Method and system for evaluating operation capability of power grid under natural disaster

Abstract

The invention provides a power grid operation capability assessment method and system under natural disasters, and relates to the technical field of power grid operation capability assessment. Extracting power transmission line node wind speed data from the power transmission line operation data, constructing a wind speed fault rate model, determining a wind speed fault coefficient of each power transmission line node according to the power transmission line node wind speed data and the wind speed fault rate model, and determining series fault features according to all the wind speed fault coefficients; acquiring meteorological information of natural typhoon disasters and geographic information of a target power grid, and inputting the meteorological information and the geographic information into a prediction model based on machine learning for power transmission line disaster damage prediction to obtain node damage indications of a power transmission line of the target power grid; according to the method, the operation capability of the target power grid can be accurately evaluated by using the operation information of the power transmission line of the target power grid under natural typhoon disasters.

Description

Technical Field

[0001] The present application relates to the technical field of power grid operation capability assessment, and more specifically, to a method and system for assessing power grid operation capability under natural disasters. Background Art

[0002] The assessment of the grid's operating capacity under natural disasters is a key link in ensuring the grid's stability and resilience, especially in extreme weather or disaster situations such as typhoons, earthquakes, and floods. The operation of the grid faces huge challenges. Natural disasters may cause problems such as broken transmission lines, substation equipment failures, and power outages. These problems not only directly affect the normal power supply of the grid, but may also cause large-scale power outages, socioeconomic losses, and public safety risks. Therefore, timely and accurate assessment of the grid's ability to continue operating after a disaster is an important means to ensure post-disaster recovery and disaster response efficiency.

[0003] In natural disasters, the real-time monitoring system of the power grid may be damaged, resulting in interruptions in data collection and transmission. Especially under extreme weather conditions, network communication infrastructure (such as optical fiber, wireless communication systems, etc.) often suffers large-scale failures or damage, resulting in the inability to transmit real-time data to the monitoring center. This means that when evaluating the operating capacity of the power grid after a disaster, it is often necessary to rely on sensor data or indirectly obtain information through drones, inspection teams, etc., which increases the uncertainty of the assessment. In addition, problems such as series failures and node damage are often interrelated and may present a complex chain reaction. For example, a transmission line break caused by a typhoon may cause damage to multiple node devices, thereby affecting the overall operating status of the power grid. Therefore, how to use the operating information of the target power grid transmission line under natural typhoon disasters to accurately assess the operating capacity of the target power grid is a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a method and system for evaluating the operating capacity of a power grid under natural disasters, which can use the operating information of the target power grid transmission lines under natural typhoon disasters to accurately evaluate the operating capacity of the target power grid.

[0005] In a first aspect, the present application provides a method for evaluating the operation capacity of a power grid under natural disasters, the evaluation method comprising the following steps:

[0006] Real-time collection of transmission line operation data of the target power grid under natural typhoon disasters;

[0007] Extracting the transmission line node wind speed data of the target power grid from the transmission line operation data, constructing a wind speed failure rate model for the transmission line of the target power grid, determining the wind speed failure coefficient of each transmission line node in the target power grid according to the transmission line node wind speed data and the wind speed failure rate model, and determining the series failure characteristics of the transmission line of the target power grid according to all the wind speed failure coefficients;

[0008] Acquire meteorological information of natural typhoon disasters and geographic information of the target power grid, input the meteorological information and the geographic information into a prediction model based on machine learning to predict transmission line damage, and then obtain node damage indications of the target power grid transmission line;

[0009] The series fault characteristics and the node damage indicators are used to evaluate the operating capacity of the target power grid transmission line, thereby obtaining an operating capacity evaluation result of the target power grid transmission line.

[0010] In this embodiment, the intelligent sensor system of the target power grid is used to collect the transmission line operation data of the target power grid under the natural typhoon disaster in real time.

[0011] In this embodiment, extracting the transmission line node wind speed data of the target power grid from the transmission line operation data specifically includes:

[0012] For each transmission line node in the target power grid, extracting the wind speed of the transmission line node from the transmission line operation data, thereby obtaining the wind speed of each transmission line node;

[0013] The data set consisting of the wind speeds borne by all transmission line nodes is used as the wind speed data of the transmission line nodes of the target power grid.

