Bird damage risk assessment method and system for power transmission line

Abstract

The invention provides a power transmission line bird damage risk assessment method and system, and relates to the technical field of power system assessment, and the assessment method comprises the steps: collecting historical bird damage behavior data, risk factor data, regional geographic information data and power system layout data, and constructing a bird damage behavior and regional damage association scene model; collecting output and load change data of the power system under historical bird damage behaviors, and establishing a power system disaster response model; based on the bird damage behavior and regional damage association scene model, determining an index for measuring the fault risk of the power system under the bird damage behavior, and performing comprehensive risk assessment on the bird damage behavior data collected in real time; according to the comprehensive risk assessment result, the influence degree of different bird damage behaviors on the electric power system is analyzed, a corresponding risk management strategy is formulated based on an electric power system disaster response model, and the bird damage risk level of the electric power system is comprehensively and accurately assessed through multi-dimensional and multi-index data collection and assessment analysis.

Description

Technical Field

[0001] This invention relates to the field of power system assessment technology, specifically to a method and system for assessing bird damage risk to transmission lines. Background Technology

[0002] In the existing technology, although the power supply capacity monitoring and analysis and impact range assessment under bird damage behavior are carried out in the assessment of power system failures, it is impossible to calculate the failure risk index of the power system under bird damage behavior. This results in an incomplete assessment of the process before and after bird damage behavior, a single assessment dimension, and thus inaccurate assessment data and insufficient coverage.

[0003] Furthermore, when conducting risk assessments of the power system, the main approach is to monitor and evaluate the situation macroscopically using bird damage behavior, damage data, and power grid data. This approach fails to provide detailed analysis of the various components of the power system, resulting in the failure to promptly identify weaknesses in the power system. Consequently, it hinders the response to and recovery from bird damage and fails to provide effective guidance for the power system.

[0004] Meanwhile, when conducting risk assessments of bird damage, the assessment indicators are too simplistic and fail to comprehensively consider multiple aspects such as equipment failure risk and user power outage risk, resulting in an inability to adapt to different power systems, poor applicability and scalability, and an inability to provide a comprehensive basis for decision-making in the planning and operation of power systems. Summary of the Invention

[0005] This invention addresses the problems existing in the prior art by providing a method for assessing bird damage risks to power transmission lines, which solves the problem that the assessment dimensions and indicators in the prior art are too limited and that the weaknesses of the power system cannot be detected in a timely manner.

[0006] Another objective of this invention is to provide a bird hazard risk assessment system for power transmission lines.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for assessing bird damage risk to power transmission lines includes the following steps:

[0009] Collect historical bird damage behavior data, risk factor data, regional geographic information data, and power system layout data to construct a scenario model linking bird damage behavior and regional victimization.

[0010] Collect historical data on power system output and load changes under bird damage behavior to establish a power system disaster response model;

[0011] Based on the scenario model of the correlation between bird damage behavior and regional victimization, indicators are determined to measure the failure risk of the power system under bird damage behavior, and a comprehensive risk assessment is carried out on the real-time collected bird damage behavior data.

[0012] Furthermore, the construction of the bird-damage behavior and regional victim association scenario model specifically includes the following steps:

[0013] Information on the time, location, and intensity of historical bird damage events was collected, along with data on the distribution and regional construction of power facilities. Bird damage events were analyzed across time, state, and structural dimensions to establish a probabilistic model of causative factors. The probabilistic model of causative factors is as follows:

[0014] P(Z(E i )|F i，1 F i，2 F i，j )

[0015] in:

[0016] P(E i ) represents the probability of the i-th type of bird-harming behavior occurring;

[0017] F i，j This represents the j-th risk factor under the i-th type of bird-damaging behavior;

[0018] Z(E i ) represents the causative factor of the i-th type of bird damage behavior;

[0019] j represents the number of risk factors;

[0020] By combining a probability model of bird damage factors with regional geographic information and power system layout, the likelihood and extent of damage to power facilities are predicted based on the probability model. A scenario model linking bird damage behavior and regional damage is constructed, represented by the degree of urban damage.

[0021] S(E, P, F)

[0022] in:

[0023] S represents the city, indicating the degree of damage to the city;

[0024] E indicates bird-related misconduct;

[0025] P represents the power system layout;

[0026] F represents risk factors;

[0027] S is a function of E, P, and F.

[0028] Furthermore, the establishment of the power system disaster response model specifically includes the following steps:

[0029] This study collects data on the output power changes of renewable energy equipment under different bird-related behaviors, as well as the changes in electricity demand from residents and businesses under bird-related behaviors. It analyzes the characteristics of renewable energy output and load changes in the time, state, and architecture dimensions, and establishes a power system disaster response model based on power system output and load changes. The power system disaster response model is as follows:

[0030] P new (t)=f(E(t))

[0031] L(t)=g(E(t))

[0032] in:

[0033] P new (t) represents the output of the new energy source at time t;

[0034] L(t) represents the load demand at time t;

[0035] E(t) represents the influencing factor of bird-damage behavior at time t;

[0036] f and g represent the functional relationships between new energy output and load demand and the influencing factors of bird damage behavior, respectively.

[0037] Furthermore, in the power system disaster response modeling, adjustment measures are introduced to establish a mathematical model of the response behavior of a high-proportion renewable energy power system, describing the dynamic response of the power system under bird damage. The state equation of the power system is then expressed as:

[0038]

[0039] The output equation is expressed as:

[0040] Y = CX + DU

[0041] Where X represents the state variable of the power system, U represents the input variable, Y represents the output variable, and A, B, C, and D represent the system matrix, input matrix, output matrix, and direct transfer matrix, respectively.

[0042] Furthermore, in the comprehensive risk assessment steps, the indicators for failure risk under bird damage include static indicators, dynamic indicators, and risk-sharing indicators. The static indicators include the average level of resilience calculated based on historical data statistical analysis, the probability of maintaining a certain operating level under disturbance calculated based on Monte Carlo simulation, and the expected level of maintaining a certain operating level under disturbance calculated based on Monte Carlo simulation. The dynamic indicators include real-time load change rate, equipment failure rate, and grid voltage fluctuation amplitude. The risk indicators include equipment failure rate, power outage probability, and power outage duration.

[0043] The aforementioned comprehensive risk is denoted as R.total =w1×R ep +w2×R out ;

[0044] R total This represents the overall risk, where w1 and w2 are weighting coefficients, and R... ep Indicates the risk of equipment failure, R out This indicates a risk of power outage for users.

[0045] Furthermore, it also includes analyzing the impact of different bird-related behaviors on the power system based on the comprehensive risk assessment results, and formulating corresponding risk management strategies and steps based on the power system disaster response model; the formulation of corresponding risk management strategies includes strengthening equipment maintenance and upgrades; optimizing the power system layout; strengthening emergency management; and formulating long-term risk management plans.

