A power distribution network vulnerability assessment method and terminal based on typhoon gale and heavy rain

Abstract

The application discloses a power distribution network vulnerability assessment method and terminal based on typhoon gale and heavy rainfall, and realizes fine identification of composite disasters by constructing a typhoon gale and heavy rainfall composite event identification matrix at an hourly level, and overcomes the limitations of traditional daily scale and single disaster factor methods. According to the composite event identification matrix, the effective influence frequency of the typhoon gale and heavy rainfall composite event is calculated, in order to quantify the composite disaster intensity, the maximum wind speed, the rain intensity, the cumulative rainfall and the effective influence frequency are fused, the composite disaster index is calculated, and the regional adaptation degree is improved. In the aspect of establishing the vulnerability index, the Logistic function is introduced to establish the nonlinear mapping between the composite disaster index and the feeder failure rate, and the vulnerability index is formed. Furthermore, in order to solve the problem of insufficient disaster loss samples in some areas, the similarity interpolation is constructed based on the Mahalanobis distance, the vulnerability parameters of the missing areas are inferred, and the global quantitative evaluation is realized.

Description

Technical Field

[0001] This invention relates to the technical field of power system risk vulnerability assessment, and in particular to a method and terminal for assessing the vulnerability of distribution networks based on typhoon winds and heavy rainfall. Background Technology

[0002] Typhoons, as a typical combined meteorological disaster of strong winds and heavy rainfall, seriously threaten the safe operation of power grids. Compared with high-voltage transmission networks, low-voltage distribution networks, due to their large scale, complex structure, and significant regional differences in wind resistance and flood prevention design standards, are more exposed and vulnerable to combined typhoon wind and heavy rainfall disasters. Furthermore, because strong winds and heavy rainfall affect different structures or components of the distribution network—for example, strong winds causing pole collapses, pole breaks, or wire breaks, while heavy rainfall causing flooding and power outages in underground substations and short circuits in overhead lines—the accuracy of risk assessments of distribution networks under the influence of a single meteorological factor is significantly lower than that considering the risk vulnerability assessment results that take into account the amplified effect of combined disasters.

[0003] Existing studies on the impact of typhoon disasters on power grids typically employ statistical models such as machine learning, Logistic regression, Poisson regression, and negative binomial functions to construct disaster damage prediction models. Machine learning is limited by the extreme nature of the samples; fitting extreme functions may lead to unrealistic extrapolations and unreasonable overestimations. To address this issue, some studies use typhoon wind field parameter models to obtain larger and more detailed typhoon wind field samples, or utilize ideal power models and Monte Carlo simulations to simulate the impact of disaster factors on the distribution network. However, typhoon simulation data differs from real-world typhoon scenarios, and the fault scenarios output by ideal distribution network models fail to reflect the impact of the actual complex structure of the distribution network. This results in vulnerability of disaster damage models and "distorted" risk assessments. Furthermore, existing studies on the impact of typhoons on power grids commonly use separate modeling based on daily-scale wind and heavy precipitation data, with less attention paid to the temporal characteristics and impacts of wind and rain combined at the hourly scale. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a method and terminal for assessing the vulnerability of power distribution networks based on typhoon winds and heavy rainfall, which can improve the accuracy of power distribution network vulnerability assessment and solve technical problems such as simulation bias, simplification of disaster-causing factors, and insufficient spatiotemporal resolution of meteorological factors.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A vulnerability assessment method for power distribution networks based on typhoon winds and heavy rainfall, comprising the following steps: Based on the hourly maximum wind speed, hourly precipitation and multi-hourly precipitation of typhoons, a composite event identification matrix is ​​constructed in different value ranges. The effective impact frequency of the composite event of typhoon strong wind and heavy precipitation is calculated based on the composite event identification matrix. Based on the effective impact frequency and the obtained cumulative precipitation, maximum wind speed, and maximum hourly rainfall intensity, the composite disaster-causing index of typhoon gale and heavy precipitation is calculated. A nonlinear mapping relationship between the composite catastrophicity index and the change in the failure rate of the distribution network feeder is established based on the Logistic curve. The nonlinear mapping relationship includes vulnerability indicators. The vulnerability parameter spatial interpolation method based on Mahalanobis distance is used to interpolate the vulnerability index to obtain a quantitative assessment result of the vulnerability of the distribution network.

