A method for lightning strike risk assessment of overhead transmission line towers

Abstract

The present invention discloses a method for lightning strike risk assessment of overhead transmission line towers, which includes the following steps: By using the reverse risk identification method, determine the extreme consequence scenarios that may lead to lightning strikes, deduce the key areas and protection objects for risk assessment, collect environmental factor data related to lightning strikes, and combine with the lightning current signals collected by Rogowski coils to construct a relationship model between the environment and the structure of overhead transmission line towers and lightning protection facilities, evaluate the vulnerability of the facilities. Based on the results of environmental factor and structural vulnerability analysis, construct a dynamic and plastic lightning strike risk scenario model, combine real-time monitoring data and historical lightning strike information, adjust the model parameters, establish a cross-dimensional feedback self-regulation mechanism, and adjust the risk assessment results in real time. According to the dynamic assessment results of the scenarios, generate protection strategies, arrange and construct lightning protection facilities in high-risk areas, optimize resource allocation, and effectively respond to lightning strike events under different risk levels.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly to a method for evaluating the lightning strike risk of overhead transmission line towers. Background Art

[0002] With the continuous development of power systems, overhead transmission lines play a crucial role in power transmission. However, damage caused by natural disasters such as lightning strikes remains a major hidden danger in the safe operation of power systems. Equipment damage caused by lightning current, power system interruptions, and equipment damage caused by lightning overvoltage bring huge economic losses and safety hazards to power companies. Therefore, how to accurately evaluate the lightning strike risk and optimize the layout of lightning protection facilities has become an important issue in power system protection.

[0003] In the prior art, lightning strike risk assessment usually relies on static environmental data and preset risk models. Although these traditional methods can conduct a preliminary assessment of lightning strike risk, in a dynamically changing environment, they have the following obvious defects:

[0004] Lack of dynamic adaptability: Most of the existing lightning strike risk assessment models are based on static meteorological data and historical lightning strike information, and cannot respond in real time to the dynamic changes of meteorological fluctuations and other environmental factors, resulting in a deviation between the assessment results and the actual situation.

[0005] Low assessment accuracy: The risk assessment models in the prior art often ignore the dynamic interaction relationship between the environment and facilities, especially the vulnerability assessment of the structure of overhead transmission line towers and lightning protection facilities is not fine enough, resulting in insufficient lightning protection resource allocation and the effectiveness of protection measures.

[0006] Lag in data processing and analysis: Due to relying on historical data and fixed models, the existing lightning strike risk assessment methods cannot effectively integrate real-time monitoring data and cannot quickly respond to changes in lightning strike risk.

[0007] Lack of a comprehensive assessment system: Traditional methods mainly rely on a single data source, lack comprehensive analysis of multi-dimensional data and a global perspective, and cannot accurately predict lightning strike risk under complex environmental conditions.

[0008] Therefore, how to provide a method for evaluating the lightning strike risk of overhead transmission line towers is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0009] An object of the present invention is to propose a method for evaluating the lightning strike risk of overhead transmission line towers. The present invention combines real-time meteorological data, lightning current signals and historical lightning strike data to construct a dynamic adaptive risk assessment model, and through multi-source data fusion and adaptive adjustment algorithms, it can evaluate and optimize the resource allocation and layout of lightning protection facilities in real time. This method has the advantages of high precision, high adaptability and real-time response.

[0010] A method for evaluating the lightning strike risk of overhead transmission line towers according to an embodiment of the present invention includes the following steps:

[0011] S1. Determine the extreme consequence scenarios that may lead to lightning strikes through the reverse risk identification method, and derive the key areas and protection objects for risk assessment based on these consequence scenarios;

[0012] S2. Collect data on meteorological and geographical environmental factors related to lightning strikes, use Rogowski coils to collect lightning current signals, construct a relationship model between the environment and the physical structure of overhead transmission line towers and lightning protection facilities, and evaluate their vulnerability;

[0013] S3. Based on the results of environmental factor and structural vulnerability analysis, construct a dynamic and plastic lightning strike risk scenario model, combine real-time monitoring data and historical lightning strike information, and adjust the model parameters to adapt to environmental changes;

[0014] S4. On the basis of the dynamic risk scenario model, establish a cross-dimensional feedback self-regulation mechanism to adjust the risk assessment results in real time according to meteorological fluctuation factors, and keep the assessment consistent with the actual environment;

[0015] S5. According to the adjusted risk model, evaluate the occurrence probability and influence range of lightning strike events under different climates through the scenario dynamic adaptive assessment method;

[0016] S6. Generate protection strategies according to the scenario dynamic assessment results, adjust the allocation of protection resources, and cope with lightning strike events under different risk levels;

[0017] S7. Based on the risk assessment results of real-time monitoring, combined with historical lightning strike data, conduct cross-cycle risk assessment, identify long-term and short-term lightning strike risk trends, and optimize the prediction ability of the assessment model;

[0018] S8. On the basis of risk assessment, adopt a feedback mechanism to optimize the lightning protection strategy, and adjust the protection measures according to real-time data and historical feedback to adapt to changing lightning strike risks.

[0019] Optionally, the S1 specifically includes:

[0020] S11. Determine the extreme consequence scenarios that may lead to lightning strikes through the reverse risk identification method, analyze the scenarios of the damage of lightning current to overhead transmission line equipment, power system interruption, and system stability impairment, and derive high-risk areas and protected objects.

[0021] S12. According to the identified extreme consequence scenarios, construct multiple lightning strike risk scenario models, calculate the occurrence conditions and probabilities of each scenario, and derive high-risk areas and key equipment based on the model output.

[0022] S13. Analyze through historical lightning strike data, extract key data related to extreme consequence scenarios, where the key data includes lightning current intensity, lightning current duration, lightning strike frequency, and lightning strike location, and derive high-risk areas and key facilities.

[0023] S14. Integrate meteorological data and geographical data, through multi-source data analysis, establish a lightning strike risk assessment model, predict the occurrence time periods and areas of extreme lightning strike events, and dynamically adjust the model parameters to adapt to environmental changes.

