Power grid risk monitoring method and system based on meteorological early warning

Abstract

The invention relates to the technical field of power grid risk prediction, and discloses a power grid risk monitoring method and system based on meteorological early warning, and the method comprises the steps: dividing a power grid monitoring region into a plurality of monitoring regions; defining a power grid risk fault type, obtaining a risk type set of different monitoring region types, and calculating a risk assessment index and a region influence index; generating a risk meteorological feature set, and calculating a meteorological influence index of the risk meteorological feature set on the risk type; and constructing a risk prediction model, performing risk prediction, and issuing meteorological risk early warning information. Through the risk monitoring method of the system, from data collection, risk assessment and meteorological influence analysis to risk prediction and early warning, the level of power grid risk management is comprehensively improved, the risk is monitored and predicted in real time, early warning information is issued in time, the response speed and efficiency for responding to the risk are improved, the occurrence probability and the influence degree of risk events are reduced, and the risk management efficiency is improved. The economic loss is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid risk prediction, and in particular to a power grid risk monitoring method and system based on meteorological early warning. Background Art

[0002] With the development of social economy and the increase in electricity demand, the safe and stable operation of power grids has become particularly important. Grid risk management is a key link in ensuring the safe and reliable operation of power systems, involving the monitoring, assessment and early warning of various risks. Traditional grid risk management mainly relies on manual experience and historical data, lacking systematic scientific methods and technical means.

[0003] Although the existing power grid risk monitoring methods and technical means have achieved certain results, the following problems still exist: power grid data and meteorological data are scattered in different systems, lacking a unified data management and integration platform, resulting in low data utilization; existing risk assessment methods mainly rely on statistical analysis of historical data, lack of comprehensive analysis of multi-dimensional data, and difficult to fully reflect the risk status; traditional prediction models often only consider a single factor and ignore the combined effect of multiple factors, resulting in low prediction accuracy, lack of real-time monitoring and early warning functions, and inability to timely detect and respond to potential risks; and existing methods fail to fully consider the characteristics of different regions and lack differentiated management strategies for different regions. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a power grid risk monitoring method based on meteorological early warning to solve the above problems.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for monitoring power grid risk based on meteorological early warning, comprising: dividing a power grid monitoring area into a plurality of monitoring areas, acquiring terrain data and power grid data of the monitoring areas, analyzing the correlation of the monitoring areas, and performing regional classification; Define the risk fault types of power grid, obtain and analyze the historical risk data of the monitoring area, obtain the risk type set of different monitoring area types, and calculate the risk assessment index and regional impact index; Defining meteorological key features according to meteorological data, extracting meteorological key features corresponding to each risk type, generating a risk meteorological feature set, and calculating the meteorological impact index of the risk meteorological feature set on the risk type; Based on the risk assessment indicators, regional impact indicators and meteorological impact indicators, a risk prediction model is constructed, real-time meteorological data of the monitoring area is obtained, risk prediction is performed through the risk prediction model, and meteorological risk warning information is issued based on the comparison of the prediction results with the preset risk warning threshold.

[0007] As a preferred solution of the method for monitoring power grid risk based on meteorological early warning according to the present invention, the analysis of the correlation of each monitoring area and the regional classification include: Divide the monitoring area into several grid cells , each grid cell represents a monitoring area; Acquire terrain data and power grid data of the monitoring area and perform data normalization processing, wherein the terrain data includes altitude, regional slope, vegetation coverage and soil type, and the power grid data includes substation capacity, transmission line length and equipment type; Based on the terrain data and the power grid data, a terrain feature vector and a power grid feature vector are respectively constructed, which are respectively expressed as: ; ; in, represents the terrain feature vector, represents the power grid characteristic vector, represents the average altitude, represents the regional slope, represents the vegetation coverage ratio, Indicates the soil type code, Indicates the total capacity of substations in the region, represents the total length of the transmission line, represents the sum of risk weights calculated based on equipment type; Calculate the comprehensive correlation index of the terrain feature vector and the power grid feature vector in each monitoring area, classify the monitoring area by clustering algorithm, and It is expressed as: ; in, Indicates the monitoring area. , Represents each element of the terrain feature vector, , Represents each element of the power grid characteristic vector, , Represent the weight coefficients of terrain characteristic vector and power grid characteristic vector respectively.

