System for analyzing influence of meteorological event on power grid based on multivariate data fusion

Abstract

The invention discloses a system for analyzing influence of meteorological events on a power grid based on multivariate data fusion, which comprises a data acquisition module, a data processing and fusion module, a visual display module and a decision support module, and is characterized in that the data acquisition module is used for acquiring meteorological data, power grid region video data and regional power grid geographic information data in a GIS (Geographic Information System); the data processing and fusion module establishes a mapping model between meteorological data and power grid equipment, the visual display module displays the influence range and the influence degree of a meteorological event on a map according to the analysis result of the mapping model, and the decision support module generates an emergency response plan according to the analysis result of the mapping model. According to the system, the mapping model between the meteorological event and the power grid fault is constructed by fusing the three data elements, and compared with a traditional single data element analysis method, the precision and reliability of power grid fault analysis are improved, and reliable and stable operation of a power grid is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of the impact of meteorological events on power grids, and in particular to an analysis system for the impact of meteorological events on power grids based on multivariate data fusion. Background Art

[0002] The Haibei area of ​​Qinghai is located in the Qinghai-Tibet Plateau, and the power line construction is widely distributed. Due to its large area, there are great differences in weather conditions in different regions during the same period. With the intensification of global climate change, extreme weather events occur frequently. Meteorological events such as typhoons, rainstorms, lightning and other natural disasters pose a serious threat to the safe and stable operation of the power grid. Traditional monitoring and evaluation of power grid equipment and transmission lines are often limited to a single data source, which makes it difficult to fully and accurately reflect the actual impact of meteorological events on the power grid. Grid staff are unable to initiate corresponding emergency plans in time according to meteorological events, resulting in disaster losses to the power grid. At the same time, in the face of extremely severe meteorological conditions, the emergency repair management of the power grid cannot be completed in time, resulting in power outages and secondary disasters.

[0003] Multi-source data fusion technology refers to the technology of using relevant means to integrate all the information obtained from investigation and analysis, and to make a unified evaluation of the information to finally obtain unified information. Its purpose is to integrate various different data information, absorb the characteristics of different data sources, and extract unified, better and richer information from them than single data, so as to improve the accuracy of related event analysis. Summary of the invention

[0004] In order to overcome the above problems, the purpose of the present invention is to provide an analysis system for the impact of meteorological events on power grids based on multivariate data fusion. The analysis system collects multivariate data of meteorological data, power grid area video data, and regional power grid geographic information data in GIS through a data acquisition module, and uses a data processing and fusion module to establish a mapping model between meteorological data and the operating status of power grid equipment, so as to achieve accurate positioning of the power grid area affected by meteorological events and quantitative evaluation of the degree of impact, and display it on a map through a visualization module, which is intuitive and effective. An emergency response plan is generated through a decision support module, which provides a scientific basis for the safe operation and maintenance of the power grid and emergency response, and ensures the safe and stable operation of the power grid.

[0005] The technical solution adopted by the present invention is: an analysis system of the impact of meteorological events on the power grid based on multivariate data fusion, including a data acquisition module, a data processing and fusion module, a visualization display module, and a decision support module.

[0006] The data acquisition module is used to collect meteorological data, power grid area video data, and regional power grid geographic information data in GIS.

[0007] The data processing and fusion module pre-processes the data collected by the data acquisition module and uses a data fusion algorithm to establish a mapping model between meteorological data and power grid equipment.

[0008] The visualization display module displays the impact range and impact degree of the meteorological event on the map according to the analysis results of the mapping model.

[0009] The decision support module generates an emergency response plan according to the analysis results of the mapping model.

[0010] As a further description of the present invention, the meteorological data of the data acquisition module is obtained from the meteorological department, including basic meteorological parameters such as temperature, humidity, air pressure, wind speed, wind direction, and precipitation;

[0011] The power grid area video data is data collected by monitoring equipment deployed on key power grid equipment, which is a real-time video image that identifies the physical state of power grid equipment and changes in the surrounding environment;

[0012] The regional power grid geographic information data in the GIS includes power grid thematic data, power grid spatial data, and transmission line channel data.

[0013] The power grid special data include wind, icing, pollution, earthquake, tremor, lightning damage, bird damage area division data,

[0014] The grid spatial data includes power plant location, line layout, substation location, converter station location, and switch station location data.

[0015] The transmission line channel data includes important planning areas, environmentally sensitive points, mineral plant areas, crossings and channel clearing data within the line channel range.