[0024] S15. According to the evaluation results, define protected objects and derive protection priorities, clarify that the protection facilities include overhead transmission line towers, insulators, and lightning protection facilities, and derive corresponding protection measures.

[0025] S16. According to the derived high-risk areas and protected objects, determine the deployment strategy of lightning protection facilities, implement reasonable allocation of protection resources, and optimize lightning protection measures.

[0026] Optionally, the specific content of S2 includes:

[0027] S21. Collect meteorological data and geographical information data related to lightning strikes. The meteorological data includes precipitation, wind speed, temperature, and humidity, and the geographical data includes the terrain, climate characteristics, and power grid distribution of the area where the overhead transmission line is located.

[0028] S22. Use Rogowski coils to collect lightning current signals. The signals include lightning current intensity, duration, and its waveform. Combine signal processing technology to denoise and filter the collected data, and extract key features.

[0029] S23. Based on meteorological and geographical data, construct a vulnerability relationship model between the environment and the overhead transmission line structure. This model evaluates the impact of environmental factors on the vulnerability of facilities by real-time updating the relationship between meteorology and the physical structure and lightning protection facilities of the overhead transmission line, and uses weighted regression analysis and multivariate statistical models to derive the potential impact of lightning strikes under different environmental conditions.

[0030] S24. Through an adaptive algorithm, combining real-time lightning current signals with environmental changes, optimize the vulnerability assessment of lightning protection facilities. The assessment method includes fusing lightning current signals with meteorological load data, calculating the vulnerability of each lightning protection facility under different lightning current scenarios, and deriving the risk level of each lightning protection facility:

[0031] ;

[0032] Among them, represents the vulnerability assessment value of the lightning protection facility, is the weight of each environmental factor, is the influence degree of each environmental factor on the lightning protection facility, is the number of environmental factors considered;

[0033] S25. Based on the vulnerability assessment results , dynamically adjust the parameters of the lightning strike risk assessment model. By simulating the responses of facilities under different lightning strike scenarios, optimize the protection priority, and derive corresponding protection measures and reasonable allocation of lightning protection resources according to the assessment results;

[0034] S26. Optimize the layout and resource allocation of lightning protection facilities in real time according to the results of the assessment model, so that the protection requirements of high-risk areas and equipment can be responded to in a timely manner, and the lightning protection facilities can be reasonably deployed.

[0035] Optionally, the specific content of S3 includes:

[0036] S31. Construct a dynamic lightning strike risk assessment model based on the analysis of environmental factors and the vulnerability of overhead transmission lines. The model combines meteorological factors, geographical features and the characteristics of lightning protection facilities to derive potential high-incidence areas of lightning strike risk;

[0037] S32. Combine real-time monitoring data with historical lightning strike information, and use the incremental learning algorithm to dynamically adjust the lightning strike risk assessment model and update the model parameters in real time. The algorithm optimizes the model based on real-time lightning current intensity, duration, and lightning strike location data:

[0038] ;

[0039] Among them, represents the adjusted model parameters, is the current model parameter, is the learning rate, is the gradient of the loss function with respect to the model parameters, is the current real-time lightning current data;

[0040] S33. Calculate the impacts of factors such as lightning current intensity, duration, and lightning strike location on overhead transmission line equipment based on the dynamically adjusted lightning strike risk assessment model, and derive the vulnerability and protection requirements of the equipment under different lightning strike scenarios;

[0041] S34. Through an adaptive optimization algorithm, combined with real-time meteorological and lightning current data, automatically adjust the prediction accuracy of the lightning strike risk scenario, enabling the assessment model to adapt to environmental changes and predict extreme lightning strike events that may occur in the future:

[0042] ;

[0043] wherein, is the optimized parameter after adjustment, is the current parameter, is the adjustment factor, is the current lightning strike risk assessment result, is the next assessment result;

[0044] S35. Based on the adjusted lightning strike risk assessment model, derive the priority of protection measures, prioritize the layout of lightning protection facilities in high-risk areas, and optimize resource allocation according to real-time data feedback;

[0045] S36. Update the model assessment result in real time, adjust the deployment strategy of lightning protection facilities according to the protection priority, enabling each protection facility to respond in a timely manner and provide sufficient protection in a changing environment.

[0046] Optionally, the specific content of S4 includes:

[0047] S41. Based on the dynamic lightning strike risk assessment model, establish a cross-dimensional self-regulating feedback mechanism. This mechanism dynamically adjusts the model parameters by collecting real-time meteorological data and geographical information and combining the structural vulnerability analysis in the model, enabling the model to respond to environmental changes and keep the assessment consistent with the actual environment;

[0048] S42. Automatically adjust the weights of environmental factors in the assessment by real-time monitoring of meteorological data and combining with the real-time updated risk model, optimize the assessment result, and derive the lightning strike risk scenario suitable for the current environmental conditions;

[0049] S43. Update the lightning strike risk assessment model according to the real-time collected lightning current data, and use the adaptive parameter adjustment mechanism to adjust the sensitivity of the assessment in real time, enabling the model to maintain high accuracy under different meteorological conditions:

[0050] ;

[0051] wherein, is the weight of environmental factors after adjustment, is the current environmental factor weight, is the adjustment factor, is the th environmental factor's lightning strike risk assessment result at time , is the assessment result of the previous moment;

[0052] S44. Through a dynamic feedback mechanism based on environmental changes, optimize the model parameters in real time, integrate meteorological fluctuations with lightning strike risk scenarios, generate new risk scenarios, and dynamically adjust the layout and protection priorities of lightning protection facilities;

[0053] S45. Combine the adjusted assessment results to dynamically optimize the layout and resource allocation of lightning protection facilities, so that lightning protection facilities are preferentially deployed in high-risk areas and critical equipment;

[0054] S46. Through real-time evaluation of the feedback results, continuously update the risk assessment model and protection measures, so that the lightning protection facilities are adjusted synchronously with environmental changes, and long-term effective risk control and protection optimization are achieved.

