Lightning stroke risk assessment method for overhead transmission line tower

Abstract

The invention discloses a lightning stroke risk assessment method for an overhead transmission line tower, and the method comprises the following steps: determining an extreme consequence situation which may cause lightning stroke through a reverse risk recognition method, deducing a key region and a protection object for risk assessment, collecting environmental factor data related to lightning stroke, and carrying out the calculation of the environmental factor data. A relation model of the environment, the overhead transmission line tower structure and the lightning protection facility is constructed by combining lightning current signals collected by a Rogowski coil, the facility vulnerability is evaluated, a dynamic and plastic lightning stroke risk scene model is constructed based on the analysis result of environmental factors and the structural vulnerability, and the real-time monitoring data and historical lightning stroke information are combined. Adjusting model parameters, establishing a cross-dimension feedback self-adjusting mechanism, adjusting a risk assessment result in real time, dynamically assessing the result according to a situation, generating a protection strategy, arranging and constructing lightning protection facilities in a high-risk area, optimizing resource allocation, and effectively coping with lightning stroke 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 lightning strike risks 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 risks, they have the following obvious defects in a dynamically changing environment: Lack of dynamic adaptability: Most 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.

[0004] Low assessment accuracy: 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.

[0005] Lag in data processing and analysis: Due to relying on historical data and fixed models, existing lightning strike risk assessment methods cannot effectively integrate real-time monitoring data and cannot quickly respond to changes in lightning strike risks.

[0006] 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 risks under complex environmental conditions.

[0007] 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

[0008] 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.

[0009] 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: 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; 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, 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 dynamically 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; 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 scenario dynamic assessment results, adjust the allocation of protection resources, and cope with 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.

[0010] Optionally, the S1 specifically includes: S11. By using the reverse risk identification method, determine the extreme consequence scenarios that may lead to lightning strikes, analyze the scenarios of damage to overhead transmission line equipment, power system interruption and system stability damage caused by lightning current, and deduce high-risk areas and protection objects; 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; S13. Analyze through historical lightning strike data to extract key data related to extreme consequence scenarios. The key data includes lightning current intensity, lightning current duration, lightning strike frequency, and lightning strike location, and deduce high-risk areas and key facilities. S14. Integrate meteorological data and geographical data, establish a lightning strike risk assessment model through multi-source data analysis, predict the occurrence time periods and regions of extreme lightning strike events, and dynamically adjust the parameters of the model to adapt to environmental changes. S15. According to the evaluation results, define the protected objects and deduce the protection priorities, clarify that the protection facilities include overhead transmission line towers, insulators, and lightning protection facilities, and deduce the corresponding protection measures. S16. According to the deduced 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.

[0011] Optionally, 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, 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. S22. Use Rogowski coils to collect lightning current signals. The signals include lightning current intensity, duration, and their waveforms, and combine signal processing techniques to denoise and filter the collected data, and extract key features. S23. Based on meteorological and geographical data, construct a vulnerability relationship model between the environment and the overhead transmission line structure. This model updates the relationship between meteorology and the physical structure and lightning protection facilities of the overhead transmission line 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 deduce the potential impact of lightning strikes under different environmental conditions. S24. Through an adaptive algorithm, combine real-time lightning current signals and 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 deducing the risk levels of each lightning protection facility: ; Wherein, 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; 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 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 assessment 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.

[0012] Optionally, the specific steps of S3 are as follows: 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 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. This algorithm optimizes the model based on real-time lightning current intensity, duration, and lightning strike location data: ; Wherein, 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; S33. Based on the dynamically adjusted lightning strike risk assessment model, calculate the impacts of factors such as lightning current intensity, duration, and lightning strike location 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 assessment model can adapt to environmental changes and predict possible future extreme lightning strike events: ; Wherein, 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; S35. Based on the adjusted lightning strike risk assessment model, derive the priorities 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, 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.