[0014] In this embodiment, constructing the wind speed failure rate model of the target power grid transmission line specifically includes:

[0015] Obtain the working wind speed and the limit wind speed of the target power grid transmission line;

[0016] Obtaining a preset correction factor;

[0017] A wind speed failure rate model of the target power grid transmission line is constructed according to the working withstand wind speed, the limit withstand wind speed and the correction coefficient.

[0018] In this embodiment, determining the wind speed failure coefficient of each transmission line node in the target power grid according to the transmission line node wind speed data and the wind speed failure rate model specifically includes:

[0019] For each transmission line node in the target power grid, obtaining the wind speed corresponding to the transmission line node in the wind speed data of the transmission line node;

[0020] Inputting the withstand wind speed into the wind speed failure rate model to obtain the wind speed failure characteristics of the transmission line node;

[0021] The wind speed fault coefficient of the transmission line node is determined according to the wind speed fault characteristic, and then the wind speed fault coefficient of each transmission line node in the target power grid is obtained.

[0022] In this embodiment, the series fault feature is a feature used to represent the overall fault probability of the target power grid transmission line.

[0023] In this embodiment, the prediction model based on machine learning is an adaptive random forest classification model.

[0024] In this embodiment, the node damage indicator is an indicator used to represent the damage probability of the transmission line node in the target power grid.

[0025] In this embodiment, the operation capability of the target power grid transmission line is evaluated using the series fault characteristics and the node damage indication, and the operation capability evaluation result of the target power grid transmission line is obtained, which specifically includes:

[0026] Inputting the series fault characteristics and the node damage indication into an evaluation model based on machine learning to evaluate the operating capacity of the target power grid transmission line, thereby obtaining an operating capacity evaluation coefficient of the target power grid transmission line;

[0027] Obtain a pre-set operational capability level table;

[0028] An operation capability evaluation result of a target power grid transmission line is generated according to the operation capability evaluation coefficient and the operation capability grade table.

[0029] In a second aspect, the present application provides a system for evaluating the operation capability of a power grid under natural disasters, which is used to perform a method for evaluating the operation capability of a power grid under natural disasters. The evaluation system includes:

[0030] Data acquisition module, used to collect real-time transmission line operation data of the target power grid under natural typhoon disasters;

[0031] A feature extraction module is used to extract the wind speed data of the transmission line nodes of the target power grid from the transmission line operation data, construct a wind speed failure rate model of the transmission line of the target power grid, determine the wind speed failure coefficient of each transmission line node in the target power grid according to the transmission line node wind speed data and the wind speed failure rate model, and determine the series failure characteristics of the transmission line of the target power grid according to all the wind speed failure coefficients;

[0032] A node damage determination module is used to obtain meteorological information of natural typhoon disasters and geographic information of the target power grid, input the meteorological information and the geographic information into a prediction model based on machine learning to predict transmission line damage, and then obtain node damage indications of the target power grid transmission line;

[0033] The operating capacity evaluation module is used to evaluate the operating capacity of the target power grid transmission line using the series fault characteristics and the node damage indication, and then obtain the operating capacity evaluation result of the target power grid transmission line.

[0034] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0035] The method collects transmission line operation data of the target power grid under natural typhoon disasters in real time; extracts transmission line node wind speed data of the target power grid from the transmission line operation data, constructs a wind speed failure rate model of the target power grid transmission line, determines the wind speed failure coefficient of each transmission line node in the target power grid according to the transmission line node wind speed data and the wind speed failure rate model, and determines the series failure characteristics of the target power grid transmission line according to all wind speed failure coefficients; obtains meteorological information of natural typhoon disasters and geographic information of the target power grid, inputs the meteorological information and the geographic information into a prediction model based on machine learning to predict transmission line disaster losses, and then obtains node damage indicators of the target power grid transmission lines; uses the series failure characteristics and the node damage indicators to evaluate the operation capacity of the target power grid transmission lines, and then obtains the operation capacity evaluation results of the target power grid transmission lines.