[0046] A bird hazard risk assessment system for power transmission lines includes:

[0047] The module for building a scenario model linking bird damage behavior and regional victimization is used to collect historical bird damage behavior data, risk factor data, regional geographic information data, and power system layout data to build a scenario model linking bird damage behavior and regional victimization.

[0048] The power system disaster response model construction module is used to collect power system output and load change data under historical bird damage behavior and establish a power system disaster response model.

[0049] The comprehensive risk assessment module, based on a scenario model linking bird damage behavior with regional victimization, identifies indicators to measure the risk of power system failures under bird damage and conducts a comprehensive risk assessment of real-time collected bird damage behavior data.

[0050] Furthermore, the module for constructing the bird damage behavior and regional victim association scenario model is used to collect information on the time, location, and intensity of historical bird damage behavior, as well as the distribution of power facilities and regional construction status. It analyzes bird damage behavior in the time, state, and structural dimensions to establish a probability model of causative factors. The probability model of causative factors is as follows:

[0051] P(Z(E i )|F i，1 F i，2 F i，j )

[0052] in:

[0053] P(E i ) represents the probability of the i-th type of bird-harming behavior occurring;

[0054] F i，j This represents the j-th risk factor under the i-th type of bird-damaging behavior;

[0055] Z(E i ) represents the causative factor of the i-th type of bird damage behavior;

[0056] j represents the number of risk factors;

[0057] By combining a probability model of bird damage factors with regional geographic information and power system layout, the likelihood and extent of damage to power facilities are predicted based on the probability model. A scenario model linking bird damage behavior and regional damage is constructed, represented by the degree of urban damage.

[0058] S(E, P, F)

[0059] in:

[0060] S represents the city, indicating the degree of damage to the city;

[0061] E indicates bird-related misconduct;

[0062] P represents the power system layout;

[0063] F represents risk factors;

[0064] S is a function of E, P, and F.

[0065] Furthermore, the power system disaster response model construction module is used to collect data on the output power changes of new energy equipment under different bird-related behaviors, as well as the changes in electricity demand of residents and businesses under bird-related behaviors. It analyzes the characteristics of new energy output and load changes in the time, state, and architecture dimensions, and establishes a power system disaster response model for power system output and load changes. The power system disaster response model is as follows:

[0066] P new (t)=f(E(t))

[0067] L(t)=g(E(t))

[0068] in:

[0069] P new (t) represents the output of the new energy source at time t;

[0070] L(t) represents the load demand at time t;

[0071] E(t) represents the influencing factor of bird-damage behavior at time t;

[0072] f and g represent the functional relationships between new energy output and load demand and the influencing factors of bird damage behavior, respectively.

[0073] Furthermore, in the power system disaster response model construction module, adjustment measures are introduced to establish a mathematical model of the response behavior of a high-proportion renewable energy power system, describing the dynamic response of the power system under bird damage. The state equation of the power system is then expressed as:

[0074]

[0075] The output equation is expressed as:

[0076] Y = CX + DU

[0077] Where X represents the state variable of the power system, U represents the input variable, Y represents the output variable, and A, B, C, and D represent the system matrix, input matrix, output matrix, and direct transfer matrix, respectively.

[0078] Furthermore, in the comprehensive risk assessment module, the indicators for failure risk under bird damage include static indicators, dynamic indicators, and risk-sharing indicators. The static indicators include the average level of resilience calculated based on historical data statistical analysis, the probability of maintaining a certain operating level under disturbance calculated based on Monte Carlo simulation, and the expected level of maintaining a certain operating level under disturbance calculated based on Monte Carlo simulation. The dynamic indicators include real-time load change rate, equipment failure rate, and grid voltage fluctuation amplitude. The risk indicators include equipment failure rate, power outage probability, and power outage duration.

[0079] The aforementioned comprehensive risk is denoted as R. total =w1×R ep +w2×R out ;

[0080] R total This represents the overall risk, where w1 and w2 are weighting coefficients, and R... ep Indicates the risk of equipment failure, R out This indicates a risk of power outage for users.

[0081] Furthermore, it also includes a results analysis and decision-making module, which analyzes the impact of different bird-related behaviors on the power system based on the comprehensive risk assessment results, and formulates corresponding risk management strategies based on the power system disaster response model. The results analysis and decision-making module formulates corresponding risk management strategies, including strengthening equipment maintenance and upgrades; optimizing the power system layout; strengthening emergency management; and formulating long-term risk management plans.

[0082] The present invention has the following beneficial effects:

[0083] 1. This invention comprehensively analyzes the risk situation of power systems under bird damage by taking into account the time, state, and architectural dimensions. It uses scientific methods such as the probability model of causative factors, the output and load change model of new energy sources, and the mathematical model of the response behavior of high-proportion new energy power systems for modeling. Based on historical data and actual conditions, it can accurately predict the possibility and extent of damage to power facilities, as well as the response of the power system under disaster scenarios. Through clear calculation formulas, it accurately calculates static, dynamic, and risk indicators. Through multi-dimensional and multi-indicator data collection and simulation evaluation and prediction, it can comprehensively and accurately assess the risk level of the power system.

[0084] 2. This invention analyzes equipment failure risks, user power outage risks, and comprehensive risks to understand the weaknesses and potential risks of the power system under bird-related behaviors. Based on the risk assessment results, corresponding risk management strategies can be formulated. As the proportion of new energy in the power system continues to increase, this assessment method considers the characteristics of new energy output and load changes, and establishes a mathematical model of the response behavior of power systems with a high proportion of new energy. This makes the assessment method adaptable to the trend of new energy development, highly practical, and provides effective risk management guidance for power systems with a high proportion of new energy.

[0085] 3. The introduction of dynamic indicators in this invention enables the assessment method to monitor changes in the power system during bird-related incidents in real time. By assessing the comprehensive risk, the overall risk level of the power system under bird-related incidents can be quickly determined. Based on the risk assessment results, emergency resources, such as human, material, and financial resources, can be allocated rationally. This assessment method is applicable to power systems of different regions and scales. It only requires appropriate adjustments and optimizations based on the specific geographical environment, power system layout, and data conditions to achieve risk assessment for different power systems. At the same time, this method is also applicable to different types of bird-related incidents, and has wide applicability and scalability. Attached Figure Description

[0086] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0087] Figure 2 This is a schematic diagram of the power system disaster risk assessment index system architecture of the present invention;

[0088] Figure 3 This is a graph showing the change in load demand of new energy power output over time in the power system according to the present invention.