[0013] Optionally, the S4 specifically includes: S41. Based on the dynamic lightning risk assessment model, establish a cross-dimensional self-regulating 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. S42. Automatically adjust the weights of environmental factors in the evaluation by monitoring meteorological data in real time and combining the risk model updated in real time, optimize the evaluation results, and deduce the lightning risk scenarios suitable for the current environmental conditions. S43. Update the lightning risk assessment model according to the lightning current data collected in real time, and use an adaptive parameter adjustment mechanism to adjust the sensitivity of the evaluation in real time, so that the model maintains high precision under different meteorological conditions. ; Wherein, is the adjusted weight of environmental factors, is the current weight of environmental factors, is the adjustment factor, is the th environmental factor at time of the lightning risk assessment result, is the evaluation 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 risk scenarios, generate new risk scenarios, and dynamically adjust the layout and protection priority of lightning protection facilities. S45. Combine the adjusted evaluation results, and dynamically optimize the layout and resource allocation of lightning protection facilities, so that lightning protection facilities are preferentially deployed in high-risk areas and on key equipment. S46. Continuously update the risk assessment model and protection measures through real-time evaluation feedback results, so that the lightning protection facilities are adjusted synchronously with environmental changes, and long-term effective risk control and protection optimization are achieved.

[0014] Optionally, the S5 specifically includes: S51. According to the adjusted lightning risk assessment model, adopt a situation dynamic adaptive assessment method to evaluate the occurrence probability and influence range of lightning events under different climate conditions, and the climate conditions include temperature, precipitation, humidity, and wind speed. S52. Dynamically collect meteorological data, evaluate the occurrence probability of lightning strike events in different climates in real time, and adjust the parameters of the evaluation model according to these scenarios to keep the evaluation results 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 impact range in different scenarios in real time, optimize the vulnerability assessment of equipment in each scenario, and derive the protection priority and resource allocation; S54. According to the evaluation results, calculate the damage probability of overhead transmission line equipment and the adaptability of lightning protection facilities in different climates, derive the protection measures and resource allocation strategies in different scenarios, and calculate the occurrence probability of lightning strike events: ; 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 th weight coefficient of meteorological factors, is the th influence value of meteorological factors on the occurrence probability of lightning strikes, is the number of meteorological factors affecting the occurrence probability of lightning strike events; S55. According to the results of dynamic evaluation of scenarios, derive the impact range of lightning strike events under various environmental conditions, and give priority to arranging protection facilities in high-risk areas, so that protection resources can be allocated in real time according to the risk assessment results; S56. Combine the real-time feedback of risk assessment results, dynamically adjust the calculation methods of the occurrence probability and impact range of lightning strike events, optimize the prediction accuracy of the model, make the risk assessment dynamically adapt to climate changes, calculate the equipment vulnerability assessment and lightning strike impact range, and optimize resource allocation through a feedback enhancement algorithm combined with real-time data streams and adaptive algorithms.

[0015] Optionally, the specific steps of S6 include: S61. Generate protection priorities and resource allocation strategies according to the results of dynamic evaluation of scenarios, and adjust protection measures according to different lightning strike risk scenarios based on real-time evaluation results and historical lightning strike data; S62. Collect meteorological data and lightning current data in real time, and determine high-risk areas through the evaluation results; 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; 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.

[0016] Optionally, the specific content of S7 is as follows: 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; 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 of each area, is the sum of lightning strike risk values of 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 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; S76. Optimize lightning protection resource allocation through historical lightning strike data, deduce the changing patterns of future lightning strike risks, and dynamically adjust protection strategies and resource allocation according to these changes, enabling lightning protection facilities to flexibly respond to long-term climate changes.

[0017] The beneficial effects of the present invention are as follows: (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.

[0018] (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.

[0019] (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 enhance the intelligence, automation, and long-term effectiveness of lightning protection facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings: 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 EMBODIMENTS

[0021] Now, the present invention will be further described in detail with reference to the drawings. These drawings are all simplified schematic diagrams, only showing the basic structure of the present invention in a schematic manner, so they only show the components related to the present invention.

[0022] Refer to Figure 1 , a method for assessing the lightning strike risk of an overhead transmission line tower, includes the following steps: S1. Determine the extreme consequence scenarios that may lead to lightning strikes through the reverse risk identification method, and deduce the key areas and protection objects for risk assessment based on these consequence scenarios; S2. Collect meteorological and geographical environmental factor data related to lightning strikes, use Rogowski coils to collect lightning current signals, construct a relationship model between the environment, 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 plastic lightning strike risk scenario model, combine real-time monitoring data with historical lightning strike information, and adjust 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, and adjust the risk assessment results in real time according to meteorological fluctuation factors to keep the assessment consistent with the actual environment; 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; S6. Generate protection strategies according to the results of situation dynamic assessment, adjust the allocation of protection resources, and deal with 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.