[0036] It can be seen that in the present application, firstly, by obtaining the wind speed data of each transmission line node and combining it with the wind speed failure rate model, the potential impact of wind speed on each transmission line node can be accurately evaluated, and the probability of failure of the transmission line node under different wind speeds can be determined, and the wind speed failure coefficient can reflect the disaster resistance of the transmission line nodes in the target power grid in real time in the wind speed change, and can help predict the overall failure probability of the target power grid based on the series fault characteristics; then, by combining meteorological information with geographic information, the accurate prediction of transmission line damage can be improved, and the transmission line disaster loss prediction based on machine learning prediction model can accurately evaluate the damage probability of the transmission line nodes in the target power grid; finally, the series fault characteristics and node damage indicators provide reliability and vulnerability analysis of the target power grid transmission lines under disaster conditions, so that by combining the series fault characteristics and node damage indicators to perform operation capacity evaluation, the operation capacity of the target power grid transmission lines can be accurately evaluated.

[0037] In summary, the technical solution adopted in this application can use the operating information of the target power grid transmission lines under natural typhoon disasters to accurately evaluate the operating capacity of the target power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0039] Figure 1 is a flow chart of a method for evaluating the operation capacity of a power grid under natural disasters provided in this application;

[0040] Figure 2 is an exemplary flow chart for determining the wind speed fault coefficient of each transmission line node in the target power grid according to the present application;

[0041] Figure 3 is an exemplary flow chart for determining an operating capability evaluation result of a target power grid transmission line according to the present application;

[0042] Figure 4 It is a module structure diagram of the power grid operation capacity assessment system under natural disasters provided by this application. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and drawings. The schematic implementation modes and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention. It should be noted that the present invention is already in the actual development and use stage.

[0044] Embodiment 1

[0045] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 As shown in FIG. 1 , this figure is an exemplary flow chart of a method for evaluating the operation capacity of a power grid under natural disasters according to this embodiment of the present application, and the evaluation method includes the following steps:

[0046] In step S1, the transmission line operation data of the target power grid under the natural typhoon disaster is collected in real time.

[0047] In specific implementation, the intelligent sensor system of the target power grid is used to collect the transmission line operation data of the target power grid under natural typhoon disasters in real time; it should be noted that, in the present application, the transmission line operation data includes the operating electrical data of the transmission line of the target power grid, the typhoon wind speed at the location of each transmission line node and the vibration data of each transmission line node, wherein the transmission line node refers to the line pole tower, substation, connection point, electric tower and distribution device in the transmission line of the target power grid, and the intelligent sensor system of the target power grid includes voltage and current sensors, wind speed sensors, vibration sensors and meteorological sensors.

[0048] In step S2, the wind speed data of the transmission line nodes of the target power grid are extracted from the transmission line operation data, a wind speed failure rate model of the transmission lines of the target power grid is constructed, the wind speed failure coefficients of each transmission line node in the target power grid are determined according to the transmission line node wind speed data and the wind speed failure rate model, and the series fault characteristics of the transmission lines of the target power grid are determined based on all the wind speed failure coefficients.

[0049] The following method can be used to extract the transmission line node wind speed data of the target power grid from the transmission line operation data, namely:

[0050] For each transmission line node in the target power grid, extracting the wind speed of the transmission line node from the transmission line operation data, thereby obtaining the wind speed of each transmission line node;

[0051] The data set consisting of the wind speeds borne by all transmission line nodes is used as the wind speed data of the transmission line nodes of the target power grid.

[0052] In the specific implementation, first, for each transmission line node in the target power grid, the wind speed that the transmission line node can withstand is extracted from the transmission line operation data, that is, the typhoon wind speed at the location of the transmission line node is used as the wind speed that the transmission line node can withstand. The wind speed that each transmission line node can withstand can be obtained in the above manner; finally, the data set composed of the wind speeds that all transmission line nodes can withstand can be used as the wind speed data of the transmission line nodes of the target power grid.

[0053] In this embodiment, the wind speed failure rate model of the target power grid transmission line may be constructed in the following manner, namely:

[0054] Obtain the working wind speed and the limit wind speed of the target power grid transmission line;

[0055] Obtaining a preset correction factor;

[0056] A wind speed failure rate model of the target power grid transmission line is constructed according to the working withstand wind speed, the limit withstand wind speed and the correction coefficient.