[0006] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows: A power distribution network vulnerability assessment terminal based on typhoon winds and heavy rainfall includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the aforementioned power distribution network vulnerability assessment method based on typhoon winds and heavy rainfall.

[0007] The beneficial effects of this invention are as follows: By constructing an hourly-level identification matrix for combined typhoon and heavy precipitation events, refined identification of combined disasters is achieved, overcoming the limitations of traditional daily-scale and single-cause methods. Based on the combined event identification matrix, the effective impact frequency of combined typhoon and heavy precipitation events is calculated. To quantify the intensity of combined disasters, maximum wind speed, rainfall intensity, cumulative precipitation, and effective impact frequency are integrated to calculate a combined disaster-causing index, improving regional adaptability. Regarding the establishment of vulnerability indicators, a Logistic function is introduced to establish a nonlinear mapping between the combined disaster-causing index and the feeder failure rate, forming a vulnerability index. Furthermore, to address the problem of insufficient disaster damage samples in some areas, similarity interpolation is constructed based on Mahalanobis distance to infer vulnerability parameters for missing areas, achieving comprehensive quantitative assessment. Attached Figure Description

[0008] Figure 1 This is a flowchart of a power distribution network vulnerability assessment method based on typhoon winds and heavy rainfall, according to an embodiment of the present invention. Figure 2 This is a schematic diagram of a power distribution network vulnerability assessment terminal based on typhoon winds and heavy rainfall, according to an embodiment of the present invention. Label Explanation: 1. A power distribution network vulnerability assessment terminal based on typhoon winds and heavy rainfall; 2. Memory; 3. Processor. Detailed Implementation

[0009] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0010] To at least solve the above problems, please refer to Figure 1 This invention provides a method for assessing the vulnerability of power distribution networks based on typhoon winds and heavy rainfall, including the following steps: Based on the hourly maximum wind speed, hourly precipitation and multi-hourly precipitation of typhoons, a composite event identification matrix is ​​constructed in different value ranges. The effective impact frequency of the composite event of typhoon strong wind and heavy precipitation is calculated based on the composite event identification matrix. Based on the effective impact frequency and the obtained cumulative precipitation, maximum wind speed, and maximum hourly rainfall intensity, the composite disaster-causing index of typhoon gale and heavy precipitation is calculated. A nonlinear mapping relationship between the composite catastrophicity index and the change in the failure rate of the distribution network feeder is established based on the Logistic curve. The nonlinear mapping relationship includes vulnerability indicators. The vulnerability parameter spatial interpolation method based on Mahalanobis distance is used to interpolate the vulnerability index to obtain a quantitative assessment result of the vulnerability of the distribution network.

[0011] As described above, the beneficial effects of this invention are as follows: By constructing an hourly-level identification matrix for combined typhoon and heavy precipitation events, refined identification of combined disasters is achieved, overcoming the limitations of traditional daily-scale and single-cause methods. Based on the combined event identification matrix, the effective impact frequency of combined typhoon and heavy precipitation events is calculated. To quantify the intensity of combined disasters, maximum wind speed, rainfall intensity, cumulative precipitation, and effective impact frequency are integrated to calculate a combined disaster-causing index, improving regional adaptability. Regarding the establishment of vulnerability indicators, a Logistic function is introduced to establish a nonlinear mapping between the combined disaster-causing index and the feeder failure rate, forming a vulnerability index. Furthermore, to address the problem of insufficient disaster damage samples in some areas, similarity interpolation is constructed based on Mahalanobis distance to infer vulnerability parameters for missing areas, achieving comprehensive quantitative assessment.

[0012] Furthermore, the effective impact frequency of the combined typhoon gale and heavy precipitation event is calculated based on the composite event identification matrix, including: The composite event identification matrix includes hourly maximum wind speed, hourly precipitation, and multi-hourly precipitation to construct corresponding levels under different value ranges; Based on the number of intervals at each level and the total frequency at each level in the composite event identification matrix, the effective impact frequency of the typhoon gale and heavy precipitation composite event is calculated. F :

[0013] In the formula, n i Represents the first event in the composite event recognition matrix. i Each level ( iThe number of intervals (i.e., 1, 2, ..., 5). f i Represents the first event in the composite event recognition matrix. i Total frequency of each level.

