Intelligent evaluation method and system for standard system of power system based on data analysis

Abstract

The invention discloses an intelligent evaluation method and system for a power system standard system based on data analysis, and relates to the technical field of voltage control. A power grid evaluation area is set, and an intelligent evaluation network is constructed based on the set power grid evaluation area; meanwhile, historical power data in a power grid evaluation area are collected based on the constructed intelligent evaluation network, the collected historical power data are processed in a data processing mode after collection is completed, and the processed historical power data are analyzed in a data analysis mode; and after the analysis is completed, comprehensive index extraction is performed on the analyzed historical power data, and a power data evaluation model and a power standard system are constructed through a comprehensive evaluation mode based on the extracted historical power data indexes, so that the reliability of power system standard system evaluation is improved.

Description

Technical Field

[0001] This invention relates to the field of voltage control technology, and more specifically to a data-based intelligent evaluation method and system for power system standard systems. Background Technology

[0002] Currently, there is a lack of scientific evaluation methods for the effectiveness of constructing a new power system technology standard system. To ensure that the standard system and technology system are well-coordinated and meet the requirements for promoting scientific and technological innovation, scientific theories and methods are needed. Based on the basic method of constructing a hierarchical and classified new power system technology standard system, this project proposes a set of effectiveness evaluation systems, constructs quantitative evaluation indicators, designs evaluation methods, and actively promotes the construction of a new power system technology standard system.

[0003] Existing technologies, such as the invention patent application with publication number CN117196134A, disclose a method for analyzing the applicability of technical standards in the standard system of new power systems. The method includes: evaluating the applicability of the standard's time, object, content, and referenced documents separately; identifying the standard object by applying the maximum membership principle; and conducting a comprehensive evaluation of multi-objective functions using the TOPSIS method. This enables a scientific quantitative analysis and multi-objective comprehensive evaluation of the adaptability of existing standards to the comprehensive needs of new power systems, thereby improving the accuracy and reliability of identifying the applicability of technical standards for new power systems and providing a foundation for the layout of the technical standard system of new power systems.

[0004] As can be seen from the above solutions, most of the current power system standards focus on the identification and evaluation of document data within the power system, neglecting the actual operation of the power system and user evaluation, which has certain limitations. Summary of the Invention

[0005] The purpose of this invention is to provide a data analysis-based intelligent evaluation method and system for power system standards, which solves the problems existing in the background technology.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides an intelligent evaluation method for power system standard system based on data analysis, specifically including the following steps:

[0007] S1. Define the power grid assessment area and build an intelligent assessment network based on the defined power grid assessment area;

[0008] S2. Historical power data within the power grid assessment area is collected based on the constructed intelligent assessment network;

[0009] The historical power data includes power grid operation safety data and power grid transmission data;

[0010] S3. The collected historical power data is processed through data processing methods to obtain processed historical power data;

[0011] S4. Analyze the processed historical power data using data analysis methods to obtain the analyzed historical power data;

[0012] S41. Analyze the power grid operation safety data in the processed historical power data through static risk analysis to obtain the analyzed power grid operation safety data.

[0013] S42. Analyze the power grid transmission data in the processed historical power data through user demand analysis to obtain the analyzed power grid transmission data.

[0014] S43. Summarize and analyze the power grid operation safety data and the power grid transmission data to obtain the analyzed historical power data;

[0015] S5. Extract comprehensive indicators from the analyzed historical power data to obtain the refined historical power data indicators.

[0016] S6. Based on the refined historical power data indicators, construct a power data evaluation model and power standard system through a comprehensive evaluation method.

[0017] Preferably, the step of setting up a power grid assessment area and constructing an intelligent assessment network based on the set power grid assessment area includes the following steps:

[0018] S11. Divide the power grid assessment area into nodes and sections, and deploy power data monitoring equipment at each node.

[0019] Each segment is numbered, and the center point of each segment is set as a node. Power data monitoring equipment is deployed at each monitoring node.

[0020] S12. Collect and save the coordinate location information of the corresponding power data monitoring equipment to build an intelligent evaluation network.