[0008] As a preferred solution of the method for monitoring power grid risk based on meteorological early warning according to the present invention, the calculation of the regional impact index includes: A plurality of risk fault types are defined, wherein the historical risk data of the monitoring area includes fault duration, fault location, risk fault type and fault range; Count all risk failure types under each area type, and calculate the comprehensive failure rate of the risk failure type, compare the comprehensive failure rate with a preset failure rate threshold, and if it is greater than the failure rate threshold, generate a risk type set for the area type according to the corresponding risk failure type; The risk assessment index is calculated based on historical risk data and is expressed as: ; in, Indicates the risk failure type, Indicates the risk type In the area The number of occurrences, Indicates in the area The first Second risk failure duration, Indicates in the area The first Second risk failure The number of users affected, Indicates in the area Internal risk failure type The number of occurrences, Indicates the area category, , , Represents the weight coefficient.

[0009] As a preferred solution of the method for monitoring power grid risk based on meteorological early warning described in the present invention, the calculation of the risk assessment index includes: Calculate regional impact indicators based on the terrain data, power grid data and risk assessment indicators , expressed as: ; ; in, represents the comprehensive impact factor, represents the transmission line length influencing factor, represents the substation capacity influencing factor, Indicates the influencing factor of equipment aging degree, represents the altitude impact factor, represents the slope influence factor, represents the vegetation cover influencing factor, Represents the soil type influencing factor.

[0010] As a preferred solution of the power grid risk monitoring method based on meteorological early warning described in the present invention, wherein: the generating risk meteorological feature set includes: Define multiple key meteorological features and obtain the historical occurrence sequence of the risk fault type corresponding to each monitoring area type , and historical data series of key meteorological characteristics , calculate the connection degree between each risk fault type and the key meteorological characteristics, expressed as: ; in, , Respectively , The average value of By comparing the connection value between each risk fault type and the key meteorological feature with the preset threshold, the key meteorological features greater than the preset threshold are extracted and arranged in descending order according to the connection degree to generate a risk meteorological feature set for the risk fault type.

[0011] As a preferred solution of the power grid risk monitoring method based on meteorological early warning described in the present invention, the calculation of the meteorological impact index of the risk meteorological feature set on the risk type includes: Based on the connection value between each risk fault type and the key meteorological characteristics, combined with the meteorological risk change trend, the meteorological impact index of the risk meteorological characteristic set on the risk type is calculated, which is expressed as: ; in, Indicates key meteorological characteristics In the The characteristic value of the period, Indicates the changing trend cycle of meteorological characteristics. Indicates key meteorological characteristics The weight coefficient of Indicates the number of meteorological key characteristics associated with the risk type.

[0012] As a preferred solution of the method for monitoring power grid risk based on meteorological warning according to the present invention, the step of constructing a risk prediction model and issuing meteorological risk warning information includes: Collecting real-time meteorological data of each type of monitoring area, calculating risk assessment indicators, regional impact indicators and meteorological impact indicators of the monitoring area, and combining all indicators into a risk feature vector; Build risk prediction models through machine learning algorithms, train models based on data using historical analysis, and validate models; The risk feature vector is input into the risk prediction model to perform risk probability prediction, output the risk prediction result, and compare it with the risk warning threshold to obtain the monitoring area corresponding to the risk warning threshold, and issue a risk warning report for the monitoring area.

[0013] In a second aspect, the present invention provides a power grid risk monitoring system based on meteorological early warning, comprising: A regional classification module is used to divide the power grid monitoring area into multiple monitoring areas, obtain the terrain data and power grid data of the monitoring areas, analyze the correlation of each monitoring area, and perform regional classification; A risk analysis module is used to define the risk fault types of the power grid, obtain and analyze the historical risk data of the monitoring area, obtain the risk type set of different monitoring area types, and calculate the risk assessment index and regional impact index; A meteorological analysis module is used to define meteorological key features according to meteorological data, extract meteorological key features corresponding to each risk type, generate a risk meteorological feature set, and calculate the meteorological impact index of the risk meteorological feature set on the risk type; The output module is used to build a risk prediction model based on the risk assessment indicators, regional impact indicators and meteorological impact indicators, obtain real-time meteorological data in the monitoring area, make risk predictions through the risk prediction model, and issue meteorological risk warning information based on the comparison of the prediction results with the preset risk warning threshold.

[0014] In a third aspect, the present invention provides an electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the power grid risk monitoring method based on meteorological warning are implemented.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the power grid risk monitoring method based on meteorological warning.