[0055] Optionally, the specific content of S5 includes:

[0056] S51. According to the adjusted lightning strike risk assessment model, use the scenario dynamic adaptive assessment method to evaluate the occurrence probability and influence range of lightning strike events under different climate conditions, where the climate conditions include temperature, precipitation, humidity, and wind speed;

[0057] S52. By dynamically collecting meteorological data, evaluate the occurrence probability of lightning strike events under different climates in real time, and adjust the parameters of the assessment model according to these scenarios to make the assessment results consistent with the actual environmental changes;

[0058] S53. Through a feedback mechanism based on real-time meteorological data, automatically adjust the occurrence probability of lightning strike risk scenarios, generate the lightning strike influence range under different scenarios in real time, optimize the vulnerability assessment of equipment under each scenario, and deduce the protection priority and resource allocation;

[0059] S54. According to the assessment results, calculate the damage probability of overhead transmission line equipment and the adaptability of lightning protection facilities under different climates, deduce the protection measures and resource allocation strategies under different scenarios, and calculate the occurrence probability of lightning strike events:

[0060] ;

[0061] where, is the adjusted occurrence probability of lightning strike events, is the current occurrence probability of lightning strike events, is the adjustment factor, is the The weight coefficient of each meteorological factor, is the influence value of the th meteorological factor on the lightning strike occurrence probability, and N is the number of meteorological factors affecting the lightning strike event occurrence probability;

[0062] S55. Based on the results of the situation dynamic assessment, deduce the influence range of lightning strike events under various environmental conditions, and preferentially arrange the protection facilities in high-risk areas, so that the protection resources can be allocated in real time according to the risk assessment results;

[0063] S56. Combine the risk assessment results of real-time feedback, dynamically adjust the calculation methods of the lightning strike event occurrence probability and influence range, optimize the prediction accuracy of the model, so that the risk assessment can dynamically adapt to the climate change, calculate the equipment vulnerability assessment and lightning strike influence range, and optimize the resource allocation through the feedback enhancement algorithm combined with the real-time data stream and the adaptive algorithm.

[0064] Optionally, the specific steps of S6 are as follows:

[0065] S61. Generate the protection priority and resource allocation strategy according to the situation dynamic assessment results, and adjust the protection measures according to the real-time assessment results and historical lightning strike data for different lightning strike risk situations;

[0066] S62. Collect meteorological data and lightning current data in real time, and determine the high-risk areas through the assessment results;

[0067] S63. According to different risk levels, adjust the priority of protection measures in real time, and rely on real-time lightning current data and meteorological data;

[0068] S64. Adjust the protection strategy through situation simulation and real-time feedback, calculate the lightning strike influence range, adjust the layout of protection measures and resource allocation, and dynamically optimize the layout of protection measures and resource allocation according to the changes of meteorological conditions and lightning current intensity. At the same time, optimize the response speed and accuracy of protection measures according to different lightning strike risk levels and environmental changes;

[0069] S65. Integrate historical lightning strike data and real-time monitoring data, adjust the priority of resource allocation, optimize protection measures, and preferentially protect high-risk areas and key equipment.

[0070] Optionally, the specific steps of S7 are as follows:

[0071] S71. Based on the lightning strike risk assessment results of real-time monitoring, combined with historical lightning strike data, construct a dynamic cross-cycle lightning strike risk assessment model. This model analyzes the long-term and short-term lightning strike risk trends and dynamically adjusts the model parameters using a recursive algorithm;

[0072] S72. Integrate meteorological data and historical lightning strike data, dynamically update the key parameters of the evaluation model, real-time correct the lightning strike risk prediction results, and reflect the impact of meteorology on lightning strike risks;

[0073] S73. Based on the cross-cycle risk assessment results, calculate the lightning strike risks in each time period in real time, and automatically adjust the resource allocation strategy of lightning protection facilities according to the assessment results. The cross-cycle risk trend analysis optimizes the risk prediction in high lightning strike periods through a time series model, and automatically derives the dynamic priority allocation of protection resources;

[0074] S74. Identify high lightning strike periods and high-risk areas according to long-term and short-term lightning strike risk trends, and adjust the priority of lightning protection resource allocation:

[0075] ;

[0076] wherein, is the adjusted lightning protection resource allocation, is the total available lightning protection resources, is the lightning strike risk assessment value of each area, is the sum of lightning strike risk values of all areas, is the number of evaluated areas;

[0077] S75. Combine the real-time feedback results, derive short-term and long-term lightning strike risk models according to different lightning strike scenarios, and adjust the allocation of protection resources:

[0078] ;

[0079] wherein, is the adjusted lightning protection resources, is the original resource allocation, is the adjustment factor, is the lightning strike risk during the prediction period, is the current risk assessment value;

[0080] S76. Optimize the lightning protection resource allocation through historical lightning strike data, derive the change pattern of future lightning strike risks, and dynamically adjust the protection strategy and resource allocation according to these changes, so that the lightning protection facilities can flexibly respond to long-term climate changes.

[0081] The beneficial effects of the present invention are:

[0082] (1) By combining a dynamic lightning strike risk assessment model, multi-source data fusion, and an adaptive adjustment algorithm, the present invention can monitor meteorological changes and historical lightning strike data in real time, providing accurate lightning strike risk prediction and optimization of protection strategies. This method can dynamically adapt to environmental changes, adjust the resource allocation and layout of lightning protection facilities in real time, improve the accuracy and response speed of protection strategies, and effectively reduce the damage caused by lightning strikes to overhead transmission lines.

[0083] (2) By combining a real-time data monitoring system with a feedback mechanism, the present invention can dynamically optimize the layout of lightning protection facilities. By identifying high-risk periods and high-risk areas, lightning protection resources are preferentially deployed to ensure timely and effective protection in different lightning strike scenarios. This mechanism not only improves the flexibility and adaptability of lightning protection measures but also effectively reduces equipment damage and system interruptions caused by lightning strikes.