[0016] As a further description of the present invention, the power grid area video data extracts equipment type and equipment operating years through a graphics processing algorithm, and extracts equipment operating history data and associated equipment information through a power grid database.

[0017] As a further description of the present invention, the data preprocessing process of the data processing and fusion module is data cleaning, data smoothing, data dimension reduction, and standardization processing.

[0018] The data cleaning includes the processing of missing values, abnormal values, duplicate values, and data conversion. The missing values ​​are directly deleted. The abnormal values ​​are extreme values ​​in the data set that are significantly different from the rest of the data. The abnormal values ​​are replaced with statistical values. The duplicate values ​​are directly deleted.

[0019] The data smoothing adopts Savitzky-Golay filtering to remove noise data;

[0020] The data dimension reduction is to map high-dimensional data to low-dimensional space through a neural network by using an autoencoder feature extraction method, thereby reducing the complexity of the data;

[0021] The standardization process adopts a normalization process.

[0022] As a further description of the present invention, the data fusion algorithm used in the data processing and fusion module is a data fusion algorithm based on convolutional neural network.

[0023] As a further description of the present invention, the steps for establishing the mapping model of the data processing and fusion module are as follows:

[0024] Step 1: Overlay the preprocessed meteorological data with the regional power grid geographic information data in GIS;

[0025] Step 2: Combine the video data of the power grid video area to identify the impact area and impact degree of the meteorological event on the power grid equipment;

[0026] Step 3: Establish a connection between the impact of meteorological events on power grid equipment and implement zoning warning for power grid equipment.

[0027] As a further description of the present invention, the specific types of step 2 are divided into precipitation, snowfall, and wind force level.

[0028] The precipitation identification process is as follows:

[0029] When the 24-hour precipitation is less than 25 mm, the meteorological event has no impact on the normal operation of the power grid equipment. The impact range is the area 10 km outward from the center of the meteorological event, and the impact level is 0.

[0030] When the 24-hour precipitation is between 25mm and 100mm, the meteorological event will cause the vibration of the conductors on the transmission equipment line, affecting the stability of power supply. The affected area is 10km outside the center of the meteorological event, and the impact level is level 1.

[0031] When the 24-hour precipitation is greater than 100 mm, the meteorological event causes the insulator flashover of the transmission line and the conductor stress to increase, causing a power outage. The affected area is 10 km outside the center of the meteorological event, and the impact level is level 2.

[0032] The snowfall recognition process is as follows:

[0033] When the 24-hour snowfall is less than 5 mm, the meteorological event has no impact on the normal operation of power grid equipment. The impact range is 10 km outside the center of the meteorological event, and the impact level is 0.

[0034] When the 24-hour snowfall is between 5mm and 100mm, the meteorological event will increase the load on the transmission equipment line and affect the power supply stability. The affected area is 10km away from the center of the meteorological event. The impact level is level 1.

[0035] When the 24-hour snowfall is greater than 10 mm, the meteorological event causes the line ice load to be greater than the tower bearing capacity, causing line breakage and swaying, and tower tilting. The affected area is 10 km outward from the center of the meteorological event, and the impact level is level 2;

[0036] The wind force level identification process is as follows:

[0037] When the wind force level is less than level 3, the meteorological event has no impact on the normal operation of power grid equipment. The impact range is 10 km outside the center of the meteorological event, and the impact level is level 0.

[0038] When the wind force level is between 3 and 7, the transmission lines and equipment are subjected to greater mechanical stress. The impact range is 10 km away from the center of the meteorological event, causing line loosening and tower deformation. The impact level is level 1.

[0039] When the wind force level is greater than Level 7, it will cause transmission lines to break or towers to collapse, causing power outages. The affected area is 10km outward from the center of the meteorological event, and the impact level is Level 2.

[0040] As a further description of the present invention, the emergency response plan includes early warning information release, power grid dispatch adjustment, and emergency repair resource deployment.

[0041] The warning information issued corresponds to the meteorological event with impact level 0.

[0042] The grid dispatch adjustment corresponds to a meteorological event with an impact level of 1.

[0043] The emergency repair resources are deployed to respond to meteorological events with impact level 2.

[0044] Beneficial effects of the present invention:

[0045] The present invention is an analysis system for the impact of meteorological events on power grids based on multivariate data fusion, comprising a data acquisition module, a data processing and fusion module, a visualization display module, and a decision support module. The data acquisition module acquires meteorological data, power grid area video data, and regional power grid geographic information data in GIS, and constructs a mapping model between meteorological events and power grid faults by fusing the above three data elements. Compared with the traditional single data element analysis method, the accuracy and reliability of power grid fault analysis are improved, thereby ensuring the reliable and stable operation of the power grid.