[0023] In this embodiment, the specific content of S1 includes: S11. Through the reverse risk identification method, determine the extreme consequence scenarios that may lead to lightning strikes, analyze the scenarios of lightning current damage to overhead transmission line equipment, power system interruption, and system stability damage, and deduce high-risk areas and protected objects; 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; 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; 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; S15. According to the assessment results, define the protected objects and deduce the protection priorities, clarify that the protection facilities include overhead transmission line towers, insulators, and lightning protection facilities, and deduce the corresponding protection measures; S16. Determine the deployment strategy of lightning protection facilities based on the derived high-risk areas and protected objects, implement reasonable allocation of protection resources, and optimize lightning protection measures.

[0024] In this embodiment, the specific steps of S2 are as follows: 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. S22. Use Rogowski coils to collect lightning current signals. The signals include lightning current intensity, duration, and waveform. Combine signal processing techniques to denoise and filter the collected data, and extract key features. 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 derive 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 vulnerability assessment of lightning protection facilities. The assessment method includes fusing the 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: ; 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; 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; 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 needs of high-risk areas and equipment can be responded to in a timely manner, and the lightning protection facilities can be reasonably deployed.

[0025] In this embodiment, the specific steps of S3 are as follows: S31. Construct a dynamic lightning strike risk assessment model based on the analysis of environmental factors and the vulnerability of the overhead transmission line structure. 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 the real-time monitoring data with the 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 the real-time lightning current intensity, duration, and lightning strike location data: ; 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; S33. Based on the dynamically adjusted lightning strike risk assessment model, calculate the impacts of factors such as lightning current intensity, duration, and lightning strike location on the overhead transmission line equipment, and deduce the vulnerability and protection requirements of the equipment under different lightning strike scenarios; S34. Through the adaptive optimization algorithm, combine the real-time meteorological and lightning current data to automatically adjust the prediction accuracy of the lightning strike risk scenario, so that the assessment model can adapt to environmental changes and predict extreme lightning strike events that may occur in the future: ; 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; S35. Based on the adjusted lightning strike risk assessment model, deduce the priority of the protection measures, give priority to the layout of lightning protection facilities in high-risk areas, and optimize the resource allocation according to the real-time data feedback; S36. Update the model assessment result in real time, and adjust the deployment strategy of the lightning protection facilities according to the protection priority, so that each protection facility can respond in a timely manner in the changing environment and provide sufficient protection.

[0026] In this embodiment, the specific steps of S4 are as follows: S41. Based on the dynamic lightning strike risk assessment model, establish a cross-dimensional self-regulating feedback mechanism. The mechanism dynamically adjusts the model parameters by collecting real-time meteorological data and geographical information and combining the structural vulnerability analysis in the model, so that the model responds to environmental changes and keeps the assessment consistent with the actual environment; S42. By real-time monitoring the meteorological data and combining with the real-time updated risk model, automatically adjust the weights of the environmental factors in the assessment, optimize the assessment result, and deduce the lightning strike risk scenarios suitable for the current environmental conditions; 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, so that the model maintains high accuracy under different meteorological conditions: ; wherein, is the adjusted environmental factor weight, is the current environmental factor weight, is the adjustment factor, is the th environmental factor, at time of the lightning strike risk assessment result, is the evaluation result of the previous moment; S44. Through the 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 evaluation 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 key equipment;

