Power grid disaster risk assessment method and system

Abstract

The invention discloses a power grid disaster risk assessment method and system, and the method comprises the steps: determining a composite disaster and a disaster-inducing factor of a regional power grid, calculating the composite strength of the disaster-inducing factor of the composite disaster based on the disaster-inducing factor, and determining a regional digital elevation model based on a satellite image and a geographic information system corresponding to the regional power grid. Calculating a forward index and a reverse index of the regional power grid serving as a disaster-bearing body, and calculating a characteristic quantitative index of the regional power grid based on the forward index and the reverse index; the disaster risk coefficient of the regional power grid is calculated according to the disaster-inducing factor composite strength of the composite disasters, the terrain slope coefficient of the region and the characteristic quantitative index of the regional power grid, and in the disaster risk assessment process, multiple composite disasters, terrain characteristics and the characteristics of the power grid are comprehensively considered. The accuracy and reliability of disaster risk assessment are improved, so that more accurate and reliable disaster risk assessment is realized.

Description

Technical Field

[0001] This invention relates to the field of disaster risk management technology, and in particular to a method and system for assessing the disaster risk of power grids. Background Technology

[0002] With the intensification of global climate change, extreme weather events are exhibiting characteristics of high frequency, complexity, and chain reactions. The spatiotemporal coupling effects of multiple disasters, such as typhoons, severe convection (including thunderstorms, hail, and short-duration heavy rainfall), earthquakes, floods, and ice storms, pose a systemic threat to power grids. Existing research shows that traditional single-disaster defense systems are no longer effective in dealing with the cumulative effects of compound disasters. For example, the coupling of Typhoon Doksuri and severe convective weather in 2023 led to wind-induced flashover and pollution flashover cascading failures in coastal power distribution networks; the superposition of extreme ice storms and secondary earthquake disasters in 2024 triggered tower collapses and line breaks in transmission channels; and the high-frequency impacts of short-duration heavy rainfall triggered by severe convection and lightning caused insulation breakdown and damage to automation terminals in industrial park power distribution equipment. These cases reveal that the damage mechanism of multiple disasters and their chain evolution on power grids has nonlinear and dynamically cumulative characteristics, indicating that traditional assessment methods based on single disasters or static scenarios have significant limitations.

[0003] Beyond the inherent hazard factors of multiple disasters themselves, topographical factors and the characteristics of the power grid itself also play a significant role in disaster risk. Complex terrain can significantly alter the intensity and propagation path of disasters. For example, typhoons in some mountainous areas can cause localized increases in wind speed due to the funneling effect, while severe convective weather in hilly areas can easily trigger localized floods and mudslides, eventually leading to the instability of power tower foundations.

[0004] The characteristics of the power grid itself, such as load density and grid reliability level, not only reflect the importance classification of the estimated power outage losses for important users and ordinary users in the power grid supply objects, but also include the power transfer capacity and even the design quality of the power grid under historical power outage conditions. Therefore, they should also be considered in disaster risk assessment.

[0005] Therefore, existing technologies have the following shortcomings: First, at the level of mechanism understanding, existing technologies mostly focus on single disaster damage modes and lack chain failure path modeling for the coupled effects of multiple disasters (such as strong wind-rainfall-landslide, lightning-hail-equipment contamination), resulting in insufficient accuracy in disaster evolution prediction; Second, in terms of considering topographic elements, existing technologies mostly adopt homogeneous geographical assumptions and lack dynamic correlation modeling of topographic factors and the combined effects of multiple disasters, resulting in insufficient consideration of topographic features; Third, at the level of power grid characteristics, existing technologies have not comprehensively considered the combination of indicators such as power grid load density and overhead line density with disaster risk assessment, resulting in insufficient consideration of the characteristics of the power grid itself. Summary of the Invention