[0046] The present invention is an analysis system for the impact of meteorological events on power grids based on multivariate data fusion. The visualization display module displays the impact range and impact degree of meteorological events on a map according to the analysis results of the mapping model. The decision support module generates an emergency response plan according to the analysis results of the mapping model. Through GIS visualization display and risk assessment, the analysis method can generate corresponding emergency plans in real time, provide timely and accurate decision support for disaster prevention and mitigation and emergency repair of power grids, help power grid managers fully understand the impact of meteorological events on power grids, optimize resource allocation and emergency response strategies, and effectively reduce disaster losses caused by meteorological events to power grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a system structure diagram of the analysis system of the impact of meteorological events on power grids based on multivariate data fusion proposed by the present invention;

[0048] Figure 2 The present invention provides a flow chart of the data processing and fusion module of the analysis system for the impact of meteorological events on power grids based on multivariate data fusion. DETAILED DESCRIPTION

[0049] The specific implementation of the present invention is described below in conjunction with the accompanying drawings and embodiments:

[0050] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions under which the present invention can be implemented. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0051] At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" cited in this specification are only for the convenience of description and are not used to limit the scope of implementation of the present invention. Changes or adjustments to their relative relationships should be regarded as the scope of implementation of the present invention without substantially changing the technical content.

[0052] like Figures 1-2 As shown, it shows a specific embodiment of the present invention:

[0053] Embodiment 1

[0054] The analysis system of the impact of meteorological events on power grids based on multivariate data fusion disclosed in the present invention includes a data acquisition module, a data processing and fusion module, a visualization display module, and a decision support module.

[0055] The data acquisition module is used to collect meteorological data, power grid area video data, and regional power grid geographic information data in GIS.

[0056] The data processing and fusion module pre-processes the data collected by the data acquisition module and uses a data fusion algorithm to establish a mapping model between meteorological data and power grid equipment.

[0057] The visualization display module displays the impact range and impact degree of the meteorological event on the map according to the analysis results of the mapping model.

[0058] The decision support module generates an emergency response plan according to the analysis results of the mapping model.

[0059] In this embodiment, Figure 1 As shown in the figure, the analysis system collects three kinds of multivariate data, namely meteorological data, power grid area video data, and regional power grid geographic information data in GIS, through the data acquisition module, and uses the data processing and fusion module to establish a mapping model between meteorological data and the operating status of power grid equipment, so as to achieve accurate positioning of the impact of meteorological events on the power grid area and quantitative evaluation of the impact degree, and display it on the map through the visualization module, which is intuitive and effective. The emergency response plan is generated through the decision support module, which provides a scientific basis for the safe operation and maintenance of the power grid and emergency response, and ensures the safe and stable operation of the power grid.

[0060] Specifically, the meteorological data of the data acquisition module is obtained from the meteorological department, including basic meteorological parameters such as temperature, humidity, air pressure, wind speed, wind direction, and precipitation;

[0061] The power grid area video data is data collected by monitoring equipment deployed on key power grid equipment, which is a real-time video image that identifies the physical state of power grid equipment and changes in the surrounding environment;

[0062] Specifically, the power grid area video data uses a graphics processing algorithm to extract equipment type and equipment operating years, and extracts equipment operating history data and related equipment information through a power grid database.

[0063] The regional power grid geographic information data in the GIS includes power grid thematic data, power grid spatial data, and transmission line channel data.

[0064] The power grid special data include wind, icing, pollution, earthquake, tremor, lightning damage, bird damage area division data,

[0065] The grid spatial data includes power plant location, line layout, substation location, converter station location, and switch station location data.

[0066] The transmission line channel data includes important planning areas, environmentally sensitive points, mineral plant areas, crossings and channel clearing data within the line channel range.

[0067] In this embodiment, the data acquisition module obtains specific meteorological parameters from the meteorological department, and can choose to obtain meteorological data in real time or periodically. Through data analysis and acquisition, the power grid area video data can obtain the real-time status of power grid equipment, and the regional power grid geographic information data in the GIS can clarify the specific power grid location information. By integrating the above three data elements, compared with the traditional single data element analysis method, the accuracy and reliability of power grid fault analysis are improved, thereby ensuring the reliable and stable operation of the power grid.

[0068] Specifically, Figure 2 As shown, the data preprocessing process of the data processing and fusion module is data cleaning, data smoothing, data dimension reduction, and standardization.