[0084] (3) By integrating historical lightning strike data and meteorological data, and based on incremental learning algorithms and adaptive optimization, the present invention provides a comprehensive lightning strike risk assessment and protection strategy optimization solution. This solution can effectively identify high-incidence periods and areas of lightning strikes, automatically optimize protection priorities according to real-time data and historical feedback, reduce dependence on manual intervention, and improve the intelligence, automation, and long-term effectiveness of lightning protection facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0086] Figure 1 is a flowchart of a method for assessing the lightning strike risk of an overhead transmission line tower proposed by the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0087] Now, the present invention will be further described in detail with reference to the drawings. These drawings are all simplified schematic diagrams, only illustrating the basic structure of the present invention in a schematic manner, so they only show the components related to the present invention.

[0088] Refer to Figure 1 , a method for assessing the lightning strike risk of an overhead transmission line tower, comprising the following steps:

[0089] S1. By using the reverse risk identification method, determine the extreme consequence scenarios that may lead to lightning strikes, and based on these consequence scenarios, deduce the key areas and protection objects for risk assessment;

[0090] S2. Collect data on meteorological and geographical environment factors related to lightning strikes, use Rogowski coils to collect lightning current signals, construct a relationship model between the environment, the physical structure of the overhead transmission line tower, and lightning protection facilities, and evaluate its vulnerability;

[0091] S3. Based on the results of environmental factor and structural vulnerability analysis, construct a dynamically plastic lightning strike risk scenario model, and combine real-time monitoring data and historical lightning strike information to adjust the model parameters to adapt to environmental changes;

[0092] S4. On the basis of the dynamic risk scenario model, establish a cross-dimensional feedback self-regulation mechanism to adjust the risk assessment results in real time according to meteorological fluctuation factors, and keep the assessment consistent with the actual environment;

[0093] S5. According to the adjusted risk model, through the method of situation dynamic adaptive assessment, evaluate the occurrence probability and influence range of lightning strike events under different climates;

[0094] S6. Generate protection strategies according to the results of situation dynamic assessment, adjust the allocation of protection resources, and cope with lightning strike events under different risk levels;

[0095] S7. Based on the risk assessment results of real-time monitoring, combined with historical lightning strike data, conduct cross-cycle risk assessment, identify long-term and short-term lightning strike risk trends, and optimize the prediction ability of the assessment model;

[0096] S8. On the basis of risk assessment, adopt a feedback mechanism to optimize the lightning protection strategy, and adjust the protection measures according to real-time data and historical feedback to adapt to the changing lightning strike risk.

[0097] In this embodiment, the specific content of S1 includes:

[0098] S11. Through the reverse risk identification method, determine the extreme consequence scenarios that may lead to lightning strikes, analyze the scenarios of the damage of lightning current to overhead transmission line equipment, power system interruption and system stability damage, and deduce high-risk areas and protected objects;

[0099] S12. According to the identified extreme consequence scenarios, construct multiple lightning strike risk scenario models, calculate the conditions and probabilities of each scenario occurring, and deduce high-risk areas and key equipment based on the model output;

[0100] S13. Analyze through historical lightning strike data, extract key data related to extreme consequence scenarios, and the key data includes lightning current intensity, lightning current duration, lightning strike frequency, lightning strike location, and deduce high-risk areas and key facilities;

[0101] S14. Integrate meteorological data and geographical data, and through multi-source data analysis, establish a lightning strike risk assessment model, predict the occurrence time period and area of extreme lightning strike events, and dynamically adjust the model parameters to adapt to environmental changes;

[0102] S15. According to the evaluation results, define the protected objects and derive the protection priorities, clarify that the protection facilities include overhead transmission line towers, insulators, and lightning protection facilities, and derive the corresponding protection measures;

[0103] S16. According to the derived high-risk areas and protected objects, determine the deployment strategy of lightning protection facilities, implement reasonable allocation of protection resources, and optimize lightning protection measures.

[0104] In this embodiment, the S2 specifically includes:

[0105] S21. Collect meteorological data and geographical information data related to lightning strikes. The meteorological data includes precipitation, wind speed, temperature, and humidity, and the geographical data includes the terrain, climate characteristics, and power grid distribution in the area where the overhead transmission line is located;

[0106] S22. Use Rogowski coils to collect lightning current signals. The signals include lightning current intensity, duration, and its waveform. Combine signal processing techniques to denoise and filter the collected data, and extract key features;

[0107] S23. Based on the meteorological and geographical data, construct a relationship model between the environment and the vulnerability of the overhead transmission line structure. This model updates the relationship between the meteorology, the physical structure of the overhead transmission line, and the lightning protection facilities in real time, and uses weighted regression analysis and multivariate statistical models to evaluate the impact of environmental factors on the vulnerability of the facilities, and derives the potential impact of lightning strikes under different environmental conditions;

[0108] S24. Through an adaptive algorithm, combine real-time lightning current signals with environmental changes to optimize the vulnerability assessment of lightning protection facilities. The assessment method includes fusing lightning current signals with meteorological load data, calculating the vulnerability of each lightning protection facility under different lightning current scenarios, and deriving the risk level of each lightning protection facility:

[0109] ;

[0110] where represents the vulnerability assessment value of the lightning protection facility, is the weight of each environmental factor, is the degree of influence of each environmental factor on the lightning protection facility, is the number of environmental factors considered;

[0111] S25. Based on the vulnerability assessment results , dynamically adjust the parameters of the lightning strike risk assessment model. By simulating the responses of facilities under different lightning strike scenarios, optimize the protection priorities, and derive the corresponding protection measures and reasonable allocation of lightning protection resources according to the assessment results;

[0112] S26. Optimize the layout and resource allocation of lightning protection facilities in real time according to the results of the evaluation model, so that the protection requirements of high-risk areas and equipment can be promptly responded to, and the lightning protection facilities can be reasonably deployed.