[0006] The technical problem to be solved by this invention is to provide a method and system for assessing the disaster risk of power grids, so as to achieve a more accurate and reliable disaster risk assessment.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: A method for assessing the disaster risk of a power grid includes the following steps: Identify the compound disasters and their causative factors in the regional power grid, and calculate the combined intensity of the causative factors of the compound disasters based on the causative factors. Based on satellite images and geographic information systems corresponding to the regional power grid, a regional digital elevation model is determined, and the terrain slope coefficient of the area where the regional power grid is located is calculated using the regional digital elevation model. Calculate the positive and negative indicators of the regional power grid as a disaster-bearing body, and calculate the characteristic quantitative indicators of the regional power grid based on the positive and negative indicators; The disaster risk coefficient of the regional power grid is calculated based on the combined intensity of the causative factors of the combined disaster, the topographic slope coefficient of the region, and the characteristic quantitative index of the regional power grid.

[0008] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows: A power grid disaster risk assessment system 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 performs the following steps: Identify the compound disasters and their causative factors in the regional power grid, and calculate the combined intensity of the causative factors of the compound disasters based on the causative factors. Based on satellite images and geographic information systems corresponding to the regional power grid, a regional digital elevation model is determined, and the terrain slope coefficient of the area where the regional power grid is located is calculated using the regional digital elevation model. Calculate the positive and negative indicators of the regional power grid as a disaster-bearing body, and calculate the characteristic quantitative indicators of the regional power grid based on the positive and negative indicators; The disaster risk coefficient of the regional power grid is calculated based on the combined intensity of the causative factors of the combined disaster, the topographic slope coefficient of the region, and the characteristic quantitative index of the regional power grid.

[0009] The beneficial effects of this invention are as follows: it identifies the compound disasters and their causative factors of a regional power grid, calculates the compound intensity of the causative factors of the compound disasters based on the causative factors, determines the regional digital elevation model based on satellite images and geographic information systems corresponding to the regional power grid, and uses the regional digital elevation model to calculate the topographic slope coefficient of the area where the regional power grid is located. It calculates the positive and negative indicators of the regional power grid as a disaster-bearing body, and calculates the characteristic quantitative indicators of the regional power grid based on them. It calculates the disaster risk coefficient of the regional power grid according to the compound intensity of the causative factors of the compound disasters, the topographic slope coefficient of the area, and the characteristic quantitative indicators of the regional power grid. In the process of disaster risk assessment, it comprehensively considers multiple compound disasters, topographic features, and the characteristics of the power grid itself, which improves the accuracy and reliability of disaster risk assessment, thereby achieving a more accurate and reliable disaster risk assessment. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the steps of a power grid disaster risk assessment method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a power grid disaster risk assessment system according to an embodiment of the present invention. Detailed Implementation

[0011] 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.

[0012] Please refer to Figure 1 A method for assessing the disaster risk of a power grid includes the following steps: Identify the compound disasters and their causative factors in the regional power grid, and calculate the combined intensity of the causative factors of the compound disasters based on the causative factors. Based on satellite images and geographic information systems corresponding to the regional power grid, a regional digital elevation model is determined, and the terrain slope coefficient of the area where the regional power grid is located is calculated using the regional digital elevation model. Calculate the positive and negative indicators of the regional power grid as a disaster-bearing body, and calculate the characteristic quantitative indicators of the regional power grid based on the positive and negative indicators; The disaster risk coefficient of the regional power grid is calculated based on the combined intensity of the causative factors of the combined disaster, the topographic slope coefficient of the region, and the characteristic quantitative index of the regional power grid.

[0013] As can be seen from the above description, the beneficial effects of the present invention are as follows: it identifies the compound disasters and their causative factors of the regional power grid, calculates the compound intensity of the causative factors of the compound disasters based on the causative factors, determines the regional digital elevation model based on the satellite image and geographic information system corresponding to the regional power grid, and uses the regional digital elevation model to calculate the topographic slope coefficient of the area where the regional power grid is located, calculates the positive and negative indicators of the regional power grid as a disaster-bearing body, and calculates the characteristic quantitative indicators of the regional power grid based on them, and calculates the disaster risk coefficient of the regional power grid according to the compound intensity of the causative factors of the compound disasters, the topographic slope coefficient of the area, and the characteristic quantitative indicators of the regional power grid. In the process of disaster risk assessment, it comprehensively considers multiple compound disasters, topographic features, and the characteristics of the power grid itself, improves the accuracy and reliability of disaster risk assessment, and thus achieves a more accurate and reliable disaster risk assessment.