[0021] Preferably, the process of processing the collected historical power data to obtain processed historical power data includes the following steps:

[0022] The collected historical electricity data is standardized through data standardization methods;

[0023] The standardized calculation formula is as follows:

[0024] ;

[0025] in, This represents the minimum value collected from historical electricity data. This represents the maximum value collected from historical electricity data. This represents the historical electricity data collected in the s-th group. This represents the s-th historical power data after standardization;

[0026] Standardized historical power data is set as processed historical power data.

[0027] Preferably, the step of analyzing the power grid operation safety data in the processed historical power data using static risk analysis to obtain the analyzed power grid operation safety data includes the following steps:

[0028] S411. Calculate the node voltage over-limit risk index in the power grid operation safety data;

[0029] S412. Calculate the branch power flow over-limit risk index in the power grid operation safety data.

[0030] Preferably, the calculation of the node voltage over-limit risk index in the power grid operation safety data includes the following steps:

[0031] Select known power grid operation safety data for M types of faults, and input the selected power grid operation safety data into a convolutional neural network for feature extraction;

[0032] Based on the input power grid operation safety data, fault features of the power grid operation safety data are extracted and saved through the convolution operation of a convolutional neural network.

[0033] The formula for calculating convolution is as follows:

[0034] ;

[0035] in, This indicates the input power grid operation safety data. This represents the weights of the corresponding convolution kernel. Indicates the bias value. This indicates the fault characteristics from which power grid operation safety data is extracted;

[0036] The power grid operation safety data in the processed historical power data is input into a convolutional neural network for feature extraction to obtain the fault features of the processed power grid operation safety data.

[0037] Fault characteristics in the processed power grid operation safety data are determined by similarity comparison.

[0038] The similarity comparison formula is as follows:

[0039] ;

[0040] in, Fault characteristics representing processed power grid operation safety data and fault characteristics for storing power grid operation safety data covariance, This represents the standard deviation of the fault characteristics in the processed power grid operation safety data. This represents the standard deviation of fault characteristics in the stored power grid operation safety data. Fault characteristics representing processed power grid operation safety data and fault characteristics for storing power grid operation safety data The similarity between them;

[0041] A similarity threshold is set. When the similarity value between the fault characteristics of the processed power grid operation safety data and the fault characteristics of the stored power grid operation safety data is greater than the set similarity threshold, it indicates that the corresponding processed power grid operation safety data is fault data; otherwise, it is normal data.

[0042] Record the frequency of faults in the corresponding power grid operation safety data in the processed historical power data, and determine the failure rate of the corresponding node based on the recorded fault frequency;

[0043] Calculate the probability of power grid outage based on the failure rate of the corresponding node;

[0044] The probability calculation formula is as follows:

[0045] ;

[0046] in, This represents the failure rate of the j-th failure type. This represents the probability of a power grid outage corresponding to the k-th node;

[0047] Based on the probability of grid outages, calculate the node voltage over-limit risk index when a grid outage occurs.

[0048] The formula for calculating the voltage over-limit risk index is as follows:

[0049] ;

[0050] in, Represents a node The voltage under the j-th fault type, Represents a node The probability density function of voltage under the j-th fault type, Representing nodes respectively The upper and lower limits of voltage, Represents a node Voltage over-limit risk index under the j-th fault type.

[0051] Preferably, the calculation of the branch power flow over-limit risk index in the power grid operation safety data includes the following steps:

[0052] The formula for calculating the risk index of branch power flow exceeding the limit during a power grid outage is as follows:

[0053] ;

[0054] in, Represents a node The maximum transmission power of the corresponding line. Represents a node The probability density function of the power transmitted by the corresponding line under the j-th fault type. Represents a node Branch power flow over-limit risk index under the j-th fault type.