[0057] In specific implementation, first, the working wind speed and the limit wind speed of the target power grid transmission line can be obtained from the design standard and construction specification manual of the power grid, wherein the working wind speed refers to the wind speed that the transmission line can continuously withstand under normal operating conditions. Exceeding the working wind speed may affect the normal operation of the equipment, but may not necessarily cause structural damage. The limit wind speed refers to the maximum wind speed that the transmission line can withstand under extreme conditions. Exceeding the limit wind speed may cause structural damage to the equipment or even complete failure; then, a pre-set correction coefficient can be obtained. The correction coefficient is a constant used to adjust the value and accuracy of the wind speed failure rate model, which can be set through historical experiments. In this application, the correction coefficient is taken as 0.17, and other values ​​can also be taken in actual implementation, which is not limited here; finally, the wind speed failure rate model of the target power grid transmission line can be constructed based on the working wind speed, the limit wind speed and the correction coefficient. In actual implementation, the wind speed failure rate model of the target power grid transmission line can be expressed in the following way:

[0058]

[0059] Among them, γ a represents the value of the wind speed failure rate model of the target power grid transmission line, S0 represents the working wind speed of the target power grid transmission line, S x represents the ultimate wind speed of the target power grid transmission line, K represents the correction coefficient, S a Indicates the wind speed that the transmission line node a in the target power grid can withstand.

[0060] Preferably, in this embodiment, reference Figure 2 As shown, this figure is an exemplary flow chart of determining the wind speed fault coefficient of each transmission line node in the target power grid in an embodiment of the present application. In this embodiment, the wind speed fault coefficient of each transmission line node in the target power grid is determined according to the transmission line node wind speed data and the wind speed fault rate model, which can be specifically implemented by the following steps:

[0061] First, in step S21, for each transmission line node in the target power grid, the corresponding wind speed of the transmission line node in the wind speed data of the transmission line node is obtained;

[0062] Then, in step S22, the withstand wind speed is input into the wind speed failure rate model to obtain the wind speed failure characteristics of the transmission line node;

[0063] Finally, in step S23, the wind speed fault coefficient of the transmission line node is determined according to the wind speed fault characteristics, and then the wind speed fault coefficient of each transmission line node in the target power grid is obtained.

[0064] In specific implementation, first, for each transmission line node in the target power grid, the corresponding wind speed of the transmission line node in the wind speed data of the transmission line node can be obtained by traversal; then, the wind speed of the transmission line node can be input into the wind speed failure rate model, so that the value result of the wind speed failure rate model can be used as the wind speed failure feature of the transmission line node, and the wind speed failure feature is a feature used to represent the failure probability of the transmission line node under natural typhoon disasters; finally, the wind speed failure coefficient of the transmission line node can be determined according to the wind speed failure feature, and the wind speed failure coefficient represents the possibility of failure of the transmission line node under natural typhoon disasters. The larger the wind speed failure coefficient, the weaker the disaster resistance of the corresponding transmission line node. In actual implementation, the wind speed failure coefficient can be determined by the following formula:

[0065]

[0066] Among them, F a represents the wind speed fault coefficient of the transmission line node a in the target power grid, λ a It represents the wind speed fault characteristic of the transmission line node a in the target power grid. The wind speed fault coefficient of each transmission line node in the target power grid can be obtained by the above method.

[0067] In this embodiment, the series fault characteristics of the target power grid transmission line are determined according to all wind speed fault coefficients; it should be noted that, in this application, the series fault characteristics are characteristics used to represent the overall fault probability of the target power grid transmission line; in specific implementation, the series fault characteristics of the target power grid transmission line can be determined by the following formula:

[0068]

[0069] Where F represents the series fault characteristics of the target power grid transmission line, F(i) represents the wind speed fault coefficient of the i-th transmission line node in the target power grid, and n represents the total number of transmission line nodes in the target power grid.

[0070] It should be noted that by obtaining the wind speed data of each transmission line node and combining it with the wind speed failure rate model, the potential impact of wind speed on each transmission line node can be accurately evaluated, and the probability of failure of transmission line nodes under different wind speeds can be determined. The wind speed failure coefficient can reflect the disaster resistance of the transmission line nodes in the target power grid in real time under wind speed changes. Based on the characteristics of series faults, it can help predict the overall failure probability of the target power grid, take zoning protection or isolation measures in advance, and avoid large-scale power grid paralysis.

[0071] In step S3, meteorological information of natural typhoon disasters and geographic information of the target power grid are obtained, and the meteorological information and the geographic information are input into a prediction model based on machine learning to predict transmission line damage, thereby obtaining node damage indications of the target power grid transmission lines.