[0069] The data cleaning includes the processing of missing values, abnormal values, duplicate values, and data conversion. The missing values ​​are directly deleted. The abnormal values ​​are extreme values ​​in the data set that are significantly different from the rest of the data. The abnormal values ​​are replaced with statistical values. The duplicate values ​​are directly deleted.

[0070] In this embodiment, data cleaning processes missing values, abnormal values, and duplicate values ​​in the collected data to ensure the integrity and accuracy of the data.

[0071] The data smoothing adopts Savitzky-Golay filtering to remove noise data;

[0072] In this embodiment, the data is smoothed using the Savitzky-Golay filtering method. The Savitzky-Golay filtering method is a filtering method based on the least squares method. It can effectively remove noise while retaining the high-frequency information of the data. It has a good smoothing effect on the noise in the data without excessively affecting the overall shape of the data. At the same time, for non-stationary data, it can better retain the periodic characteristics of the data.

[0073] The data dimension reduction is to map high-dimensional data to low-dimensional space through a neural network by using an autoencoder feature extraction method, thereby reducing the complexity of the data;

[0074] In this embodiment, data dimensionality reduction can improve data processing efficiency, reduce computational complexity, and help discover patterns and trends in data by removing redundant and irrelevant features and retaining information that is useful for analysis.

[0075] The standardization process adopts a normalization process.

[0076] In this embodiment, a normalization method is used to implement standardized processing of data to improve the accuracy and efficiency of computer calculations. It can narrow the differences between features and reduce the impact of features with larger values ​​on features with smaller values, thereby improving the accuracy and efficiency of calculations, making the model more stable, reducing the risk of overfitting, and thus improving the generalization ability of the model.

[0077] The purpose of normalization is to compress the data within the range of [0,1], including the boundary numbers 0 and 1. The calculation formula is:

[0078]

[0079] Among them, X represents the original data, X min Represents the minimum value in the data, X max Indicates the maximum value in the data, X 归一化 Represents the processed data.

[0080] Specifically, Figure 2 As shown, the data fusion algorithm used in the data processing and fusion module is a data fusion algorithm based on convolutional neural network.

[0081] In this embodiment, the data fusion algorithm based on the convolutional neural network has strong feature extraction capability. The convolutional neural network can automatically learn the local structure and global features of the image through multi-layer convolution operations, and capture high-level semantic information. The convolutional neural network adopts parameter sharing and sparse connection methods, which greatly reduces the number of network parameters and the amount of calculation. The weight of the convolution kernel is shared across the entire image, so that the network can better process large-scale image data and reduce the risk of overfitting. The convolution operation has the characteristics of translation invariance, that is, it is insensitive to image translation, which makes the convolutional neural network have a certain robustness to the position and size changes of objects in the image. In practical applications, even if the position of the target object changes slightly, the network can still accurately identify and locate it. The good model generalization ability of the convolutional neural network can adapt to a variety of input data. In the network, the convolution layer greatly reduces the amount of calculation of the model by sharing weight parameters. At the same time, downsampling through the pooling layer further reduces the dimension of the data and improves the calculation efficiency.

[0082] Embodiment 2

[0083] Specifically, the steps for establishing the mapping model of the data processing and fusion module are as follows:

[0084] Step 1: Overlay the preprocessed meteorological data with the regional power grid geographic information data in GIS;

[0085] Step 2: Combine the video data of the power grid video area to identify the impact area and impact degree of the meteorological event on the power grid equipment;

[0086] Step 3: Establish a connection between the impact of meteorological events on power grid equipment and implement zoning warning for power grid equipment.

[0087] In this embodiment, a mapping model is established to determine the correlation between the meteorological events represented by the meteorological data and the power grid equipment, so as to achieve real-time warning and take corresponding measures in time to ensure the stable operation of the power grid equipment.

[0088] Specifically, the specific types of step 2 are precipitation, snowfall, and wind force level.

[0089] The precipitation identification process is as follows:

[0090] When the 24-hour precipitation is less than 25 mm, the meteorological event has no impact on the normal operation of the power grid equipment. The impact range is the area 10 km outward from the center of the meteorological event, and the impact level is 0.

[0091] When the 24-hour precipitation is between 25mm and 100mm, the meteorological event will cause the vibration of the conductors on the transmission equipment line, affecting the stability of power supply. The affected area is 10km outside the center of the meteorological event, and the impact level is level 1.