[0055] Preferably, the step of analyzing the power grid transmission data in the processed historical power data through user demand analysis to obtain the analyzed power grid transmission data includes the following steps:

[0056] Summarize the historical power consumption data collected from users on each power line to construct a sample set B={ };

[0057] in, This represents the historical power consumption collected on the first power line in the sample set;

[0058] The power grid transmission data collected by each node in the intelligent assessment network is set as follows: ;

[0059] ;

[0060] in, To intelligently assess the power stored in each node within the network, n represents the number of power lines;

[0061] Set power balance constraints within the intelligent evaluation network;

[0062] ;

[0063] in, This indicates that the network nodes are intelligently evaluated at time t. The active power of China's new energy power generation This indicates that the network nodes are intelligently evaluated at time t. Power exchange between domestic and international power grids This indicates that the network nodes are intelligently evaluated at time t. The discharge power in This indicates that the network nodes are intelligently evaluated at time t. Load loss power in the middle, This indicates that the network nodes are intelligently evaluated at time t. The charging power in the middle, This indicates that the network nodes are intelligently evaluated at time t. Active power in the middle;

[0064] Collect historical voltage and current consumption data of users on each power line, and calculate the user load threshold for each power line based on the collected historical voltage and current consumption data;

[0065] ;

[0066] in, Indicates the user's electricity load threshold. This indicates the user's historical electricity voltage. This indicates the user's historical electricity consumption current;

[0067] The power grid transmission data after analysis is determined based on the calculated user electricity load threshold and the power balance constraints within the intelligent assessment network.

[0068] The calculation formula for the analyzed power grid transmission data is shown below:

[0069] ;

[0070] in, This represents the analyzed power grid transmission data.

[0071] Preferably, the step of extracting comprehensive indicators from the analyzed historical power data to obtain the extracted historical power data indicators includes the following steps:

[0072] S51. Standardize the various data in the analyzed historical power data using standardized calculation formulas;

[0073] S52. Extract comprehensive indicators from the standardized data;

[0074] The maximum value among all data points is defined as the positive ideal solution, and the minimum value among all data points is defined as the negative ideal solution.

[0075] Calculate the distance between the standardized data and the positive and negative ideal solutions;

[0076] positive distance = The difference between the ideal solution and the standardized data;

[0077] negative distance = The difference between the standardized data and the negative ideal solution;

[0078] The formula for extracting comprehensive indicators is shown below:

[0079] ;

[0080] in, This represents the relative score of each data point in the historical electricity data after the i-th group of analysis. This represents the negative distance between each data point in the historical power data after the i-th group of analysis. This represents the positive distance between each data point in the historical power data after the i-th group of analysis;

[0081] The relative scores of each data point in the analyzed historical power data are set as the refined historical power data indicators.

[0082] Preferably, the construction of a power data evaluation model and power standard system based on refined historical power data indicators through a comprehensive evaluation method includes the following steps:

[0083] Based on the analyzed historical power data, experts were asked to score various data points in the analyzed historical power data.

[0084] Each expert score is used as a set of power standard system data, and all power standard system data and refined historical power data indicators are aggregated. A power data evaluation model and power standard system are constructed through a comprehensive evaluation method.

[0085] The formula for the power data evaluation model is shown below:

[0086] ;

[0087] in, This represents a power data assessment model. This represents the data of the i-th group of power standard system. This represents the weighting parameter for the overall score;

[0088] The power data evaluation model is used to calculate and score various data points in the analyzed historical power data, and the scoring results are summarized.

[0089] A standard threshold is set based on the scoring results, and a power standard system is constructed by filtering the corresponding data range based on the set standard threshold.

[0090] This invention also provides a data analysis-based intelligent evaluation system for power system standards, used to implement a data analysis-based intelligent evaluation method for power system standards. The system includes: a data acquisition module, a data processing module, a data analysis module, a data extraction module, and a standard setting module.

[0091] The data acquisition module is used to collect power data within the power grid assessment area;

[0092] The data processing module is used to process the power data collected within the power grid assessment area to obtain processed power data.

[0093] The data analysis module is used to analyze the processed power data to obtain the analyzed power data;

[0094] The data extraction module is used to extract the analyzed power data to obtain refined power data indicators.

[0095] The standard setting module is used to construct a power standard system based on the refined historical power data indicators through a comprehensive evaluation method.

