Star-ground information-mountain fire model driven power transmission corridor fault risk prediction method and device

Abstract

The application provides a mountain fire model driving power transmission corridor fault risk prediction method and device, and relates to the technical field of power system safety risk assessment. Through the fusion of multi-source information, the fire line evolution dynamic prediction and satellite observation correction are realized, the line segment level interval exposure state variable is constructed, the cumulative fault probability model is established, the whole line risk assessment is generated combined with the spatial correlation aggregation, the technical problem that the traditional method cannot capture the real-time interaction of fire line dynamic evolution and line state is effectively solved, the mountain fire dynamic characteristics can be responded in real time, the line segment level risk accumulation process can be accurately described, and the spatial correlation is comprehensively considered, and the resilience defense capability of the power grid under extreme disasters is improved.

Description

Technical Field

[0001] This application relates to the field of power system safety risk assessment technology, and in particular to a method and device for predicting transmission corridor fault risks driven by satellite-ground information-wildfire model. Background Technology

[0002] Wildfires pose a serious threat to power infrastructure, especially transmission lines, which are the backbone of the power grid. Their physical characteristics result in high-risk exposure: these lines traverse vast areas, passing through complex terrains such as mountains and forests, and are exposed to open environments for extended periods, lacking effective physical barriers. During the spread of wildfires, transmission lines are continuously subjected to multiple coupled effects, including a sudden rise in conductor temperature caused by high-intensity thermal radiation, insulator surface contamination due to smoke particle deposition, a decrease in air dielectric strength caused by high-temperature smoke, and mechanical stress impacts from environmental disturbances such as sudden changes in wind direction. These factors work together to easily induce faults such as instantaneous line tripping, insulation flashover, or conductor melting, and in severe cases, can trigger cross-regional cascading outages, causing widespread power interruptions.

[0003] The evolving nature of wildfires further complicates risk assessment. Fire spread is dynamically influenced by multiple factors, including undulating terrain, rapidly changing weather conditions, and uneven distribution of vegetation and fuel, exhibiting highly nonlinear characteristics. The spatiotemporal states of different transmission line segments exposed to fire vary significantly: the proximity of the fire line to the line changes rapidly over time; heat radiation intensity fluctuates locally due to terrain shielding; fault propagation is linked between adjacent segments due to spatial continuity; and the cumulative risk effect increases nonlinearly with exposure duration. Traditional risk assessment methods are mostly based on historical statistics or static scenario modeling, considering only environmental parameters at a single point in time or simplifying assumptions, failing to capture the real-time interaction between the dynamic evolution of the fire line and the line status. When dealing with complex scenarios such as multiple overlapping fire points and rapid fire shifts, these methods often underestimate risk levels or overlook critical vulnerable sections, leading to delayed early warnings and ineffective control measures.

[0004] The construction of new power systems is accelerating, and power grids are exhibiting new characteristics such as large-scale interconnection, high penetration of renewable energy, and highly flexible operation. Transmission channels play an increasingly crucial role in cross-regional power exchange, renewable energy consumption, and system stability support. Their failures can trigger cascading failures, leading to systemic consequences such as widespread power outages, loss of power to critical loads, exceeding operational constraints, and soaring emergency dispatch costs. Current technological frameworks lack an effective mechanism for integrating multi-source heterogeneous information, including satellite remote sensing of wildfire dynamics, refined weather forecasts, three-dimensional terrain data, vegetation and fuel characteristics, and power grid topology. This makes it difficult to construct a complete chain from fire line evolution prediction to line fault probability quantification and system risk assessment. Therefore, there is an urgent need to develop a prediction method that can respond in real-time to the dynamic characteristics of wildfires, accurately characterize the risk accumulation process at the line segment level, and comprehensively consider spatial correlations to enhance the resilience of the power grid under extreme disasters.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] This application provides a method and device for predicting transmission corridor fault risks driven by satellite-ground information and wildfire model. It is capable of responding to the dynamic characteristics of wildfires in real time, accurately depicting the risk accumulation process at the line segment level, and comprehensively considering spatial correlations, thereby enhancing the resilience of the power grid under extreme disasters.

[0007] Firstly, the satellite-ground information-wildfire model-driven transmission corridor fault risk prediction method provided in this application adopts the following technical solution: A satellite-ground information-wildfire model-driven method for predicting transmission corridor fault risks includes: Acquire multi-source information, which includes at least satellite remote sensing observation information, terrain information, meteorological information, fuel information, and power transmission line topology information; Based on the aforementioned multi-source information, a wildfire spread model is used to predict the evolution of the fire line, and satellite observations of the fire line are used to dynamically correct the prediction results, generating a corrected fire line analysis set. Based on the corrected live wire analysis set, segment-level dynamic features are extracted to construct interval exposure state variables for transmission lines; Based on the interval exposure state variables, a line segment-level cumulative fault probability calculation model is established to calculate the line segment-level cumulative fault probability. Considering the spatial correlation between adjacent line segments, the cumulative fault probabilities at the line segment level are aggregated to generate the overall line fault probability. By combining the overall failure probability of the entire line with the importance of the line, a risk assessment of wildfire risk in the power transmission corridor is conducted to generate a risk value.

