Power grid facility flood disaster vulnerability dynamic assessment method

Abstract

The invention belongs to the technical field of power grid flood disaster assessment, and particularly relates to a power grid facility flood disaster vulnerability dynamic assessment method, which comprises the following steps: acquiring a historical flood event data set of a power grid facility in a target area, extracting a homogeneous flood sequence group, and calculating a failure growth rate; and the disaster bearing vulnerability score is counted by integrating multiple factors and is dynamically updated. Quantitative analysis of cumulative damage of the power grid facility in multiple flood events is achieved, the cumulative degradation trend of the performance of the power grid facility is accurately captured, the vulnerability change rule of the facility in continuous disasters is effectively revealed, a reliable basis is provided for scientific assessment of the risk condition of the power grid facility under long-term flood threats, and the risk assessment efficiency is improved. And meanwhile, an elevation protection coefficient is set based on the elevation of the facility from the ground, the original fault degree is corrected in combination with a flood intensity influence factor, and the flood vulnerability of the facilities at different positions can be accurately evaluated in a two-factor correction mode.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid flood disaster assessment, and in particular relates to a method for dynamically assessing the vulnerability of power grid facilities to flood disasters. Background Art

[0002] As urbanization accelerates, the density of power grid equipment increases. Flood disasters often lead to widespread power outages and severe equipment damage, significantly impacting urban operations. To ensure timely warnings for flood disasters, it's necessary to assess the damage to power grid facilities.

[0003] Existing technologies, such as the Chinese invention patent application with application number 2023105426264, disclose a method and system for flood prevention and forecasting of power grid facilities. This method objectively processes raw data using K-means cluster analysis and constructs a deep learning model using the back-propagation algorithm, thereby solving the problem of low forecast accuracy, achieving more accurate flood prevention and forecasting of power grid facilities, and reducing the risk of equipment damage.

[0004] Existing technologies, such as the method for assessing the rainstorm risk of power grid facilities disclosed in the Chinese invention patent application with application number 2016103179949, collect disaster information, rainstorm, socio-economic, geographical, and power grid data, and based on these data, efficiently calculate the four major factors of disaster-causing factors, disaster-prone environment, vulnerability of disaster-bearing bodies, and disaster prevention capabilities, and ultimately perform risk rating and level classification. This solves the problems of one-sided, inefficient, and abstract assessment results, and achieves efficient, accurate, and comprehensive rainstorm risk assessment. It also outputs intuitive risk indexes and levels to facilitate staff decision-making.

[0005] Regarding existing technical solutions, it is obvious that the current flood disaster assessment of power grid facilities mainly focuses on real-time risk prediction and static structural vulnerability, which is a timely analysis, that is, each flood event is handled independently. There are still the following deficiencies: 1. No quantitative analysis of the cumulative damage to facilities caused by multiple flood events is considered, and there is a lack of exploration of the growth pattern of historical equipment failures. The cumulative degradation trend of power grid facility performance is not captured, resulting in subsequent risk assessments underestimating the actual risk and making it difficult to reveal the changing pattern of facility vulnerability in continuous disasters.

[0006] 2. The correlation between flood severity and failure rate growth was ignored. The growth differences in failure rates of the same facility under different flood intensities were not analyzed. The strengthening effect of continuous disasters and the impact of the length of time between disasters on equipment recovery were not considered, which weakened the effectiveness of the early warning.

[0007] 3. During the evaluation process, the equipment elevation above the ground was regarded as a fixed parameter, which did not reflect the performance degradation differences of equipment at different elevations in the same flood, and did not quantify the gradient of the impact of different flooding depths on equipment failure rates. Summary of the Invention

[0008] In view of this, in order to solve the above problems, a dynamic assessment method for the vulnerability of power grid facilities to flood disasters is proposed.

[0009] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides a method for dynamically assessing the vulnerability of power grid facilities to flood disasters, the method comprising: S1, obtaining the ground elevation of each power grid facility in the target area and a historical flood event data set of the target area, each record containing a facility identifier, trigger time, characteristic data of a single flood event, and a quantitative facility failure degree value and failure type after the flood.

[0010] S2. Set the flood intensity impact factor based on the flood event characteristic data, and set the elevation protection factor based on the facility elevation above the ground. Use these two factors to correct the original fault degree and output the balanced fault degree.

[0011] S3. Extract continuous flood events whose flood intensity influencing factors belong to the same preset flood intensity range to form a homogeneous flood sequence group, and calculate the failure growth rate of power grid facilities in the group based on the balanced setting fault degree.

[0012] S4. The comprehensive failure growth rate and the number of groups whose failure growth rate exceeds the preset warning value are counted to obtain the disaster carrying vulnerability score and provide feedback.

[0013] S5. When a new flood event occurs, repeat steps S1-S4 to update the comprehensive disaster carrying vulnerability score.

[0014] Compared with the existing technology, the present invention has the following beneficial effects: (1) By acquiring a historical flood event dataset of power grid facilities in the target area, extracting a homogeneous flood sequence group to calculate the failure growth rate, and comprehensively analyzing multiple factors to calculate the disaster-bearing vulnerability score and dynamically updating it, the present invention achieves a quantitative analysis of the cumulative damage of power grid facilities in multiple flood events, deeply explores the growth pattern of historical equipment failures, and accurately captures the cumulative degradation trend of power grid facility performance, thereby avoiding the situation where subsequent risk assessment underestimates the actual risk, effectively reveals the vulnerability change pattern of facilities in continuous disasters, and provides a reliable basis for scientifically assessing the risk status of power grid facilities under the threat of long-term floods.