[0016] Compared with the prior art, the present invention has the following beneficial effects: the present invention comprehensively improves the level of power grid risk management through a systematic risk monitoring method, from data collection, risk assessment, meteorological impact analysis to risk prediction and early warning, monitors and predicts risks in real time, releases early warning information in a timely manner, improves the response speed and efficiency to risks, reduces the probability and impact of risk events, and reduces economic losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0018] Figure 1 The present invention is a schematic diagram of the overall process of a method for monitoring power grid risks based on meteorological warning according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0022] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0023] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0024] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0025] Reference Figure 1 , as an embodiment of the present invention, provides a power grid risk monitoring method based on meteorological early warning, comprising: S101, dividing the power grid monitoring area into multiple monitoring areas, acquiring terrain data and power grid data of the monitoring areas, analyzing the correlation of each monitoring area, and performing regional classification.

[0026] In a preferred embodiment, analyzing the correlation of each monitoring area and classifying the areas includes: Step 1: Divide the monitoring area into several grid units , each grid cell represents a monitoring area; Specifically, the entire monitoring area is divided into several regular grid units. , each grid cell represents a monitoring area.

[0027] Step 2: Obtain terrain data and power grid data of the monitoring area and perform data normalization. The terrain data includes altitude, regional slope, vegetation cover and soil type. The power grid data includes substation capacity, transmission line length and equipment type. Specifically, terrain data can be obtained from GIS (geographic information system), and power grid data can be obtained from the power database. The altitude data in the terrain data obtains the digital elevation model (DEM) data of the monitored area through the geographic information system (GIS), and directly reads the altitude value of each monitoring point. The regional slope data uses GIS software to perform slope analysis on the DEM data and calculate the average slope value of each monitoring area. The vegetation coverage data uses satellite remote sensing images to analyze vegetation indices (such as normalized vegetation index, NDVI), and count the vegetation coverage ratio of each monitoring area. By processing multi-spectral remote sensing images, the NDVI value is calculated, and then the vegetation coverage area is determined according to the distribution of the NDVI value, and then the vegetation coverage ratio is obtained. The soil type data refers to the soil geographical survey data to determine the main soil type of each monitoring area, and is classified and coded according to the soil's electrical conductivity, water holding capacity and other characteristics. The substation capacity data in the power grid data obtains the rated capacity data of each substation from the power grid management database, and the unit is megavolt-ampere (MVA). The transmission line length data uses the power grid geographic information system to measure the total length of the transmission line in each monitoring area, and the unit is kilometer (km). The equipment type data classifies and counts the equipment types of the transmission lines, such as according to the conductor material (copper, aluminum, etc.), pole tower structure (angle steel tower, steel pipe tower, etc.), etc., and assigns corresponding risk weights to different types of equipment.

[0028] Exemplarily, the formula for the average slope value of the monitoring area is: ; in, is the regional slope, and is the altitude of the adjacent monitoring point, is the horizontal distance between adjacent monitoring points, is the number of monitoring points.

[0029] Step 3: For each monitoring area, a terrain feature vector and a power grid feature vector are constructed based on the terrain data and the power grid data, respectively, and are expressed as: ; ; in, represents the terrain feature vector, represents the power grid characteristic vector, represents the average altitude, represents the regional slope, represents the vegetation coverage ratio, Indicates the soil type code, Indicates the total capacity of substations in the region, represents the total length of the transmission line, It represents the sum of risk weights calculated based on equipment type. The sum of risk weights of equipment type can be obtained by multiplying the number of different types of equipment by their corresponding risk weights. Step 4: Calculate the comprehensive correlation index of the terrain feature vector and the power grid feature vector in each monitoring area, classify the monitoring area by clustering algorithm, and set the monitoring area and Region The terrain feature vectors are and , the grid characteristic vectors are and , comprehensive correlation index It is expressed as: ; in, Indicates the monitoring area. , Represents each element of the terrain feature vector, , Represents each element of the power grid characteristic vector, , The weight coefficients representing the terrain characteristic vector and the power grid characteristic vector respectively can be determined based on expert experience or data analysis.

[0030] Specifically, the K-means algorithm is used to cluster the monitoring area. Assume that the area is to be divided into Class, the algorithm steps are as follows: initialization Cluster centers; Calculate the distance between each region and each cluster center and assign it to the category to which the nearest cluster center belongs; Update the cluster center to the average value of each type of area; Repeat the above steps until the cluster center no longer changes or the maximum number of iterations is reached.