[0089] Figure 4 This is a schematic block diagram of the system structure of the present invention. Detailed Implementation

[0090] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Specific Implementation Example 1

[0092] like Figure 1-3 As shown, a method for assessing bird damage risk to power transmission lines includes the following steps:

[0093] Historical data on bird damage behavior was collected, including the time, location, and intensity of faults caused by nesting on power lines, excrement adhering to power lines, and pecking damage to power lines. Regional geographic information, power system layout, and related risk factor data were also collected, including the distribution of power facilities and urban development. Bird damage behavior was analyzed across time, state, and structural dimensions. The frequency and trends of bird damage behavior over time were analyzed to determine the probability of bird damage behavior in different time periods. In the state dimension, the impact of the intensity and duration of bird damage behavior on regional harm was considered. In the structural dimension, the relationship between the structure and layout of the power system and the urban geographic environment was analyzed, and a probability model of the causative factors was established. The probability model of the causative factors is as follows:

[0094] P(z(E i )|F i，1 F i，2 F i，j )

[0095] in:

[0096] P(E i ) represents the probability of the i-th type of bird-harming behavior occurring;

[0097] F i，j This represents the j-th risk factor under the i-th type of bird-damaging behavior;

[0098] Z(E i ) represents the causative factor of the i-th type of bird damage behavior;

[0099] j represents the number of risk factors;

[0100] The probability model of harmful factors represents the various risk factors F under a given i types of bird-harming behavior. i，j Under the condition of, the causative factor Z(E) of the i-th bird damage behavior iBy statistical analysis and model fitting of historical data, the probability model of causative factors can determine the probability of harm caused by different bird damage behaviors based on historical bird damage behavior data and risk factors, providing a basis for constructing victim-related scenarios.

[0101] By combining the probability model of causative factors with regional geographic information and power system layout, the likelihood and extent of damage to power facilities are predicted based on the probability model. The degree of urban disaster is expressed as follows:

[0102] S(E, P, F)

[0103] in:

[0104] S represents the city, indicating the degree of damage to the city;

[0105] E indicates bird-related misconduct;

[0106] P represents the power system layout;

[0107] F represents risk factors;

[0108] S is a function of E, P, and F;

[0109] By constructing a related scenario, the degree of urban damage S is represented as a function of bird damage behavior E, power system layout P, ​​and risk factors F. Through comprehensive analysis of these factors, a foundation is provided for subsequent risk assessment.

[0110] This study collects historical data on power system output and load changes under bird-related behaviors, including changes in the output power of renewable energy equipment such as wind turbines and solar panels under different bird-related behaviors, as well as changes in electricity demand from residents and businesses under bird-related behaviors. It analyzes the characteristics of renewable energy output and load changes, including trends, magnitude, and correlations. In the time dimension, it considers the impact of the duration of bird-related behaviors on renewable energy output and load. In the state dimension, it analyzes the operating status and reliability of renewable energy equipment. In the architecture dimension, it considers the impact of the power system topology and the location of renewable energy access on system response. A power system disaster response model based on power system output and load changes is established to simulate and extrapolate disaster scenarios. Data from the simulated disaster scenarios are collected and analyzed in the time, state, and architecture dimensions. The power system disaster response model based on power system output and load changes is as follows:

[0111] P new (t)=f(E(t))

[0112] L(t)=g(E(t))

[0113] in:

[0114] P new(t) represents the output of the new energy source at time t;

[0115] L(t) represents the load demand at time t;

[0116] E(t) represents the influencing factor of bird-damage behavior at time t;

[0117] f and g are the functional relationships between new energy output and load demand and the influencing factors of bird damage behavior, respectively;

[0118] This model represents the output P of new energy sources. new The load demand L(t) and the bird damage behavior influence factor E(t) are functions of the bird damage behavior influence factor. By analyzing the data and fitting the model, the specific forms of the functions f and g can be determined, thereby describing the changing patterns of new energy output and load under bird damage behavior. This can accurately reflect the impact of bird damage behavior on new energy and load, and provide a reference for the planning and operation of the power system.

[0119] Considering the characteristics of new energy sources, the topology of the power system, load demand, and other factors, a mathematical model of the response behavior of a high-proportion new energy power system is established. Simultaneously, considering the intermittency and volatility of new energy sources, regulation methods such as energy storage devices are introduced to improve the stability and reliability of the power system. In the power system disaster response modeling, a mathematical model of the response behavior of a high-proportion new energy power system is established to describe the dynamic response of the power system under extreme weather events. The state equation of the power system is then expressed as:

[0120]

[0121] The output equation can be expressed as:

[0122] Y = CX + DU

[0123] Where: X represents the state variable of the power system, U represents the input variable, Y represents the output variable, and A, B, C, and D represent the system matrix, input matrix, output matrix, and direct transfer matrix, respectively;

[0124] The above formula represents the rate of change of the state variable X of the power system over time. The system matrix A, input variable U, and state variable X are functions of the system matrix A, input variable U, and state variable X. The output variable Y is a function of the state variable X, input variable U, and direct transfer matrix D. The system matrix A, input matrix B, output matrix C, and direct transfer matrix D are determined by these factors.

[0125] Based on a scenario model linking bird damage behavior to regional damage, risk indicators, static indicators, and dynamic indicators are identified to measure the fault risk of the power system under bird damage. Using an assessment algorithm for equipment fault risk and user power outage risk, combined with static and dynamic indicators, a comprehensive assessment of power system risk is conducted based on real-time collected bird damage behavior data. In power system risk assessment, static indicators emphasize the inherent attributes and long-term average levels of the system, remaining unchanged regardless of specific disaster conditions. Indicators reflecting system average levels such as probability and expectation can be obtained through disaster simulation. The static indicators further include the following:

[0126] The average resilience level is calculated based on statistical analysis of historical data: The number of perturbations experienced in the historical data is N, and the resilience after each perturbation is R. i (i = 1, 2, ..., N), then the average level of restoring force

[0127] The probability of maintaining a certain operating level under disturbance is calculated based on Monte Carlo simulation: If M Monte Carlo simulations are performed, and m of them satisfy the condition for maintaining a certain operating level, then the probability of maintaining that operating level is calculated.

[0128] Based on Monte Carlo simulation, the expected operating level is maintained under disturbances: the index value for maintaining a certain operating level is M in each simulation. i (i = 1, 2, ..., M), then the expected value is...

[0129] In power system risk assessment, dynamic indicators emphasize the system's performance under specific disasters. These indicators are related to real-time monitoring and early warning during the disaster process. Dynamic indicators include real-time load change rate, equipment failure rate, and grid voltage fluctuation amplitude.

[0130] Real-time load change rate: Let the load value at a certain moment be L. t The load value at the previous moment was L. t-1 Then the real-time load change rate

[0131] Equipment failure rate: Let N be the total number of devices within a certain time period. equip The number of devices that failed is N. fault The equipment failure rate

[0132] Voltage fluctuation amplitude of the power grid: Let the voltage value at a certain moment be V. t The average voltage during normal operation is The voltage fluctuation amplitude of the grid structure

[0133] Risk indicators in power system risk assessment are used to measure the failure risk of the power system under bird-related behaviors, including equipment failure rate, outage probability, and outage duration, among which:

[0134] Equipment failure risk: R eq Indicates the risk of equipment failure, representing P. fail The probability of equipment failure is expressed as R. eq =P fail .