[0015] (2) The present invention sets a flood intensity impact factor based on flood event characteristic data and calculates the failure growth rate of power grid facilities within different flood intensity intervals. This fully considers the correlation between flood severity and failure growth rate, not only analyzing the growth differences in failure rates of the same facility under different flood intensities, but also reflecting the strengthening effect of consecutive disasters and the impact of the length of time between disasters on equipment recovery. This significantly enhances the effectiveness of early warning and also facilitates the subsequent prediction of changes in equipment failure risks based on different flood intensities, allowing for early prevention preparations.

[0016] (3) The present invention sets the elevation protection factor based on the elevation of the facility above the ground, and corrects the original fault degree in combination with the flood intensity influencing factor. The dual-factor correction method is used to quantify the gradient of the impact of different flooding depths on the equipment failure rate. This can accurately reflect the performance degradation differences of equipment at different elevations in the same flood, thereby carefully and accurately evaluating the flood vulnerability of facilities at different locations. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 The figure is a schematic diagram of the overall implementation process of the method of the present invention.

[0019] Figure 2 This is a schematic diagram of the flow chart for setting the flood intensity influencing factors of the present invention.

[0020] Figure 3 This is a schematic diagram of the elevation protection factor setting process of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] See also Figure 1 As shown, the present invention provides a method for dynamically assessing the vulnerability of power grid facilities to flood disasters, the method comprising: S1, obtaining the ground elevation of each power grid facility in the target area and a historical flood event dataset of the target area, each record comprising a facility identifier, a triggering time, characteristic data of a single flood event, and a quantitative post-flood facility failure degree value and failure type.

[0023] S2. Set the flood intensity impact factor based on the flood event characteristic data, and set the elevation protection factor based on the facility elevation above the ground. Use these two factors to correct the original fault degree and output the balanced fault degree.

[0024] S3. Extract continuous flood events whose flood intensity influencing factors belong to the same preset flood intensity range to form a homogeneous flood sequence group, and calculate the failure growth rate of power grid facilities in the group based on the balanced setting fault degree.

[0025] S4. The comprehensive failure growth rate and the number of groups whose failure growth rate exceeds the preset warning value are counted to obtain the disaster carrying vulnerability score and provide feedback.

[0026] S5. When a new flood event occurs, repeat steps S1-S4 to update the comprehensive disaster carrying vulnerability score.

[0027] This embodiment of the present invention obtains a historical flood event dataset for power grid facilities in a target area, extracts homogeneous flood sequence groups, calculates failure growth rates, and dynamically updates a multi-factor statistical disaster vulnerability score. This approach quantitatively analyzes the cumulative damage to power grid facilities from multiple flood events, deeply explores the growth patterns of historical equipment failures, and accurately captures the cumulative degradation trends in power grid facility performance. This prevents subsequent risk assessments from underestimating actual risks, effectively reveals the changing patterns of facility vulnerability to successive disasters, and provides a reliable basis for scientifically assessing the risk status of power grid facilities under the threat of long-term floods.

[0028] Regarding step S1, it should be added that the characteristic data of a single flood event include flow velocity series, flood depth series, and total flood duration.

[0029] See also Figure 2 As shown, the specific setting process of the flood intensity influencing factor in step S2 is as follows: A1. Extract the maximum value of the flow velocity sequence of a single flood as the target flood flow velocity, and at the same time calculate the standard deviation of the flow velocity sequence to obtain the flow velocity fluctuation.

[0030] A2. Extract the maximum value of the inundation depth sequence of a single flood as the target flood inundation depth, and calculate the inundation depth growth rate through differential calculation.

[0031] A3. If the flow velocity fluctuation or the inundation depth growth rate exceeds the corresponding preset threshold, the flow velocity fluctuation and the inundation depth growth rate are normalized and then introduced into the Sigmoid function to output the flood intensity assessment compensation factor.

[0032] A4. Obtain the total duration of a single flood as the target duration of the flood.

[0033] A5. Weight coefficients of target flood velocity, target flood inundation depth, and target duration assigned by analytic hierarchy process.

[0034] A5. After normalizing the target flood velocity, target flood inundation depth, and target duration, the basic flood intensity impact factor is obtained through weighted summation. The final flood intensity impact factor is then output after compensation using the flood intensity assessment compensation factor.

[0035] It can be understood that the normalization adopts linear normalization, namely Min-Max normalization. Taking the normalization of flow velocity fluctuation as an example, the minimum and maximum flow velocity fluctuations are found from historical flood data, and the calculated flow velocity fluctuation is imported into the setting formula of Min-Max normalization to obtain the normalized flow velocity fluctuation. When the flow velocity fluctuation is lower than the historical lowest value, the normalized result is directly assigned to 0.