[0027] In this embodiment, the specific content of S5 includes: 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, and the climate conditions include temperature, precipitation, humidity, and wind speed; S52. Through dynamically collecting meteorological data, evaluate the occurrence probability of lightning strike events in different climates in real time, and adjust the parameters of the assessment model according to these scenarios, so that the evaluation results are consistent with the actual environmental changes; 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 in different scenarios in real time, optimize the vulnerability assessment of equipment in each scenario, and derive the protection priority and resource allocation; S54. According to the evaluation results, calculate the damage probability of overhead transmission line equipment and the adaptability of lightning protection facilities under different climates, derive the protection measures and resource allocation strategies in different scenarios, and calculate the occurrence probability of lightning strike events: ; wherein, is the adjusted occurrence probability of lightning strike events, is the current occurrence 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 lightning strike occurrence probability, is the number of meteorological factors affecting the lightning strike event occurrence probability; S55. According to the results of the situation dynamic assessment, deduce the influence range of lightning strike events under various environmental conditions, and prioritize the layout of 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 lightning strike event occurrence probability and influence range, optimize the prediction accuracy of the model, enable the risk assessment to dynamically adapt to climate changes, calculate the equipment vulnerability assessment and lightning strike influence range, and optimize the resource allocation through the feedback enhancement algorithm combined with real-time data streams and adaptive algorithms.

[0028] In this embodiment, the S6 specifically includes: S61. Generate a 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; S62. Collect meteorological data and lightning current data in real time, and determine high-risk areas through the assessment results; 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; 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 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 resource allocation priority, optimize the protection measures, and give priority to protecting high-risk areas and key equipment.

[0029] In this embodiment, the S7 specifically includes: S71. Based on the real-time monitored lightning strike risk assessment results, combined with historical lightning strike data, construct a dynamic cross-cycle lightning strike risk assessment model. This model dynamically adjusts the model parameters by analyzing the long-term and short-term lightning strike risk trends and 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 influence of meteorology on lightning strike risks; 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 in the high - lightning - occurrence period through a time - series model, and automatically derives the dynamic priority configuration of protection resources. S74. Identify the high - lightning - occurrence 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: ; Among them, 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 of all areas, is the number of areas evaluated; S75. Combine the real - time feedback results, derive the short - term and long - term lightning strike risk models according to different lightning strike scenarios, and adjust the allocation of protection resources: ; Among them, 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; 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.

[0030] Example 1: 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, an adaptive adjustment algorithm, and a scenario - dynamic assessment model to improve the accuracy and adaptability of prediction.

[0031] In this simulation experiment, the method of the present invention is applied to the lightning strike risk assessment of the power system and the optimization of lightning protection facilities. First, a Rogowski coil is used to collect lightning current signals, and key parameters such as lightning current intensity, duration, and waveform are recorded in real time. At the same time, meteorological data is transmitted to the monitoring system in real time through sensors. The system fuses this data and constructs a comprehensive lightning strike risk assessment model. This model combines historical lightning strike data and real-time meteorological information, and uses an adaptive adjustment algorithm and a context dynamic assessment method to dynamically assess the lightning strike risk.

[0032] During the entire assessment process, the real-time collected data is used to deduce high-risk areas, and the parameters of the risk assessment model are adjusted through the cross-dimensional feedback self-regulation mechanism of the present invention to adapt to changing environmental conditions. When the model identifies high-risk periods, lightning protection resources are preferentially deployed in high-risk areas to reduce the impact of lightning strikes on equipment. During periods with a relatively high lightning strike risk, the system automatically adjusts the priority of lightning protection facilities and updates the deployment strategy of lightning protection facilities in real time to ensure that the protection resources are optimally configured.

[0033] Based on the simulation data, the present invention evaluates the number and proportion of poles and 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 the proportion of poles and towers during the test period: Table 1 Lightning Strike Risk Level and Proportion of Poles and Towers

[0034] By analyzing the table data, we can see that there is a close relationship between the lightning strike risk and the tripping rate. The simulation data shows that the proportion of poles and towers with an annual tripping rate less than 0.5% is 10%, and these poles and towers belong to the low-risk area. The proportion of poles and towers with an annual tripping rate between 0.5% and 5% is 45%, and these poles and towers belong to the medium-low risk area and need to be strengthened in protection. The proportion of poles and towers with an annual tripping rate between 5% and 15% is 35%, which belongs to the medium-high risk area, and this part of the poles and towers needs to be focused on. The proportion of poles and towers with an annual tripping rate greater than 15% is 10%, and these poles and towers belong to the high-risk area and should be given priority to implement protection measures.

[0035] The lightning strike risk assessment method of the present invention can effectively predict the 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 suitable for the risk prediction and management of overhead transmission lines.