[0135] User power outage risk: R out Indicates the risk of power outage for users, P out T represents the probability of a user experiencing a power outage. out If the user's power outage time is represented by R, then the user's power outage risk can be represented as R. out =P out ×T out ;

[0136] The calculation of static indicators such as the average level of resilience, the probability of maintaining a certain operating level, and the expected value can reflect the inherent properties and long-term stability of the system. The calculation of dynamic indicators such as the real-time load change rate, equipment failure rate, and grid voltage fluctuation amplitude can capture the changes in the system during disasters in a timely manner. The calculation of risk indicators such as equipment failure risk and user power outage risk can accurately measure the failure risk of the power system under bird damage. By combining these indicators, the risk level of the power system can be comprehensively and accurately assessed.

[0137] The comprehensive assessment in power system risk assessment takes into account both equipment failure risk and user power outage risk, and evaluates the overall risk level of the power system under bird-related activities, R. total This represents the overall risk, where w1 and w2 are weighting coefficients, and R... ep Indicates the risk of equipment failure, R out If the risk of power outage for users is represented by R, then the overall risk can be expressed as R. total =w1×R ep +w2×R out .

[0138] By comprehensively assessing power risks, the comprehensive risk R total Equipment failure risk R ep and user power outage risk R out The weighted sum, with weighting coefficients w1 and w2 determined according to actual conditions, reflects the importance of equipment failure risk and user power outage risk in the comprehensive risk assessment. By comprehensively assessing the risks of the power system, a scientific basis can be provided for formulating risk management strategies.

[0139] Based on the comprehensive assessment results, the risks of equipment failure, power outages, and overall risks are analyzed to understand the weaknesses and potential risks of the power system under bird damage. The impact of different bird damage behaviors on the power system is analyzed. Based on the risk assessment results and the power system disaster response model, corresponding risk management strategies are formulated, such as strengthening equipment maintenance and upgrades to improve equipment resilience; optimizing the power system layout to improve system reliability; and strengthening emergency management to improve the ability to respond to bird damage. Considering the changing trends of static and dynamic indicators, a long-term risk management plan is developed to improve the resilience and reliability of the power system. Specific Implementation Example 2

[0141] like Figure 1-3 As shown, based on the content of the above specific embodiments, the following content is further disclosed:

[0142] In constructing a scenario model linking bird damage behavior with regional victimization, the time dimension, state dimension, and architecture dimension further include the following:

[0143] The time dimension describes the complete evolution of power system resilience, including pre-disaster, during-disaster, and post-disaster indicators. Pre-disaster indicators primarily assess the resilient grid's ability to prevent bird-related incidents before they occur, reflecting the distribution network's robustness to disaster predictions and providing a reference for relevant departments in bird-related early warning and emergency response. These indicators mainly cover aspects such as grid construction, bird-related prediction, anticipated damage, and emergency plans. During-disaster indicators primarily assess the resilient grid's ability to withstand bird-related incidents, reflecting real-time changes in the distribution network's operating status and providing a valuable reference for emergency dispatch. These indicators mainly cover aspects such as bird-related monitoring, fault propagation, dispatch status, and real-time damage. Post-disaster indicators primarily assess the resilient grid's recovery ability after bird-related incidents, reflecting the distribution network's post-disaster recovery efficiency and providing a basis for relevant departments in post-disaster recovery decisions. These indicators provide a reference for policy decisions and mainly include aspects such as power grid recovery strategies, recovery capabilities, flexible resource allocation, and recovery efficiency. By combining indicators in three stages—before, during, and after a disaster—they describe the complete changes in the resilience of the power system, thus reflecting the multiple connotations of power system resilience. The time dimension covers the complete change process before, during, and after a disaster, ensuring a clear understanding of the risks at different stages. Based on the risk assessment results, corresponding risk management strategies can be formulated. Before a disaster, preventive measures can be strengthened, such as improving the resilience of equipment, optimizing the power system layout, and strengthening risk early warning. During a disaster, effective emergency measures can be taken, such as quickly restoring power supply and ensuring the power supply to important loads. After a disaster, the recovery speed can be accelerated and the recovery quality improved, such as rationally allocating resources and strengthening equipment maintenance. These strategies can effectively reduce the risks to the power system under bird damage and improve the reliability and stability of the system.

[0144] State dimension: This includes static and dynamic indicators. Static indicators reflect the resilience level of the power system within a certain range over a period of time, reflecting the system's elasticity performance. Dynamic indicators reflect the real-time changes in the power system's elasticity at a certain node. The state dimension distinguishes between static and dynamic indicators, considering both the long-term average level of the system and real-time changes, making the assessment more accurate. The introduction of dynamic indicators enables the assessment method to monitor the changes in the power system during disasters in real time, such as real-time load change rate, equipment failure rate, and grid voltage fluctuation amplitude. These indicators can reflect the system's operating status in a timely manner, providing important basis for emergency decision-making.

[0145] Architectural Dimension: This describes the process by which a power system maintains and recovers normal operation under bird damage conditions, utilizing internal and external factors. This includes load, equipment, grid structure, resilient resources, emergency management, and geological and meteorological conditions. Environmental indicators primarily characterize the impact of the external environment on the distribution network's resilience. Generally, the more severe the external environment, the weaker the power system's ability to resist external disturbances, and the more easily the grid's resilience is affected. These indicators mainly include meteorological conditions, geological conditions, and disaster characteristics. Grid indicators characterize the distribution system's performance characteristics in response to bird damage, primarily considering the impact of grid topology, equipment operation, and load distribution on the power system's resilience. These indicators include equipment conditions, grid structure, and load distribution. Load distribution, etc.; technical indicators characterize the power sector's emergency management and resource allocation capabilities for resilient distribution networks. When the power grid's technical level and resource quantity improve, the power system can enhance its stable operation and rapid recovery capabilities. These indicators include emergency management and dispatch, resilient resource allocation, and fault analysis. By combining these structural indicators, the impact of different factors on the resilience of the power system can be reflected, and relevant personnel can be provided with ideas to take appropriate measures to strengthen and improve the resilience of the power system from the perspective of the corresponding objects. The structural dimension evaluates multiple aspects such as load, equipment, grid structure, resilient resources, emergency management, and geological and meteorological conditions, leaving no factor that may affect the risks of the power system unchecked. Specific Implementation Example 3

[0147] like Figure 1-3 As shown, based on the content of the above specific embodiments, the following content is further disclosed:

[0148] The above algorithm was applied to different power system architectures and operating conditions, as detailed below:

[0149] This section addresses performance across different power system architectures, primarily focusing on existing power system architectures, including centralized, distributed, and microgrid architectures.