[0113] In this embodiment, the S3 specifically includes:

[0114] S31. Construct a dynamic lightning strike risk assessment model based on the analysis of environmental factors and the vulnerability of overhead transmission line structures. The model combines meteorological factors, geographical features, and the characteristics of lightning protection facilities to deduce potential high-incidence areas of lightning strike risk;

[0115] S32. Combine real-time monitoring data and historical lightning strike information, and use an incremental learning algorithm to dynamically adjust the lightning strike risk assessment model and update the model parameters in real time. The algorithm optimizes the model based on real-time lightning current intensity, duration, and lightning strike location data:

[0116] ;

[0117] Among them, represents the adjusted model parameters, is the current model parameter, is the learning rate, is the gradient of the loss function with respect to the model parameters, is the current real-time lightning current data;

[0118] S33. Based on the dynamically adjusted lightning strike risk assessment model, calculate the impact of factors such as lightning current intensity, duration, and lightning strike location on overhead transmission line equipment, and deduce the vulnerability and protection requirements of equipment under different lightning strike scenarios;

[0119] S34. Through an adaptive optimization algorithm, combine real-time meteorological and lightning current data to automatically adjust the prediction accuracy of lightning strike risk scenarios, so that the evaluation model can adapt to environmental changes and predict possible extreme lightning strike events in the future:

[0120] ;

[0121] Among them, is the adjusted optimization parameter, is the current parameter, is the adjustment factor, is the current lightning strike risk assessment result, is the next assessment result;

[0122] S35. Based on the adjusted lightning strike risk assessment model, deduce the priority of protection measures, give priority to the layout of lightning protection facilities in high-risk areas, and optimize resource allocation according to real-time data feedback;

[0123] S36. Update the model evaluation results in real time, adjust the deployment strategy of lightning protection facilities according to the protection priority, so that each protection facility can respond in a timely manner and provide sufficient protection in a changing environment.

[0124] In this embodiment, the specific steps of S4 are as follows:

[0125] S41. Based on the dynamic lightning risk assessment model, establish a cross-dimensional self-adjusting feedback mechanism. This mechanism dynamically adjusts the model parameters by collecting meteorological data and geographical information in real time and combining the structural vulnerability analysis in the model, enabling the model to respond to environmental changes and keep the evaluation consistent with the actual environment.

[0126] S42. By monitoring meteorological data in real time and combining with the risk model updated in real time, automatically adjust the weights of environmental factors in the evaluation, optimize the evaluation results, and deduce the lightning risk scenarios suitable for the current environmental conditions.

[0127] S43. According to the lightning current data collected in real time, use the adaptive parameter adjustment mechanism to update the lightning risk assessment model and adjust the sensitivity of the evaluation in real time, so that the model maintains high precision under different meteorological conditions.

[0128] ;

[0129] Wherein, is the adjusted weight of environmental factors, is the current weight of environmental factors, is the adjustment factor, is the th lightning risk assessment result of the environmental factor at time is the evaluation result of the previous moment;

[0130] S44. Through the dynamic feedback mechanism based on environmental changes, optimize the model parameters in real time, integrate meteorological fluctuations with lightning risk scenarios, generate new risk scenarios, and dynamically adjust the layout and protection priority of lightning protection facilities.

[0131] S45. Combine the adjusted evaluation results, dynamically optimize the layout and resource allocation of lightning protection facilities, and give priority to the deployment of lightning protection facilities in high-risk areas and on key equipment.

[0132] S46. Through the real-time evaluation feedback results, continuously update the risk assessment model and protection measures, so that the lightning protection facilities are adjusted synchronously with environmental changes, and long-term effective risk control and protection optimization are achieved.

[0133] In this embodiment, the specific steps of S5 are as follows:

[0134] S51. According to the adjusted lightning strike risk assessment model, adopt the scenario dynamic adaptive assessment method to evaluate the occurrence probability and influence range of lightning strike events under different climate conditions, where the climate conditions include temperature, precipitation, humidity, and wind speed;

[0135] S52. By dynamically collecting meteorological data, evaluate the occurrence probability of lightning strike events under different climates in real time, and adjust the parameters of the assessment model according to these scenarios to make the assessment results consistent with the actual environmental changes;

[0136] S53. Through the feedback mechanism based on real-time meteorological data, automatically adjust the occurrence probability of lightning strike risk scenarios, generate the lightning strike influence range under different scenarios in real time, optimize the vulnerability assessment of equipment under each scenario, and deduce the protection priority and resource allocation;

[0137] S54. According to the assessment results, calculate the damage probability of overhead transmission line equipment and the adaptability of lightning protection facilities under different climates, deduce the protection measures and resource allocation strategies under different scenarios, and calculate the occurrence probability of lightning strike events:

[0138] ;

[0139] where, is the occurrence probability of the adjusted lightning strike event, is the current occurrence probability of the lightning strike event, is the adjustment factor, is the weight coefficient of the th meteorological factor, is the influence value of the th meteorological factor on the occurrence probability of lightning strikes, is the number of meteorological factors affecting the occurrence probability of lightning strike events;

[0140] S55. According to the results of the scenario dynamic assessment, deduce the influence range of lightning strike events under various environmental conditions, and give priority to arranging the protection facilities in high-risk areas so that the protection resources can be allocated in real time according to the risk assessment results;

[0141] S56. Combining the risk assessment results of real-time feedback, dynamically adjust the calculation methods of the occurrence probability and influence range of lightning strike events, optimize the prediction accuracy of the model, make the risk assessment dynamically adapt to the climate change, calculate the equipment vulnerability assessment and the lightning strike influence range, and optimize the resource allocation through the feedback enhancement algorithm combined with the real-time data stream and the adaptive algorithm.

[0142] In this embodiment, the specific content of S6 includes:

[0143] S61. Generate a protection priority and resource allocation strategy based on the dynamic assessment results of the scenario, and adjust the protection measures according to the real-time assessment results and historical lightning strike data for different lightning strike risk scenarios;

[0144] S62. Collect meteorological data and lightning current data in real time, and determine high-risk areas through the assessment results;

[0145] S63. Adjust the priority of protection measures in real time according to different risk levels, and rely on real-time lightning current data and meteorological data;

[0146] S64. Adjust the protection strategy through scenario simulation and real-time feedback, calculate the lightning strike impact range, adjust the layout and resource allocation of protection measures, and dynamically optimize the layout and resource allocation of protection measures according to the changes in meteorological conditions and lightning current intensity. At the same time, optimize the response speed and accuracy of protection measures according to different lightning strike risk levels and environmental changes;

[0147] S65. Integrate historical lightning strike data and real-time monitoring data, adjust the priority of resource allocation, optimize protection measures, and give priority to protecting high-risk areas and key equipment.