[0008] Optionally, the step of using a wildfire spread model to predict fire line evolution based on the multi-source information, and dynamically correcting the prediction results using satellite observations of the fire line, includes: By integrating the terrain information, meteorological information, fuel information, and fire information, a wildfire spread scenario is constructed, and the FARSITE model is used to predict the ensemble background of the fire line evolution, generating a prediction set. Discretize the fire line boundary and represent it as a state vector to construct a fire line state set; By introducing the observation front obtained from the satellite remote sensing observation information, a dynamic update mechanism under observation constraints is established to perform rolling correction on the prediction set; Anomaly adaptive reconstruction is performed on the corrected set to restore the set dispersion and generate the corrected fire analysis set.

[0009] Optionally, the step of extracting segment-level dynamic features and constructing interval exposure state variables for transmission lines based on the corrected live wire analysis set includes: Based on the corrected live wire analysis set, time-varying covariates are extracted from the segments of the transmission line. The time-varying covariates include at least the duration of continuous exposure, equivalent thermal intensity, minimum approach distance of the live wire, conductor temperature, dynamic current carrying capacity, and equivalent breakdown critical level. A rough approximation-driven interval representation method is used to represent each of the time-varying covariates in interval form; By combining the time-varying covariates in the form of intervals, the interval exposure state variables are constructed.

[0010] Optionally, establishing a segment-level cumulative fault probability calculation model based on the interval exposure state variables includes: Thermal aging, tripping and breakdown are three types of fault mechanisms as embedded driving factors, which are uniformly incorporated into the line segment-level time-varying fault intensity model to generate a unified fault intensity. Based on the unified fault intensity, a time-varying hazard model is constructed; Based on the time-varying hazard model, the cumulative fault probability at the line segment level is calculated by accumulating at discrete time steps.

[0011] Optionally, the step of considering the spatial correlation between adjacent line segments and aggregating the cumulative fault probabilities at the line segment level to generate the overall line fault probability includes: Obtain relevant structural parameters of the internal structure of the line; Based on the relevant structural parameters, a joint survival aggregation operator consistent with the relevant structural parameters is introduced; Based on the joint survival aggregation operator, the cumulative fault probabilities of each line segment are aggregated into the overall line fault probability, which is expressed in interval form.

[0012] Optionally, the step of combining the overall failure probability of the entire line with the line importance to conduct a wildfire risk assessment of the power transmission corridor includes: Define the overall importance of the line, which shall at least integrate the Birnbaum type line importance, the risk amplification index after line fault, and the risk reduction index after line priority protection; Based on the standard power transmission test system operation scenario set, a comprehensive system consequence function is defined. The comprehensive system consequence function uniformly characterizes the impact of line status changes on power supply service loss, critical load guarantee, operation safety constraint satisfaction, rescheduling cost and network transmission capacity. Based on the comprehensive system consequence function, the importance of the Birnbaum type line, the risk amplification index, and the risk reduction index are calculated. The risk value is generated by multiplying the overall failure probability of the entire production line by the overall importance.

[0013] Optionally, the dynamic correction of the prediction results using satellite observation data includes: A nonlinear state-space model is constructed, which includes a prediction model and an observation model. The prediction model is used to characterize the evolution of the fire line state, and the observation model is used to characterize the mapping relationship between the satellite-observed fire line and the fire line state vector. Based on the prediction model, the fire state set is advanced forward to obtain the prediction set; Based on the observation model, the satellite observation line is mapped to the observation space, and the prediction perturbation matrix and the observation error covariance matrix in the observation space are calculated. Calculate the Kalman gain, which is used to comprehensively balance the relative effects of background prediction and satellite observation; Based on the Kalman gain, the prediction set is updated to generate a corrected fire analysis set.

[0014] Optionally, anomaly adaptive reconstruction includes: Calculate the analytical mean, which is the mean of the corrected set members; Based on the local linearization form of the observation operator, adaptive reconstruction coefficients are calculated, which are used to maintain the set discreteness. Based on the aforementioned adaptive reconstruction coefficients, the analysis disturbance matrix is ​​reconstructed; Based on the reconstructed analysis perturbation matrix, the members of each analysis set are updated to generate the corrected fireline analysis set.

[0015] Secondly, this application also provides a satellite-ground information-wildfire model-driven transmission corridor fault risk prediction device, comprising: The information acquisition module is used to acquire multi-source information, which includes at least satellite remote sensing observation information, terrain information, meteorological information, fuel information, and power transmission line topology information; The prediction module is used to predict the evolution of the fire line based on the multi-source information and the wildfire spread model, and to dynamically correct the prediction results using satellite observations of the fire line, thereby generating a corrected fire line analysis set. The variable construction module is used to extract line segment-level dynamic features based on the corrected live wire analysis set and construct interval exposure state variables for transmission lines. The cumulative failure probability calculation module is used to establish a line segment-level cumulative failure probability calculation model based on the interval exposure state variables, and to calculate the line segment-level cumulative failure probability. The comprehensive fault probability calculation module is used to consider the spatial correlation between adjacent line segments, aggregate the cumulative fault probabilities at the line segment level, and generate the comprehensive fault probability of the entire line. The output module is used to combine the overall failure probability of the entire line with the importance of the line to conduct a wildfire risk assessment of the power transmission corridor and generate a risk value.