[0150] Centralized power system architecture: In a centralized power system, large power plants transmit electricity to load centers through high-voltage transmission lines. Risk assessment algorithms can accurately evaluate the impact of bird damage on large power plants and major transmission lines. Due to the relatively simple structure of centralized systems and fewer risk factors, assessment algorithms can more focusedly analyze the risks of key equipment and lines, such as large generators, transformers, and long-distance transmission lines. For the probability model of causative factors, based on historical data and known risk factors, the probability of bird damage affecting these key equipment can be predicted relatively accurately. Since a failure in a centralized system may affect a large area of ​​power supply, the reliability and recovery capability of the system need to be given more attention during the assessment process. For example, when calculating the risk of equipment failure and the risk of power outage for users, the cascading effects of the failure and the possibility of large-scale power outages need to be considered.

[0151] Distributed power system architecture: A distributed power system consists of multiple small distributed power sources and loads connected through medium- and low-voltage distribution networks. Under this architecture, risk assessment algorithms need to consider more distributed power sources and complex network structures. Distributed systems have higher flexibility and reliability. Since distributed power sources are scattered in different geographical locations, the impact of bird damage on the entire system is relatively small. Risk assessment algorithms can better consider the independence and complementarity of different distributed power sources, improving the system's resilience. Due to the complexity of distributed systems, more risk factors need to be considered, such as the reliability, intermittency, and volatility of distributed power sources, as well as the coordination and control issues between different distributed power sources. In addition, the network structure of distributed systems is more complex, and the fault propagation and recovery process is more difficult to predict. This requires risk assessment algorithms to analyze and simulate different fault scenarios more precisely.

[0152] Microgrid Architecture: A microgrid is a relatively independent small-scale power system that can operate independently even under main grid failures or bird damage. Risk assessment algorithms need to be adjusted and optimized for the characteristics of microgrids. Microgrids possess high autonomy and flexibility, enabling rapid response to extreme weather events and ensuring power supply to critical loads. Risk assessment algorithms can focus more on the energy management and control strategies within the microgrid, assessing the risk level under different operating modes. For example, in islanded operation mode, it is necessary to assess the matching degree between the microgrid's power capacity and load demand, as well as the role and reliability of energy storage devices. Microgrid technology and equipment are relatively new, lacking sufficient historical data and experience, which poses certain challenges to the accuracy and reliability of risk assessment algorithms.

[0153] The specific details are as follows, depending on the different operating conditions:

[0154] High proportion of renewable energy integration: As the proportion of renewable energy in the power system continues to increase, risk assessment algorithms need to consider the characteristics and uncertainties of renewable energy. For example, wind power and photovoltaic power generation are intermittent and volatile, and bird damage may have a greater impact on the output of renewable energy. The integration of renewable energy can improve the sustainability and environmental friendliness of the power system. In the risk assessment process, the decentralization and complementarity of renewable energy can be considered to reduce dependence on traditional fossil fuels and improve the system's disaster resistance.

[0155] Applications of smart grid technology: The application of smart grid technology can improve the monitoring, control, and management of power systems, and will also affect risk assessment algorithms. For example, smart sensors and communication technologies can collect real-time operating data of the power system, providing more accurate information for risk assessment. Smart grid technology can realize real-time monitoring and early warning of the power system, improving the timeliness and accuracy of risk assessment. For example, through smart sensors, the operating status of equipment and environmental parameters can be monitored in real time, and potential fault hazards can be detected in a timely manner. At the same time, the self-healing capability and flexible control strategies of the smart grid can reduce the risk of equipment failure and power outage for users, and improve the reliability and recovery capability of the system.

[0156] Different load characteristics: Different load types and characteristics will also affect the risk assessment of the power system. For example, industrial loads and residential loads have different demand characteristics and different impacts on power outages caused by bird damage. Understanding different load characteristics can more accurately assess the power outage risk to users and the system's recovery needs. For example, for important industrial loads, special protection measures and backup power supplies can be taken to improve their power supply reliability. For residential loads, demand response and load management strategies can be used to reduce peak loads and improve the system's stability and disaster resistance.

[0157] In summary, risk assessment algorithms will perform differently under different power system architectures and operating conditions. They need to be adjusted and optimized according to specific circumstances to improve the accuracy and reliability of risk assessment and provide a scientific basis for the planning, operation and management of power systems. Specific Implementation Example 4

[0159] like Figure 4 As shown, a system for assessing bird damage risk to power transmission lines includes:

[0160] The module for building a scenario model linking bird damage behavior with regional victims is used to collect historical bird damage behavior data, including the time, location, and intensity of faults caused by nesting on power lines, excrement adhering to power lines, and pecking damage to power lines. It also collects regional geographic information, power system layout, and related risk factor data, including the distribution of power facilities and urban construction. The module analyzes bird damage behavior across time, state, and architectural dimensions. It analyzes the frequency and trends of bird damage behavior over time to determine the probability of bird damage behavior in different time periods. In the state dimension, it considers the impact of the intensity and duration of bird damage behavior on regional victims. In the architectural dimension, it analyzes the relationship between the structure and layout of the power system and the urban geographic environment, establishing a probability model for the causative factors. The probability model for the causative factors is as follows:

[0161] P(z(E i )|F i，1 F i，2 F i，j )

[0162] in:

[0163] P(E i ) represents the probability of the i-th type of bird-harming behavior occurring;

[0164] F i，j This represents the j-th risk factor under the i-th type of bird-damaging behavior;

[0165] Z(E i ) represents the causative factor of the i-th type of bird damage behavior;

[0166] k represents the number of risk factors;

[0167] The probability model of harmful factors represents the various risk factors F under a given i types of bird-harming behavior. i，j Under the condition of, the causative factor Z(E) of the i-th bird damage behavior i By statistical analysis and model fitting of historical data, the probability model of causative factors can determine the probability of harm caused by different bird damage behaviors based on historical bird damage behavior data and risk factors, providing a basis for constructing victim-related scenarios.

[0168] By combining the probability model of causative factors with regional geographic information and power system layout, the likelihood and extent of damage to power facilities are predicted based on the probability model. The degree of urban disaster is expressed as follows:

[0169] S(E, P, F)

[0170] in:

[0171] S represents the city, indicating the degree of damage to the city;

[0172] E indicates bird-related misconduct;

[0173] P represents the power system layout;

[0174] F represents risk factors;

[0175] S is a function of E, P, and F;

[0176] By constructing a related scenario, the degree of urban damage S is represented as a function of bird damage behavior E, power system layout P, ​​and risk factors F. Through comprehensive analysis of these factors, a foundation is provided for subsequent risk assessment.

