated from the grid history phase overall load index zzl The calculation formula of (2) is as follows:wherein C is the group number of grouping the historical load data of each power grid node according to the set historical data sampling period, delta is the growth index to calculate the modulation parameter, and e is the natural constant.
In this embodiment, the historical load data are firstly grouped according to the historical data sampling period, then the change condition of the overall load index of the historical stage of the power grid in the adjacent historical data sampling period is calculated, and the change rate is calculated to obtain the power grid load increase index Γ zzl Can be used to describe the load increase trend of the historical stage of the power grid. Growth index calculation modulation parameterThe algorithm is more universal due to the use of the number and the natural constant, and can be adjusted according to specific requirements so as to adapt to analysis requirements of different power grids.
Specifically, as shown in fig. 4, the process of calculating the power supply capacity index of the power grid according to the power supply amount of the power grid at the present stage, the node load data of each power grid node at the present stage and the power supply growth index of the power grid is as follows: obtaining the highest load quantity of each power grid node in the current stage; integral load index lambda based on highest load and current stage of power grid d Calculating the analog load FH required by the current power grid mn The method comprises the steps of carrying out a first treatment on the surface of the Calculating power supply growth index gamma of power grid based on historical power supply quantity of power grid dw The method comprises the steps of carrying out a first treatment on the surface of the Based on required analog load capacity lambda of current stage electric network mn Current stage power supply DG w And grid power supply growth index Γ dw Calculating the power supply capacity index ψ of a power grid dw The calculation formula is as follows:
in this embodiment, the highest load of each grid node is obtained from the grid data, which is the load peak that the grid may face, and the current integral load index Λ of the grid is used d The method comprises the steps of combining the highest load quantity, calculating the analog load quantity required by the power grid, obtaining the historical power supply quantity of the power grid, obtaining the historical power supply quantity from a historical database, and calculating the power supply growth index gamma of the power grid by using the historical power supply quantity dw To learn about historical changes in grid power supply capacity.
Utilize required analog load FH of electric wire netting of present stage mn Historical power supply DG of power grid w Grid power supply growth index Γ dw Grid load growth index Γ zzl The method comprises the steps of calculating according to a given calculation formula, combining actual data and historical information of the power grid to evaluate whether the power supply capacity of the power grid is enough or not, considering historical trend of power supply capacity and load growth of the power grid, and helping a power grid manager to plan and decide better so as to ensure availability and reliability of the power grid.
Specifically, the calculation formula of the analog load quantity required by the current-stage power grid is as follows:wherein->Calculating supplementary parameters for the load difference, ζ is the calculated modulation factor,>is the highest load of the ith grid node.
In this embodiment, the highest load of each grid node is obtained from the grid data, the previously calculated overall load index of the current stage of the grid is used, for each grid node, the load difference is calculated, the load difference is multiplied by the load difference to calculate the complementary parameter, the above results are summed to obtain a sum, the sum is multiplied by the load difference to calculate the complementary parameter, and finally, the result is multiplied by the calculated modulation factor to obtain the required simulated load of the current stage of the grid, and the simulated load requirement of the current stage of the grid is estimated according to the highest load of each grid node, the overall load condition and the adjustment parameter.
The load difference value calculation supplementary parameters are introduced into the formula, so that the algorithm is more flexible, the algorithm can be adjusted according to specific conditions, different power grids and analysis requirements are met, and the calculated modulation factor in the formula allows the calculated sensitivity to be adjusted according to the requirements, so that the method is better suitable for the characteristics of different power grids.
Specifically, a calculation formula for calculating a power supply growth index of the power grid based on the historical power supply amount of the power grid is as follows:wherein C is the group number of grouping the historical load data of each power grid node according to the set historical data sampling period, χ is the power supply quantity increase index, and a modulation factor is calculated,/->And->The historical power supply quantity of the power grid is the c and the c+1 respectively.
In this embodiment, the historical power supply amount data of the power grid in each historical data sampling period is obtained from the historical database, the power supply increase index of the power grid is calculated, the power supply increase index of the power grid is output, and the historical increase trend of the power supply capacity of the power grid can be quantified by considering the change condition of the historical power supply amount and combining with calculating the modulation factor.
In summary, the present application has at least the following effects:
the data processing system based on the statistical method of the power grid data nodes integrates modules such as data acquisition, load statistics, load growth rate calculation, power supply capacity assessment and storage, so that a power grid manager can monitor the power grid performance in real time, analyze historical performance trend, estimate load demand, assess power supply capacity, effectively manage and maintain the power grid, improve stability and usability of the power grid, and provide support for future planning.
The statistical method based on the grid data nodes not only allows a grid manager to monitor the grid performance in real time but also supports historical performance analysis by collecting the real-time grid node data and historical data, and provides deep knowledge of the overall load condition and performance trend of the grid by screening, classifying and summarizing the data, so that the grid manager can plan and optimize the grid operation better, improve the reliability and efficiency of the grid operation, and generate detailed evaluation reports to support decision making and maintenance work.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of systems, apparatuses (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The statistical method based on the power grid data nodes is characterized by comprising the following steps of:
determining grid nodes to be subjected to data statistics, and acquiring node data of each grid node based on a set sampling period;
selecting power grid data to be summarized from the obtained node data, and preprocessing the power grid data;
acquiring power grid historical data from a historical database, and preprocessing the power grid historical data;
classifying and summarizing the preprocessed power grid data and the preprocessed power grid historical data according to a set classification standard;
evaluating the performance of the power grid based on the classified and summarized power grid data and power grid historical data;
an assessment report is generated.