[0014] Furthermore, the calculation of the combined intensity of the causative factors of the combined disaster based on the causative factors includes: Obtain historical data corresponding to the disaster-causing factors; The intensity coefficient of the disaster-causing factor is obtained by predicting the historical data. The strength coefficient is normalized to obtain the normalized strength coefficient; The weights of the disaster-causing factors are determined using an objective weighting method; The combined intensity of the causative factors of the combined disaster is calculated based on the normalized intensity coefficient and the weight.

[0015] As described above, calculating the combined intensity of disaster-causing factors by using the intensity coefficient and weight of disaster-causing factors can more accurately reflect the overall disaster-causing capacity of combined disasters, which helps to achieve more accurate disaster risk assessment and effectively reduce power grid disaster losses.

[0016] Furthermore, determining the weights of the disaster-causing factors using the objective weighting method includes: The weights of the disaster-causing factors are determined using a combination of standard and conflict methods, including: Each category of disaster-causing factors is used as an evaluation index, and each category of disaster-causing factors includes multiple samples to be evaluated. Generate an original indicator data matrix based on the evaluation indicators and the samples to be evaluated; Calculate the standard deviation of the evaluation index based on the original index data matrix; Calculate the conflict of the evaluation indicators; The information content of the evaluation index is calculated based on the standard deviation and the index conflict. The weight of the disaster-causing factor is calculated based on the amount of information.

[0017] As can be seen from the above description, using the objective weighting method to determine the weights of disaster-causing factors can determine the weights of each disaster-causing factor based on the characteristics and inherent laws of the data itself, avoiding interference from subjective factors and making the weight allocation more scientific and objective.

[0018] Furthermore, the calculation of the terrain slope coefficient of the area where the regional power grid is located using the regional digital elevation model includes: Select multiple feature points from the region where the regional power grid is located; The longitude, latitude, and altitude of the multiple feature points are obtained from the regional digital elevation model; Calculate the slope value between two adjacent points based on the longitude, latitude, and altitude of the multiple feature points; The terrain slope coefficient of the region is calculated based on the slope value between the two adjacent points.

[0019] As described above, considering the effects of terrain elevation on disasters, such as its role in fostering geological disasters like landslides and debris flows, and its amplification effect on wind speed, a regional digital elevation model (DEM) can be used to first calculate the slope value between two adjacent points, and then calculate the regional terrain slope coefficient based on the slope value between the two adjacent points. This can more accurately describe the characteristics of the regional terrain, provide a more detailed understanding of the slope changes within the region, and ensure the accuracy of subsequent disaster risk assessments.

[0020] Furthermore, the calculation of the slope value between two adjacent points based on the longitude, latitude, and altitude of the multiple feature points specifically involves: ; In the formula, s k This represents the slope value between two adjacent points. z k+1 Representing feature points k +1 altitude, z k Representing feature points k altitude x k+1 Representing feature points k Longitude +1 x k Representing feature points k longitude, y k+1 Representing feature points k +1 latitude, y k Representing feature points k Latitude; The calculation of the terrain slope coefficient of the region based on the slope value between the two adjacent points is specifically as follows: ; In the formula, SI represents the terrain slope coefficient of the region. n This represents the total slope values ​​between two adjacent points.

[0021] As described above, by obtaining the longitude, latitude, and altitude of multiple feature points from the regional digital elevation model to calculate the terrain slope coefficient, the characteristics of the regional terrain are described more accurately.