[0177] The power system disaster response model construction module is used to collect historical data on power system output and load changes under bird damage, including the output power changes of renewable energy equipment such as wind turbines and solar panels under different bird damage behaviors, as well as the changes in electricity demand of residents and enterprises under bird damage. It analyzes the characteristics of renewable energy output and load changes, including trends, magnitude, and correlations. In the time dimension, it considers the impact of the duration of bird damage on renewable energy output and load. In the state dimension, it analyzes the operating status and reliability of renewable energy equipment. In the architecture dimension, it considers the impact of the power system topology and the location of renewable energy access on system response. A power system disaster response model based on power system output and load changes is established to simulate and extrapolate disaster scenarios. Data from the simulated disaster scenarios is collected and analyzed in the time, state, and architecture dimensions. The power system disaster response model based on power system output and load changes is as follows:

[0178] P new (t)=f(E(t))

[0179] L(t)=g(E(t))

[0180] in:

[0181] P new (t) represents the output of the new energy source at time t;

[0182] L(t) represents the load demand at time t;

[0183] E(t) represents the influencing factor of bird-damage behavior at time t;

[0184] f and g are the functional relationships between new energy output and load demand and the influencing factors of bird damage behavior, respectively;

[0185] This model represents the output P of new energy sources. newThe load demand L(t) and the bird damage behavior influence factor E(t) are functions of the bird damage behavior influence factor. By analyzing the data and fitting the model, the specific forms of the functions f and g can be determined, thereby describing the changing patterns of new energy output and load under bird damage behavior. This can accurately reflect the impact of bird damage behavior on new energy and load, and provide a reference for the planning and operation of the power system.

[0186] Considering the characteristics of new energy sources, the topology of the power system, load demand, and other factors, a mathematical model of the response behavior of a high-proportion new energy power system is established. Simultaneously, considering the intermittency and volatility of new energy sources, regulation methods such as energy storage devices are introduced to improve the stability and reliability of the power system. In the power system disaster response modeling, a mathematical model of the response behavior of a high-proportion new energy power system is established to describe the dynamic response of the power system under extreme weather events. The state equation of the power system is then expressed as:

[0187]

[0188] The output equation can be expressed as:

[0189] Y = CX + DU

[0190] Where: X represents the state variable of the power system, u represents the input variable, Y represents the output variable, and A, B, C, and D represent the system matrix, input matrix, output matrix, and direct transfer matrix, respectively;

[0191] The above formula represents the rate of change of the state variable X of the power system over time. The system matrix A, input variable U, and state variable X are functions of the system matrix A, input variable U, and state variable X. The output variable Y is a function of the state variable X, input variable U, and direct transfer matrix D. The system matrix A, input matrix B, output matrix C, and direct transfer matrix D are determined by these factors.

[0192] The comprehensive risk assessment module, based on a scenario model linking bird damage behavior to regional victimization, identifies risk indicators, static indicators, and dynamic indicators to measure the risk of power system failures under bird damage. Using an assessment algorithm for equipment failure risk and user power outage risk, combined with static and dynamic indicators, it comprehensively assesses power system risk based on real-time collected bird damage behavior data. In power system risk assessment, static indicators emphasize inherent system attributes and long-term average levels, remaining unchanged regardless of specific disaster conditions. Indicators reflecting system average levels such as probability and expectation can be obtained through disaster simulation. The static indicators further include the following:

[0193] The average resilience level is calculated based on statistical analysis of historical data: The number of perturbations experienced in the historical data is N, and the resilience after each perturbation is R. i (i = 1, 2, ..., N), then the average level of restoring force

[0194] The probability of maintaining a certain operating level under disturbance is calculated based on Monte Carlo simulation: If M Monte Carlo simulations are performed, and m of them satisfy the condition for maintaining a certain operating level, then the probability of maintaining that operating level is calculated.

[0195] Based on Monte Carlo simulation, the expected operating level is maintained under disturbances: the index value for maintaining a certain operating level is M in each simulation. i (i = 1, 2, ..., M), then the expected value is...

[0196] In power system risk assessment, dynamic indicators emphasize the system's performance under specific disasters. These indicators are related to real-time monitoring and early warning during the disaster process. Dynamic indicators include real-time load change rate, equipment failure rate, and grid voltage fluctuation amplitude.

[0197] Real-time load change rate: Let the load value at a certain moment be L. t The load value at the previous moment was L. t-1 Then the real-time load change rate

[0198] Equipment failure rate: Let N be the total number of devices within a certain time period. equip The number of devices that failed is N. fault The equipment failure rate

[0199] Voltage fluctuation amplitude of the power grid: Let the voltage value at a certain moment be V. t The average voltage during normal operation is The voltage fluctuation amplitude of the grid structure

[0200] Risk indicators in power system risk assessment are used to measure the failure risk of the power system under bird-related behaviors, including equipment failure rate, outage probability, and outage duration, among which:

[0201] Equipment failure risk: R eq Indicates the risk of equipment failure, representing P. fail The probability of equipment failure is expressed as R. eq =P fail .

[0202] User power outage risk: R out Indicates the risk of power outage for users, P out T represents the probability of a user experiencing a power outage. out If the user's power outage time is represented by R, then the user's power outage risk can be represented as R. out=P out ×T out ;

[0203] The calculation of static indicators such as the average level of resilience, the probability of maintaining a certain operating level, and the expected value can reflect the inherent properties and long-term stability of the system. The calculation of dynamic indicators such as the real-time load change rate, equipment failure rate, and grid voltage fluctuation amplitude can capture the changes in the system during disasters in a timely manner. The calculation of risk indicators such as equipment failure risk and user power outage risk can accurately measure the failure risk of the power system under bird damage. By combining these indicators, the risk level of the power system can be comprehensively and accurately assessed.

[0204] The comprehensive assessment in power system risk assessment takes into account both equipment failure risk and user power outage risk, and evaluates the overall risk level of the power system under bird-related activities, R. total This represents the overall risk, where w1 and w2 are weighting coefficients, and R... ep Indicates the risk of equipment failure, R out If the risk of power outage for users is represented by R, then the overall risk can be expressed as R. total =w1×R ep +w2×R out .

[0205] By comprehensively assessing power risks, the comprehensive risk R total Equipment failure risk R ep and user power outage risk R out The weighted sum, with weighting coefficients w1 and w2 determined according to actual conditions, reflects the importance of equipment failure risk and user power outage risk in the comprehensive risk assessment. By comprehensively assessing the risks of the power system, a scientific basis can be provided for formulating risk management strategies.

[0206] The results analysis and decision-making module analyzes equipment failure risk, user power outage risk, and overall risk based on the comprehensive assessment results. It identifies weaknesses and potential risks in the power system under bird damage, analyzes the impact of different bird damage behaviors on the power system, and formulates corresponding risk management strategies based on the power system disaster response model. These strategies include strengthening equipment maintenance and upgrades to improve equipment resilience; optimizing the power system layout to improve system reliability; and strengthening emergency management to enhance the ability to respond to bird damage. Considering the changing trends of static and dynamic indicators, it develops long-term risk management plans to improve the power system's resilience and reliability.