2. A statistical method based on grid data nodes according to claim 1, characterized in that: the power grid data to be summarized comprise node load data of each power grid node at the current stage and power supply quantity of the power grid at the current stage;
the power grid historical data comprise historical load data of each power grid node and historical power supply quantity of the power grid.
3. A statistical method based on grid data nodes according to claim 1, characterized in that: the evaluation of the power grid performance comprises the evaluation of the current stage integral load index of the power grid, the historical stage integral load index of the power grid, the load increase index of each node, the load increase index of the power grid and the power supply capacity index of the power grid.
4. A data processing system for a statistical method based on grid data nodes, which is characterized by comprising a data acquisition module, a node load statistical module, a load growth rate calculation module, a grid power supply capacity assessment module and a storage module, wherein:
the data acquisition module is used for acquiring node load data of each power grid node at the current stage, the power supply quantity of the power grid at the current stage, and historical load data and power grid historical power supply quantity of each power grid node stored in the storage module;
the node load statistics module is used for calculating the whole load index of the current stage of the power grid according to the node load data of each power grid node acquired by the data acquisition module, and calculating the whole load index of the historical stage of the power grid according to the historical load data of each power grid node;
the load increase rate calculation module is used for calculating a power grid load increase index according to the overall load index in the power grid history stage;
the power supply capacity evaluation module is used for evaluating the power supply capacity of the power grid according to the current power supply capacity of the power grid node load data of each power grid node and a power grid power supply growth index at the present stage are used for calculating a power grid power supply capacity index;
the storage module is used for storing historical load data of each power grid node and historical power supply quantity of the power grid.
5. The data processing system for a statistical method based on grid data nodes according to claim 4, wherein the process of calculating the current stage overall load index of the grid according to the node load data of each grid node acquired by the data acquisition module and calculating the historical stage overall load index of the grid according to the historical load data of each grid node is as follows:
deleting abnormal values in node load data of each power grid node;
calculating the node load index of each node according to the periodically acquired node load data of each power grid node, summarizing the node load indexes to obtain the integral load index lambda at the current stage of the power grid d ；
Grouping the historical load data of each power grid node according to the set historical data sampling period, calculating the historical load index of each node in the historical data sampling period based on the grouped historical load data, and summarizing the historical load indexes to obtain the overall load index of the power grid historical stage in each historical data sampling period.
6. A data processing system for a statistical method based on grid data nodes according to claim 5, characterized in that the grid current stage overall load index Λ d The calculation formula of (2) is as follows:
for the number of grid nodes, j=1, 2,3,..m is the number of node load data per grid node, where fh ij Calculating compensation factors for the j-th node load data of the i-th power grid node, wherein gamma is the load of a single power grid node, and beta is the load of the power grid;
the whole load index of the historical stage of the power gridThe calculation formula of (2) is as follows: />Where i=1, 2, 3..n is the number of grid nodes, j=1, 2, 3..m is the number of historical load data per grid node, where fh ij The j historical load data of the ith power grid node, gamma l Calculating compensation factors, beta, for individual grid node historical load differences l Calculating compensation parameters, fh, for historical loads of the power grid p And (5) a mean value of historical load data of each power grid node.
7. A data processing system for a statistical method based on grid data nodes according to claim 6, characterized in that the grid load increase index Γ is calculated from the grid history phase overall load index zzl The calculation formula of (2) is as follows:
wherein C is the group number of grouping the historical load data of each power grid node according to the set historical data sampling period, delta is the growth index to calculate the modulation parameter, and e is the natural constant.
8. A data processing system for a statistical method based on grid data nodes according to claim 7, wherein the process of calculating the grid power supply capacity index from the current grid power supply quantity, the node load data of each grid node at the current stage and the grid power supply growth index is as follows:
obtaining the highest load quantity of each power grid node in the current stage;
integral load index lambda based on highest load and current stage of power grid d Calculating the analog load FH required by the current power grid mn ；
Calculating power supply growth index gamma of power grid based on historical power supply quantity of power grid dw ；
Based on required analog load capacity lambda of current stage electric network mn Current stage power supply DG w And grid power supply growth index Γ dw Calculating the power supply capacity index ψ of a power grid dw The calculation formula is as follows:
9. a data processing system for a statistical method based on grid data nodes according to claim 8, wherein the calculation formula of the analog load amount required by the current stage grid is:
wherein->Calculating supplementary parameters for the load difference, ζ is the calculated modulation factor,>is the highest load of the ith grid node.
10. A data processing system for a statistical method based on grid data nodes according to claim 9, wherein the calculation formula for calculating the grid power supply growth index based on the historical power supply amount of the grid is:wherein C is the group number of grouping the historical load data of each power grid node according to the set historical data sampling period, χ is the power supply quantity increase index, and a modulation factor is calculated,/->And->The historical power supply quantity of the power grid is the c and the c+1 respectively.
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