[0022] Furthermore, the calculation of the positive and negative indicators of the regional power grid as a disaster-bearing entity includes: Obtain the total length of overhead lines, the area of ​​the power supply zone, and the maximum power load of the power grid in the region; The overhead line density of the regional power grid is calculated based on the total length of the overhead lines and the area of ​​the power supply zone. The load density of the regional power grid is calculated based on the maximum power load and the area of ​​the power supply area; The overhead line density and the load density are used as positive indicators; Calculate the average power outage time for users in the aforementioned regional power grid; Obtain the total length of cable lines and the total length of power lines in the regional power grid; The cable coverage rate of the regional power grid is calculated based on the total length of the cable lines and the total length of the power lines. The average power outage time for users and the cable coverage rate are used as inverse indicators.

[0023] As described above, using overhead line density and load density as positive indicators, and average user outage time and cable coverage rate as negative indicators, considers a series of indicators that directly or indirectly affect the degree of disaster impact on the power grid. The larger the positive indicator, the greater the degree of disaster impact on the power grid, and the larger the negative indicator, the smaller the degree of disaster impact on the power grid. By using positive and negative indicators, the characteristics of the power grid can be more effectively combined into the disaster risk assessment.

[0024] Furthermore, the calculation of the characteristic quantification index of the regional power grid based on the positive index and the negative index includes: The positive and negative indices are normalized to obtain normalized positive and negative indices respectively. The weights of the normalized positive index and the weights of the normalized negative index are determined using an objective weighting method. The characteristic quantification index of the regional power grid is calculated based on the normalized positive index, the normalized negative index, the weight of the normalized positive index, and the weight of the normalized negative index.

[0025] As described above, normalizing positive and negative indicators separately can unify indicators with different dimensions and orders of magnitude onto the same scale, avoiding calculation errors caused by differences in dimensions. Using the objective weighting method to determine the weights of normalized positive and negative indicators can allocate weights based on the characteristics and inherent laws of the data itself, reducing the interference of subjective factors and more realistically reflecting the actual characteristics of the regional power grid.

[0026] Furthermore, the step of calculating the characteristic quantification index of the regional power grid based on the normalized positive index, the normalized negative index, the weight of the normalized positive index, and the weight of the normalized negative index specifically involves: ; In the formula, GI represents the quantitative index of the regional power grid characteristics, and o represents the ordinal number of the positive index. G i Indicators i This includes positive and negative indicators. S i Indicators i The weight of the inverse indicator is m, where m represents the index of the inverse indicator.

[0027] As described above, by comprehensively considering positive and negative indicators and combining their weights to calculate the characteristic quantitative indicators of the regional power grid, the characteristics of the power grid can be comprehensively evaluated.

[0028] Furthermore, the calculation of the disaster risk coefficient of the regional power grid based on the combined intensity of the causative factors of the combined disaster, the topographic slope coefficient of the region, and the characteristic quantitative index of the regional power grid specifically involves: ; In the formula, RI represents the disaster risk coefficient of the regional power grid, HI represents the combined intensity of the disaster-causing factors of the compound disaster, SI represents the topographic slope coefficient of the region, and GI represents the characteristic quantitative index of the regional power grid.

[0029] As described above, the disaster risk coefficient of the regional power grid is calculated based on the combined intensity of the causative factors of the compound disaster, the regional topographic slope coefficient, and the characteristic quantitative indicators of the regional power grid. This improves the accuracy and reliability of the power grid disaster risk assessment and can effectively guide the power grid disaster emergency response and disaster prevention planning and design.

[0030] Please refer to Figure 2Another embodiment of the present invention provides a power grid disaster risk assessment system, 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 each step of the above-described power grid disaster risk assessment method.

[0031] The power grid disaster risk assessment method and system described above are applicable to power grid disaster risk assessment scenarios. The following detailed embodiments illustrate these methods: Please refer to Figure 1 Embodiment 1 of the present invention is as follows: A method for assessing the disaster risk of a power grid includes the following steps: S1. Determine the compound disasters and their causative factors of the regional power grid, and calculate the combined intensity of the causative factors of the compound disasters based on the causative factors, specifically including S1.1-S1.6: S1.1 Determine the complex disasters and their causative factors in the regional power grid.