[0096] The beneficial effects of this invention are as follows:

[0097] (1) This invention sets up a power grid assessment area and builds an intelligent assessment network based on the set power grid assessment area. At the same time, it collects historical power data within the power grid assessment area based on the constructed intelligent assessment network. After the collection is completed, the collected historical power data is processed by data processing and analyzed by data analysis. After the analysis is completed, comprehensive indicators are extracted from the analyzed historical power data. Based on the extracted historical power data indicators, a power data assessment model and a power standard system are constructed through a comprehensive assessment method, which improves the reliability of the power system standard system assessment.

[0098] (2) This invention divides the power grid assessment area into nodes and segments, and deploys power data monitoring equipment at each node. At the same time, it constructs an intelligent assessment network based on the coordinate location information of the corresponding power data monitoring equipment after the division, thereby improving the accuracy of power system data collection.

[0099] (3) This invention improves the rationality of power system assessment by calculating risk indicators and historical power consumption of users in power grid operation safety data and analyzing them through data analysis.

[0100] (4) This invention standardizes the various data in the analyzed historical power data through standardized calculation formulas, and extracts comprehensive indicators from the standardized data. After the extraction is completed, the power data evaluation model and power standard system are constructed through a comprehensive evaluation method, in combination with the expert's score of the various data in the analyzed historical power data, thus ensuring the accuracy of the power standard system evaluation. Attached Figure Description

[0101] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0102] Figure 1 This is a schematic diagram of the intelligent evaluation method for the power system standard system of the present invention. Detailed Implementation

[0103] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0104] In a specific embodiment of the present invention,

[0105] Reference Figure 1 As shown, this invention provides an intelligent evaluation method for power system standard systems based on data analysis, including:

[0106] S1. Define the power grid assessment area and build an intelligent assessment network based on the defined power grid assessment area;

[0107] S2. Historical power data within the power grid assessment area is collected based on the constructed intelligent assessment network;

[0108] The historical power data includes power grid operation safety data and power grid transmission data;

[0109] S3. The collected historical power data is processed through data processing methods to obtain processed historical power data;

[0110] S4. Analyze the processed historical power data using data analysis methods to obtain the analyzed historical power data;

[0111] S41. Analyze the power grid operation safety data in the processed historical power data through static risk analysis to obtain the analyzed power grid operation safety data.

[0112] S42. Analyze the power grid transmission data in the processed historical power data through user demand analysis to obtain the analyzed power grid transmission data.

[0113] S43. Summarize and analyze the power grid operation safety data and the power grid transmission data to obtain the analyzed historical power data;

[0114] S5. Extract comprehensive indicators from the analyzed historical power data to obtain the refined historical power data indicators.

[0115] S6. Based on the refined historical power data indicators, construct a power data evaluation model and a power standard system through a comprehensive evaluation method;

[0116] Furthermore, referring to Figure 1 As shown, defining a power grid assessment area and constructing an intelligent assessment network based on that area includes the following steps:

[0117] S11. Divide the power grid assessment area into nodes and sections, and deploy power data monitoring equipment at each node.

[0118] Each segment is numbered, and the center point of each segment is set as a node. Power data monitoring equipment is deployed at each monitoring node.

[0119] S12. Collect and save the coordinate location information of the corresponding power data monitoring equipment to construct an intelligent evaluation network;

[0120] Furthermore, referring to Figure 1 As shown, the collected historical power data is processed using data processing methods to obtain the processed historical power data, including the following steps:

[0121] The collected historical electricity data is standardized through data standardization methods;

[0122] The standardized calculation formula is as follows:

[0123] ;

[0124] in, This represents the minimum value collected from historical electricity data. This represents the maximum value collected from historical electricity data. This represents the historical electricity data collected in the s-th group. This represents the s-th historical power data after standardization;

[0125] Standardized historical power data is defined as processed historical power data;

[0126] Furthermore, referring to Figure 1As shown, the power grid operation safety data in the processed historical power data is analyzed using static risk analysis. The resulting power grid operation safety data includes the following steps:

[0127] S411. Calculate the node voltage over-limit risk index in the power grid operation safety data;

[0128] Select known power grid operation safety data for M types of faults, and input the selected power grid operation safety data into a convolutional neural network for feature extraction;

[0129] Based on the input power grid operation safety data, fault features of the power grid operation safety data are extracted and saved through the convolution operation of a convolutional neural network.