[0036] Understandably, the preset thresholds for flow velocity fluctuation and inundation depth growth rates are determined by combining historical flood data with the impact of power grid facilities. Specifically, data on flow velocity fluctuation and inundation depth growth rates from a large number of historical flood events are collected, statistically analyzed, and statistical quantities such as mean, standard deviation, and quantiles are calculated. For example, by calculating the 95th percentile, data exceeding this value is considered to have a high probability of being abnormal. Simultaneously, historical flood data that caused varying degrees of power grid facility failures is referenced to determine the limits of flow velocity fluctuation and inundation depth growth rates that trigger significant disaster impacts. On this basis, power industry experts, meteorological experts, and others can be invited to evaluate and adjust the statistical analysis results based on their professional experience, ultimately determining reasonable preset thresholds.

[0037] It should be noted that in flood intensity assessments, when flow velocity fluctuations or inundation depth growth rates exceed preset thresholds, this indicates abnormal changes in flood characteristics, resulting in more complex and severe impacts on power grid facilities. The Sigmoid function, due to its unique S-shaped curve characteristics, can map normalized input data to the range of 0 to 1, achieving smoothing and nonlinear transformation of abnormal data. Furthermore, its nonlinear characteristics can better simulate the complexity of flood disaster impacts, avoiding the over-amplification or neglect of abnormal data by simple linear calculations. This allows the compensation factor for flood intensity assessment to better reflect the actual disaster impact, thereby improving the accuracy and reliability of flood intensity assessments and providing a more scientific basis for power grid facility vulnerability assessments.

[0038] It should be noted that when both the flow velocity fluctuation and the inundation depth growth rate exceed the corresponding preset thresholds, the weighted sum of the normalized results of the two needs to be used as input. Among them, the weight setting of the inundation depth growth rate of flow velocity fluctuation can be achieved through the hierarchical analysis method and combined with machine learning algorithms, such as random forests, support vector machines, etc., by using historical flood data and power grid facility disaster data for model training, and setting through model feature importance evaluation, etc. Among them, the weight setting of the hierarchical analysis method and machine learning algorithm are both existing common weight processing methods, which will not be repeated here.

[0039] This embodiment of the present invention sets a flood intensity impact factor based on flood event characteristic data and calculates the failure growth rate of power grid facilities within different flood intensity ranges. This fully considers the correlation between flood severity and failure growth rate, not only analyzing the differences in failure rate growth for the same facility under different flood intensities, but also reflecting the reinforcing effect of consecutive disasters and the impact of the duration of disaster intervals on equipment recovery. This significantly enhances the effectiveness of early warnings and also facilitates subsequent prediction of changes in equipment failure risk based on different flood intensities, allowing for proactive preparedness.

[0040] See also Figure 3 As shown, the specific setting process of the elevation protection factor in step S2 includes: B1, based on the elevation above the ground, matching the interference inundation depth of each power grid facility from a preset elevation above the ground and interference inundation depth mapping table.

[0041] B2. Determine the effectiveness of flood interference protection based on the interference inundation depth.

[0042] B3. Calculate the ratio of the number of historical floods judged to be effective for each power grid setting to the total number of historical floods to obtain the flood protection effectiveness ratio.

[0043] B4. Calculate the mean flood inundation depth of each historical flood judged to be effective for protection, and subtract it from the interference inundation depth to obtain the inundation depth difference. At the same time, calculate the average of the total duration of each historical flood judged to be effective for protection to obtain the average effective flood protection duration.

[0044] B5. Calculate the normalized values ​​of the inundation depth difference and the average effective flood protection duration respectively, and then obtain the protection correction factor by weighted summation based on the preset weights of the inundation depth difference and the average effective flood protection duration.

[0045] B6. Output the elevation protection coefficient after correcting the flood protection effectiveness ratio using the protection correction factor.

[0046] It should be added that the calculation method for the normalized values ​​of the inundation depth difference and the average effective flood protection time is the same as the normalization method of the flow velocity fluctuation and the inundation depth growth rate, and the examples will not be repeated.

[0047] Understandably, the preset mapping table of ground elevation and interference inundation depth is set through statistical analysis of historical data and training of machine learning algorithms. That is, by collecting the ground elevation data of a large number of power grid facilities in the target area, combined with the inundation depth and facility damage in historical flood events, the ground elevation is divided into multiple intervals, such as every 5 meters as an interval. For each elevation interval, the probability, type and severity of facility failure at different historical inundation depths are statistically analyzed to calculate the average interference level. Then, using machine learning algorithms such as regression analysis and decision trees, the model is trained with ground elevation as the independent variable and interference inundation depth (that is, the inundation depth corresponding to the quantified interference level) as the dependent variable. After optimizing the parameters, a mapping relationship is generated.

[0048] It's important to note that the data in this table has been cross-validated. The parameters and mapping relationships in the mapping table are regularly updated based on new flood event data and feedback on facility damage, ensuring that the data in the table dynamically reflects the actual impact of flooding. The aforementioned improved analysis and training methods and related algorithms are existing processing methods and will not be explained in detail.

[0049] Understandably, for each power grid facility, we first screen all historical flood events with effective protection, calculate the average flood inundation depth for these events, and then subtract this average from the interference inundation depth obtained in step 1 to obtain the inundation depth difference. This difference reflects the degree of deviation between the actual inundation depth and the interference threshold, given that the facility is able to effectively protect. A larger difference indicates that the facility can maintain effective protection even in the face of deeper floods, and thus has stronger protection capabilities. Simultaneously, we calculate the average total duration of these effective flood protection events to obtain the average effective flood protection duration. This duration reflects the facility's ability to sustain flooding while effectively protected. A longer duration indicates greater protection stability. Therefore, we use the two factors of inundation depth difference and effective flood protection duration to set a protection correction factor, compensating for the current limitation of focusing on the number of events while ignoring the degree of protection.