[0036] As described above, it is only the preferred specific implementation manner 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 of the present invention and its inventive concept, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A method for assessing the risk of lightning strikes on overhead transmission line towers, characterized in that: The steps include: S1. Through the reverse risk identification method, determine the extreme consequence scenarios that may lead to lightning strikes, and derive the key areas and protection objects for risk assessment based on these consequence scenarios; S2. Collect data on meteorological and geographical environmental factors related to lightning strikes, use Rogowski coils to collect lightning current signals, build a relationship model between the environment and the physical structure of overhead transmission line towers and lightning protection facilities, and assess their vulnerability; S3. Based on the results of environmental factors and structural vulnerability analysis, a dynamic and plastic lightning risk scenario model is constructed. The model parameters are adjusted to adapt to environmental changes by combining real-time monitoring data with historical lightning strike information. S4. Based on the dynamic risk scenario model, a cross-dimensional feedback self-adjustment mechanism is established to adjust the risk assessment results in real time according to meteorological fluctuation factors to keep the assessment consistent with the actual environment; S5. Based on the adjusted risk model, the probability and impact range of lightning strikes under different climates are evaluated through a situational dynamic adaptive assessment method; S6. Generate protection strategies based on the results of dynamic situational assessment and adjust the allocation of protection resources to deal with lightning strikes at different risk levels; S7. Based on the risk assessment results of real-time monitoring and combined with historical lightning strike data, cross-period risk assessment is carried out to identify long-term and short-term lightning strike risk trends and optimize the prediction ability of the assessment model; S8. Based on risk assessment, a feedback mechanism is used to optimize lightning protection strategies, and protective measures are adjusted according to real-time data and historical feedback to adapt to changing lightning strike risks.

2. The method for assessing the risk of lightning strike on overhead transmission line towers according to claim 1, characterized in that: The S1 specifically includes: S11. Through the reverse risk identification method, determine the extreme consequences that may lead to lightning strikes, analyze the damage of lightning current to overhead transmission line equipment, power system interruption and system stability damage, and derive high-risk areas and protection objects; S12. Based on the identified extreme consequence scenarios, construct multiple lightning strike risk scenario models, calculate the conditions and probability of each scenario, and derive high-risk areas and key equipment based on the model output; S13. Analyze historical lightning strike data to extract key data related to extreme consequence scenarios, including lightning current intensity, lightning current duration, lightning strike frequency, and lightning strike location, and deduce high-risk areas and key facilities; S14. Integrate meteorological data and geographic data, establish a lightning risk assessment model through multi-source data analysis, predict the time period and area of ​​occurrence of extreme lightning events, and dynamically adjust the parameters of the model to adapt to environmental changes; S15. Based on the assessment results, define the protection objects and derive the protection priority, clarify the protection facilities including overhead transmission line towers, insulators, and lightning protection facilities, and derive the corresponding protection measures; S16. Determine the deployment strategy of lightning protection facilities based on the derived high-risk areas and protection objects, implement reasonable allocation of protection resources, and optimize lightning protection measures.

3. The method for assessing the risk of lightning strike on overhead transmission line towers according to claim 1, characterized in that: The S2 specifically includes: S21. Collect meteorological data and geographic information data related to lightning strikes, wherein the meteorological data includes precipitation, wind speed, temperature, and humidity, and the geographic data includes the topography, climate characteristics, and power grid distribution of the area where the overhead transmission line is located; S22, using a Rogowski coil to collect lightning current signals, the signals including lightning current intensity, duration and waveform, and combining signal processing technology to denoise and filter the collected data to extract key features; S23. Based on meteorological and geographic data, a model of the relationship between the environment and the vulnerability of overhead transmission line structures is constructed. The model updates the relationship between meteorology and the physical structure of overhead transmission lines and lightning protection facilities in real time, uses weighted regression analysis and multivariate statistical models to assess the impact of environmental factors on facility vulnerability, and derives the potential impact of lightning strikes under different environmental conditions. S24. Through adaptive algorithms, combined with real-time lightning current signals and environmental changes, the vulnerability assessment of lightning protection facilities is optimized. 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: ； in, It represents the vulnerability assessment value of the lightning protection facilities. For each environmental factor, is the degree of influence of various environmental factors on lightning protection facilities, The number of environmental factors considered; S25. Based on the vulnerability assessment results , dynamically adjust the parameters of the lightning risk assessment model, optimize the protection priority by simulating the response of facilities under different lightning strike scenarios, and derive the corresponding protection measures and reasonable configuration of lightning protection resources based on the assessment results; S26. Optimize the layout of lightning protection facilities and resource allocation based on the evaluation model results in real time, so that the protection needs of high-risk areas and equipment can be responded to in a timely manner and lightning protection facilities can be reasonably deployed.