[0130] The formula for calculating convolution is as follows:

[0131] ;

[0132] in, This indicates the input power grid operation safety data. This represents the weights of the corresponding convolution kernel. Indicates the bias value. This indicates the fault characteristics from which power grid operation safety data is extracted;

[0133] The power grid operation safety data in the processed historical power data is input into a convolutional neural network for feature extraction to obtain the fault features of the processed power grid operation safety data.

[0134] Fault characteristics in the processed power grid operation safety data are determined by similarity comparison.

[0135] The similarity comparison formula is as follows:

[0136] ;

[0137] in, Fault characteristics representing processed power grid operation safety data and fault characteristics for storing power grid operation safety data covariance, This represents the standard deviation of the fault characteristics in the processed power grid operation safety data. This represents the standard deviation of fault characteristics in the stored power grid operation safety data. Fault characteristics representing processed power grid operation safety data and fault characteristics for storing power grid operation safety data The similarity between them;

[0138] A similarity threshold is set. When the similarity value between the fault characteristics of the processed power grid operation safety data and the fault characteristics of the stored power grid operation safety data is greater than the set similarity threshold, it indicates that the corresponding processed power grid operation safety data is fault data; otherwise, it is normal data.

[0139] Furthermore, the frequency of faults corresponding to the power grid operation safety data in the processed historical power data is recorded, and the fault rate of the corresponding node is determined based on the recorded fault frequency.

[0140] Furthermore, based on the failure rate of the corresponding node, the probability of a power grid outage is calculated;

[0141] The probability calculation formula is as follows:

[0142] ;

[0143] in, This represents the failure rate of the j-th failure type. This represents the probability of a power grid outage corresponding to the k-th node;

[0144] Furthermore, based on the probability of grid outages, a node voltage over-limit risk index is calculated when a grid outage occurs.

[0145] The formula for calculating the voltage over-limit risk index is as follows:

[0146] ;

[0147] in, Represents a node The voltage under the j-th fault type, Represents a node The probability density function of voltage under the j-th fault type, Representing nodes respectively The upper and lower limits of voltage, Represents a node Voltage over-limit risk index under the j-th fault type;

[0148] S412. Calculate the branch power flow over-limit risk index in the power grid operation safety data;

[0149] The formula for calculating the risk index of branch power flow exceeding the limit during a power grid outage is as follows:

[0150] ;

[0151] in, Represents a node The maximum transmission power of the corresponding line. Represents a node The probability density function of the power transmitted by the corresponding line under the j-th fault type. Represents a node Branch power flow exceeding limit risk index under the j-th type of fault;

[0152] Furthermore, referring to Figure 1 As shown, the power grid transmission data in the processed historical power data is analyzed using a user demand analysis method. The analyzed power grid transmission data includes the following steps:

[0153] Summarize the historical power consumption data collected from users on each power line to construct a sample set B={ };

[0154] in, This represents the historical power consumption collected on the first power line in the sample set;

[0155] The power grid transmission data collected by each node in the intelligent assessment network is set as follows: ;

[0156] ;

[0157] in, To intelligently assess the power stored in each node within the network, n represents the number of power lines;

[0158] Furthermore, power balance constraints are set within the intelligent evaluation network;

[0159] ;

[0160] in, This indicates that the network nodes are intelligently evaluated at time t. The active power of China's new energy power generation This indicates that the network nodes are intelligently evaluated at time t. Power exchange between domestic and international power grids This indicates that the network nodes are intelligently evaluated at time t. The discharge power in This indicates that the network nodes are intelligently evaluated at time t. Load loss power in the middle, This indicates that the network nodes are intelligently evaluated at time t. The charging power in the middle, This indicates that the network nodes are intelligently evaluated at time t. Active power in the middle;

[0161] Furthermore, historical voltage and current consumption of users on each power line are collected, and the user load threshold for each power line is calculated based on the collected historical voltage and current consumption.

[0162] ;

[0163] in, Indicates the user's electricity load threshold. This indicates the user's historical electricity voltage. This indicates the user's historical electricity consumption current;

[0164] Furthermore, the analyzed power grid transmission data is determined based on the calculated user electricity load threshold and the power balance constraints within the intelligent assessment network.