[0032] Specifically, statistical analysis is performed on historical disaster data of the regional power grid to identify composite disasters causing power outages. These composite disasters are then decomposed to obtain causative factors. For example, the analyzed composite disasters include strong (typhoon) winds, heavy rainfall, and lightning strikes. Typical causative factors obtained from the decomposition include wind speed, precipitation, lightning strike density, maximum temperature, minimum temperature, and humidity. As shown in Table 1, Table 1 displays the causative factors for typical composite disaster types in the power grid.

[0033] Table 1. Multiple causative factors of typical power grid compound disasters

[0034] S1.2 Obtain historical data corresponding to the disaster-causing factors.

[0035] S1.3. Predict the intensity coefficient of the disaster-causing factor based on the historical data.

[0036] In one optional implementation, the historical extreme values, historical average values, or medians of the historical data are calculated, and the historical extreme values, historical average values, or medians are used as the intensity coefficients of the disaster-causing factor.

[0037] S1.4. Normalize the strength coefficient to obtain the normalized strength coefficient.

[0038] The normalization process involves converting data of different dimensions and orders of magnitude to a level that allows for horizontal comparison and weighted summation. This is typically achieved using the maximum-minimum method, with the specific calculation formula as follows: ; In the formula,X i,norm Represents the normalized data X i , X min Representing data X i The minimum value, X max Representing data X i The maximum value.

[0039] S1.5. Use the objective weighting method to determine the weights of the disaster-causing factors.

[0040] In an optional implementation, the weights of the disaster-causing factors are determined using the CRITIC method. The core idea of ​​this method is to establish the objective weights of the evaluation indicators, and its theoretical basis involves two core concepts. First, contrast strength, which reflects the degree of difference in values ​​taken by different evaluation schemes under the same evaluation indicator, expressed in the form of standard deviation. The magnitude of the standard deviation reveals the difference in values ​​taken by the evaluation schemes on a certain indicator; the larger the standard deviation, the more significant the difference in values ​​between the schemes. Second, the conflict between evaluation indicators, which is measured based on the correlation between indicators. For example, when there is a strong positive correlation between two indicators, it indicates that the conflict between these two indicators is low. Specifically, this includes S1.5.1-S1.5.6: S1.5.1 Each of the disaster-causing factors in each category is used as an evaluation index, and each category of disaster-causing factors includes multiple samples to be evaluated.

[0041] S1.5.2. Generate an original indicator data matrix based on the evaluation indicators and the samples to be evaluated, specifically as follows: ; In the formula, X Represents the original indicator data matrix. x np Indicates the first p The first evaluation indicator n Data for the sample to be evaluated.

[0042] S1.5.3 Calculate the standard deviation of the evaluation index based on the original index data matrix, specifically as follows: ; In the formula, Indicates the first j The average value of each evaluation indicator Indicates the first j The standard deviation of each evaluation indicator.

[0043] S1.5.4 Calculate the conflict of the evaluation indicators, specifically as follows: ; In the formula, R j Indicates the first j The conflict between evaluation indicators r ij Indicates the first j Individual indicators and other indicators i Pearson correlation coefficient, p This represents the total number of other indicators i.

[0044] The correlation coefficient is calculated to measure the conflict of indicators. The stronger the correlation with other indicators, the less conflict there is between the indicator and other indicators. The more identical information is reflected, the more repetitive the evaluation content is. To a certain extent, this indicates that the weight allocation of the indicator should be reduced.

[0045] S1.5.5 Calculate the information content of the evaluation index based on the standard deviation and the index conflict, specifically as follows: ; In the formula, C j Indicates the first j The amount of information in each evaluation indicator.