[0072] In specific implementation, meteorological information of natural typhoon disasters can be obtained through the intelligent sensor system of the target power grid, and the geographic information of the target power grid can be extracted from the geographic information system, where the meteorological information includes real-time meteorological data, typhoon path and wind speed data of the typhoon, and the geographic information includes the topological structure diagram of the target power grid and the type of transmission line. The topological structure diagram contains the geographical location of each transmission line node and the connection relationship between each transmission line node.

[0073] In this implementation, the meteorological information and the geographic information are input into a prediction model based on machine learning to predict transmission line damage, thereby obtaining node damage indicators of the target power grid transmission line; it should be noted that in this application, the prediction model based on machine learning is an adaptive random forest classification model, and the adaptive random forest (Adaptive Random Forest, ARF) is an improved version of the random forest algorithm with powerful classification capabilities, suitable for processing complex, multi-dimensional input data; the node damage indicator is an indicator used to represent the damage probability of the transmission line node in the target power grid; in specific implementation, the adaptive random forest classification model can be trained using historical meteorological data and power grid geographic information, so that the meteorological information and geographic information are input into the adaptive random forest classification model for transmission line damage prediction, and the adaptive random forest classification model will predict the damage probability of the transmission line node in the output target power grid, and the damage probability can be used as the node damage indicator of the target power grid transmission line.

[0074] It should be noted that by combining meteorological information with geographic information, the accuracy of prediction of transmission line damage can be improved. By using a machine learning-based prediction model to predict transmission line damage, the damage probability of transmission line nodes in the target power grid can be accurately assessed, and potential risks of the target power grid can be identified in advance.

[0075] In step S4, the operation capability of the target power grid transmission line is evaluated using the series fault characteristics and the node damage indication, thereby obtaining an operation capability evaluation result of the target power grid transmission line.

[0076] Preferably, in this embodiment, reference Figure 3As shown in FIG. 1 , this figure is an exemplary flow chart for determining the operating capacity evaluation result of the target power grid transmission line in an embodiment of the present application. In this embodiment, the operating capacity of the target power grid transmission line is evaluated using the series fault characteristics and the node damage indication, and then the operating capacity evaluation result of the target power grid transmission line is obtained, which can be specifically implemented by the following steps:

[0077] First, in step S41, the series fault characteristics and the node damage indication are input into an evaluation model based on machine learning to evaluate the operating capacity of the target power grid transmission line, thereby obtaining an operating capacity evaluation coefficient of the target power grid transmission line;

[0078] Then, in step S42, a preset operation capability level table is obtained;

[0079] Finally, in step S43, an operation capability evaluation result of the target power grid transmission line is generated according to the operation capability evaluation coefficient and the operation capability grade table.

[0080] In specific implementation, the series fault characteristics and node damage indicators can be input into the machine learning-based evaluation model to evaluate the operating capacity of the target power grid transmission line. In the present application, the machine learning-based evaluation model used is a support vector machine. The support vector machine can be used to evaluate the operating capacity of the target power grid, especially when the relationship between the series fault characteristics and the node damage indicators is more complicated. It can provide a more accurate evaluation. The series fault characteristics and node damage indicators are used as input data and passed into the trained support vector machine to obtain the operating capacity evaluation coefficient of the target power grid transmission line. The operating capacity evaluation coefficient represents the disaster-bearing operating capacity of the target power grid transmission line under natural typhoon disasters. The larger the operating capacity evaluation coefficient, the stronger the disaster-bearing operating capacity of the target power grid transmission line.

[0081] In addition, during the specific implementation, a pre-set operation capability level table can be obtained, and the operation capability level table can map the operation capability evaluation coefficient to a specific operation capability level. In this application, the operation capability level table includes the following levels:

[0082] Level 1 (when the operating capacity assessment coefficient is in the first set interval): It means that the target power grid transmission line has extremely strong disaster resistance and operating capabilities under natural typhoon disasters, and the probability of damage is extremely low.

[0083] Level 2 (when the operating capacity assessment coefficient is in the second set interval): It means that the target power grid transmission line has strong disaster resistance and operating capabilities, but may still suffer a certain degree of damage in extreme disaster situations.

[0084] Level 3 (when the operating capacity assessment coefficient is in the third set interval): It means that the target power grid transmission lines may be greatly affected by natural typhoon disasters and may need certain reinforcement or adjustment.

[0085] Level 4 (when the operating capacity assessment coefficient is in the fourth set interval): It means that the target power grid transmission lines are at greater risk in natural typhoon disasters and preventive measures such as reinforcement and suspension of operations are required.