[0031] It should be noted that this embodiment analyzes the correlation of each monitoring area and can identify areas with similar characteristics. According to the correlation analysis results, the monitoring areas are classified to form different area types. Through area division, different areas can be managed in a refined manner to improve management efficiency. Through correlation analysis, redundant data can be reduced and the efficiency of data analysis can be improved. In addition, regional classification helps to adopt different management strategies for different types of areas, thereby improving the pertinence and effectiveness of management.

[0032] S102, define the risk fault type of the power grid, obtain and analyze the historical risk data of the monitoring area, obtain the risk type set of different monitoring area types, and calculate the risk assessment index and regional impact index.

[0033] In a preferred embodiment, calculating the regional impact index includes: Define multiple risk fault types, and monitor the historical risk data of the area including fault duration, fault location, risk fault type, and fault range, as well as historical meteorological data; Specifically, risk failure types may be lightning strikes, landslides, floods, short circuits, equipment aging, human damage, animal interference, etc. Historical risk data is collected and data cleaning and preprocessing are performed.

[0034] Count all risk failure types under each area type, calculate the comprehensive failure rate of the risk failure types, compare the comprehensive failure rate with the preset failure rate threshold, and if it is greater than the failure rate threshold, generate a risk type set for the area type according to the corresponding risk failure type, where the preset failure rate threshold can be set according to the actual application scenario; Specifically, historical data is classified according to the defined risk fault types, all fault types occurring in the area are extracted, and risk types of different monitoring area types are obtained. For each monitoring area, the comprehensive fault frequency is calculated and a risk type set is generated. This can effectively generate a reasonable set of risk types under the same area classification. These sets will provide important data support for subsequent risk assessment and prediction, helping the power grid to better manage and respond to potential risks.

[0035] The risk assessment index is calculated based on historical risk data and is expressed as: ; in, Indicates the risk failure type, Indicates the risk type In the area The number of occurrences, Indicates in the area The first Second risk failure duration, Indicates in the area The first Second risk failure The number of users affected, Indicates in the area Internal risk failure type The number of occurrences, Indicates the area category, , , Represents the weight coefficient.

[0036] It should be noted that the risk level can be quantified by calculating the risk index of each risk failure type through the risk failure rate, average failure duration and failure impact rate.

[0037] In a preferred embodiment, calculating the risk assessment index includes: Calculate regional impact indicators based on terrain data, power grid data and risk assessment indicators , expressed as: ; ; in, represents the comprehensive impact factor, represents the transmission line length influence factor, represents the substation capacity influencing factor, Indicates the influencing factor of equipment aging degree, represents the altitude impact factor, represents the slope influence factor, represents the vegetation cover influencing factor, Represents the soil type influencing factor.

[0038] For example, for each influencing factor, its influencing factor on the occurrence of risk failure is calculated. The influencing factor can be standardized and normalized to ensure the comparability of different factors. Specifically, the influencing factor of the transmission line length is It can be expressed as: ; in, represents the length of the transmission line, , Respectively represent the maximum and minimum transmission line lengths in the monitoring area.

[0039] It should be noted that the types of grid risk failures are defined, the historical risk data of the monitored areas are obtained and analyzed, the risk distribution of different areas is understood, a set of risk types for different types of areas is generated, the evaluation indicators for each risk type are calculated, the degree of risk is quantified, the impact indicators for each area are calculated, and the overall risk level of the area is evaluated. Based on historical data analysis, the scientificity and accuracy of risk assessment are ensured. By calculating risk assessment indicators and regional impact indicators, the risks are quantified for easy comparison and analysis. Differentiated management measures are taken for different areas based on the risk type set and impact indicators to improve the targeted management.

[0040] S103, defining meteorological key features according to meteorological data, extracting meteorological key features corresponding to each risk type, generating a risk meteorological feature set, and calculating meteorological impact indicators of the risk meteorological feature set on the risk type.

[0041] In a preferred embodiment, generating a risk meteorological feature set includes: Define multiple key meteorological features and obtain the historical occurrence sequence of risk fault types corresponding to each monitoring area type , and historical data series of key meteorological characteristics , calculate the connection degree between each risk fault type and the key meteorological characteristics, expressed as: ; in, , Respectively , The average value of By comparing the connection value between each risk fault type and the key meteorological feature with the preset threshold, the key meteorological features greater than the preset threshold are extracted and arranged in descending order according to the connection degree to generate a risk meteorological feature set for the risk fault type.