[0016] In summary, this application achieves dynamic prediction of fire line evolution and satellite observation correction by integrating multi-source information, constructs segment-level interval exposure state variables and establishes a cumulative fault probability model, and generates a whole-line risk assessment by combining spatial correlation aggregation. It effectively solves the technical problem that traditional methods cannot capture the dynamic evolution of fire lines and the real-time interaction of line status. It has the ability to respond to the dynamic characteristics of wildfires in real time, accurately depict the risk accumulation process at the segment level and comprehensively consider spatial correlation, thereby improving the resilience and defense capabilities of the power grid under extreme disasters. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the first embodiment of the wildfire model-driven transmission corridor fault risk prediction method of this application; Figure 2 This is a structural block diagram of the first embodiment of the wildfire model-driven transmission corridor fault risk prediction device of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] Traditional methods for assessing the safety of transmission lines primarily rely on static scenarios or single-moment analyses, making it difficult to accurately capture the dynamic, spatially correlated, and temporally cumulative impacts of wildfires on transmission lines. This results in limitations in current methods for assessing the risk situation of transmission networks and mitigating disasters in complex situations involving continuous wildfire expansion and multiple overlapping fire points.

[0020] This application provides a wildfire model-driven method for predicting transmission corridor fault risks, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the wildfire model-driven transmission corridor fault risk prediction method of this application.

[0021] In this embodiment, the wildfire model-driven transmission corridor fault risk prediction method includes the following steps: Step S10: Obtain multi-source information, which includes at least satellite remote sensing observation information, terrain information, meteorological information, fuel information, and power transmission line topology information; For ease of understanding, the following explains some key terms in this embodiment: Multi-source information refers to multiple data sources used to describe the occurrence, development, and impact of wildfires on power transmission lines. This includes at least satellite remote sensing data, topographic information, meteorological information, fuel information, and power transmission line topology information. This information is used to comprehensively depict the wildfire scenario and the operational status of power transmission lines.

[0022] A wildfire spread model is a mathematical or physical model used to simulate and predict the propagation path, speed, and intensity of wildfires under specific environmental conditions. This model extrapolates the future location and shape of the fire front by considering factors such as topography, weather, and fuel.

[0023] Fire line evolution prediction refers to using wildfire spread models to estimate the dynamic change trend of fire line boundaries over a future period of time based on currently known multi-source information.

[0024] Satellite-observed fire lines refer to real-time or near-real-time data on the location and extent of fire lines obtained through satellite remote sensing technology. This data is used to validate and correct the prediction results of wildfire spread models.

[0025] The corrected fireline analysis set refers to a collection of multiple possible fireline evolution scenarios after dynamic correction based on satellite observations. This set reflects the uncertainty in fireline evolution prediction and provides more reliable dynamic information about the fireline.

[0026] Segment-level dynamic characteristics refer to the time-varying properties of various segments of a transmission line during the spread of a wildfire, such as duration of continuous exposure and intensity of thermal effects. These characteristics directly reflect the degree to which the segment is affected by the wildfire.

[0027] Interval exposure state variables refer to variables that represent line-segment level dynamic characteristics in interval form. These variables can capture the uncertainty of dynamic characteristics and provide more comprehensive input for subsequent failure probability calculations.

[0028] The cumulative fault probability at the line segment level refers to the cumulative probability that a single segment of a transmission line will experience a fault due to wildfires within a specific time period. This probability takes into account the combined effects of multiple fault mechanisms.

[0029] The overall failure probability refers to the probability of failure for the entire transmission line obtained by aggregating the cumulative failure probabilities of all segments within the line, taking into account the spatial correlation between segments. This probability is expressed in interval form, reflecting the overall risk of the entire line.

[0030] Line importance refers to the criticality of a transmission line within a power system or the severity of its impact on system operation due to a fault. This importance can be defined and calculated based on various indicators.

[0031] Wildfire risk assessment of power transmission corridors refers to the quantitative evaluation of the overall risk level of power transmission corridors in wildfire scenarios by combining the overall failure probability of the entire line and the importance of the line.

[0032] The risk value is the final quantitative result of the wildfire risk assessment of power transmission corridors. It is usually expressed in numerical form and is used to guide risk management and decision-making.

[0033] Step S20: Based on the multi-source information, a wildfire spread model is used to predict the evolution of the fire line, and satellite observations of the fire line are used to dynamically correct the prediction results, generating a corrected fire line analysis set.

[0034] It should be noted that the process of predicting fire line evolution using a wildfire spread model based on the multi-source information and dynamically correcting the prediction results using satellite observations of the fire line includes: fusing the terrain information, meteorological information, fuel information, and ignition information to construct a wildfire spread scenario; using the FARSITE model to predict the ensemble background of the fire line evolution and generate a prediction set; discretizing the fire line boundary and representing it as a state vector to construct a fire line state set; introducing the observed fire line obtained from the satellite remote sensing observation information, establishing a dynamic update mechanism under observation constraints, and performing rolling correction on the prediction set; and performing anomaly adaptive reconstruction on the corrected set to restore the set dispersion and generate the corrected fire line analysis set.