[0050] In one specific embodiment, a protection correction factor is adjusted based on the inundation depth difference and the average effective flood protection duration to further correct the flood protection effectiveness ratio. This correction is achieved by multiplying the sum of 1 and the protection correction factor by the flood protection effectiveness ratio to obtain the elevation protection coefficient. This coefficient comprehensively considers the grid facility's elevation above ground level, its historical flood protection performance, and the characteristics of floods when protection is effective. It accurately quantifies the facility's ability to protect against floods and provides a key basis for flood risk assessment of grid facilities.

[0051] It is understandable that the preset weight setting of the inundation depth difference and the average effective protection time for floods needs to comprehensively consider subjective experience and objective data. The values ​​can be directly assigned through expert evaluation, or the weights can be calculated based on the correlation between the inundation depth difference, the average effective protection time and facility failures in historical flood events. The final value of the preset weight will be the average of the weights assigned and calculated by the expert evaluation.

[0052] Furthermore, the specific judgment process for judging the effectiveness of flood interference protection described in step B2 includes: B21, traversing the inundation depth data of each historical flood event, comparing it one by one with the interference inundation depth corresponding to each power grid facility, and at the same time extracting the total duration of each flood, and comparing it with the preset flood interference duration threshold.

[0053] B22. For each flood event, the flood protection of the power grid facility is determined to be effective if the following conditions are met at the same time: the inundation depth is less than or equal to the interference inundation depth of the facility.

[0054] The total duration of flooding exceeds the preset flood interference duration.

[0055] No faults due to flooding were triggered.

[0056] If any of the above conditions is not met, the flood protection of the power grid facility is deemed ineffective.

[0057] Understandably, the depth of flooding directly determines the degree of exposure of power grid facilities to floodwaters. Different facilities, such as transformers and line towers, have varying tolerances to water depths. Comparing the actual flooding depth with the preset interference flooding depth can determine whether floodwaters have breached the basic protection thresholds of the facilities. The duration of flooding is positively correlated with the aging of facility materials and the degradation of insulation performance.

[0058] It should be added that in step S2, the balanced fault degree can be obtained by multiplying the original fault degree by the flood intensity impact factor and then multiplying the result by the difference between 1 and the elevation protection factor.

[0059] Understandably, this formula accurately captures the antagonistic relationship between disasters and facility protection, using the logic that flood intensity positively amplifies the severity of faults, while the protection factor negatively attenuates their impact. The flood intensity impact factor multiplied by the original fault severity reflects the physical nature of the situation: stronger disasters result in more severe damage to facilities. The difference between 1 and the protection factor quantifies the risk discount associated with facility elevation: the stronger the protection capability, the more pronounced the attenuation of fault severity. This calculation logic aligns with the classic model of power industry risk as the product of disaster intensity and vulnerability. In extreme scenarios, for example, outputting 0 when the protection factor is 1 reasonably reflects the effectiveness of flood protection.

[0060] The embodiment of the present invention sets an elevation protection factor based on the facility's elevation above the ground, and corrects the original fault degree in combination with the flood intensity influencing factor. Through a dual-factor correction method, this method quantifies the gradient of the impact of different inundation depths on the equipment failure rate. This can accurately reflect the performance degradation differences of equipment at different elevations in the same flood, thereby carefully and accurately assessing the flood vulnerability of facilities in different locations.

[0061] The extraction of the homogeneous flood sequence group in step S3 must meet the following conditions: the standard deviation of the flood intensity influencing factors within the group is less than or equal to the preset fluctuation threshold.

[0062] The events in the group are continuous and the interval length is less than or equal to the preset maximum allowed interval.

[0063] The specific calculation process of the failure growth rate of the power grid facilities described in step S3 includes: C1, taking the difference between the balanced fault degrees of the first and last flood history in the group, and dividing it by the average triggering time interval of adjacent historical floods to obtain the failure growth rate of each power grid facility.

[0064] C2. Fit the balance fault degree change curve of each power grid setting with time as the horizontal axis and the balance fault degree as the vertical axis to determine whether failure growth rate compensation is needed.

[0065] C3. If it is determined that failure growth rate compensation is required, the ratio of the total length of the curve above the average balanced fault degree to the total length of the variation curve is extracted from the curve as a failure growth rate compensation factor.

[0066] C4. Compensating the failure growth rate by a failure growth rate compensation factor to obtain the failure growth rate of each power grid facility.

[0067] C5. Calculate the failure growth rate of each power grid facility by averaging to obtain the final failure growth rate of the power grid facility.

[0068] It's important to note that plotting balanced fault severity curves and determining whether compensation is appropriate aims to identify nonlinear trends. When fault growth accelerates, decelerates, or fluctuates, such as when equipment aging accelerates or when temporary maintenance reduces faults, simple linear calculations can produce deviations. In these cases, it's necessary to analyze the underlying patterns of change through curve characteristics to avoid underestimating or overestimating failure risk.