[0042] Specifically, key meteorological characteristics include temperature, humidity, rainfall, wind speed, air pressure, lightning and other characteristics. Among them, temperature-related characteristics can be the daily maximum temperature, daily minimum temperature, and temperature change rate, which are related to the risk of failure caused by icing and shrinkage. Humidity-related characteristics can be relative humidity and dew point temperature, which are critical for determining whether condensation, rime and other phenomena will affect the operation of the power grid, and are related to risk types such as icing of conductors and moisture corrosion on the surface of towers. Precipitation-related characteristics can be precipitation amount and precipitation duration. Wind speed-related characteristics can be maximum wind speed and wind direction stability. Air pressure-related characteristics can be air pressure change values. Lightning-related characteristics can be the number of lightning activities and lightning intensity.

[0043] In a preferred embodiment, calculating the meteorological impact index of the risk meteorological feature set on the risk type includes: Based on the connection value between each risk fault type and the key meteorological characteristics, combined with the meteorological risk change trend, the meteorological impact index of the risk meteorological characteristic set on the risk type is calculated, which is expressed as: ; in, Indicates key meteorological characteristics In the The characteristic value of the period, Indicates the changing trend cycle of meteorological characteristics. Indicates key meteorological characteristics The weight coefficient reflects the overall impact of meteorological factors on specific risk types. The value range is between and . The closer the value is, the higher the positive correlation is. Indicates the number of meteorological key characteristics associated with the risk type.

[0044] Specifically, for each key meteorological feature, analyze its For example, for the temperature change rate feature, the average temperature change rate in each cycle can be calculated, and then the change trend of these average values ​​can be analyzed. The meteorological impact index reflects the comprehensive impact of the changing trend of meteorological factors on a specific risk type. If the meteorological impact index is greater than 0, it means that the changing trend of meteorological factors tends to increase the possibility of the occurrence of this risk type; if the meteorological impact index is less than 0, it means that the changing trend of meteorological factors tends to reduce the possibility of the occurrence of this risk type.

[0045] It should be noted that through the steps of this embodiment, key meteorological features can be defined more comprehensively, feature sets related to risk types can be extracted, and indicators that can reflect the impact of meteorological factors on power grid risks can be calculated, providing a strong meteorological basis for power grid risk prediction and early warning. It comprehensively considers multiple meteorological characteristics such as temperature, humidity, rainfall, wind speed and air pressure, improves the comprehensiveness and accuracy of the assessment, and uses multiple methods such as correlation coefficients and feature impact scores to ensure the reliability and robustness of the assessment and improve the prediction accuracy of the model.

[0046] S104, based on risk assessment indicators, regional impact indicators and meteorological impact indicators, a risk prediction model is constructed to obtain real-time meteorological data in the monitoring area, risk prediction is performed through the risk prediction model, and meteorological risk warning information is issued based on the comparison of the prediction results with the preset risk warning threshold.

[0047] In a preferred embodiment, constructing a risk prediction model and issuing meteorological risk warning information includes: Collect real-time meteorological data for each type of monitoring area, calculate the risk assessment index, regional impact index and meteorological impact index of the monitoring area, and combine all indicators into a risk feature vector; Build risk prediction models through machine learning algorithms, use historical analysis to train and validate the models based on data, including logistic regression, random forest, support vector machine, neural network, etc., and then use cross-validation methods to evaluate the performance of the models; The risk feature vector is input into the risk prediction model to perform risk probability prediction, output the risk prediction result, and compare it with the risk warning threshold to obtain the monitoring area corresponding to the risk warning threshold, and issue a risk warning report for the monitoring area.

[0048] It should be noted that this embodiment uses real-time and scientific risk prediction to timely release risk warning information, help relevant departments to prepare in advance, formulate reasonable emergency plans, reduce risk losses, and through accurate risk prediction, reasonably allocate resources, improve resource utilization efficiency, monitor and predict risks in real time, and improve the response speed and efficiency of dealing with risks.