[0165] The calculation formula for the analyzed power grid transmission data is shown below:

[0166] ;

[0167] in, This represents the analyzed power grid transmission data;

[0168] Furthermore, referring to Figure 1 As shown, the process of extracting comprehensive indicators from the analyzed historical power data includes the following steps:

[0169] S51. Standardize the various data in the analyzed historical power data using standardized calculation formulas;

[0170] S52. Extract comprehensive indicators from the standardized data;

[0171] The maximum value among all data points is defined as the positive ideal solution, and the minimum value among all data points is defined as the negative ideal solution.

[0172] Calculate the distance between the standardized data and the positive and negative ideal solutions;

[0173] positive distance = The difference between the ideal solution and the standardized data;

[0174] negative distance = The difference between the standardized data and the negative ideal solution;

[0175] The formula for extracting comprehensive indicators is shown below:

[0176] ;

[0177] in, This represents the relative score of each data point in the historical electricity data after the i-th group of analysis. This represents the negative distance between each data point in the historical power data after the i-th group of analysis. This represents the positive distance between each data point in the historical power data after the i-th group of analysis;

[0178] The relative scores of each data point in the analyzed historical power data are set as the refined historical power data indicators.

[0179] Furthermore, referring to Figure 1 As shown, based on the refined historical power data indicators, the construction of a power data evaluation model and power standard system through a comprehensive evaluation method includes the following steps:

[0180] Based on the analyzed historical power data, experts were asked to score various data points in the analyzed historical power data.

[0181] Each expert score is used as a set of power standard system data, and all power standard system data and refined historical power data indicators are aggregated. A power data evaluation model and power standard system are constructed through a comprehensive evaluation method.

[0182] The formula for the power data evaluation model is shown below:

[0183] ;

[0184] in, This represents a power data assessment model. This represents the data of the i-th group of power standard system. This represents the weighting parameter for the overall score;

[0185] Furthermore, based on the constructed power data evaluation model, scores are calculated for each data point in the analyzed historical power data, and the scoring results are summarized.

[0186] Furthermore, standard thresholds are set based on the scoring results, and a power standard system is constructed by filtering the corresponding data ranges based on the set standard thresholds;

[0187] In one specific embodiment, the intelligent evaluation system for power system standards based on data analysis is used to implement an intelligent evaluation method for power system standards based on data analysis. The system includes: a data acquisition module, a data processing module, a data analysis module, a data extraction module, and a standard setting module.

[0188] The data acquisition module is used to collect power data within the power grid assessment area;

[0189] The data processing module is used to process the power data collected within the power grid assessment area to obtain processed power data.

[0190] The data analysis module is used to analyze the processed power data to obtain the analyzed power data;

[0191] The data extraction module is used to extract the analyzed power data to obtain refined power data indicators.

[0192] The standard setting module is used to construct a power standard system based on the refined historical power data indicators through a comprehensive evaluation method.

[0193] It should be noted that

[0194] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A method for intelligent evaluation of power system standard system based on data analysis, characterized in that, Includes the following steps: S1. Define the power grid assessment area and build an intelligent assessment network based on the defined power grid assessment area; S2. Historical power data within the power grid assessment area is collected based on the constructed intelligent assessment network; The historical power data includes power grid operation safety data and power grid transmission data; S3. The collected historical power data is processed through data processing methods to obtain processed historical power data; S4. Analyze the processed historical power data using data analysis methods to obtain the analyzed historical power data; S41. Analyze the power grid operation safety data in the processed historical power data through static risk analysis to obtain the analyzed power grid operation safety data. S42. Analyze the power grid transmission data in the processed historical power data through user demand analysis to obtain the analyzed power grid transmission data. S43. Summarize and analyze the power grid operation safety data and the power grid transmission data to obtain the analyzed historical power data; S5. Extract comprehensive indicators from the analyzed historical power data to obtain the refined historical power data indicators. S6. Based on the refined historical power data indicators, construct a power data evaluation model and power standard system through a comprehensive evaluation method.