[0035] Through the above technical solutions, this embodiment can significantly improve the accuracy and reliability of wildfire spread prediction. First, by fusing multi-source information and using the FARSITE model for ensemble background prediction, the complex influencing factors of wildfire spread can be fully considered, and the uncertainty of the initial prediction can be quantified, laying a solid foundation for subsequent correction. Second, discretizing the fireline boundary into a state vector allows for accurate and efficient calculation and management of the fireline state. More importantly, introducing satellite remote sensing observation information and establishing a dynamic update mechanism ensures that the prediction results are consistent with actual observations in real time, effectively correcting model biases. Finally, by performing anomaly adaptive reconstruction on the corrected ensemble, the potential ensemble collapse problem during data assimilation is successfully avoided, ensuring that the predicted ensemble continuously reflects the true range of uncertainty. This provides more robust and reliable fireline evolution information for wildfire risk assessment in power transmission corridors, greatly enhancing the accuracy and practicality of risk prediction.

[0036] It should be noted that the dynamic correction of the prediction results using satellite-observed fire lines includes: constructing a nonlinear state-space model, which includes a prediction model and an observation model. The prediction model is used to characterize the evolution of the fire line state, and the observation model is used to characterize the mapping relationship between the satellite-observed fire line and the fire line state vector; based on the prediction model, the fire line state set is forward-progressed to obtain a prediction set; based on the observation model, the satellite-observed fire line is mapped to the observation space, and the prediction perturbation matrix and the observation error covariance matrix in the observation space are calculated; the Kalman gain is calculated, which is used to comprehensively balance the relative effects of background prediction and satellite observation; based on the Kalman gain, the prediction set is updated to generate a corrected fire line analysis set.

[0037] Through the above technical solution, this embodiment can comprehensively and meticulously capture the dynamic characteristics of transmission lines under the influence of wildfires, and effectively quantify and manage the inherent uncertainties of these characteristics in an interval format. This method avoids the biases that may be caused by traditional point-value assessments, making the exposure risk assessment of transmission line segments more accurate and robust. By constructing comprehensive interval exposure state variables, a more reliable and comprehensive input is provided for subsequent segment-level cumulative fault probability calculations, thereby significantly improving the overall accuracy and reliability of wildfire risk prediction for transmission corridors.

[0038] Understandably, the anomaly adaptive reconstruction includes: calculating the analysis mean, which is the mean of the corrected set members; calculating the adaptive reconstruction coefficients based on the local linearization form of the observation operator, which are used to maintain the set dispersion; reconstructing the analysis perturbation matrix based on the adaptive reconstruction coefficients; and updating each analysis set member based on the reconstructed analysis perturbation matrix to generate the corrected fireline analysis set.

[0039] The above technical solution effectively addresses the issue of reduced ensemble dispersion after dynamic correction of the live-line prediction ensemble. Specifically, by calculating the analytical mean of the corrected ensemble members and then using adaptive reconstruction coefficients based on the locally linearized form of the observation operator, the ensemble dispersion can be accurately assessed and adjusted. Subsequently, these adaptive reconstruction coefficients are used to reconstruct the analytical perturbation matrix and update each analytical ensemble member, thereby generating a corrected live-line analysis ensemble that integrates observational information while maintaining a reasonable range of uncertainty. This ensures the robustness and reliability of live-line evolution prediction, avoids underestimation of prediction uncertainty due to insufficient ensemble dispersion, and thus improves the accuracy and reliability of subsequent transmission corridor fault risk assessment.

[0040] In its implementation, this embodiment integrates topographic, meteorological, fuel, and ignition information to construct a wildfire spread scenario. Topographic, fuel, and meteorological conditions jointly determine the environmental constraints of fire spread, while ignition information is used to determine the initial fire line. Based on this, the FARSITE model is used to simulate the wildfire spread process, and its core mechanism follows the Rothermel surface fire spread model, i.e. In the formula, The rate of fire spread; The strength of the reaction; The propagation flux ratio; and These are the wind speed correction factor and the slope correction factor, respectively. Absolutely dry density; The effective heating number; This is for preheating and ignition.

[0041] To facilitate ensemble prediction and observation updates, the fire line boundaries need to be discretized and represented as state vectors. Let each fire line consist of... It consists of discrete fire points, among which Indicates the number of sampled fire points, then time... The fire state can be represented as: in, Indicates time No. The planar coordinates of each fire point. This unified representation allows fire information from different times and sources to be mapped to the same state space, laying the foundation for subsequent model prediction and observation correction.

[0042] It is understandable that, considering the uncertainty of the initial firing line, in the initial state... Surrounding, based on the initial covariance matrix Construct the initial set. Specifically, the first... Each set member is defined as in, Indicates the size of the set, superscript This indicates the analytical state.

[0043] By using FARSITE to perform forward propagation on the set members, the prediction set is obtained: Among them, superscript Represents the predicted state, where each set member satisfies: In the formula, This represents the nonlinear prediction operator induced by FARSITE. This indicates environmentally driven inputs, including terrain, weather, and fuel information.

[0044] The statistics for the prediction set are defined as follows: the prediction mean is: The predicted perturbation matrix is: The prediction error covariance matrix is: in, The average evolution result characterizing the predicted state of the fire line. Describing the degree of discrepancy of a set, This quantifies the uncertainty of predicting the state of the front line.