[0049] The present invention uses a systematic risk monitoring method, from data collection, risk assessment, meteorological impact analysis to risk prediction and early warning, to comprehensively improve the level of power grid risk management, monitor and predict risks in real time, release early warning information in a timely manner, improve the response speed and efficiency of coping with risks, reduce the probability and impact of risk events, and reduce economic losses.

[0050] The above is a schematic scheme of a grid risk monitoring method based on meteorological warning in this embodiment. It should be noted that the technical scheme of the grid risk monitoring system based on meteorological warning and the technical scheme of the grid risk monitoring method based on meteorological warning belong to the same concept. The details of the technical scheme of the grid risk monitoring system based on meteorological warning in this embodiment that are not described in detail can be found in the description of the technical scheme of the grid risk monitoring method based on meteorological warning.

[0051] The power grid risk monitoring system based on meteorological early warning in this embodiment includes: The regional classification module is used to divide the power grid monitoring area into multiple monitoring areas, obtain the terrain data and power grid data of the monitoring area, analyze the correlation of each monitoring area, and perform regional classification; The risk analysis module is used to define the risk fault types of the power grid, obtain and analyze the historical risk data of the monitoring area, obtain the risk type set of different monitoring area types, and calculate the risk assessment index and regional impact index; The meteorological analysis module is used to define meteorological key features according to meteorological data, extract meteorological key features corresponding to each risk type, generate a risk meteorological feature set, and calculate the meteorological impact index of the risk meteorological feature set on the risk type; The output module is used to build a risk prediction model based on risk assessment indicators, regional impact indicators and meteorological impact indicators, obtain real-time meteorological data in the monitoring area, make risk predictions through the risk prediction model, and issue meteorological risk warning information based on the comparison of the prediction results with the preset risk warning thresholds.

[0052] This embodiment also provides an electronic device, which is applicable to power grid risk monitoring based on meteorological warning, including: Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the power grid risk monitoring method based on meteorological warning as proposed in the above embodiment.

[0053] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for monitoring power grid risks based on meteorological warning as proposed in the above embodiment is implemented.

[0054] The storage medium proposed in this embodiment and the method for implementing a power grid risk monitoring method based on meteorological warning proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0055] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0056] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for monitoring power grid risk based on meteorological early warning, characterized in that: include: Divide the power grid monitoring area into multiple monitoring areas, obtain terrain data and power grid data of the monitoring areas, analyze the correlation of each monitoring area, and perform regional classification; Define the risk fault types of power grid, obtain and analyze the historical risk data of the monitoring area, obtain the risk type set of different monitoring area types, and calculate the risk assessment index and regional impact index; Defining meteorological key features according to meteorological data, extracting meteorological key features corresponding to each risk type, generating a risk meteorological feature set, and calculating the meteorological impact index of the risk meteorological feature set on the risk type; Based on the risk assessment indicators, regional impact indicators and meteorological impact indicators, a risk prediction model is constructed, real-time meteorological data of the monitoring area is obtained, risk prediction is performed through the risk prediction model, and meteorological risk warning information is issued based on the comparison of the prediction results with the preset risk warning threshold.

2. The method for monitoring power grid risk based on meteorological early warning according to claim 1, characterized in that: The analysis of the correlation of each monitoring area and the regional classification include: Divide the monitoring area into several grid cells , each grid cell represents a monitoring area; Acquire terrain data and power grid data of the monitoring area and perform data normalization processing, wherein the terrain data includes altitude, regional slope, vegetation coverage and soil type, and the power grid data includes substation capacity, transmission line length and equipment type; Based on the terrain data and the power grid data, a terrain feature vector and a power grid feature vector are respectively constructed, which are respectively expressed as: ； ； in, represents the terrain feature vector, represents the power grid characteristic vector, represents the average altitude, represents the regional slope, represents the vegetation coverage ratio, Indicates the soil type code, Indicates the total capacity of substations in the region, represents the total length of the transmission line, represents the sum of risk weights calculated based on equipment type; Calculate the comprehensive correlation index of the terrain feature vector and the power grid feature vector in each monitoring area, classify the monitoring area by clustering algorithm, and It is expressed as: ； in, Indicates the monitoring area. , Represents each element of the terrain feature vector, , Represents each element of the power grid characteristic vector, , Represent the weight coefficients of terrain characteristic vector and power grid characteristic vector respectively.