4. The method for assessing the risk of lightning strike on overhead transmission line towers according to claim 1, characterized in that: The S3 specifically includes: S31. Construct a dynamic lightning risk assessment model based on environmental factors and overhead transmission line structural vulnerability analysis. The model combines meteorological factors, geographical features and characteristics of lightning protection facilities to derive potential high-incidence areas of lightning strike risks; S32. Combining real-time monitoring data with historical lightning strike information, an incremental learning algorithm is used to dynamically adjust the lightning strike risk assessment model and update model parameters in real time. The algorithm optimizes the model based on real-time lightning current intensity, duration, and lightning strike location data: ； in, represents the adjusted model parameters, are the current model parameters, is the learning rate, is the gradient of the loss function with respect to the model parameters, It is the current real-time lightning current data; S33. Based on the dynamically adjusted lightning risk assessment model, calculate the impact of lightning current intensity, duration, lightning strike location and other factors on overhead transmission line equipment, and derive the vulnerability and protection requirements of equipment under different lightning strike scenarios; S34. Through adaptive optimization algorithms, combined with real-time meteorological and lightning current data, the prediction accuracy of lightning risk scenarios is automatically adjusted, so that the assessment model can adapt to environmental changes and predict extreme lightning events that may occur in the future: ； in, is the optimized parameter after adjustment, is the current parameter, is the adjustment factor, is the current lightning risk assessment result, is the result of the next assessment; S35. Based on the adjusted lightning risk assessment model, derive the priority of protective measures, prioritize the deployment of lightning protection facilities in high-risk areas, and optimize resource allocation based on 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 promptly and provide adequate protection in a changing environment.

5. The method for evaluating the risk of lightning strike on overhead transmission line towers according to claim 1, characterized in that: The S4 specifically includes: S41. On the basis of the dynamic lightning risk assessment model, a cross-dimensional self-adjusting feedback mechanism is established. The mechanism collects meteorological data and geographic information in real time, combines the structural vulnerability analysis in the model, and dynamically adjusts the model parameters so that the model responds to environmental changes and keeps the assessment consistent with the actual environment. S42. By real-time monitoring of meteorological data and combining it with a real-time updated risk model, the weights of environmental factors in the assessment are automatically adjusted to optimize the assessment results and derive a lightning risk scenario that is adapted to the current environmental conditions; S43. Based on the real-time collected lightning current data, the adaptive parameter adjustment mechanism is used to update the lightning risk assessment model and adjust the assessment sensitivity in real time to ensure that the model maintains high accuracy under different meteorological conditions: ； in, is the adjusted environmental factor weight, is the current environmental factor weight, is the adjustment factor, It is Environmental factors at the moment The lightning risk assessment results are is the evaluation result of the previous moment; S44. Through a dynamic feedback mechanism based on environmental changes, the model parameters are optimized in real time, meteorological fluctuations are integrated with lightning risk scenarios, new risk scenarios are generated, and the layout and protection priority of lightning protection facilities are dynamically adjusted; S45. Combined with the adjusted assessment results, dynamically optimize the layout and resource allocation of lightning protection facilities, so that lightning protection facilities are deployed preferentially in high-risk areas and on key equipment; S46. Through real-time evaluation feedback results, risk assessment models and protection measures are continuously updated so that lightning protection facilities can be adjusted synchronously with environmental changes to achieve long-term and effective risk control and protection optimization.