[0014] As described above, the effective impact frequency of the combined typhoon-type gale and heavy precipitation event is constructed by weighted summation based on the frequency and interval of each level in the identification matrix, so as to facilitate the subsequent calculation of the composite disaster-causing index.

[0015] Furthermore, combining the effective impact frequency with the obtained cumulative precipitation, maximum wind speed, and maximum hourly rainfall intensity, the composite disaster-causing index of typhoon gale and heavy precipitation is calculated, including:

[0016] In the formula, a The weights representing the effective frequency of influence. W max This indicates the maximum wind speed. b The weight representing the maximum wind speed. R max This indicates the maximum hourly rainfall intensity. c The weight representing the maximum hourly rainfall intensity. ACC This indicates the cumulative precipitation. d This indicates the weight of the cumulative precipitation.

[0017] As described above, in addition to the frequency of typhoon-type gale and heavy precipitation events, hourly maximum wind speed, hourly rainfall intensity, and cumulative precipitation are also key disaster-causing factors. Each disaster-causing factor needs to be normalized and then weighted and multiplied to construct a composite disaster-causing index, which helps to calculate the comprehensive disaster situation of each power grid area under the influence of a single typhoon.

[0018] Furthermore, calculating the combined destructiveness index of typhoon gales and heavy rainfall also includes: The weights of effective impact frequency, maximum wind speed, maximum hourly rainfall intensity, and cumulative precipitation are arranged in multiple combinations with a step size of 0.1, with the total weights being 1. The optimal weight combination is determined based on the fitting results of the failure rate change corresponding to each combination.

[0019] As described above, the main disaster-causing factors that lead to the vulnerability of the power distribution network differ for different jurisdictions. In order to achieve the best fitting effect, the optimal weighting method is used to assign weights to the four disaster-causing factors for each jurisdiction. The weights reflect the contribution of the disaster-causing factors of typhoon-type strong winds and heavy precipitation in the jurisdiction.

[0020] Furthermore, a nonlinear mapping relationship between the composite catastrophicity index and the change in the fault rate of the distribution network feeder is established based on the Logistic curve, including: Constructing nonlinear mapping relationships:

[0021] In the formula, FFR represents the fault rate of the distribution network feeder; UL represents the saturation fault rate; TP represents the inflection point parameter, which represents the composite catastrophicity index corresponding to the fault rate reaching half of the saturation fault rate; SL represents the growth rate parameter, which is the sensitivity of the fault rate to changes in the composite catastrophicity index. The saturation failure rate, the inflection point parameter, and the growth rate parameter are vulnerability indicators.

[0022] As described above, the nonlinear mapping effectively characterizes the process by which the failure rate initially increases slowly, then rises sharply, and finally reaches saturation as the catastrophicity of a disaster accumulates. This aligns with the physical laws governing infrastructure failure under extreme weather conditions. UL represents the theoretical upper limit approached by the failure rate when HI is maximized; TP represents the failure rate reaching its maximum value. Time corresponding This reflects the overall disaster resilience threshold of the distribution network; SL determines the curve at... The steepness of the slope when nearby; TP, SL and UL are relatively independent of each other and can be used to assess the vulnerability of the distribution network individually.

[0023] Furthermore, based on the Mahalanobis distance-based vulnerability parameter spatial interpolation method, the vulnerability index is interpolated to obtain a quantitative assessment result of the distribution network vulnerability, including: For each jurisdiction's distribution network, establish a corresponding index column vector for all vulnerability indicators; Calculate the Mahalanobis distance between the target territory and other known vulnerable territories one by one:

[0024] In the formula, x and y represent the indicator column vectors of the two jurisdictions, respectively; S represents the sample population, which is an n×n dimensional covariance matrix. -1 It is the inverse matrix of S; T denotes the vector transpose; The known regional parameters corresponding to the minimum Mahalanobis distance are used as approximate estimates of the target region to conduct a quantitative assessment of the vulnerability of the distribution network in all power grid jurisdictions within the assessment range.

[0025] As described above, by calculating the Mahalanobis distance between the target area and other known vulnerable areas one by one, the known area parameters corresponding to the minimum Mahalanobis distance are used as approximate estimates of the target area. This enables a quantitative assessment of the vulnerability of the distribution network to combined typhoon and wind and rain disasters in all power grid areas within the assessment scope, thus solving the problem of insufficient disaster damage samples in some areas.