[0148] In this embodiment, the specific steps of S7 are as follows:

[0149] S71. Based on the lightning strike risk assessment results of real-time monitoring and combined with historical lightning strike data, construct a dynamic cross-cycle lightning strike risk assessment model. This model analyzes the long-term and short-term lightning strike risk trends and dynamically adjusts the model parameters using a recursive algorithm;

[0150] S72. Integrate meteorological data and historical lightning strike data, dynamically update the key parameters of the assessment model, and real-time correct the lightning strike risk prediction results to reflect the impact of meteorology on lightning strike risk;

[0151] S73. Based on the cross-cycle risk assessment results, calculate the lightning strike risk in each time period in real time, and automatically adjust the lightning protection facility resource allocation strategy according to the assessment results. The cross-cycle risk trend analysis optimizes the risk prediction of high lightning strike periods through a time series model and automatically derives the dynamic priority configuration of protection resources;

[0152] S74. Identify high lightning strike periods and high-risk areas according to the long-term and short-term lightning strike risk trends, and adjust the priority of lightning protection resource allocation:

[0153] ;

[0154] Wherein, is the adjusted lightning protection resource allocation, is the total available lightning protection resources, is the lightning strike risk assessment value of each area, is the sum of the lightning strike risk values for all regions, is the number of regions evaluated;

[0155] S75. Combining the real-time feedback results, short-term and long-term lightning strike risk patterns are derived according to different lightning strike scenarios, and the allocation of protection resources is adjusted:

[0156] ;

[0157] wherein, is the adjusted lightning protection resources, is the original resource allocation, is the adjustment factor, is the lightning strike risk during the prediction period, is the current risk assessment value;

[0158] S76. Through historical lightning strike data, optimize the allocation of lightning protection resources, derive the change pattern of future lightning strike risks, and dynamically adjust the protection strategy and resource configuration according to these changes, so that the lightning protection facilities can flexibly respond to long-term climate changes.

[0159] Example 1:

[0160] To verify the feasibility of the present invention in implementation, the present invention was applied to evaluate the lightning strike risk of overhead transmission lines based on simulation data. The simulation data simulated the impact of lightning strike events on transmission lines and was analyzed in combination with environmental factors. The lightning strike risk assessment method adopted multi-source data fusion, adaptive adjustment algorithm and scenario dynamic assessment model to improve the accuracy and adaptability of prediction.

[0161] In this simulation experiment, the method of the present invention was applied to the lightning strike risk assessment and lightning protection facility optimization of the power system. First, the lightning current signal was collected through a Rogowski coil, and key parameters such as lightning current intensity, duration and waveform were recorded in real time. At the same time, meteorological data was transmitted to the monitoring system in real time through sensors, and the system fused and processed these data and constructed a comprehensive lightning strike risk assessment model. This model combined historical lightning strike data and real-time meteorological information, and used an adaptive adjustment algorithm and scenario dynamic assessment method to dynamically assess the lightning strike risk.

[0162] During the whole evaluation process, the real-time collected data was used to derive high-risk regions, and the parameters of the risk assessment model were adjusted through the cross-dimensional feedback self-regulation mechanism of the present invention to adapt to changing environmental conditions. When the model identified high-risk periods, lightning protection resources were preferentially deployed in high-risk regions to reduce the impact of lightning strikes on equipment. During periods with high lightning strike risks, the system would automatically adjust the priority of lightning protection facilities and update the deployment strategy of lightning protection facilities in real time to ensure that the protection resources were optimally configured.

[0163] Based on simulation data, the present invention evaluates the number and proportion of transmission towers under different risk levels. Using the annual tripping rate (S) as the main evaluation index, the lightning strike risk is divided into four levels: low risk, medium-low risk, medium-high risk, and high risk. The following are some of the lightning strike risk levels and tower proportions during the test period:

[0164] Table 1 Lightning Strike Risk Levels and Tower Proportions

[0165]

[0166] By analyzing the table data, we can see that there is a close relationship between lightning strike risk and tripping rate. The simulation data shows that the proportion of transmission towers with an annual tripping rate less than 0.5% is 10%, and these towers belong to the low-risk area. The proportion of transmission towers with an annual tripping rate between 0.5% and 5% is 45%, and these towers belong to the medium-low risk area and need enhanced protection. The proportion of transmission towers with an annual tripping rate between 5% and 15% is 35%, belonging to the medium-high risk area, and this part of the towers requires key attention. The proportion of transmission towers with an annual tripping rate greater than 15% is 10%, and these towers belong to the high-risk area, and protective measures should be implemented preferentially.

[0167] The lightning strike risk assessment method of the present invention can effectively predict lightning strike risk and provide risk assessment results through multi-source data fusion, real-time simulation analysis, and dynamic adjustment, providing decision-making support for the optimal configuration of lightning protection facilities. Compared with traditional static assessment methods, this method is more accurate and dynamically adaptable, and is applicable to the risk prediction and management of overhead transmission lines.

[0168] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent replacements or changes, shall be covered by the protection scope of the present invention.

Claims

1. A method for evaluating the lightning strike risk of overhead transmission line towers, characterized in that, It includes the following steps: S1. By using the reverse risk identification method, determine the consequence scenarios that may lead to lightning strikes, and based on these consequence scenarios, derive the key areas for risk assessment and the protection objects; S2. Collect meteorological and geographical environment factor data related to lightning strikes, use Rogowski coils to collect lightning current signals, construct a relationship model between the environment and the physical structure of overhead transmission line towers and lightning protection facilities, and evaluate their vulnerability; S3. Based on the results of environmental factor and structural vulnerability analysis, construct a dynamic and adaptable lightning risk scenario model, and combine real-time monitoring data and historical lightning strike information to adjust the model parameters to adapt to environmental changes; S4. On the basis of the dynamic risk scenario model, establish a cross-dimensional feedback self-regulation mechanism to adjust the risk assessment results in real time according to meteorological fluctuation factors, and keep the assessment consistent with the actual environment; S5. According to the adjusted risk model, through the scenario dynamic adaptive assessment method, evaluate the occurrence probability and influence range of lightning strike events under different climates; S6. Generate protection strategies according to the results of scenario dynamic assessment, adjust the allocation of protection resources, and respond to lightning strike events under different risk levels; S7. Based on the risk assessment results of real-time monitoring, combined with historical lightning strike data, conduct cross-cycle risk assessment, identify long-term and short-term lightning strike risk trends, and optimize the prediction ability of the assessment model; S8. On the basis of risk assessment, adopt a feedback mechanism to optimize the lightning protection strategy, and adjust the protection measures according to real-time data and historical feedback to adapt to changing lightning strike risks.