6. The method for evaluating the risk of lightning strike on overhead transmission line towers according to claim 1, characterized in that: The S5 specifically includes: S51. According to the adjusted lightning strike risk assessment model, a situational dynamic adaptive assessment method is used to assess the probability of occurrence and impact range of lightning strike events under different climatic conditions, wherein the climatic conditions include temperature, precipitation, humidity, and wind speed; S52. By dynamically collecting meteorological data, the probability of lightning strikes in different climates is evaluated in real time, and the parameters of the evaluation model are adjusted according to these scenarios to keep the evaluation results consistent with actual environmental changes; S53. Through the feedback mechanism based on real-time meteorological data, the probability of occurrence of lightning risk scenarios is automatically adjusted, the lightning impact range under different scenarios is generated in real time, the vulnerability assessment of equipment under each scenario is optimized, and the protection priority and resource allocation are derived; S54. Based on the evaluation results, calculate the damage probability of overhead power transmission line equipment and the adaptability of lightning protection facilities under different climates, derive protection measures and resource allocation strategies under different scenarios, and calculate the probability of lightning strikes: ； in, is the adjusted probability of a lightning strike event, is the probability of the current lightning strike event, is the adjustment factor, It is The weight coefficient of each meteorological factor is It is The impact of meteorological factors on the probability of lightning strikes is is the number of meteorological factors that affect the probability of lightning strikes; S55. Based on the results of the dynamic situation assessment, the impact range of lightning strikes under various environmental conditions is derived, and protective facilities in high-risk areas are arranged in priority, so that protective resources can be deployed in real time according to the risk assessment results; S56. Combined with the risk assessment results with real-time feedback, dynamically adjust the calculation method of the probability of occurrence and impact range of lightning strikes, optimize the prediction accuracy of the model, enable risk assessment to dynamically adapt to climate changes, calculate equipment vulnerability assessment and lightning strike impact range, and optimize resource allocation through feedback enhancement algorithms combined with real-time data streams and adaptive algorithms.

7. The method for evaluating the risk of lightning strike on overhead transmission line towers according to claim 1, characterized in that: The S6 specifically includes: S61. Generate protection priorities and resource allocation strategies based on the dynamic situation assessment results, and adjust protection measures for different lightning risk situations 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 evaluation results; S63. Adjust the priority of protective measures in real time according to different risk levels, 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 impact range of lightning strikes, adjust the layout of protection measures and resource allocation, and dynamically optimize the layout of protection measures and resource allocation according to 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 risk levels and environmental changes; S65. Integrate historical lightning strike data with real-time monitoring data, adjust resource allocation priorities, optimize protective measures, and give priority to protecting high-risk areas and key equipment.

8. The method for evaluating the risk of lightning strike on overhead transmission line towers according to claim 1, characterized in that: The S7 specifically includes: S71. Based on the lightning risk assessment results of real-time monitoring and combined with the changes in historical lightning data, a dynamic cross-period lightning risk assessment model is constructed. The model analyzes the long-term and short-term lightning risk trends and uses a recursive algorithm to dynamically adjust the model parameters. S72. Integrate meteorological data and historical lightning strike data, dynamically update key parameters of the assessment model, and correct lightning strike risk prediction results in real time to reflect the impact of meteorology on lightning strike risk; S73. Based on the cross-period risk assessment results, the lightning strike risk in each time period is calculated in real time, and the resource allocation strategy of lightning protection facilities is automatically adjusted according to the assessment results. The cross-period risk trend analysis optimizes the risk prediction of the high-incidence period of lightning strikes through the time series model, and automatically derives the dynamic priority configuration of protection resources; S74. Based on the long-term and short-term lightning risk trends, identify high-incidence periods and high-risk areas, and adjust the priority of lightning protection resource allocation: ； in, is the adjusted allocation of lightning protection resources, is the total available lightning protection resource, is the lightning risk assessment value for each area, is the sum of the lightning risk values ​​of all areas, is the number of regions evaluated; S75. Combine the real-time feedback results, derive short-term and long-term lightning risk patterns according to different lightning strike scenarios, and adjust the allocation of protection resources: ； in, It is the adjusted lightning protection resource. is the original resource allocation, is the adjustment factor, is the lightning strike risk during the forecast period, is the current risk assessment value; S76. Optimize the allocation of lightning protection resources through historical lightning strike data, deduce the changing pattern of future lightning strike risks, and dynamically adjust protection strategies and resource allocation based on these changes.

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