[0026] Please refer to Figure 2 Another embodiment of the present invention provides a power distribution network vulnerability assessment terminal based on typhoon winds and heavy rainfall, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the above-mentioned power distribution network vulnerability assessment method based on typhoon winds and heavy rainfall.

[0027] The above-described method and terminal for assessing the vulnerability of power distribution networks based on typhoon winds and heavy rainfall is applicable to improving the accuracy of power distribution network vulnerability assessment and solving technical problems such as simulation bias, simplification of disaster-causing factors, and insufficient spatiotemporal resolution of meteorological factors. The following detailed implementation methods illustrate these methods: Please refer to Figure 1 One embodiment of the present invention is as follows: A method and terminal for assessing the vulnerability of power distribution networks based on typhoon winds and heavy rainfall, including the following steps: S1. Data collection and statistics.

[0028] (1) Obtain the optimal path dataset of typhoon tropical cyclones and downscale the typhoon path to 1-hour resolution; obtain the latitude, longitude and intensity information of the typhoon center location every 3 or 6 hours.

[0029] (2) Within the assessment area, obtain the hourly maximum wind speed (or the collected maximum wind speed multiplied by a coefficient of 1.66 to approximate the 3s instantaneous maximum wind speed) and hourly precipitation data of the ground meteorological observation station (in some embodiments, this includes regional automatic stations) or high-precision grid during the typhoon impact period.

[0030] (3) Within the assessment area, obtain the following statistical data on the distribution network in the jurisdiction (customizable, such as prefecture-level, county-level or street-level): the number of feeder faults per hour under the influence of each typhoon (delete possible duplicate records based on the feeder name, start and end time of the impact in the disaster record, and clean and quality control the disaster data), the number and total length of the 10kV distribution network and its overhead and cable lines, the total load density and the average power outage time per household, the design wind speed of the power grid, the number of emergency rescue teams prepared during each typhoon, the total number of users with restored power, the total number of users with power outages, and the average age of the equipment, etc.

[0031] (4) Obtain economic, social and geographical data: GDP per unit population in the jurisdiction, average DEM or DEM standard deviation, urban land use ratio, river network density, etc.

[0032] S2, Identification of combined disaster events of typhoon-type strong winds and heavy precipitation.

[0033] Referring to the identification methods for typhoon-induced precipitation and strong winds in the Climate Index Typhoon (QXT 574-2020), the stations and periods affected by each typhoon (maximum wind speed ≥ 17.2 m / s) in the study area were identified, and hourly maximum wind speeds and precipitation were recorded. To include the impact of heavy precipitation caused by the residual circulation after a typhoon ceases to be recorded, a threshold of 10% of the maximum sum of hourly precipitation for all stations or high-precision grid points in the 24 hours prior to the typhoon's cessation of recording was used. When the total hourly precipitation for these stations was less than this threshold at a certain time after the typhoon ceases to be recorded as the end of the typhoon's impact.

[0034] Referring to the "Technical Specification for Monitoring, Assessment and Visualization of Power Grid Operation Risks" (GB / T 40585-2021) and the "Measures for Issuance and Dissemination of Meteorological Disaster Warning Signals" (Order No. 16 of the China Meteorological Administration), a composite event identification matrix for hourly maximum wind speed, hourly precipitation, and 3-hour precipitation caused by typhoons was constructed across different value ranges, as shown in Table 1. Considering that a maximum wind speed at a certain moment can damage distribution network lines and cause power outages, and that heavy precipitation within a certain period before and after this point may further amplify the disaster impact by causing equipment flooding or conductor short circuits, when a maximum wind speed at a certain moment coincides with the corresponding precipitation within 12 hours before and after, it is recorded as a composite event of strong wind and heavy precipitation within the corresponding range. The frequency of the composite events of strong wind and heavy precipitation obtained by this method shows a high correlation with the distribution network disaster situation under multiple typhoon impacts.

[0035] Table 1. Identification Matrix of Typhoon and Heavy Rainfall Composite Events Based on Power Grid Operation Safety

[0036] S3 is the combined disaster-causing index of typhoon-type gale and heavy precipitation.

[0037] Based on the objective model of typhoon-type gale and heavy precipitation composite events, the effective impact frequency index of typhoon-type gale and heavy precipitation composite events is constructed by weighted summation of the frequency of 16 intervals and 5 levels in the identification matrix (Table 1), as follows: (1) In the formula, n i and f i They are respectively the first in the matrix i Each level ( i The number of intervals and total frequency of (1, 2, ..., 5).