[0086] Level 5 (when the operational capacity assessment coefficient is in the fifth setting interval): It indicates that the target power grid transmission line has extremely weak disaster resistance in natural typhoon disasters and may suffer major failures, requiring immediate repair or replacement.

[0087] It should be noted that in the present application, the operating capacity level table and the setting intervals therein can be set based on historical data and the actual performance of the target power grid transmission line, which will not be repeated here; the operating capacity evaluation result of the target power grid transmission line can be generated according to the operating capacity evaluation coefficient and the operating capacity level table. The operating capacity evaluation result is an evaluation result of the overall operating capacity of the target power grid transmission line, including the operating capacity evaluation coefficient and the operating capacity level of the target power grid transmission line. By comparing the operating capacity evaluation coefficient with the operating capacity level table, the operating capacity evaluation result of the target power grid transmission line can be generated.

[0088] It should be noted that the series fault characteristics and node damage indicators provide reliability and vulnerability analysis of the target power grid transmission lines under disaster conditions. Therefore, by combining the series fault characteristics and node damage indicators to conduct operation capacity assessment, the operation capacity of the target power grid transmission lines can be accurately assessed.

[0089] It can be seen that in the present application, firstly, by obtaining the wind speed data of each transmission line node and combining it with the wind speed failure rate model, the potential impact of wind speed on each transmission line node can be accurately evaluated, and the probability of failure of the transmission line node under different wind speeds can be determined, and the wind speed failure coefficient can reflect the disaster resistance of the transmission line nodes in the target power grid in real time in the wind speed change, and can help predict the overall failure probability of the target power grid based on the series fault characteristics; then, by combining meteorological information with geographic information, the accurate prediction of transmission line damage can be improved, and the transmission line disaster loss prediction based on machine learning prediction model can accurately evaluate the damage probability of the transmission line nodes in the target power grid; finally, the series fault characteristics and node damage indicators provide reliability and vulnerability analysis of the target power grid transmission lines under disaster conditions, so that by combining the series fault characteristics and node damage indicators to perform operation capacity evaluation, the operation capacity of the target power grid transmission lines can be accurately evaluated.

[0090] In summary, the technical solution adopted in this application can use the operating information of the target power grid transmission lines under natural typhoon disasters to accurately evaluate the operating capacity of the target power grid.

[0091] Embodiment 2

[0092] This application provides a system for evaluating the operation capacity of power grids under natural disasters. Figure 4 As shown, this figure is a schematic diagram of an evaluation system according to this embodiment of the present application, and the evaluation system includes:

[0093] The data acquisition module 100 is used to collect the transmission line operation data of the target power grid under the natural typhoon disaster in real time;

[0094] A feature extraction module 200 is used to extract the wind speed data of the transmission line nodes of the target power grid from the transmission line operation data, construct a wind speed failure rate model of the transmission line of the target power grid, determine the wind speed failure coefficient of each transmission line node in the target power grid according to the transmission line node wind speed data and the wind speed failure rate model, and determine the series failure characteristics of the transmission line of the target power grid according to all the wind speed failure coefficients;

[0095] A node damage determination module 300 is used to obtain meteorological information of natural typhoon disasters and geographic information of a target power grid, and input the meteorological information and the geographic information into a prediction model based on machine learning to perform transmission line damage prediction, thereby obtaining node damage indications of the transmission lines of the target power grid;

[0096] The operation capability evaluation module 400 is used to evaluate the operation capability of the target power grid transmission line using the series fault characteristics and the node damage indication, and then obtain the operation capability evaluation result of the target power grid transmission line.

[0097] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating the operation capacity of a power grid under natural disasters, characterized in that: The evaluation method comprises the following steps: Real-time collection of transmission line operation data of the target power grid under natural typhoon disasters; Extracting the transmission line node wind speed data of the target power grid from the transmission line operation data, constructing a wind speed failure rate model for the transmission line of the target power grid, determining the wind speed failure coefficient of each transmission line node in the target power grid according to the transmission line node wind speed data and the wind speed failure rate model, and determining the series failure characteristics of the transmission line of the target power grid according to all the wind speed failure coefficients; Acquire meteorological information of natural typhoon disasters and geographic information of the target power grid, input the meteorological information and the geographic information into a prediction model based on machine learning to predict transmission line damage, and then obtain node damage indications of the target power grid transmission line; The series fault characteristics and the node damage indicators are used to evaluate the operating capacity of the target power grid transmission line, thereby obtaining an operating capacity evaluation result of the target power grid transmission line.