[0045] In practice, after obtaining the prediction set, satellite observations of the fire line are used to update the fire line status. The evolution and observation process of wildfires can be represented by the following nonlinear state-space model: In the formula, For a moment The fire state vector, For prediction models, Environment-driven input, This refers to model error; For the observation vector, For the observation operator, This refers to observation error. It is typically assumed that model error and observation error satisfy the following conditions: in, and These are the model error covariance matrix and the observation error covariance matrix, respectively.

[0046] At any moment First, the observed fire line is resampled and represented as... The same state vector form is used to obtain the observation vector. .set up: Indicates the first The result of mapping each member of the prediction set to the observation space is: The mean of the corresponding observation space is: The prediction perturbation matrix in the observation space is defined as: Meanwhile, the observation error covariance matrix is ​​written as: in, The standard deviation of the observation error. It is the identity matrix of compatible dimension.

[0047] Furthermore, the deviation between the observed fire line and the predicted fire line is defined as: in, It characterizes the degree of mismatch between the satellite-observed fire line and the average predicted fire line in the observation space.

[0048] Step S30: Based on the corrected live wire analysis set, extract segment-level dynamic features and construct interval exposure state variables for transmission lines.

[0049] It should be noted that the step of extracting segment-level dynamic features and constructing interval exposure state variables for transmission lines based on the corrected live wire analysis set includes: extracting time-varying covariates from the segments of the transmission line based on the corrected live wire analysis set; the time-varying covariates include at least the duration of continuous exposure, equivalent thermal intensity, minimum live wire approach distance, conductor temperature, dynamic current carrying capacity, and equivalent breakdown critical level; using a rough approximation-driven interval representation method, representing each of the time-varying covariates in interval form; and synthesizing the time-varying covariates in interval form to construct the interval exposure state variables.

[0050] In specific implementation, a time setting The resulting set of corrected fireline analyses is Based on this set, the lines... The Extract time-varying covariates driven by the fireline evolution from each line segment and construct their interval representations. Let the line segment at time be... The duration of continuous exposure, equivalent thermal intensity, minimum approach distance to the fire line, conductor temperature, dynamic current carrying capacity, and equivalent breakdown critical level are respectively , , , , and The time-varying covariate interval at the line segment level can then be denoted as: in In the formula, This is the critical threshold for the thermal state of the conductor. This is the operating voltage of the line. Taking into account ambient temperature, wind speed, the effects of wildfire heat, and the heat dissipation conditions of the conductor, and ensuring that the conductor temperature does not exceed [a certain value], [further considerations are taken into account]. To constrain and determine dynamic current carrying limits; The equivalent breakdown critical voltage was determined after taking into account the high temperature of wildfires, smoke and dust, and the air insulation condition. The aforementioned covariates characterize the combined effect of the fire line on the line segment from aspects such as persistence, intensity, proximity, thermal state, current carrying capacity, and insulation capacity. Due to the ensemble uncertainty of the fire line prediction and observation correction results, each covariate is included in the subsequent fault modeling in interval form.

[0051] Step S40: Based on the interval exposure state variables, establish a line segment-level cumulative fault probability calculation model and calculate the line segment-level cumulative fault probability.

[0052] It should be noted that the establishment of the segment-level cumulative fault probability calculation model based on the interval exposure state variables includes: taking thermal aging, tripping and breakdown as three types of fault mechanisms as embedded driving factors, uniformly incorporating them into the segment-level time-varying fault intensity model to generate a unified fault intensity; constructing a time-varying hazard model based on the unified fault intensity; and calculating the segment-level cumulative fault probability by accumulating at discrete time steps based on the time-varying hazard model.

[0053] In practical implementation, the calculation of the cumulative fault probability interval for a line segment includes: Considering that thermal aging, tripping, and breakdown are not entirely independent terminal probability events at the same level, this paper does not construct three separate parallel fault probability models. Instead, it incorporates these three mechanisms as embedded driving factors into the line segment-level time-varying fault intensity. (Conditions and covariates) Assuming the fault process of a line segment follows a time-varying hazard model, the uniform fault intensity is written as: in, As the baseline fault strength, Let be the parameter vector to be calibrated. If we further assume that the influence of each covariate on the fault intensity is monotonically constant, then the line segment at time _____ The fault intensity of the interval can be expressed as: In this formula, the temperature exceedance term mainly reflects the cumulative effect of thermal aging and thermal damage, the dynamic current carrying margin term reflects the degree to which the line approaches the tripping threshold under the thermal effects of wildfires, and the insulation margin term reflects the increasing trend of the risk of insulation breakdown under the influence of fire. Therefore, this paper replaces the separate modeling of the probabilities of the three parallel terminals with a unified fault intensity, making fault probability prediction more compact while maintaining physical interpretability.

[0054] at discrete time step Below, the line The The line segment at the predicted time The cumulative failure probability interval is defined as: This formula is essentially an expression of the cumulative failure probability based on time-varying hazard, which naturally reflects the increasing failure probability over time caused by the continuous accumulation of wildfire thermal effects. Compared with methods that estimate failure probability based solely on exposure status at a single moment, this form can more accurately describe the dynamic impact of continuous approaching wildfires, continuous exposure, and continuous thermal effects on the line failure formation process.