[0038] Besides the frequency of combined typhoon-type gale and heavy precipitation events, hourly maximum wind speed, hourly rainfall intensity, and cumulative precipitation are also key disaster-causing factors. Therefore, a combined typhoon-type gale and heavy precipitation disaster-causing index is constructed, and the calculation formula is as follows: (2) In the formula, each disaster-causing factor needs to be normalized before being weighted and multiplied, where a, b, c, and d are their respective weights. When assessing a single typhoon, the effective impact frequency of the cumulative average composite events occurring at stations within the power grid's service area from the start of the typhoon's strong winds and heavy rainfall impact to the current time is used. F Cumulative precipitation ACC And the maximum values ​​of maximum wind speed and hourly rainfall intensity at the station or high-precision grid ( W max , R max The comprehensive disaster susceptibility index for each power grid region under the influence of a single typhoon is calculated using this formula. When assessing historical typhoons over multiple years, the average effective impact frequency of composite events occurring at stations under the cumulative influence of multiple historical typhoons is used as the unit for each power grid region. F and the largest cumulative rainfall of a typhoon ACC Extreme wind speed W max and hourly rain intensity R max The values ​​are substituted into the formula for calculation. Each jurisdiction typically has multiple meteorological observation stations or high-precision grid points. To reflect the extreme wind and rain conditions at a certain time, the maximum wind speed and hourly rainfall intensity are taken from the maximum values ​​of the stations or high-precision grid points within the jurisdiction.

[0039] S4. Vulnerability assessment of distribution networks.

[0040] S41. Construct vulnerability indicators.

[0041] To quantify the impact of combined typhoon-type gale and heavy rainfall disasters on the power distribution network, analysis of power and meteorological data revealed that the variation of the S-shaped Logistic function most closely approximates the relationship between the catastrophic nature of meteorological disasters and the variation of power line failure rates. Therefore, the S-shaped Logistic function was used to establish a correlation between the catastrophic nature index HI of previous combined typhoon-type gale and heavy rainfall events and the power distribution network feeder failure rate. This function effectively depicts the nonlinear mapping relationship between changes in failure rate and disaster severity. It describes the process by which the failure rate initially increases slowly, then rises sharply, and finally reaches saturation, consistent with the physical laws governing infrastructure failure under extreme weather conditions. The calculation formula is as follows: (3) In the formula, This represents the ratio of the number of faulty feeders in the current distribution network to the total number of faults in the area. Indicates saturation failure rate, representing when At its maximum, the failure rate approaches the theoretical upper limit. This represents the inflection point parameter, indicating when the failure rate reaches its peak. Time corresponding This reflects the overall disaster resilience threshold of the power distribution network. ( The growth rate parameter determines the curve at... The steepness of the slope when near the typhoon, i.e., the failure rate of the typhoon. Sensitivity to changes. The three parameters of this curve, TP, SL, and UL, are relatively independent and can be used to assess the vulnerability of the distribution network individually.

[0042] S42. Use the optimal weighting method to determine the weights.

[0043] The main disaster-causing factors leading to the vulnerability of the distribution network differ across different jurisdictions. To achieve the best fitting effect, for each jurisdiction, the optimal weighting method is used to assign weights to the four disaster-causing factors included in the disaster-causing index in equation (2). That is, under the premise that the sum of the weights of the four disaster-causing factors is 1, multiple weight combinations are arranged and combined with a step size of 0.1 to obtain the fitting results of the disaster-causing index and the change in failure rate under multiple combinations. Finally, the fitting R is obtained. 2 Among combinations with a coefficient of determination greater than 0.6, the combination with the smallest RMSE (root mean square error) is selected as the optimal weighted combination for that region. Although a high R-value may be obtained even with a small number of typhoon samples. 2 While RMSE is considered, to avoid poor generalization and overfitting due to insufficient sample size, the number of typhoons involved in the fitting is generally ≥3. The weights reflect the contribution of the combined disaster-causing factors of typhoon-type strong winds and heavy precipitation within the jurisdiction.

[0044] S43, Vulnerability Indicator Verification.