[0069] It's also worth noting that the compensation factor uses the percentage of curve length above the average equilibrium failure severity. This is because above-average failure growth segments more closely reflect accelerating failure trends or abnormal deterioration. A larger percentage indicates more rapid failure growth, necessitating a larger compensation factor for the base growth rate. Conversely, a smaller compensation factor is used to precisely adjust the growth rate to match actual failure characteristics.

[0070] Understandably, using a compensation factor to modify the base growth rate eliminates nonlinear interference and makes the growth rate more closely reflect the actual failure process of the facility. Furthermore, averaging the growth rates of each facility to obtain the final result balances individual differences, reflecting the average failure growth rate of facilities within the group at a holistic level, and providing a unified risk assessment benchmark for power grid operations and maintenance.

[0071] It's important to note that the failure growth rate of power grid facilities is corrected using the aforementioned calculated failure growth assessment compensation factor and the power grid facility failure growth anomaly coefficient. Specifically, these factors are weighted together with the original failure growth rate, allowing the corrected failure growth rate to more accurately reflect the failure trends of equipment in actual disaster environments.

[0072] Understandably, the weighting of the failure growth rate and failure growth anomaly coefficient for power grid facilities can be determined through a combination of data-driven and expert experience. Specifically, by analyzing a large amount of historical flood and power grid facility failure data, the correlation between the failure growth rate and failure growth anomaly coefficient and actual failure patterns is statistically analyzed, with indicators with high correlations being given higher weights. Furthermore, experts in fields such as power engineering and disaster prevention and mitigation, drawing on their expertise and practical experience, were invited to score and assess the importance of the two indicators in reflecting facility failure risk, and then determine the weightings.

[0073] Furthermore, the determination of whether failure growth rate compensation is required in step C2 includes extracting the total number of peak and valley points from the balanced fault degree change curve, and taking the ratio of the total number of peak and valley points to the total length of the curve as the change frequency.

[0074] If the frequency of change exceeds a preset threshold, it is determined that failure growth rate compensation is required; otherwise, it is determined that failure growth rate compensation is not required.

[0075] Understandably, the peaks and valleys in the balanced failure severity curve represent significant increases and decreases in the severity of the failure, reflecting sudden changes in the equipment failure process. More peaks and valleys indicate more complex failure modes associated with flooding, such as re-damage after temporary repairs and nonlinear responses to floods of varying intensities. Measuring the frequency of changes by the ratio of the total number of peaks and valleys to the total length of the curve converts the curve's fluctuations into standardized values, eliminating subjective judgments. This metric considers both the number of fluctuations and the time span (i.e., the curve length) to comprehensively reflect the dynamic nature of failure changes. A higher ratio indicates more dramatic fluctuations in the failure severity per unit time, and a lower confidence level in the underlying growth rate.

[0076] In one specific embodiment, the frequency threshold for change can be set by comprehensively considering historical data, equipment characteristics, and engineering requirements. Typically, this involves statistically analyzing the peak and valley distribution of historical curves for similar equipment, selecting the median or 90th percentile as the threshold. Alternatively, the threshold can be set by simulating growth rate errors under different fluctuation scenarios based on a physical model of equipment failure, with the frequency corresponding to an error exceeding 10% being used.

[0077] Regarding step S4, the specific statistical process of the disaster carrying vulnerability score includes: D1, calculating the failure growth assessment compensation factor based on the triggering time of each historical flood in the same group and the flood intensity influencing factor.

[0078] D2. Calculate the abnormal growth coefficient of power grid facility failure based on the balanced failure degree and failure type of different power grid facilities in the same group during various historical floods.

[0079] D3. Correcting the failure growth rate of the power grid facilities based on the failure growth assessment compensation factor and the power grid facility failure growth anomaly coefficient.

[0080] D4. Calculate the average flood intensity impact factor differences and the corrected failure growth rate differences of different groups, and match the corresponding reference failure growth rate differences based on the flood intensity impact factor differences.

[0081] D5. The ratio of the number of adjacent groups whose statistically corrected failure growth rate difference exceeds the reference failure growth rate difference to the total number of sequence groups is recorded as the failure growth rate difference excess ratio.

[0082] D6. The ratio of the number of groups whose failure growth rate exceeds the preset warning value to the total number of sequence groups is recorded as the failure growth rate exceeding limit ratio.

[0083] D7. Based on the weights corresponding to the failure growth rate difference excess ratio and the failure growth rate excess ratio, a weighted sum of the growth rate difference excess ratio and the efficiency growth rate excess ratio is performed to obtain a comprehensive assessment coefficient, and the comprehensive assessment coefficient is multiplied by the preset disaster carrying vulnerability total score to obtain a disaster carrying vulnerability score.

[0084] In a specific embodiment, the preset total score of the disaster carrying vulnerability can be a 10-point system or a 100-point system, which is not limited here.

[0085] It should be added that the weights corresponding to the failure growth rate difference excess ratio and the failure growth rate excess ratio are usually determined by combining professional experience and historical data verification. That is, experts in the fields of power engineering and disaster prevention and mitigation directly assign weights based on their professional judgment of the failure growth rate difference and the impact of the failure growth rate on vulnerability. They also use the actual results of the vulnerability scores of historical flood events to perform reverse fitting calculations to make the scores most realistic.