[0046] S1.5.6 Calculate the weight of the disaster-causing factor based on the amount of information, specifically as follows: ; In the formula, W j Indicates the first j The weights of each evaluation indicator (disaster-causing factor).

[0047] S1.6. Calculate the combined intensity of the causative factors of the combined disaster based on the normalized intensity coefficient and the weight, specifically as follows: ; In the formula, HI This indicates the combined intensity of the causative factors in a complex disaster. W i Indicates the first i The weight of each disaster-causing factor F i,norm This represents the normalized intensity coefficient.

[0048] S2. Based on satellite imagery and geographic information system data corresponding to the regional power grid, determine the regional digital elevation model, and use the regional digital elevation model to calculate the terrain slope coefficient of the area where the regional power grid is located, specifically including S2.1-S2.5: S2.1 Determine the regional digital elevation model based on the satellite imagery and geographic information system corresponding to the regional power grid.

[0049] S2.2 Select multiple feature points from the region where the regional power grid is located.

[0050] S2.3 Obtain the longitude, latitude, and altitude of the multiple feature points from the regional digital elevation model.

[0051] S2.4 Calculate the slope value between two adjacent points based on the longitude, latitude, and altitude of the multiple feature points, specifically as follows: ; In the formula, s k This represents the slope value between two adjacent points. z k+1 Representing feature points k +1 altitude, z k Representing feature points k altitude x k+1 Representing feature points k Longitude +1 x k Representing feature points k longitude, y k+1 Representing feature points k +1 latitude, y k Representing feature points k Latitude.

[0052] S2.5. Calculate the terrain slope coefficient of the area based on the slope value between the two adjacent points, specifically as follows: ; In the formula, SI represents the terrain slope coefficient of the region. n This represents the total slope values ​​between two adjacent points.

[0053] S3. Calculate the positive and negative indicators of the regional power grid as a disaster-bearing entity, and calculate the characteristic quantitative indicators of the regional power grid based on the positive and negative indicators, specifically including S3.1-S3.11: S3.1 Obtain the total length of overhead lines, the area of ​​the power supply zone, and the maximum power load of the power grid in the region.

[0054] S3.2 Calculate the overhead line density of the regional power grid based on the total length of the overhead lines and the area of ​​the power supply area, specifically as follows: ; In the formula, This indicates the density of overhead power lines in a regional power grid (unit: km / km²). This indicates the total length of overhead lines in the regional power grid (unit: kilometers). This indicates the area of ​​the power supply zone of the regional power grid (unit: square kilometers).

[0055] S3.3 Calculate the load density of the regional power grid based on the maximum power load and the area of ​​the power supply area, specifically as follows: ; In the formula, This indicates the load density of the regional power grid (unit: megawatts per square kilometer). P load This indicates the maximum power load of the regional power grid (unit: megawatts).

[0056] S3.4. The overhead line density and the load density are used as positive indicators.

[0057] S3.5 Calculate the average user outage time (AIHC) of the regional power grid, specifically as follows: .

[0058] S3.6 Obtain the total length of cable lines and the total length of power lines in the regional power grid.

[0059] S3.7 Calculate the cable coverage rate of the regional power grid based on the total length of the cable lines and the total length of the power lines, specifically as follows: ; In the formula, This indicates the cable penetration rate of the regional power grid (unit: %). This indicates the total length of cable lines in the regional power grid (unit: kilometers). This indicates the total length of power lines in the regional power grid (unit: kilometers).

[0060] S3.8. Use the average power outage time of the user and the cable coverage rate as inverse indicators.

[0061] S3.9. Normalize the positive index and the negative index respectively to obtain normalized positive index and normalized negative index.

[0062] S3.10. Use the objective weighting method to determine the weights of the normalized positive index and the weights of the normalized negative index.