2. The intelligent evaluation method for power system standard system based on data analysis according to claim 1, characterized in that, The process of setting up a power grid assessment area and constructing an intelligent assessment network based on that area includes the following steps: S11. Divide the power grid assessment area into nodes and sections, and deploy power data monitoring equipment at each node. Each segment is numbered, and the center point of each segment is set as a node. Power data monitoring equipment is deployed at each monitoring node. S12. Collect and save the coordinate location information of the corresponding power data monitoring equipment to build an intelligent evaluation network.

3. The intelligent evaluation method for power system standard system based on data analysis according to claim 1, characterized in that, The process of processing the collected historical power data to obtain processed historical power data includes the following steps: The collected historical electricity data is standardized through data standardization methods; The standardized calculation formula is as follows: ； in, This represents the minimum value collected from historical electricity data. This represents the maximum value collected from historical electricity data. This represents the historical electricity data collected in the s-th group. This represents the s-th historical power data after standardization; Standardized historical power data is set as processed historical power data.

4. The intelligent evaluation method for power system standard system based on data analysis according to claim 1, characterized in that, The process of analyzing power grid operation safety data from processed historical power data using static risk analysis to obtain analyzed power grid operation safety data includes the following steps: S411. Calculate the node voltage over-limit risk index in the power grid operation safety data; S412. Calculate the branch power flow over-limit risk index in the power grid operation safety data.

5. The intelligent evaluation method for power system standard system based on data analysis according to claim 4, characterized in that, The calculation of the node voltage over-limit risk index in the power grid operation safety data includes the following steps: Select known power grid operation safety data for M types of faults, and input the selected power grid operation safety data into a convolutional neural network for feature extraction; Based on the input power grid operation safety data, fault features of the power grid operation safety data are extracted and saved through the convolution operation of a convolutional neural network. The formula for calculating convolution is as follows: ； in, This indicates the input power grid operation safety data. This represents the weights of the corresponding convolution kernel. Indicates the bias value. This indicates the fault characteristics from which power grid operation safety data is extracted; The power grid operation safety data in the processed historical power data is input into a convolutional neural network for feature extraction to obtain the fault features of the processed power grid operation safety data. Fault characteristics in the processed power grid operation safety data are determined by similarity comparison. The similarity comparison formula is as follows: ； in, Fault characteristics representing processed power grid operation safety data and fault characteristics for storing power grid operation safety data covariance, This represents the standard deviation of the fault characteristics in the processed power grid operation safety data. This represents the standard deviation of fault characteristics in the stored power grid operation safety data. Fault characteristics representing processed power grid operation safety data and fault characteristics for storing power grid operation safety data The similarity between them; A similarity threshold is set. When the similarity value between the fault characteristics of the processed power grid operation safety data and the fault characteristics of the stored power grid operation safety data is greater than the set similarity threshold, it indicates that the corresponding processed power grid operation safety data is fault data; otherwise, it is normal data. Record the frequency of faults in the corresponding power grid operation safety data in the processed historical power data, and determine the failure rate of the corresponding node based on the recorded fault frequency; Calculate the probability of power grid outage based on the failure rate of the corresponding node; The probability calculation formula is as follows: ； in, This represents the failure rate of the j-th failure type. This represents the probability of a power grid outage corresponding to the k-th node; Based on the probability of grid outages, calculate the node voltage over-limit risk index when a grid outage occurs. The formula for calculating the voltage over-limit risk index is as follows: ； in, Represents a node The voltage under the j-th fault type, Represents a node The probability density function of voltage under the j-th fault type, Representing nodes respectively The upper and lower limits of voltage, Represents a node Voltage over-limit risk index under the j-th fault type.

6. The intelligent evaluation method for power system standard system based on data analysis according to claim 4, characterized in that, The calculation of the branch power flow over-limit risk index in the power grid operation safety data includes the following steps: The formula for calculating the risk index of branch power flow exceeding the limit during a power grid outage is as follows: ； in, Represents a node The maximum transmission power of the corresponding line. Represents a node The probability density function of the power transmitted by the corresponding line under the j-th fault type. Represents a node Branch power flow over-limit risk index under the j-th fault type.