[0086] It should be noted that the difference in the average flood intensity impact factor reflects the difference in disaster intensity between groups of homogeneous flood sequences, and the difference in the corrected failure growth rate reflects the difference in the failure growth rate of equipment in different groups. The failure growth rate of power grid facilities can be corrected by multiplying the compensation factor and the anomaly coefficient by the corresponding allocation weights determined based on expert experience and historical data verification, and then multiplying the sum of the results and 1 by the power grid facility failure growth rate to obtain the corrected failure growth rate.

[0087] Understandably, based on the difference in average flood intensity impact factors, a pre-established comparison table or mathematical mapping relationship is used to match the corresponding reference failure growth rate difference. This comparison table or mapping relationship can be trained through a large amount of historical data and is used to measure the normal range of equipment failure growth rate differences under the same intensity difference.

[0088] Furthermore, the specific calculation of the failure growth assessment compensation factor in step D1 is as follows: D11. Count the ratio of the number of consecutive floods in the same group to the total number of floods to obtain the ratio of consecutive floods.

[0089] D12. Calculate the average flood intensity impact factor corresponding to each historical flood in the group to obtain the average flood intensity impact factor.

[0090] D13. Calculate the duration sequence of adjacent events based on the trigger time;

[0091] D14. The flood triggering density is obtained by calculating the ratio of the number of continuous floods with a triggering interval shorter than the preset interval to the total number of historical floods.

[0092] D15. Calculate the average trigger interval duration of historical flood events within the group and compare it with the preset reference interval duration. If the average interval is greater than the reference duration, set the continuity to 0. Otherwise, calculate the ratio of the absolute value of the difference between the two and the reference duration as the flood trigger continuity.

[0093] D16. Perform weighted summation on the ratio of consecutive flood times, the average work intensity influencing factor, the flood triggering density, and the flood triggering continuity to obtain the failure assessment compensation factor.

[0094] Understandably, continuous flooding events can lead to a lack of time for equipment repair and recovery, leading to exponential accumulation of damage. This indicator quantifies how the continuity of a disaster sequence accelerates failure. The intensity of a single flood determines the immediate extent of damage to equipment, while the average intensity reflects the overall level of damage in the sequence group, avoiding assessment bias caused by individual high-intensity or low-intensity events. Flooding events occurring within a short interval can exceed the fatigue limit of equipment. This indicator quantifies the amplification effect of the density of disaster time distribution on failure. Comparing the average trigger interval with a preset reference value can identify whether flooding is creating persistent pressure, supplementing long-term, continuous threat scenarios not covered by the density.

[0095] It should be noted that the weightings for the ratio of consecutive floods, the average flood intensity impact factor, the flood trigger density, and the flood trigger continuity should be based on the combined impact of each indicator on equipment failure. The ratio of consecutive floods reflects the cumulative effect of the disaster. Multiple consecutive floods can exacerbate equipment fatigue damage and can be assigned a higher weight, such as 0.3. The average flood intensity impact factor reflects the overall level of damage caused by the disaster and directly determines the immediate extent of equipment damage. It can be assigned a lower weight, such as 0.25. The flood trigger density measures the frequency of disasters in the short term. Intensive flood impacts can shorten equipment recovery time. It can be weighted as 0.25. The flood trigger continuity is used to quantify the ongoing pressure of the disaster and has a weight of 0.2.

[0096] Furthermore, the calculation process of the abnormal coefficient of failure growth of power grid facilities in step D2 is as follows: D21. Sort the historical floods in the same group in chronological order.

[0097] D22. Match the preset penalty coefficient according to the fault type, and correct the fault degree using the penalty coefficient to obtain the comprehensive fault degree.

[0098] D23. Perform differential calculation on the comprehensive fault degree sequence of the same power grid facility to obtain the comprehensive fault degree change rate between adjacent events.

[0099] D24. For each power grid facility, calculate the maximum and average values ​​of the change rates of all adjacent events, and determine whether the maximum value is less than or equal to 0.

[0100] D25. If the maximum value is less than or equal to 0, the basic failure growth anomaly coefficient of the power grid facility is assigned a value of 0; otherwise, the maximum change rate is normalized to obtain the basic anomaly growth coefficient.

[0101] D26. If the average value is greater than 0, the ratio of the average value to the maximum value is used as an abnormal growth correction factor, and the failure growth abnormal coefficient is corrected by the abnormal growth correction factor.

[0102] D27. Calculate the mean of the corrected failure growth anomaly coefficients of all power grid facilities as the failure growth anomaly coefficient of the power grid facilities of the entire homogeneous sequence group.

[0103] Understandably, sorting historical floods within the same group by time can visually demonstrate the evolution of damage to power grid facilities during successive floods. Preset penalty coefficients are applied based on fault type matching, taking into account the significant differences in the impact of different fault types on the performance and subsequent operation of power grid facilities. For example, structural damage to key components poses a greater potential threat to facility failure than surface corrosion. Penalty coefficients quantify this difference, making the assessment more accurate and tailored to actual risks.

[0104] It should be added that the preset penalty coefficient is obtained by grading and classifying the impact of the power grid facility failure type on dimensions such as safety, reliability, repair cost, and life loss, and then combining historical flood failure data statistics, such as failure frequency, repair time, and subsequent failure probability, with reference to power industry standards and expert experience and quantifying the comprehensive impact of each failure type through hierarchical analysis and normalization. The normalization is the same as the aforementioned normalization method and will not be explained again.