[0055] Step S50: Considering the spatial correlation between adjacent line segments, aggregate the cumulative fault probabilities at the line segment level to generate the overall line fault probability.

[0056] It should be noted that the process of considering the spatial correlation of adjacent line segments and aggregating the cumulative fault probabilities at the line segment level to generate the overall line fault probability includes: obtaining relevant structural parameters within the line; introducing a joint survival aggregation operator consistent with the relevant structural parameters based on the relevant structural parameters; and aggregating the cumulative fault probabilities at the line segment level into the overall line fault probability based on the joint survival aggregation operator, wherein the overall line fault probability is expressed in interval form.

[0057] In practical implementation, the aggregation of the overall line failure probability intervals is considered based on spatial correlation.

[0058] Set up the line Depend on It consists of several line segments, and its time is recorded as _____. The line segment failure probability vector is: Since adjacent line segments typically exhibit significant spatial correlation under the same continuous live wire, the overall line fault probability cannot be simply obtained by multiplying the fault probabilities of each segment independently. Therefore, this paper introduces structural parameters related to the internal structure of the line. A consistent joint survival aggregation operator, and the entire line at time... The failure probability interval is represented as: in, and Representing the related structures respectively A consistent lower and upper joint survival aggregation operator. This formula preserves the overall line failure probability range characteristics caused by the uncertainty of fire line prediction and the spatial correlation of line segments, thus providing dynamic probability input for subsequent risk assessment considering the importance of the line.

[0059] Step S60: Combine the overall failure probability of the entire line with the importance of the line to conduct a wildfire risk assessment of the power transmission corridor and generate a risk value.

[0060] It should be noted that the process of combining the overall line failure probability with the line importance to conduct a wildfire risk assessment of the transmission corridor includes: defining the overall importance of the line, which at least integrates the Birnbaum-type line importance, the risk amplification index after line failure, and the risk reduction index after priority protection of the line; defining an integrated system consequence function based on a set of standard transmission test system operating scenarios, which uniformly characterizes the impact of line status changes on power supply service losses, critical load guarantees, operational safety constraint satisfaction, rescheduling costs, and network transmission capacity; calculating the Birnbaum-type line importance, the risk amplification index, and the risk reduction index based on the integrated system consequence function; and multiplying the overall line failure probability by the overall importance to generate the risk value.

[0061] By introducing a comprehensive importance index for power lines and combining it with the overall probability of line failure, this embodiment overcomes the limitations of single importance in traditional risk assessment. Specifically, by integrating Birnbaum-type line importance, risk amplification indicators after line failures, and risk reduction indicators after priority protection of lines, and quantifying the multi-dimensional impacts such as power supply service loss, critical load assurance, operational safety constraint satisfaction, rescheduling costs, and network transmission capacity based on a comprehensive system consequence function, this embodiment generates a more comprehensive and accurate risk value. This multi-dimensional and systematic risk assessment method leads to a deeper understanding of wildfire risks in transmission corridors, enabling more accurate identification of high-risk lines with the greatest impact on the power system. This provides a scientific basis for developing targeted fire prevention strategies, optimizing resource allocation, and enhancing grid resilience, effectively reducing the potential threat of wildfires to power system operation.

[0062] In practical implementation, after obtaining the probability of future time-series faults in transmission lines under the influence of wildfires, if only based on... Ranking only indicates the probability of a line failing due to wildfires in the future; it cannot further distinguish the differences in the impact of different line failures on the system's power supply and operational safety. For identifying vulnerable links in the transmission network, the real focus should be on critical lines that have both a high probability of future failure and significant systemic importance within the grid. Therefore, based on the failure probability prediction results, a line importance rating should be further introduced. Furthermore, a dynamic risk assessment model that considers the importance of the route was constructed.

[0063] First Defined Line In the future The risk value is: in, For wildfires caused power lines In the future The probability of failure, For the line The overall importance of the power grid side This indicates the line At any moment The dynamic risk level is thus determined. Therefore, the risk assessment results simultaneously characterize two aspects: "whether the line is prone to failure under wildfire conditions" and "whether the impact of a line failure on the system is significant."

[0064] Furthermore, to balance local sensitivity and global worth, the route is defined. The overall importance is: in, Birnbaum type line importance, used to characterize the local marginal sensitivity of line state changes to system consequences; It is a risk amplification index after a line fault, used to characterize the degree of deterioration of the system consequences relative to the baseline operating condition after the line is taken out of operation; It is a risk reduction index after prioritizing line protection, used to characterize the degree of improvement in system consequences that may result from prioritizing the protection of this line; and These are used to adjust the relative weights of local sensitivity information and global worth information, as well as the relative weights of "loss cost" and "protection gain" worth information. This approach avoids the scaling inconsistency problem caused by simply linearly adding multiple importance values, and also maintains the multiplicative risk amplification characteristic of the overall importance.