3. The method for monitoring power grid risk based on meteorological early warning according to claim 2, characterized in that: Calculation of the regional impact index includes: A plurality of risk fault types are defined, wherein the historical risk data of the monitoring area includes fault duration, fault location, risk fault type and fault range; Count all risk failure types under each area type, and calculate the comprehensive failure rate of the risk failure type, compare the comprehensive failure rate with a preset failure rate threshold, and if it is greater than the failure rate threshold, generate a risk type set for the area type according to the corresponding risk failure type; The risk assessment index is calculated based on historical risk data and is expressed as: ； in, Indicates the risk failure type, Indicates the risk type In the area The number of occurrences, Indicates in the area The first Second risk failure duration, Indicates in the area The first Second risk failure The number of users affected, Indicates in the area Internal risk failure type The number of occurrences, Indicates the area category, , , Represents the weight coefficient.

4. The method for monitoring power grid risk based on meteorological early warning according to claim 3, characterized in that: Calculating the risk assessment index includes: Calculate regional impact indicators based on the terrain data, power grid data and risk assessment indicators , expressed as: ； ； in, represents the comprehensive impact factor, represents the transmission line length influence factor, represents the substation capacity influencing factor, Indicates the influencing factor of equipment aging degree, represents the altitude impact factor, represents the slope influence factor, represents the vegetation cover influencing factor, Represents the soil type influencing factor.

5. The method for monitoring power grid risk based on meteorological early warning according to claim 1 or 4, characterized in that: The generating risk meteorological feature set comprises: Define multiple key meteorological features and obtain the historical occurrence sequence of the risk fault type corresponding to each monitoring area type , and historical data series of key meteorological characteristics , calculate the connection degree between each risk fault type and the key meteorological characteristics, expressed as: ； in, , Respectively , The average value of By comparing the connection value between each risk fault type and the key meteorological feature with the preset threshold, the key meteorological features greater than the preset threshold are extracted and arranged in descending order according to the connection degree to generate a risk meteorological feature set for the risk fault type.

6. The method for monitoring power grid risk based on meteorological early warning according to claim 5, characterized in that: The calculating of the meteorological impact index of the risk meteorological feature set on the risk type includes: Based on the connection value between each risk fault type and the key meteorological characteristics, combined with the meteorological risk change trend, the meteorological impact index of the risk meteorological characteristic set on the risk type is calculated, which is expressed as: ； in, Indicates key meteorological characteristics In the The characteristic value of the period, Indicates the changing trend cycle of meteorological characteristics. Indicates key meteorological characteristics The weight coefficient of Indicates the number of meteorological key characteristics associated with the risk type.

7. The method for monitoring power grid risk based on meteorological early warning according to claim 1, characterized in that: The construction of the risk prediction model and the issuance of meteorological risk warning information include: Collecting real-time meteorological data of each type of monitoring area, calculating risk assessment indicators, regional impact indicators and meteorological impact indicators of the monitoring area, and combining all indicators into a risk feature vector; Build risk prediction models through machine learning algorithms, train models based on data using historical analysis, and validate models; The risk feature vector is input into the risk prediction model to perform risk probability prediction, output the risk prediction result, and compare it with the risk warning threshold to obtain the monitoring area corresponding to the risk warning threshold, and issue a risk warning report for the monitoring area.

8. A power grid risk monitoring system based on meteorological early warning, characterized in that: include: A regional classification module is used to divide the power grid monitoring area into multiple monitoring areas, obtain the terrain data and power grid data of the monitoring areas, analyze the correlation of each monitoring area, and perform regional classification; A risk analysis module is used to define the types of power grid risk faults, obtain and analyze the historical risk data of the monitoring area, obtain a set of risk types for different monitoring area types, and calculate risk assessment indicators and regional impact indicators; A meteorological analysis module is used to define meteorological key features according to meteorological data, extract meteorological key features corresponding to each risk type, generate a risk meteorological feature set, and calculate the meteorological impact index of the risk meteorological feature set on the risk type; The output module is used to build a risk prediction model based on the risk assessment indicators, regional impact indicators and meteorological impact indicators, obtain real-time meteorological data in the monitoring area, make risk predictions through the risk prediction model, and issue meteorological risk warning information based on the comparison of the prediction results with the preset risk warning thresholds.

9. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the power grid risk monitoring method based on meteorological warning according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: It stores computer executable instructions, which, when executed by a processor, implement the steps of the power grid risk monitoring method based on meteorological warning as described in any one of claims 1 to 7.

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