[0105] Understanding this, differential calculation of the failure severity of each historical flood at the same facility, and obtaining the rate of change of failure severity between adjacent floods, can capture the specific changes in facility performance after each flood. Compared to simply focusing on the absolute value of the failure severity, the rate of change better reflects the degradation trend of the facility during successive disasters, providing dynamic data support for failure growth assessment.

[0106] The maximum and average failure severity change rates were selected. When the maximum failure severity change rate is less than or equal to 0, it indicates that the facility performance has not deteriorated due to flooding, or even improved. In this case, the foundation failure growth anomaly coefficient is assigned a value of 0 to reasonably reflect its stable state. If it is greater than 0, the maximum failure severity change rate is normalized to obtain the foundation coefficient, which highlights the degree of facility degradation under the worst-case scenario.

[0107] When the average failure severity change rate is greater than 0, the ratio of it to the maximum failure severity change rate is used as a correction factor. This factor takes into account that the maximum change rate may be affected by extreme conditions and may be accidental, while the average change rate reflects the overall trend. This ratio correction balances the relationship between extreme values ​​and the overall trend, allowing the assessment to focus on both sudden severe degradation and long-term stable degradation trends.

[0108] Finally, by averaging the corrected failure growth anomaly coefficients of each power grid facility, the degree of failure growth anomaly of the target area's power grid facilities under a specific flood sequence group can be reflected from a holistic perspective, avoiding interference from the particularity of a single facility on the assessment results and providing a more reliable and unified quantitative indicator for the disaster-bearing vulnerability assessment of power grid facilities.

[0109] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. A method for dynamically assessing the vulnerability of power grid facilities to flood disasters, characterized in that: include: S1. Obtain the ground elevation of each power grid facility in the target area and a historical flood event dataset for the target area. Each record contains the facility identifier, trigger time, characteristic data of a single flood event, and the quantitative facility fault severity and fault type after the flood. S2. Set the flood intensity impact factor based on the flood event characteristic data, and set the elevation protection factor based on the facility's ground elevation. Use these factors to correct the original fault level and output the balanced fault level. S3. Extract consecutive flood events whose flood intensity influencing factors fall within the same preset flood intensity range to form a homogeneous flood sequence group. Calculate the failure growth rate of power grid facilities in this group based on the balanced fault level. S4. Calculate the comprehensive failure growth rate and the number of groups whose failure growth rate exceeds the preset warning value to obtain the disaster carrying vulnerability score and provide feedback; S5. When a new flood event occurs, repeat steps S1-S4 to update the comprehensive disaster carrying vulnerability score.

2. The method for dynamically assessing the vulnerability of power grid facilities to flood disasters according to claim 1, wherein: The specific setting process of the flood intensity influencing factor is as follows: The maximum value of the velocity sequence of a single flood is extracted as the target flood velocity, and the standard deviation of the velocity sequence is calculated to obtain the velocity fluctuation. The maximum value of the flood depth sequence of a single flood is extracted as the target flood depth, and the flood depth growth rate is obtained by differential calculation; If the flow velocity fluctuation or the flood depth growth rate exceeds the corresponding preset threshold, the flow velocity fluctuation and the flood depth growth rate are normalized and then introduced into the Sigmoid function to output the flood intensity assessment compensation factor; Obtain the total duration of a single flood as the target duration of the flood; weight coefficients for target flood velocity, target flood inundation depth, and target duration assigned through the analytic hierarchy process; The target flood velocity, target flood inundation depth and target duration are normalized and then weighted summed to obtain the basic flood intensity impact factor, which is then compensated by the flood intensity assessment compensation factor to output the final flood intensity impact factor.

3. The method for dynamically assessing the vulnerability of power grid facilities to flood disasters according to claim 1, wherein: The specific setting process of the elevation protection factor includes: Obtaining a matching interference submergence depth for each power grid facility based on the above-ground elevation from a preset above-ground elevation and interference submergence depth mapping table; Determine the effectiveness of flood interference protection based on the interference inundation depth; The flood protection effectiveness ratio is calculated by counting the ratio of the number of historical floods judged to be effective for each power grid setting to the total number of historical floods. The flood inundation depth of each historical flood judged to be effective was averaged and subtracted from the interference inundation depth to obtain the inundation depth difference. At the same time, the total duration of each historical flood judged to be effective was calculated to obtain the average effective flood protection duration. After calculating the normalized values ​​of the inundation depth difference and the average effective flood protection duration respectively, the protection correction factor is obtained by weighted summation based on the preset weights of the inundation depth difference and the average effective flood protection duration; The elevation protection coefficient is output after correcting the flood protection effectiveness ratio through the protection correction factor.

4. The method for dynamically assessing the vulnerability of power grid facilities to flood disasters according to claim 3, wherein: The specific process of judging the effectiveness of flood interference protection includes: Traverse the inundation depth data of each historical flood event and compare it with the interference inundation depth of each power grid facility one by one. At the same time, extract the total duration of each flood and compare it with the preset flood interference duration threshold; For each flood event, if the following conditions are met at the same time, the flood protection of the power grid facilities is considered effective: The inundation depth is less than or equal to the interference inundation depth of the facility; The total duration of flooding exceeds the preset flood interference duration; No faults caused by flooding were triggered; If any of the above conditions is not met, the flood protection of the power grid facility is deemed ineffective.