[0065] To standardize the measurement of the impact of line condition changes on system operation consequences, we first tested the standard transmission test system operating scenario set. Define the consequences function of the integrated system. Let... Indicates the line The availability status, of which This indicates that the line is operating normally. This indicates that the line has been taken out of service; This is the state vector of the entire network lines; For the running scenario The weights satisfy Then the scene The comprehensive system consequence function is defined as follows: in, Indicates the first The normalized system consequence quantity uniformly represents the impact of line state changes on electrical performance in different dimensions, such as power supply service loss, critical load guarantee, operational safety constraint satisfaction, rescheduling cost, and network transmission capacity. Weights for corresponding consequences; To avoid the problem of zero values, a very small positive number is introduced. Therefore, Essentially, it describes the multiplicative amplification effect of line state disturbances on the overall operational consequences of the system.

[0066] Based on this, and drawing on the concept of "partial derivative sensitivity of system performance to changes in component state" from B-importance, the circuit is defined as follows: The importance of the Birnbaum type is: in, This indicates a baseline condition where all lines are operating normally. This indicator reflects the situation near the baseline operating condition where all lines are functioning normally. The relative sensitivity of the system's overall consequences to the increase caused by minor degradation. If the analytical partial derivatives are difficult to obtain directly, a numerical approximation can be made using the finite difference method.

[0067] Furthermore, to characterize the amplified system consequences of a line being taken out of service, a line is defined. The risk amplification Worth index is: in, Indicates except for the line All other lines are in normal condition. This indicator measures the line's performance. Once the system is shut down, the overall consequences of the system are magnified relative to the baseline state, thus reflecting the meaning of "this line is the most indispensable part of the system's operation".

[0068] Meanwhile, to characterize the degree of improvement in system consequences that may result from prioritizing the protection of a certain line, the line is defined. The risk reduction value index is: in, Indicates the line The system consequence function value obtained after implementing priority protection, capacity enhancement, or equivalent elimination of bottleneck constraints. A larger value indicates that the priority protection line... The more valuable it is in reducing systemic consequences, the more it can provide a basis for prioritizing the investment of subsequent prevention and control resources.

[0069] Reference Figure 2 , Figure 2This is a structural block diagram of the first embodiment of the wildfire model-driven transmission corridor fault risk prediction device of this application.

[0070] like Figure 2 As shown in the embodiment of this application, the wildfire model-driven transmission corridor fault risk prediction device includes: The information acquisition module 10 is used to acquire multi-source information, which includes at least satellite remote sensing observation information, terrain information, meteorological information, fuel information and power transmission line topology information; The prediction module 20 is used to predict the evolution of the fire line based on the multi-source information using a wildfire spread model, and to dynamically correct the prediction results using satellite observations of the fire line, thereby generating a corrected fire line analysis set. The variable construction module 30 is used to extract line segment-level dynamic features based on the corrected live wire analysis set and construct interval exposure state variables for transmission lines. The cumulative failure probability calculation module 40 is used to establish a line segment-level cumulative failure probability calculation model based on the interval exposure state variables, and to calculate the line segment-level cumulative failure probability. The comprehensive fault probability calculation module 50 is used to consider the spatial correlation between adjacent line segments, aggregate the cumulative fault probabilities at the line segment level, and generate the comprehensive fault probability of the entire line. Output module 60 is used to combine the overall failure probability of the entire line with the importance of the line to conduct a wildfire risk assessment of the power transmission corridor and generate a risk value.

[0071] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solution of this application. In specific applications, those skilled in the art can make settings as needed, and this application does not impose any restrictions on this.

[0072] This embodiment achieves dynamic prediction of fire line evolution and satellite observation correction by integrating multi-source information, constructs line segment-level interval exposure state variables and establishes a cumulative fault probability model, and generates a whole-line risk assessment by combining spatial correlation aggregation. It effectively solves the technical problem that traditional methods cannot capture the dynamic evolution of fire lines and the real-time interaction of line status. It has the ability to respond to the dynamic characteristics of wildfires in real time, accurately depict the line segment-level risk accumulation process and comprehensively consider spatial correlation, thereby improving the resilience and defense capability of the power grid under extreme disasters.

[0073] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this application. In practical applications, those skilled in the art can select some or all of it to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0074] In addition, for technical details not described in detail in this embodiment, please refer to the method for predicting the risk of power transmission corridor faults driven by wildfire model provided in any embodiment of this application, which will not be repeated here.