[0045] To verify the significance of the three vulnerability indices obtained from the Logistic function fitting for the distribution network vulnerability, cross-correlation analysis was conducted using the Pearson correlation coefficient method with the obtained socio-economic, geographical, and distribution network indices within the power grid area, respectively. To mitigate the impact of extreme values ​​and stabilize variance, the distribution of all indices was examined before performing the correlation analysis. Indices that were right-skewed and had a skewness greater than 0.5 were logarithmically transformed before cross-correlation analysis. Left-skewed indices could be converted to right-skew using reflection transformation methods, such as taking the reciprocal or the difference between the maximum value and the left-skewed index. Appropriate methods were selected based on the actual data.

[0046] The following is a verification example: A study of county-level power grid areas in Fujian Province revealed that total power generation (TP) showed a significant negative correlation (p < 0.01) with four indicators: terrain height, unit length of overhead lines, and the reciprocal of the theoretical design standard. Conversely, it showed a significant positive correlation with the proportion of urbanized land, load density, number of 10kV distribution lines, cable coverage rate, disaster relief team reserves, and GDP per unit population. This can reasonably explain why higher terrains, such as mountainous areas, may experience further enhancement of local rainfall intensity and instantaneous wind speed due to atmospheric dynamics and thermodynamics. Complex terrain may also provide a favorable environment for secondary disasters such as flash floods, landslides, and debris flows under the influence of typhoon-type heavy rainfall. Increases in these geographical indicators all contribute to an earlier onset of disaster inflection points. The proportion of urbanized land, distribution network load density, and the number of 10kV lines primarily reflect the characteristics of urban core areas or key load centers. These areas, with higher network redundancy (such as ring networks and multiple connection points), can quickly restore power during disasters through power transfer, reducing the impact of power outages. While longer overhead power lines may reduce the risk of tripping due to lightning strikes and tree obstructions by decreasing intermediate branch points, longer lines also increase the risk of line breakage due to fewer support points on towers and line galloping caused by strong winds. A higher cable penetration rate, on the other hand, reduces the exposure of the power grid and lowers the risk of line faults. Per capita GDP reflects local investment in disaster mitigation measures and vulnerability mitigation capabilities. Higher disaster relief team reserves and theoretical design standards delay the occurrence of the disaster inflection point from both disaster relief and prevention perspectives. Therefore, the vulnerability curve inflection point can be interpreted as the critical threshold for the combined typhoon-induced disaster damage of the distribution network, at which systemic collapse begins. The higher the TP value, the stronger the disaster resilience of the regional power grid.

[0047] The factors showing a significant positive correlation (p<0.01) with the SL parameter are mainly river network density and the reciprocal of the restored power user ratio. This indicates that in areas with higher river network density, the speed of flooding is slowed down due to stronger rainwater runoff and flood detention regulation capabilities during heavy rainfall. The reciprocal of the restored power user ratio reflects the recovery capability of the power system; the smaller the value, the stronger the recovery capability and the slower the rate of disaster loss. Therefore, SL reflects the system's sensitivity to changes in catastrophicity, that is, the resilience of the distribution network to the cumulative effects of combined typhoon and rainstorm disasters.

[0048] The UL parameter reflects the exposure of the distribution network within the jurisdiction, i.e. the potential loss scale. It is worth noting that, limited by the sample size of the typhoons involved in the fitting, if the sample size is small, the simulated UL value may deviate significantly from the actual value. However, since it is significantly positively correlated with the total length of the 10kV distribution network, it indicates that the simulation results can reflect the differences in the exposure of the distribution network between jurisdictions to a certain extent.

[0049] In summary, by fitting the Logistic function, three vulnerability indices, TP, SL, and UL, were obtained, representing the vulnerability of the distribution network to typhoon-induced combined wind and rain disasters from three aspects: disaster resistance threshold, resilience, and potential loss scale.