[0207] Through the steps and methods described above, the overall approach to multi-dimensional risk assessment of power system faults achieves comprehensiveness, accuracy, practicality, and scalability. Specific details are as follows:

[0208] In terms of comprehensiveness, this is reflected in the time dimension, the content of the indicators, and the scope of the evaluation objects:

[0209] The algorithm takes into account all time dimensions: it analyzes the situation from three time stages: before, during, and after the damage. Before the damage, it emphasizes the ability to prevent bird damage behavior and take measures in advance to reduce the possibility and impact of bird damage. During the damage, it emphasizes the ability to resist bird damage behavior and ensure that the power system can maintain stable operation as much as possible when bird damage occurs. After the damage, it emphasizes the ability to recover and quickly restore power supply to reduce the impact of bird damage on the social economy.

[0210] The indicators are rich in nature and dimensions: they are divided into static indicators and dynamic indicators. Static indicators reflect the inherent attributes and long-term average levels of the system, providing basic data and reference standards for risk assessment. Dynamic indicators emphasize the real-time performance of the system under specific bird damage, and can capture changes in the bird damage process in a timely manner, making risk assessment more accurate and timely.

[0211] The assessment covers a wide range of dimensions: the distribution network resilience assessment indicators are divided from multiple aspects such as load, equipment, grid structure, flexible resources, and emergency management, covering all key links and influencing factors of the power system, and can comprehensively assess the risk status of the power system under bird damage.

[0212] Regarding accuracy, this is reflected in multi-data-driven approaches, multi-model integration, and real-time monitoring and adjustment:

[0213] Data-driven: By collecting a large amount of historical bird damage behavior data, power system output and load change data, and using Monte Carlo simulation for model building and calculation, the accuracy of risk assessment can be improved.

[0214] Multiple models are combined: The power system is analyzed from different perspectives by comprehensively using multiple models, such as the probability model of causative factors, the power output and load change model of new energy sources, and the mathematical model of the response behavior of power systems with high proportion of new energy sources. These models complement each other and can more accurately reflect the operating status and risk of the power system under bird damage.

[0215] Real-time monitoring and adjustment: The introduction of dynamic indicators enables the risk assessment algorithm to monitor changes in the power system during bird damage in real time and adjust and optimize based on real-time data. This helps improve the accuracy and timeliness of risk assessment and provides a more reliable basis for emergency decision-making.

[0216] Its practicality is reflected in its scientific basis, emergency management, and adaptation to the development of new energy sources:

[0217] Providing a scientific basis for decision-making: Risk assessment algorithms can provide specific indicators such as equipment failure risk, user power outage risk, and comprehensive risk, providing a scientific basis for the planning, operation, and maintenance of power systems. Decision-makers can formulate corresponding risk management strategies based on the risk assessment results to improve the disaster resistance and reliability of power systems.

[0218] Guiding Emergency Management: By assessing post-disaster recovery capabilities, risk assessment algorithms can provide guidance for emergency management, such as identifying priority areas and equipment for recovery, allocating resources rationally, improving emergency response efficiency, and shortening power outage time;

[0219] Adapting to the development of new energy sources: As the proportion of new energy sources in the power system continues to increase, this algorithm takes into account the characteristics of new energy output and load changes, and establishes a mathematical model of the response behavior of power systems with a high proportion of new energy sources. This enables the risk assessment algorithm to adapt to the trend of new energy development and provide an effective risk assessment method for power systems with a high proportion of new energy sources.

[0220] Scalability is reflected in the integration of multiple factors and adaptation to multi-regional scenarios:

[0221] New factors can be incorporated: The risk assessment algorithm framework has a certain degree of flexibility and can incorporate new factors and indicators according to actual needs. For example, the impact of socio-economic factors, environmental factors and other factors on power system risks can be considered to further expand the application scope of the algorithm.

[0222] Applicable to power systems of different regions and sizes: The principle and method of this algorithm have a certain degree of universality and can be applied to power systems of different regions and sizes. It only requires appropriate adjustments and optimizations based on the specific geographical environment, power system layout and data conditions to achieve risk assessment for different power systems.

[0223] In summary, the multi-dimensional risk assessment algorithm for power system failures caused by bird damage has advantages such as comprehensiveness, accuracy, practicality, and scalability, and can provide strong support for the safe and stable operation of power systems.

[0224] This invention also provides a storage medium storing a computer program. When executed by a processor, the computer program implements some or all of the steps in the various embodiments of the power transmission line bird hazard risk assessment method provided by this invention. The storage medium may be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0225] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0226] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a reference structure" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0227] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for assessing bird damage risk to power transmission lines, characterized in that, Includes the following steps: Collect historical bird damage behavior data, risk factor data, regional geographic information data, and power system layout data to construct a scenario model linking bird damage behavior and regional victimization. Collect historical data on power system output and load changes under bird damage behavior to establish a power system disaster response model; Based on the scenario model of the correlation between bird damage behavior and regional victimization, indicators are determined to measure the failure risk of the power system under bird damage behavior, and a comprehensive risk assessment is carried out on the real-time collected bird damage behavior data.

2. The method for assessing bird damage risk to transmission lines according to claim 1, characterized in that, The construction of the bird damage behavior and regional victim association scenario model specifically includes the following steps: Information on the time, location, and intensity of historical bird damage events was collected, along with data on the distribution and regional construction of power facilities. Bird damage events were analyzed across time, state, and structural dimensions to establish a probabilistic model of causative factors. The probabilistic model of causative factors is as follows: P(Z(E i )|F i，1 ,F i，2 ,…,F i，j ) in: P(E i ) represents the probability of the i-th type of bird-harming behavior occurring; F i，j This represents the j-th risk factor under the i-th type of bird-damaging behavior; Z(E i ) represents the causative factor of the i-th type of bird-damaging behavior; j represents the number of risk factors; By combining a probability model of bird damage factors with regional geographic information and power system layout, the likelihood and extent of damage to power facilities are predicted based on the probability model. A scenario model linking bird damage behavior and regional damage is constructed, represented by the degree of urban damage. S(E, P, F) in: S represents the city, indicating the degree of damage to the city; E indicates bird-related misconduct; P represents the power system layout; F represents risk factors; S is a function of E, P, and F.

3. The method for assessing bird damage risk to transmission lines according to claim 1, characterized in that, The establishment of a power system disaster response model specifically includes the following steps: This study collects data on the output power changes of renewable energy equipment under different bird-related behaviors, as well as the changes in electricity demand from residents and businesses under bird-related behaviors. It analyzes the characteristics of renewable energy output and load changes in the time, state, and architecture dimensions, and establishes a power system disaster response model based on power system output and load changes. The power system disaster response model is as follows: P new (t)=f(E(t)) L(t)=g(E(t)) in: P new (t) represents the output of the new energy source at time t; L(t) represents the load demand at time t; E(t) represents the influencing factor of bird-damage behavior at time t; f and g represent the functional relationships between new energy output and load demand and the influencing factors of bird damage behavior, respectively.