[0092] When the 24-hour precipitation is greater than 100 mm, the meteorological event causes the insulator flashover of the transmission line and the conductor stress to increase, causing a power outage. The affected area is 10 km outside the center of the meteorological event, and the impact level is level 2.

[0093] The snowfall recognition process is as follows:

[0094] When the 24-hour snowfall is less than 5 mm, the meteorological event has no impact on the normal operation of power grid equipment. The impact range is 10 km outside the center of the meteorological event, and the impact level is 0.

[0095] When the 24-hour snowfall is between 5mm and 100mm, the meteorological event will increase the load on the transmission equipment line and affect the power supply stability. The affected area is 10km away from the center of the meteorological event. The impact level is level 1.

[0096] When the 24-hour snowfall is greater than 10 mm, the meteorological event causes the line ice load to be greater than the tower bearing capacity, causing line breakage and swaying, and tower tilting. The affected area is 10 km outward from the center of the meteorological event, and the impact level is level 2;

[0097] The wind force level identification process is as follows:

[0098] When the wind force level is less than level 3, the meteorological event has no impact on the normal operation of power grid equipment. The impact range is 10 km outside the center of the meteorological event, and the impact level is level 0.

[0099] When the wind force level is between 3 and 7, the transmission lines and equipment are subjected to greater mechanical stress. The impact range is 10 km away from the center of the meteorological event, causing line loosening and tower deformation. The impact level is level 1.

[0100] When the wind force level is greater than Level 7, it will cause transmission lines to break or towers to collapse, causing power outages. The affected area is 10km outward from the center of the meteorological event, and the impact level is Level 2.

[0101] In this embodiment, the precipitation, snowfall, and wind force level are the most common events in meteorological events, and their probability of occurrence is relatively high. They are used as a link to establish the connection between meteorological events and the scope and degree of influence of power grid equipment. At the same time, when a meteorological event occurs, not only one of them occurs, but it is often accompanied by a variety of different meteorological events. The degree and scope of influence of different meteorological events can be weighted and superimposed to determine the final scope and degree of influence.

[0102] Embodiment 3

[0103] Specifically, the emergency response plan includes the release of early warning information, grid dispatch adjustment, and emergency repair resource deployment.

[0104] The warning information issued corresponds to the meteorological event with impact level 0.

[0105] The grid dispatch adjustment corresponds to a meteorological event with an impact level of 1.

[0106] The emergency repair resources are deployed to respond to meteorological events with impact level 2.

[0107] In this embodiment, the visualization display module displays the impact scope and impact degree of the meteorological event on the map according to the analysis results of the mapping model. The decision support module generates an emergency response plan according to the analysis results of the mapping model. Through GIS visualization display and risk assessment, the analysis method can generate corresponding emergency plans in real time, and correspond to different emergency response plans according to different levels of impact, so as to provide timely and accurate decision support for the disaster prevention and mitigation and emergency repair of the power grid, help power grid managers to fully understand the impact of meteorological events on the power grid, optimize resource allocation and emergency response strategies, and effectively reduce the disaster losses of meteorological events to the power grid.

[0108] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

[0109] Many other changes and modifications may be made without departing from the concept and scope of the present invention.It should be understood that the present invention is not limited to the specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. An analysis system for the impact of meteorological events on power grids based on multivariate data fusion, characterized in that: It includes data acquisition module, data processing and fusion module, visualization module and decision support module. The data acquisition module is used to collect meteorological data, power grid area video data, and regional power grid geographic information data in GIS. The data processing and fusion module pre-processes the data collected by the data acquisition module and uses a data fusion algorithm to establish a mapping model between meteorological data and power grid equipment. The visualization display module displays the impact range and impact degree of the meteorological event on the map according to the analysis results of the mapping model. The decision support module generates an emergency response plan according to the analysis results of the mapping model.

2. The analysis system of the impact of meteorological events on power grids based on multivariate data fusion according to claim 1 is characterized in that: The meteorological data of the data acquisition module is obtained from the meteorological department, including basic meteorological parameters such as temperature, humidity, air pressure, wind speed, wind direction, and precipitation; The power grid area video data is data collected by monitoring equipment deployed on key power grid equipment, which is a real-time video image that identifies the physical state of power grid equipment and changes in the surrounding environment; The regional power grid geographic information data in the GIS includes power grid thematic data, power grid spatial data, and transmission line channel data. The power grid special data include wind, icing, pollution, earthquake, tremor, lightning damage, bird damage area division data, The grid spatial data includes power plant location, line layout, substation location, converter station location, and switch station location data. The transmission line channel data includes important planning areas, environmentally sensitive points, mineral plant areas, crossings and channel clearing data within the line channel range.