2. The lightning strike risk assessment method for overhead transmission line towers according to claim 1, wherein The specific content of S1 includes: S11. By using the reverse risk identification method, determine the consequence scenarios that may lead to lightning strikes, analyze the scenarios of damage to overhead transmission line equipment, power system interruption and impaired system stability caused by lightning current, and derive high-risk areas and protection objects; S12. According to the identified consequence scenarios, construct multiple lightning risk scenario models, calculate the conditions and probabilities of each scenario occurring, and based on the model output, derive high-risk areas and key equipment; S13. Analyze through historical lightning strike data, extract key data related to the consequence scenarios, and the key data includes lightning current intensity, lightning current duration, lightning strike frequency, lightning strike location, and derive high-risk areas and key facilities; S14. Integrate meteorological data and geographical data, through multi-source data analysis, establish a lightning risk assessment model, predict the occurrence time period and area of lightning strike events, and dynamically adjust the model parameters to adapt to environmental changes; S15. According to the assessment results, define the protection objects and derive the protection priorities, clarify that the protection facilities include overhead transmission line towers, insulators, and lightning protection facilities, and derive the corresponding protection measures; S16. According to the derived high-risk areas and protection objects, determine the deployment strategy of lightning protection facilities, implement reasonable allocation of protection resources, and optimize lightning protection measures.

3. A method for evaluating the lightning strike risk of overhead transmission line towers according to claim 1, characterized in that, The specific content of S2 includes: S21. Collect meteorological data and geographical information data related to lightning strikes. The meteorological data includes precipitation, wind speed, temperature, humidity, and the geographical information data includes the terrain, climate characteristics and power grid distribution of the area where the overhead transmission line is located; S22. Collect lightning current signals using Rogowski coils. The signals include lightning current intensity, duration, and its waveform. Combine signal processing techniques to denoise and filter the collected data and extract key features. S23. Based on meteorological and geographical data, construct a relationship model between the environment and the vulnerability of overhead transmission line structures. This model updates the relationship between meteorology and the physical structure and lightning protection facilities of overhead transmission lines in real time, and uses weighted regression analysis and multivariate statistical models to evaluate the impact of environmental factors on the vulnerability of facilities, and derives the potential impact of lightning strikes under different environmental conditions. S24. Through an adaptive algorithm, combine real-time lightning current signals with environmental changes to optimize the evaluation of the vulnerability of lightning protection facilities. The evaluation method includes fusing lightning current signals with meteorological load data, calculating the vulnerability of each lightning protection facility under different lightning current scenarios, and deriving the risk levels of each lightning protection facility. ； Among them, represents the vulnerability assessment value of the lightning protection facilities, is the weight of each environmental factor, is the influence degree of each environmental factor on the lightning protection facilities, is the number of environmental factors considered; S25. Based on the vulnerability assessment results , dynamically adjust the parameters of the lightning strike risk assessment model, optimize the protection priorities by simulating the responses of facilities under different lightning strike scenarios, and derive corresponding protection measures and reasonable allocation of lightning protection resources according to the assessment results; S26. Optimize the layout and resource allocation of lightning protection facilities in real time according to the results of the evaluation model, so that the protection requirements of high-risk areas and equipment can be responded to in a timely manner, and the lightning protection facilities can be reasonably deployed.

4. A method for evaluating the lightning strike risk of overhead transmission line towers according to claim 1, characterized in that, The specific content of S3 includes: S31. Construct a dynamic lightning strike risk assessment model based on the analysis of environmental factors and the vulnerability of overhead transmission line structures. This model combines meteorological factors, geographical features, and the characteristics of lightning protection facilities to derive potential high-incidence areas of lightning strike risk. S32. Combine real-time monitoring data with historical lightning strike information, and use an incremental learning algorithm to dynamically adjust the lightning strike risk assessment model and update model parameters in real time. This algorithm optimizes the model based on real-time lightning current intensity, duration, and lightning strike location data. ； Among them, represents the adjusted model parameters, is the current model parameters, is the learning rate, is the gradient of the loss function with respect to the model parameters, is the current real-time lightning current data; S33. Based on the dynamically adjusted lightning strike risk assessment model, calculate the impact of lightning current intensity, duration, and lightning strike location factors on overhead transmission line equipment, and derive the vulnerability and protection requirements of equipment under different lightning strike scenarios. S34. Through an adaptive optimization algorithm, combine real-time meteorological and lightning current data to automatically adjust the prediction accuracy of lightning strike risk scenarios, so that the evaluation model can adapt to environmental changes and predict possible future lightning strike events. ； Among them, is the optimized parameter after adjustment, is the current parameter, is the adjustment factor, is the current lightning strike risk assessment result, is the next assessment result; S35. Based on the adjusted lightning strike risk assessment model, derive the priority of protection measures, give priority to the layout of lightning protection facilities in high-risk areas, and optimize resource allocation according to real-time data feedback. S36. Update the model evaluation results in real time, and adjust the deployment strategy of lightning protection facilities according to the protection priority, so that each protection facility can respond in a timely manner and provide protection in a changing environment.