5. The method for dynamically assessing the vulnerability of power grid facilities to flood disasters according to claim 1, wherein: The extraction of the homogeneous flood sequence group must meet the following conditions: The standard deviation of the flood intensity influencing factors within the group is less than or equal to the preset fluctuation threshold; The events in the group are continuous and the interval length is less than or equal to the preset maximum allowed interval.

6. The method for dynamically assessing the vulnerability of power grid facilities to flood disasters according to claim 1, wherein: The specific calculation process of the failure growth rate of the power grid facilities includes: The failure growth rate of each power grid facility is obtained by taking the difference in the equilibrium fault degree between the first and last flood history within the group and dividing it by the average triggering time interval between adjacent historical floods; Using time as the horizontal axis and the balance fault degree as the vertical axis, the balance fault degree change curve of each power grid is fitted to determine whether failure growth rate compensation is needed; If it is determined that failure growth rate compensation is required, the ratio of the total length of the curve above the average balanced fault degree to the total length of the variation curve is extracted from the curve as a failure growth rate compensation factor; Compensating the failure growth rate by a failure growth rate compensation factor to obtain a failure growth rate of each power grid facility; The failure growth rate of each power grid facility is calculated by averaging to obtain the final failure growth rate of the power grid facility.

7. A method for dynamically assessing the vulnerability of power grid facilities to flood disasters according to claim 6, characterized in that: The determination of whether failure growth rate compensation is required includes: Extracting the total number of peak and valley points from the balanced fault degree change curve, and taking the ratio of the total number of peak and valley points to the total length of the curve as the frequency of change; If the frequency of change exceeds a preset threshold, it is determined that failure growth rate compensation is required; otherwise, it is determined that failure growth rate compensation is not required.

8. The method for dynamically assessing the vulnerability of power grid facilities to flood disasters according to claim 1, wherein: The specific statistical process of the disaster carrying vulnerability score includes: The failure growth assessment compensation factor is calculated based on the triggering time of each historical flood and the flood intensity influencing factor within the same group; Based on the balanced failure degree and failure type of different power grid facilities in the same group during various historical floods, the abnormal growth coefficient of power grid facility failure is calculated; Correcting the failure growth rate of the power grid facility based on the failure growth assessment compensation factor and the power grid facility failure growth anomaly coefficient; Calculating the average flood intensity impact factor differences and the corrected failure growth rate differences of different groups, and matching the corresponding reference failure growth rate differences based on the flood intensity impact factor differences; The ratio of the number of adjacent groups whose statistically corrected failure growth rate difference exceeds the reference failure growth rate difference to the total number of sequence groups is recorded as the failure growth rate difference excess ratio; The ratio of the number of groups whose failure growth rate exceeds the preset warning value to the total number of sequence groups is recorded as the failure growth rate exceeding limit ratio; A comprehensive assessment coefficient is obtained by weighted summation based on the growth rate difference excess ratio and the effective growth rate excess ratio, and the disaster carrying vulnerability score is obtained by multiplying the comprehensive assessment coefficient with the preset disaster carrying vulnerability total score.

9. The method for dynamically assessing the vulnerability of power grid facilities to flood disasters according to claim 8, characterized in that: The specific calculation of the failure growth assessment compensation factor is as follows: The ratio of consecutive floods to the total number of floods in the same group was calculated to obtain the ratio of consecutive floods; Calculate the average flood intensity impact factor of each historical flood in the group to obtain the average flood intensity impact factor; According to the trigger time, calculate the sequence of interval lengths between adjacent events; The flood triggering density is obtained by calculating the ratio of the number of continuous floods with a triggering interval shorter than the preset interval to the total number of historical floods. Calculate the average trigger interval duration of historical flood events within the group and compare it with the preset reference interval duration. If the average interval is greater than the reference duration, set the continuity to 0. Otherwise, calculate the ratio of the absolute value of the difference between the two and the reference duration as the flood trigger continuity. The failure assessment compensation factor is obtained by weighted summing the ratio of consecutive flood times, average flood intensity influencing factor, flood triggering density and flood triggering continuity.

10. The method for dynamically assessing the vulnerability of power grid facilities to flood disasters according to claim 8, characterized in that: The calculation process of the abnormal coefficient of failure growth of power grid facilities is as follows: Sort the historical floods in the same group in chronological order; Match the preset penalty coefficient according to the fault type, and correct the fault degree by the penalty coefficient to obtain the comprehensive fault degree; Perform differential calculation on the comprehensive fault degree sequence of the same power grid facility to obtain the comprehensive fault degree change rate between adjacent events; For each power grid facility, calculate the maximum and average values ​​of the change rates of all adjacent events, and determine whether the maximum value is less than or equal to 0; If the maximum value is less than or equal to 0, the basic failure growth anomaly coefficient of the power grid facility is assigned a value of 0, otherwise the maximum change rate is normalized to obtain the basic anomaly growth coefficient; If the average value is greater than 0, the ratio of the average value to the maximum value is used as an abnormal growth correction factor, and the failure growth abnormal coefficient is corrected by the abnormal growth correction factor; The mean of the corrected failure growth anomaly coefficients of all power grid facilities is calculated as the failure growth anomaly coefficient of the power grid facilities of the entire homogeneous sequence group.

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