4. The method for assessing bird damage risk to transmission lines according to claim 3, characterized in that, In the power system disaster response modeling, regulation measures are introduced to establish a mathematical model of the response behavior of a high-proportion renewable energy power system, describing the dynamic response of the power system under bird damage. The state equation of the power system is then expressed as: The output equation is expressed as: Y = CX + DU Where X represents the state variable of the power system, U represents the input variable, Y represents the output variable, and A, B, C, and D represent the system matrix, input matrix, output matrix, and direct transfer matrix, respectively.

5. The method for assessing bird damage risk to transmission lines according to claim 1, characterized in that, In the comprehensive risk assessment steps described above, the indicators for failure risk under bird damage include static indicators, dynamic indicators, and risk-sharing indicators. The static indicators include the average level of resilience calculated based on historical data statistical analysis, the probability of maintaining a certain operating level under disturbance calculated based on Monte Carlo simulation, and the expected level of maintaining a certain operating level under disturbance calculated based on Monte Carlo simulation. The dynamic indicators include real-time load change rate, equipment failure rate, and grid voltage fluctuation amplitude. The risk indicators include equipment failure rate, power outage probability, and power outage duration. The aforementioned comprehensive risk is denoted as R. total =w1×R ep +w2×R out ; R total This represents the overall risk, where w1 and w2 are weighting coefficients, and R... ep Indicates the risk of equipment failure, R out This indicates a risk of power outage for users.

6. The method for assessing bird damage risk to transmission lines according to claim 1, characterized in that, It also includes analyzing the impact of different bird-related behaviors on the power system based on the comprehensive risk assessment results, and formulating corresponding risk management strategies and steps based on the power system disaster response model; The aforementioned development of corresponding risk management strategies includes strengthening equipment maintenance and upgrades; optimizing the power system layout; Strengthen emergency management and develop long-term risk management plans.

7. The assessment system for a method of assessing bird damage risk to transmission lines according to any one of claims 1-6, characterized in that, include: The module for building a scenario model linking bird damage behavior and regional victimization is used to collect historical bird damage behavior data, risk factor data, regional geographic information data, and power system layout data to build a scenario model linking bird damage behavior and regional victimization. The power system disaster response model construction module is used to collect power system output and load change data under historical bird damage behavior and establish a power system disaster response model. The comprehensive risk assessment module, based on a scenario model linking bird damage behavior with regional victimization, identifies indicators to measure the risk of power system failures under bird damage and conducts a comprehensive risk assessment of real-time collected bird damage behavior data.

8. The bird hazard risk assessment system for transmission lines according to claim 7, characterized in that, The aforementioned module for constructing a scenario model linking bird damage behavior and regional victimization is used to collect information on the time, location, and intensity of historical bird damage behavior, as well as the distribution of power facilities and regional construction status. It analyzes bird damage behavior across time, state, and structural dimensions to establish a probability model of causative factors. The probability model of causative factors is as follows: P(Z(E i )|F i，1 ,F i，2 ,…,F i，j ) in: P(E i ) represents the probability of the i-th type of bird-harming behavior occurring; F i，j This represents the j-th risk factor under the i-th type of bird-damaging behavior; Z(E i ) represents the causative factor of the i-th type of bird-damaging behavior; j represents the number of risk factors; By combining a probability model of bird damage factors with regional geographic information and power system layout, the likelihood and extent of damage to power facilities are predicted based on the probability model. A scenario model linking bird damage behavior and regional damage is constructed, represented by the degree of urban damage. S(E, P, F) in: S represents the city, indicating the degree of damage to the city; E indicates bird-related misconduct; P represents the power system layout; F represents risk factors; S is a function of E, P, and F.

9. A bird hazard risk assessment system for transmission lines according to claim 7, characterized in that, The power system disaster response model construction module is used to collect data on the output power changes of new energy equipment under different bird-related behaviors, as well as the changes in electricity demand of residents and businesses under bird-related behaviors. It analyzes the characteristics of new energy output and load changes in the time, state, and architecture dimensions, and establishes a power system disaster response model based on power system output and load changes. The power system disaster response model is as follows: P new (t)=f(E(t)) L(t)=g(E(t)) in: P new (t) represents the output of the new energy source at time t; L(t) represents the load demand at time t; E(t) represents the influencing factor of bird-damage behavior at time t; f and g represent the functional relationships between new energy output and load demand and the influencing factors of bird damage behavior, respectively.

10. A bird hazard risk assessment system for transmission lines according to claim 9, characterized in that, In the power system disaster response model construction module, regulation measures are introduced to establish a mathematical model of the response behavior of a high-proportion renewable energy power system, describing the dynamic response of the power system under bird damage. The state equation of the power system is then expressed as: The output equation is expressed as: Y = CX + DU Where X represents the state variable of the power system, U represents the input variable, Y represents the output variable, and A, B, C, and D represent the system matrix, input matrix, output matrix, and direct transfer matrix, respectively.

11. The bird hazard risk assessment system for transmission lines according to claim 7, characterized in that, In the comprehensive risk assessment module, the indicators for failure risk under bird damage include static indicators, dynamic indicators, and risk-sharing indicators. The static indicators include the average level of resilience calculated based on historical data statistical analysis, the probability of maintaining a certain operating level under disturbance calculated based on Monte Carlo simulation, and the expected level of maintaining a certain operating level under disturbance calculated based on Monte Carlo simulation. The dynamic indicators include real-time load change rate, equipment failure rate, and grid voltage fluctuation amplitude. The risk indicators include equipment failure rate, power outage probability, and power outage duration. The aforementioned comprehensive risk is denoted as R. total =w1×R ep +w2×R out ; R total This represents the overall risk, where w1 and w2 are weighting coefficients, and R... ep Indicates the risk of equipment failure, R out This indicates a risk of power outage for users.

12. The bird hazard risk assessment system for transmission lines according to claim 7, characterized in that, It also includes a results analysis and decision-making module, which analyzes the impact of different bird-related behaviors on the power system based on the comprehensive risk assessment results, and formulates corresponding risk management strategies based on the power system disaster response model; the results analysis and decision-making module formulates corresponding risk management strategies, including strengthening equipment maintenance and upgrading; and optimizing the power system layout; Strengthen emergency management and develop long-term risk management plans.

13. A computer device, characterized in that, include: processor; And a memory configured to store machine-readable instructions, which, when executed by the processor, perform a method for assessing bird damage risks to transmission lines as described in any one of claims 1-7.

14. A storage medium, characterized in that, The storage medium stores a computer program, which is executed by a processor as described in any one of claims 1-7, a method for assessing bird damage risks to power transmission lines.

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