[0075] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system 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 system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0076] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0077] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application. The above are only preferred embodiments of this application and do not limit the patent scope of this application. All equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for predicting transmission corridor fault risk driven by satellite-ground information and wildfire model, characterized in that, include: Acquire multi-source information, which includes at least satellite remote sensing observation information, terrain information, meteorological information, fuel information, and power transmission line topology information; Based on the aforementioned multi-source information, a wildfire spread model is used to predict the evolution of the fire line, and satellite observations of the fire line are used to dynamically correct the prediction results, generating a corrected fire line analysis set. Based on the corrected live wire analysis set, segment-level dynamic features are extracted to construct interval exposure state variables for transmission lines; Based on the interval exposure state variables, a line segment-level cumulative fault probability calculation model is established to calculate the line segment-level cumulative fault probability. Considering the spatial correlation between adjacent line segments, the cumulative fault probabilities at the line segment level are aggregated to generate the overall line fault probability. By combining the overall failure probability of the entire line with the importance of the line, a risk assessment of wildfire risk in the power transmission corridor is conducted to generate a risk value. The step of establishing a line segment-level cumulative fault probability calculation model based on the interval exposure state variables includes: Thermal aging, tripping and breakdown are three types of fault mechanisms as embedded driving factors, which are uniformly incorporated into the line segment-level time-varying fault intensity model to generate a unified fault intensity. Based on the unified fault intensity, a time-varying hazard model is constructed; Based on the time-varying hazard model, the cumulative fault probability at the line segment level is calculated by accumulating at discrete time steps. The step of extracting segment-level dynamic features and constructing interval exposure state variables for transmission lines based on the corrected live wire analysis set includes: Based on the corrected live wire analysis set, time-varying covariates are extracted from the segments of the transmission line. The time-varying covariates include at least the duration of continuous exposure, equivalent thermal intensity, minimum approach distance of the live wire, conductor temperature, dynamic current carrying capacity, and equivalent breakdown critical level. A rough approximation-driven interval representation method is used to represent each of the time-varying covariates in interval form; By combining the time-varying covariates in the form of intervals, the interval exposure state variables are constructed. The method of combining the overall failure probability of the entire line with the importance of the line to conduct a wildfire risk assessment of the power transmission corridor includes: Define the overall importance of the line, which shall at least integrate the Birnbaum type line importance, the risk amplification index after line fault, and the risk reduction index after line priority protection; Based on the standard power transmission test system operation scenario set, a comprehensive system consequence function is defined. The comprehensive system consequence function uniformly characterizes the impact of line status changes on power supply service loss, critical load guarantee, operation safety constraint satisfaction, rescheduling cost and network transmission capacity. Based on the comprehensive system consequence function, the importance of the Birnbaum type line, the risk amplification index, and the risk reduction index are calculated. The risk value is generated by multiplying the overall failure probability of the entire production line by the overall importance.

2. The method according to claim 1, characterized in that, The process of predicting fire line evolution using a wildfire spread model based on the multi-source information, and dynamically correcting the prediction results using satellite observations of the fire line, includes: By integrating the terrain information, meteorological information, fuel information, and fire information, a wildfire spread scenario is constructed, and the FARSITE model is used to predict the ensemble background of the fire line evolution, generating a prediction set. Discretize the fire line boundary and represent it as a state vector to construct a fire line state set; By introducing the observation front obtained from the satellite remote sensing observation information, a dynamic update mechanism under observation constraints is established to perform rolling correction on the prediction set; Anomaly adaptive reconstruction is performed on the corrected set to restore the set dispersion and generate the corrected fire analysis set.

3. The method according to claim 1, characterized in that, The process of considering the spatial correlation between adjacent line segments and aggregating the cumulative fault probabilities at the line segment level to generate the overall line fault probability includes: Obtain relevant structural parameters of the internal structure of the line; Based on the relevant structural parameters, a joint survival aggregation operator consistent with the relevant structural parameters is introduced; Based on the joint survival aggregation operator, the cumulative fault probabilities of each line segment are aggregated into the overall line fault probability, which is expressed in interval form.

4. The method according to claim 1, characterized in that, The method of dynamically correcting the prediction results using satellite observations includes: A nonlinear state-space model is constructed, which includes a prediction model and an observation model. The prediction model is used to characterize the evolution of the fire line state, and the observation model is used to characterize the mapping relationship between the satellite-observed fire line and the fire line state vector. Based on the prediction model, the fire state set is advanced forward to obtain the prediction set; Based on the observation model, the satellite observation line is mapped to the observation space, and the prediction perturbation matrix and the observation error covariance matrix in the observation space are calculated. Calculate the Kalman gain, which is used to comprehensively balance the relative effects of background prediction and satellite observation; Based on the Kalman gain, the prediction set is updated to generate a corrected fire analysis set.

5. The method according to claim 1, characterized in that, Anomaly adaptive reconstruction includes: Calculate the analytical mean, which is the mean of the corrected set members; Based on the local linearization form of the observation operator, adaptive reconstruction coefficients are calculated, which are used to maintain the set discreteness. Based on the aforementioned adaptive reconstruction coefficients, the analysis disturbance matrix is ​​reconstructed; Based on the reconstructed analysis perturbation matrix, the members of each analysis set are updated to generate the corrected fireline analysis set.

6. A satellite-ground information-wildfire model-driven transmission corridor fault risk prediction device, characterized in that, Performing the method as described in claim 1 includes: The information acquisition module is used to acquire multi-source information, which includes at least satellite remote sensing observation information, terrain information, meteorological information, fuel information, and power transmission line topology information. The prediction module is used to predict the evolution of the fire line based on the multi-source information and the wildfire spread model, and to dynamically correct the prediction results using satellite observations of the fire line, thereby generating a corrected fire line analysis set. The variable construction module is used to extract line segment-level dynamic features based on the corrected live wire analysis set and construct interval exposure state variables for transmission lines. The cumulative failure probability calculation module is used to establish a line segment-level cumulative failure probability calculation model based on the interval exposure state variables, and to calculate the line segment-level cumulative failure probability. The comprehensive fault probability calculation module is used to consider the spatial correlation between adjacent line segments, aggregate the cumulative fault probabilities at the line segment level, and generate the comprehensive fault probability of the entire line. The output module is used to combine the overall failure probability of the entire line with the importance of the line to conduct a wildfire risk assessment of the power transmission corridor and generate a risk value.

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