[0063] S3.11. Calculate the characteristic quantification index of the regional power grid based on the normalized positive index, the normalized negative index, the weight of the normalized positive index, and the weight of the normalized negative index, specifically as follows: ; In the formula, GI represents the quantitative index of the regional power grid characteristics, and o represents the ordinal number of the positive index. G i Indicators i This includes positive and negative indicators. S i Indicators i The weights are given by m, where m represents the index of the contrarian indicator. That is... G 1,…, G o As a positive indicator, G o+1 ,…, G m As a contrarian indicator, S 1,…, S o The weight of positive indicators, S o+1 ,…, S m The weight of the inverse indicator.

[0064] S4. Calculate the disaster risk coefficient of the regional power grid based on the combined intensity of the causative factors of the combined disaster, the topographic slope coefficient of the region, and the characteristic quantitative indicators of the regional power grid, specifically as follows: ; In the formula, RI represents the disaster risk coefficient of the regional power grid.

[0065] Please refer to Figure 2 Embodiment two of the present invention is as follows: A power grid disaster risk assessment system 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 grid disaster risk assessment method in Embodiment 1.

[0066] In summary, this invention provides a method and system for assessing the disaster risk of a power grid. It identifies compound disasters and their causative factors within a regional power grid, calculates the combined intensity of these causative factors, determines a regional digital elevation model (DEM) based on satellite imagery and geographic information systems, calculates the topographic slope coefficient of the area where the power grid is located using the DEM, calculates positive and negative indicators of the power grid as a disaster-bearing entity, and calculates quantitative indicators of the power grid's characteristics. Finally, it calculates the disaster risk coefficient of the regional power grid based on the combined intensity of the causative factors of the compound disaster, the topographic slope coefficient of the region, and the quantitative indicators of the power grid's characteristics. This method and system are then used to assess the disaster risk. During the assessment process, multiple complex disasters, terrain features, and the characteristics of the power grid itself were comprehensively considered, which improved the accuracy and reliability of disaster risk assessment, thus achieving a more accurate and reliable disaster risk assessment. In addition, overhead line density and load density were used as positive indicators, while average user outage time and cable coverage rate were used as negative indicators. A series of indicators that directly or indirectly affect the degree of disaster to the power grid were considered. The larger the positive indicator, the greater the degree of disaster impact on the power grid. The larger the negative indicator, the smaller the degree of disaster impact on the power grid. By using positive and negative indicators, the characteristics of the power grid can be more effectively combined into the disaster risk assessment.

[0067] 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 disaster risk of a power grid, characterized in that, Including the following steps: Identify the compound disasters and their causative factors in the regional power grid, and calculate the combined intensity of the causative factors of the compound disasters based on the causative factors. Based on satellite images and geographic information systems corresponding to the regional power grid, a regional digital elevation model is determined, and the terrain slope coefficient of the area where the regional power grid is located is calculated using the regional digital elevation model. Calculate the positive and negative indicators of the regional power grid as a disaster-bearing body, and calculate the characteristic quantitative indicators of the regional power grid based on the positive and negative indicators; The disaster risk coefficient of the regional power grid is calculated based on the combined intensity of the causative factors of the combined disaster, the topographic slope coefficient of the region, and the characteristic quantitative index of the regional power grid.

2. The power grid disaster risk assessment method according to claim 1, characterized in that, The calculation of the combined intensity of the combined disaster factors based on the disaster-causing factors includes: Obtain historical data corresponding to the disaster-causing factors; The intensity coefficient of the disaster-causing factor is obtained by predicting the historical data. The strength coefficient is normalized to obtain the normalized strength coefficient; The weights of the disaster-causing factors are determined using an objective weighting method; The combined intensity of the causative factors of the combined disaster is calculated based on the normalized intensity coefficient and the weight.

3. The power grid disaster risk assessment method according to claim 2, characterized in that, The determination of the weights of the disaster-causing factors using the objective weighting method includes: The weights of the disaster-causing factors are determined using a combination of standard and conflict methods, including: Each category of disaster-causing factors is used as an evaluation index, and each category of disaster-causing factors includes multiple samples to be evaluated. Generate an original indicator data matrix based on the evaluation indicators and the samples to be evaluated; Calculate the standard deviation of the evaluation index based on the original index data matrix; Calculate the conflict of the evaluation indicators; The information content of the evaluation index is calculated based on the standard deviation and the index conflict. The weight of the disaster-causing factor is calculated based on the amount of information.