7. The intelligent evaluation method for power system standard system based on data analysis according to claim 1, characterized in that, The process of analyzing power grid transmission data from processed historical power data using user demand analysis to obtain analyzed power grid transmission data includes the following steps: Summarize the historical power consumption data collected from users on each power line to construct a sample set B={ }; in, This represents the historical power consumption collected on the first power line in the sample set; The power grid transmission data collected by each node in the intelligent assessment network is set as follows: ; ； in, To intelligently assess the power stored in each node within the network, n represents the number of power lines; Set power balance constraints within the intelligent evaluation network; ； in, This indicates that the network nodes are intelligently evaluated at time t. The active power of China's new energy power generation This indicates that the network nodes are intelligently evaluated at time t. Power exchange between domestic and international power grids This indicates that the network nodes are intelligently evaluated at time t. The discharge power in This indicates that the network nodes are intelligently evaluated at time t. Load loss power in the middle, This indicates that the network nodes are intelligently evaluated at time t. The charging power in the middle, This indicates that the network nodes are intelligently evaluated at time t. Active power in the middle; Collect historical voltage and current consumption data of users on each power line, and calculate the user load threshold for each power line based on the collected historical voltage and current consumption data; ； in, Indicates the user's electricity load threshold. This indicates the user's historical electricity voltage. This indicates the user's historical electricity consumption current; The power grid transmission data after analysis is determined based on the calculated user electricity load threshold and the power balance constraints within the intelligent assessment network. The calculation formula for the analyzed power grid transmission data is shown below: ； in, This represents the analyzed power grid transmission data.

8. The intelligent evaluation method for power system standard system based on data analysis according to claim 1, characterized in that, The process of extracting comprehensive indicators from the analyzed historical power data to obtain the refined historical power data indicators includes the following steps: S51. Standardize the various data in the analyzed historical power data using standardized calculation formulas; S52. Extract comprehensive indicators from the standardized data; The maximum value among all data points is defined as the positive ideal solution, and the minimum value among all data points is defined as the negative ideal solution. Calculate the distance between the standardized data and the positive and negative ideal solutions; positive distance = The difference between the ideal solution and the standardized data; negative distance = The difference between the standardized data and the negative ideal solution; The formula for extracting comprehensive indicators is shown below: ； in, This represents the relative score of each data point in the historical electricity data after the i-th group of analysis. This represents the negative distance between each data point in the historical power data after the i-th group of analysis. This represents the positive distance between each data point in the historical power data after the i-th group of analysis; The relative scores of each data point in the analyzed historical power data are set as the refined historical power data indicators.

9. The intelligent evaluation method for power system standard system based on data analysis according to claim 1, characterized in that, The process of constructing a power data evaluation model and power standard system based on refined historical power data indicators through a comprehensive evaluation method includes the following steps: Based on the analyzed historical power data, experts were asked to score various data points in the analyzed historical power data. Each expert score is used as a set of power standard system data, and all power standard system data and refined historical power data indicators are aggregated. A power data evaluation model and power standard system are constructed through a comprehensive evaluation method. The formula for the power data evaluation model is shown below: ； in, This represents a power data assessment model. This represents the data of the i-th group of power standard system. This represents the weighting parameter for the overall score; The power data evaluation model is used to calculate and score various data points in the analyzed historical power data, and the scoring results are summarized. A standard threshold is set based on the scoring results, and a power standard system is constructed by filtering the corresponding data range based on the set standard threshold.

10. A system for implementing the intelligent evaluation method for power system standard system based on data analysis as described in claims 1-9, characterized in that, The system includes: a data acquisition module, a data processing module, a data analysis module, a data refinement module, and a standards development module; The data acquisition module is used to collect power data within the power grid assessment area; The data processing module is used to process the power data collected within the power grid assessment area to obtain processed power data. The data analysis module is used to analyze the processed power data to obtain the analyzed power data; The data extraction module is used to extract the analyzed power data to obtain refined power data indicators. The standard setting module is used to construct a power standard system based on the refined historical power data indicators through a comprehensive evaluation method.

Images