[0050] S44, Vulnerability Index Approximate Imputation If the sample size of power distribution network damage under typhoon disasters within the assessment area is limited, leading to the failure to obtain vulnerability indicators for some jurisdictions, Mahalanobis distance can be used as a similarity criterion. Based on the economic and social indicators selected in the vulnerability indicator verification step that are significantly correlated (p<0.01) with the three vulnerability indicators TP, SL, and UL, Mahalanobis distance is used to measure the spatial similarity of multiple indicators between unknown and known vulnerable areas. Mahalanobis distance, due to the introduction of the covariance matrix between indicators, has a significant advantage over Euclidean distance in handling linear correlations between indicators. For the x and y datasets of two jurisdictions with n indicators, the indicators are first standardized using z-scores, and then the Mahalanobis distance is calculated. : (4) In the formula, x and y are n×1 column vectors, representing all indicator values ​​of the two jurisdictions, respectively. S is the n×n covariance matrix of the sample population. -1 It is the inverse matrix of S. T represents the vector transpose. By calculating the Mahalanobis distance between the target area and other known vulnerable areas one by one, the known area parameters corresponding to the minimum Mahalanobis distance are used as approximate estimates of the target area, thereby achieving a quantitative assessment of the vulnerability of the distribution network to typhoon and wind-rain combined disasters in all power grid areas within the assessment scope.

[0051] According to another aspect of the invention, Figure 2 This is a schematic diagram illustrating a power distribution network vulnerability assessment terminal based on typhoon winds and heavy rainfall according to an embodiment of the present invention. It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the power distribution network vulnerability assessment method based on typhoon winds and heavy rainfall as described above.

[0052] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for assessing the vulnerability of power distribution networks based on typhoon winds and heavy rainfall, characterized in that, Including the following steps: Based on the hourly maximum wind speed, hourly precipitation and multi-hourly precipitation of typhoons, a composite event identification matrix is ​​constructed in different value ranges. The effective impact frequency of the composite event of typhoon strong wind and heavy precipitation is calculated based on the composite event identification matrix. Based on the effective impact frequency and the obtained cumulative precipitation, maximum wind speed, and maximum hourly rainfall intensity, the composite disaster-causing index of typhoon gale and heavy precipitation is calculated. A nonlinear mapping relationship between the composite catastrophicity index and the change in the failure rate of the distribution network feeder is established based on the Logistic curve. The nonlinear mapping relationship includes vulnerability indicators. The vulnerability parameter spatial interpolation method based on Mahalanobis distance is used to interpolate the vulnerability index to obtain a quantitative assessment result of the vulnerability of the distribution network.

2. The method for assessing the vulnerability of a power distribution network based on typhoon winds and heavy rainfall as described in claim 1, characterized in that, The effective impact frequency of the combined typhoon gale and heavy precipitation event is calculated based on the composite event identification matrix, including: The composite event identification matrix includes hourly maximum wind speed, hourly precipitation, and multi-hourly precipitation to construct corresponding levels under different value ranges; Based on the number of intervals at each level and the total frequency at each level in the composite event identification matrix, the effective impact frequency of the typhoon gale and heavy precipitation composite event is calculated. F : In the formula, n i Represents the first event in the composite event recognition matrix. i Each level ( i The number of intervals (i.e., 1, 2, ..., 5). f i Represents the first event in the composite event recognition matrix. i Total frequency of each level.

3. The method for assessing the vulnerability of a power distribution network based on typhoon winds and heavy rainfall as described in claim 2, characterized in that, Based on the effective impact frequency and the obtained cumulative precipitation, maximum wind speed, and maximum hourly rainfall intensity, the composite disaster-causing index of typhoon gale and heavy precipitation is calculated, including: In the formula, a The weights representing the effective frequency of influence. W max This indicates the maximum wind speed. b The weight representing the maximum wind speed. R max This indicates the maximum hourly rainfall intensity. c The weight representing the maximum hourly rainfall intensity. ACC This indicates the cumulative precipitation. d This indicates the weight of the cumulative precipitation.

4. The method for assessing the vulnerability of a power distribution network based on typhoon winds and heavy rainfall as described in claim 3, characterized in that, The calculation of the combined destructiveness index of typhoon gales and heavy precipitation also includes: The weights of effective impact frequency, maximum wind speed, maximum hourly rainfall intensity, and cumulative precipitation are arranged in multiple combinations with a step size of 0.1, with the total weights being 1. The optimal weight combination is determined based on the fitting results of the failure rate change corresponding to each combination.

5. The method for assessing the vulnerability of a power distribution network based on typhoon winds and heavy rainfall as described in claim 3, characterized in that, A nonlinear mapping relationship between the composite catastrophicity index and the change in the failure rate of distribution network feeders is established based on the Logistic curve, including: Constructing nonlinear mapping relationships: In the formula, FFR represents the fault rate of the distribution network feeder; UL represents the saturation fault rate; TP represents the inflection point parameter, which represents the composite catastrophicity index corresponding to the fault rate reaching half of the saturation fault rate; SL represents the growth rate parameter, which is the sensitivity of the fault rate to changes in the composite catastrophicity index. The saturation failure rate, the inflection point parameter, and the growth rate parameter are vulnerability indicators.