5. A method for lightning strike risk assessment of overhead transmission line towers according to claim 1, characterized in that The specific content of S4 includes: S41. Based on the dynamic lightning strike risk assessment model, establish a cross-dimensional self-regulating feedback mechanism. This mechanism dynamically adjusts model parameters by collecting meteorological data and geographical information in real time and combining the structural vulnerability analysis in the model, so that the model responds to environmental changes and keeps the evaluation consistent with the actual environment. S42. Automatically adjust the weights of environmental factors in the evaluation by real-time monitoring of meteorological data and combining with the real-time updated risk model, optimize the evaluation results, and derive lightning strike risk scenarios suitable for current environmental conditions. S43. Update the lightning strike risk assessment model according to the real-time collected lightning current data, and apply an adaptive parameter adjustment mechanism to adjust the sensitivity of the assessment in real time, so that the model can maintain high precision under different meteorological conditions: ； Among them, is the adjusted environmental factor weight, is the current environmental factor weight, is the adjustment factor, is the th environmental factor at time lightning strike risk assessment result, is the assessment result of the previous moment; S44. Through a dynamic feedback mechanism based on environmental changes, optimize the model parameters in real time, integrate meteorological fluctuations with lightning strike risk scenarios, generate new risk scenarios, and dynamically adjust the layout and protection priorities of lightning protection facilities; S45. Combine the adjusted assessment results to dynamically optimize the layout and resource allocation of lightning protection facilities, so that lightning protection facilities are preferentially deployed in high-risk areas and critical equipment; S46. Through real-time evaluation of feedback results, continuously update the risk assessment model and protection measures, so that the lightning protection facilities are adjusted synchronously with environmental changes, and long-term effective risk control and protection optimization are achieved.

6. The lightning strike risk assessment method for an overhead transmission line tower according to claim 1, wherein The specific content of S5 includes: S51. According to the adjusted lightning strike risk assessment model, adopt a scenario dynamic adaptive assessment method to evaluate the occurrence probability and influence range of lightning strike events under different climate conditions, where the climate conditions include temperature, precipitation, humidity, and wind speed; S52. Through dynamically collecting meteorological data, evaluate the occurrence probability of lightning strike events under different climates in real time, and adjust the parameters of the assessment model according to these scenarios, so that the assessment results are consistent with the actual environmental changes; S53. Through a feedback mechanism based on real-time meteorological data, automatically adjust the occurrence probability of lightning strike risk scenarios, generate the lightning strike influence range under different scenarios in real time, optimize the vulnerability assessment of equipment under each scenario, and deduce the protection priority and resource allocation; S54. According to the assessment results, calculate the damage probability of overhead transmission line equipment and the adaptability of lightning protection facilities under different climates, deduce the protection measures and resource allocation strategies under different scenarios, and calculate the occurrence probability of lightning strike events: ； Among them, is the adjusted probability of lightning strike events, is the current probability of lightning strike events, is the adjustment factor, is the weight coefficient of the th meteorological factor, is the influence value of the th meteorological factor on the probability of lightning strike, and is the number of meteorological factors affecting the probability of lightning strike events; S55. According to the results of scenario dynamic assessment, deduce the influence range of lightning strike events under various environmental conditions, and preferentially arrange the protection facilities in high-risk areas, so that the protection resources can be allocated in real time according to the risk assessment results; S56. Combine the real-time feedback risk assessment results, dynamically adjust the calculation methods of the occurrence probability and influence range of lightning strike events, optimize the prediction accuracy of the model, so that the risk assessment can dynamically adapt to climate changes, calculate the equipment vulnerability assessment and lightning strike influence range, and optimize the resource allocation through a feedback enhancement algorithm combined with real-time data streams and adaptive algorithms.

7. A lightning strike risk assessment method for overhead transmission line towers according to claim 1, characterized in that The specific content of S6 includes: S61. Generate protection priorities and resource allocation strategies according to the results of scenario dynamic assessment, and adjust the protection measures according to different lightning strike risk scenarios based on real-time assessment results and historical lightning strike data; S62. Collect meteorological data and lightning current data in real time, and determine high-risk areas through assessment results; S63. According to different risk levels, adjust the priorities of protection measures in real time, and rely on real-time lightning current data and meteorological data; S64. Adjust the protection strategy through scenario simulation and real-time feedback, calculate the lightning strike impact range, adjust the layout of protection measures and resource allocation, and dynamically optimize the layout of protection measures and resource allocation according to the changes in meteorological conditions and lightning current intensity. At the same time, optimize the response speed and accuracy of protection measures according to different lightning strike risk levels and environmental changes; S65. Integrate historical lightning strike data and real-time monitoring data, adjust the priority of resource allocation, optimize protection measures, and give priority to protecting high-risk areas and key equipment.

8. A method for evaluating the lightning strike risk of overhead transmission line towers according to claim 1, characterized in that, The specific content of the above-mentioned S7 includes: S71. Based on the lightning strike risk assessment results of real-time monitoring and combined with the changes in historical lightning strike data, construct a dynamic cross-cycle lightning strike risk assessment model. This model analyzes the long-term and short-term lightning strike risk trends and dynamically adjusts the model parameters using a recursive algorithm; S72. Integrate meteorological data and historical lightning strike data, dynamically update the key parameters of the assessment model, and real-time correct the lightning strike risk prediction results to reflect the impact of meteorology on lightning strike risk; S73. Based on the cross-cycle risk assessment results, calculate the lightning strike risk in each time period in real time, and automatically adjust the resource allocation strategy of lightning protection facilities according to the assessment results. The cross-cycle risk trend analysis optimizes the risk prediction of high lightning strike periods through a time series model and automatically derives the dynamic priority configuration of protection resources; S74. Identify high lightning strike periods and high-risk areas according to long-term and short-term lightning strike risk trends, and adjust the priority of lightning protection resource allocation: ； Among them, is the adjusted lightning protection resource allocation, is the total available lightning protection resources, is the lightning strike risk assessment value for each area, is the sum of the lightning strike risk values for all areas, is the number of areas evaluated; S75. Combine the real-time feedback results, derive short-term and long-term lightning strike risk patterns according to different lightning strike scenarios, and adjust the allocation of protection resources: ； Among them, is the adjusted lightning protection resource, is the original resource allocation, is the adjustment factor, is the lightning strike risk during the prediction period, is the current risk assessment value; S76. Optimize the allocation of lightning protection resources through historical lightning strike data, derive the change patterns of future lightning strike risks, and dynamically adjust the protection strategy and resource configuration according to these changes.

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