3. The analysis system of the impact of meteorological events on power grids based on multivariate data fusion according to claim 2 is characterized in that: The power grid area video data is extracted using a graphics processing algorithm to extract equipment type and equipment operating years, and the equipment operating history data and associated equipment information are extracted through a power grid database.

4. The analysis system of the impact of meteorological events on power grids based on multivariate data fusion according to claim 1 is characterized in that: The data preprocessing process of the data processing and fusion module includes data cleaning, data smoothing, data dimension reduction, and standardization. The data cleaning includes the processing of missing values, abnormal values, duplicate values, and data conversion. The missing values ​​are directly deleted. The abnormal values ​​are extreme values ​​in the data set that are significantly different from the rest of the data. The abnormal values ​​are replaced with statistical values. The duplicate values ​​are directly deleted. The data smoothing adopts Savitzky-Golay filtering to remove noise data; The data dimension reduction is to map high-dimensional data to low-dimensional space through a neural network by using an autoencoder feature extraction method, thereby reducing the complexity of the data; The standardization process adopts a normalization process.

5. The analysis system of the impact of meteorological events on power grids based on multivariate data fusion according to claim 1 is characterized in that: The data fusion algorithm used in the data processing and fusion module is a data fusion algorithm based on convolutional neural network.

6. The analysis system of the impact of meteorological events on power grids based on multivariate data fusion according to claim 1 is characterized in that: The steps for establishing the mapping model of the data processing and fusion module are as follows: Step 1: Overlay the preprocessed meteorological data with the regional power grid geographic information data in GIS; Step 2: Combine the video data of the power grid video area to identify the impact area and impact degree of the meteorological event on the power grid equipment; Step 3: Establish a connection between the impact of meteorological events on power grid equipment and implement zoning warning for power grid equipment.

7. The analysis system of the impact of meteorological events on power grids based on multivariate data fusion according to claim 6 is characterized in that: The specific types of step 2 are precipitation, snowfall, and wind force level. The precipitation identification process is as follows: When the 24-hour precipitation is less than 25 mm, the meteorological event has no impact on the normal operation of the power grid equipment. The impact range is the area 10 km outward from the center of the meteorological event, and the impact level is 0. When the 24-hour precipitation is between 25mm and 100mm, the meteorological event will cause the vibration of the conductors on the transmission equipment line, affecting the stability of power supply. The affected area is 10km outside the center of the meteorological event, and the impact level is level 1. When the 24-hour precipitation is greater than 100 mm, the meteorological event causes the insulator flashover of the transmission line and the conductor stress to increase, causing a power outage. The affected area is 10 km outside the center of the meteorological event, and the impact level is level 2. The snowfall recognition process is as follows: When the 24-hour snowfall is less than 5 mm, the meteorological event has no impact on the normal operation of power grid equipment. The impact range is 10 km outside the center of the meteorological event, and the impact level is 0. When the 24-hour snowfall is between 5mm and 100mm, the meteorological event will increase the load on the transmission equipment line and affect the power supply stability. The affected area is 10km away from the center of the meteorological event. The impact level is level 1. When the 24-hour snowfall is greater than 10 mm, the meteorological event causes the line ice load to be greater than the tower bearing capacity, causing line breakage and swaying, and tower tilting. The affected area is 10 km outward from the center of the meteorological event, and the impact level is level 2; The wind force level identification process is as follows: When the wind force level is less than level 3, the meteorological event has no impact on the normal operation of power grid equipment. The impact range is 10 km outside the center of the meteorological event, and the impact level is level 0. When the wind force level is between 3 and 7, the transmission lines and equipment are subjected to greater mechanical stress. The impact range is 10 km away from the center of the meteorological event, causing line loosening and tower deformation. The impact level is level 1. When the wind force level is greater than Level 7, it will cause transmission lines to break or towers to collapse, causing power outages. The affected area is 10km outward from the center of the meteorological event, and the impact level is Level 2.

8. The analysis system of the impact of meteorological events on power grids based on multivariate data fusion according to claim 1 is characterized in that: The emergency response plan includes early warning information release, power grid dispatch adjustment, and emergency repair resource deployment. The warning information issued corresponds to the meteorological event with impact level 0. The grid dispatch adjustment corresponds to a meteorological event with an impact level of 1. The emergency repair resources are deployed to respond to meteorological events with impact level 2.

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