6. The method for assessing the vulnerability of a power distribution network based on typhoon winds and heavy rainfall as described in claim 1, characterized in that, A vulnerability parameter spatial interpolation method based on Mahalanobis distance is used to interpolate the vulnerability indices, resulting in a quantitative assessment of the distribution network vulnerability, including: For each jurisdiction's distribution network, establish a corresponding index column vector for all vulnerability indicators; Calculate the Mahalanobis distance between the target territory and other known vulnerable territories one by one: In the formula, x and y represent the indicator column vectors of the two jurisdictions, respectively; S represents the sample population, which is an n×n dimensional covariance matrix. -1 It is the inverse matrix of S; T denotes the vector transpose; The known regional parameters corresponding to the minimum Mahalanobis distance are used as approximate estimates of the target region to conduct a quantitative assessment of the vulnerability of the distribution network in all power grid jurisdictions within the assessment range.

7. A power distribution network vulnerability assessment terminal based on typhoon winds and heavy rainfall, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it performs the following steps: Based on the hourly maximum wind speed, hourly precipitation and multi-hourly precipitation of typhoons, a composite event identification matrix is ​​constructed in different value ranges. The effective impact frequency of the composite event of typhoon strong wind and heavy precipitation is calculated based on the composite event identification matrix. Based on the effective impact frequency and the obtained cumulative precipitation, maximum wind speed, and maximum hourly rainfall intensity, the composite disaster-causing index of typhoon gale and heavy precipitation is calculated. A nonlinear mapping relationship between the composite catastrophicity index and the change in the failure rate of the distribution network feeder is established based on the Logistic curve. The nonlinear mapping relationship includes vulnerability indicators. The vulnerability parameter spatial interpolation method based on Mahalanobis distance is used to interpolate the vulnerability index to obtain a quantitative assessment result of the vulnerability of the distribution network.

8. A power distribution network vulnerability assessment terminal based on typhoon winds and heavy rainfall as described in claim 7, characterized in that, The effective impact frequency of the combined typhoon gale and heavy precipitation event is calculated based on the composite event identification matrix, including: The composite event identification matrix includes hourly maximum wind speed, hourly precipitation, and multi-hourly precipitation to construct corresponding levels under different value ranges; Based on the number of intervals at each level and the total frequency at each level in the composite event identification matrix, the effective impact frequency of the typhoon gale and heavy precipitation composite event is calculated. F : In the formula, n i Represents the first event in the composite event recognition matrix. i Each level ( i The number of intervals (i.e., 1, 2, ..., 5). f i Represents the first event in the composite event recognition matrix. i Total frequency of each level.

9. A power distribution network vulnerability assessment terminal based on typhoon winds and heavy rainfall as described in claim 8, characterized in that, Based on the effective impact frequency and the obtained cumulative precipitation, maximum wind speed, and maximum hourly rainfall intensity, the composite disaster-causing index of typhoon gale and heavy precipitation is calculated, including: In the formula, a The weights representing the effective frequency of influence. W max This indicates the maximum wind speed. b The weight representing the maximum wind speed. R max This indicates the maximum hourly rainfall intensity. c The weight representing the maximum hourly rainfall intensity. ACC This indicates the cumulative precipitation. d This indicates the weight of the cumulative precipitation.

10. A power distribution network vulnerability assessment terminal based on typhoon winds and heavy rainfall as described in claim 9, characterized in that, A nonlinear mapping relationship between the composite catastrophicity index and the change in the failure rate of distribution network feeders is established based on the Logistic curve, including: Constructing nonlinear mapping relationships: In the formula, FFR represents the fault rate of the distribution network feeder; UL represents the saturation fault rate; TP represents the inflection point parameter, which represents the composite catastrophicity index corresponding to the fault rate reaching half of the saturation fault rate; SL represents the growth rate parameter, which is the sensitivity of the fault rate to changes in the composite catastrophicity index. The saturation failure rate, the inflection point parameter, and the growth rate parameter are vulnerability indicators.

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