2. A method for evaluating the operation capacity of a power grid under natural disasters according to claim 1, characterized in that: The intelligent sensor system of the target power grid is used to collect the transmission line operation data of the target power grid under natural typhoon disasters in real time.

3. A method for evaluating the operation capacity of a power grid under natural disasters according to claim 1, characterized in that: Extracting the transmission line node wind speed data of the target power grid from the transmission line operation data specifically includes: For each transmission line node in the target power grid, extracting the wind speed of the transmission line node from the transmission line operation data, thereby obtaining the wind speed of each transmission line node; The data set consisting of the wind speeds borne by all transmission line nodes is used as the wind speed data of the transmission line nodes of the target power grid.

4. A method for evaluating the operation capacity of a power grid under natural disasters according to claim 1, characterized in that: Constructing the wind speed failure rate model of the target power grid transmission line specifically includes: Obtain the working wind speed and the limit wind speed of the target power grid transmission line; Obtaining a preset correction factor; A wind speed failure rate model of the target power grid transmission line is constructed according to the working withstand wind speed, the limit withstand wind speed and the correction coefficient.

5. A method for evaluating the operation capacity of a power grid under natural disasters according to claim 1, characterized in that: Determining the wind speed failure coefficient of each transmission line node in the target power grid according to the transmission line node wind speed data and the wind speed failure rate model specifically includes: For each transmission line node in the target power grid, obtaining the wind speed corresponding to the transmission line node in the wind speed data of the transmission line node; Inputting the withstand wind speed into the wind speed failure rate model to obtain the wind speed failure characteristics of the transmission line node; The wind speed fault coefficient of the transmission line node is determined according to the wind speed fault characteristic, and then the wind speed fault coefficient of each transmission line node in the target power grid is obtained.

6. A method for evaluating the operation capacity of a power grid under natural disasters according to claim 1, characterized in that: The series fault feature is a feature used to represent the overall fault probability of the target power grid transmission line.

7. A method for evaluating the operation capacity of a power grid under natural disasters according to claim 1, characterized in that: The machine learning-based prediction model is an adaptive random forest classification model.

8. A method for evaluating the operation capability of a power grid under natural disasters according to claim 1, characterized in that: The node damage indicator is an indicator used to represent the damage probability of the transmission line node in the target power grid.

9. A method for evaluating the operation capacity of a power grid under natural disasters according to claim 1, characterized in that: The operation capability of the target power grid transmission line is evaluated by using the series fault characteristics and the node damage indication, and then the operation capability evaluation result of the target power grid transmission line is obtained, which specifically includes: Inputting the series fault characteristics and the node damage indication into an evaluation model based on machine learning to evaluate the operating capacity of the target power grid transmission line, thereby obtaining an operating capacity evaluation coefficient of the target power grid transmission line; Obtain a pre-set operational capability rating table; An operation capability evaluation result of a target power grid transmission line is generated according to the operation capability evaluation coefficient and the operation capability grade table.

10. A system for evaluating the operation capability of a power grid under natural disasters, used to execute a method for evaluating the operation capability of a power grid under natural disasters as claimed in any one of claims 1 to 9, characterized in that: The evaluation system comprises: Data acquisition module, used to collect real-time transmission line operation data of the target power grid under natural typhoon disasters; A feature extraction module is used to extract the wind speed data of the transmission line nodes of the target power grid from the transmission line operation data, construct a wind speed failure rate model of the transmission line of the target power grid, determine the wind speed failure coefficient of each transmission line node in the target power grid according to the transmission line node wind speed data and the wind speed failure rate model, and determine the series failure characteristics of the transmission line of the target power grid according to all the wind speed failure coefficients; A node damage determination module is used to obtain meteorological information of natural typhoon disasters and geographic information of the target power grid, input the meteorological information and the geographic information into a prediction model based on machine learning to predict transmission line damage, and then obtain node damage indications of the target power grid transmission line; The operating capacity evaluation module is used to evaluate the operating capacity of the target power grid transmission line using the series fault characteristics and the node damage indication, and then obtain the operating capacity evaluation result of the target power grid transmission line.

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