4. The power grid disaster risk assessment method according to claim 1, characterized in that, The terrain slope coefficient of the area where the regional power grid is located, calculated using the regional digital elevation model, includes: Select multiple feature points from the region where the regional power grid is located; The longitude, latitude, and altitude of the multiple feature points are obtained from the regional digital elevation model; Calculate the slope value between two adjacent points based on the longitude, latitude, and altitude of the multiple feature points; The terrain slope coefficient of the region is calculated based on the slope value between the two adjacent points.

5. The power grid disaster risk assessment method according to claim 4, characterized in that, The calculation of the slope value between two adjacent points based on the longitude, latitude, and altitude of the multiple feature points is specifically as follows: ； In the formula, s k This represents the slope value between two adjacent points. z k+1 Representing feature points k +1 altitude, z k Representing feature points k altitude x k+1 Representing feature points k Longitude +1 x k Representing feature points k longitude, y k+1 Representing feature points k +1 latitude, y k Representing feature points k Latitude; The calculation of the terrain slope coefficient of the region based on the slope value between the two adjacent points is specifically as follows: ； In the formula, SI represents the terrain slope coefficient of the region. n This represents the total slope values ​​between two adjacent points.

6. The power grid disaster risk assessment method according to claim 1, characterized in that, The calculation of the positive and negative indicators of the regional power grid as a disaster-bearing entity includes: Obtain the total length of overhead lines, the area of ​​the power supply zone, and the maximum power load of the power grid in the region; The overhead line density of the regional power grid is calculated based on the total length of the overhead lines and the area of ​​the power supply zone. The load density of the regional power grid is calculated based on the maximum power load and the area of ​​the power supply area; The overhead line density and the load density are used as positive indicators; Calculate the average power outage time for users in the aforementioned regional power grid; Obtain the total length of cable lines and the total length of power lines in the regional power grid; The cable coverage rate of the regional power grid is calculated based on the total length of the cable lines and the total length of the power lines. The average power outage time for users and the cable coverage rate are used as inverse indicators.

7. The power grid disaster risk assessment method according to claim 1, characterized in that, The calculation of the characteristic quantification indicators of the regional power grid based on the positive and negative indicators includes: The positive and negative indices are normalized to obtain normalized positive and negative indices respectively. The weights of the normalized positive index and the weights of the normalized negative index are determined using an objective weighting method. The characteristic quantification index of the regional power grid is calculated based on the normalized positive index, the normalized negative index, the weight of the normalized positive index, and the weight of the normalized negative index.

8. The power grid disaster risk assessment method according to claim 7, characterized in that, The step of calculating the characteristic quantification index of the regional power grid based on the normalized positive index, the normalized negative index, the weight of the normalized positive index, and the weight of the normalized negative index specifically involves: ； In the formula, GI represents the quantitative index of the regional power grid characteristics, and o represents the ordinal number of the positive index. G i Indicators i This includes positive and negative indicators. S i Indicators i The weight of the inverse indicator is m, where m represents the index of the inverse indicator.

9. The power grid disaster risk assessment method according to claim 1, characterized in that, The calculation of the disaster risk coefficient of the regional power grid based on the combined intensity of the causative factors of the combined disaster, the topographic slope coefficient of the region, and the characteristic quantitative indicators of the regional power grid is specifically as follows: ； In the formula, RI represents the disaster risk coefficient of the regional power grid, HI represents the combined intensity of the disaster-causing factors of the compound disaster, SI represents the topographic slope coefficient of the region, and GI represents the characteristic quantitative index of the regional power grid.

10. A power grid disaster risk assessment system, 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 implements each step of the power grid disaster risk assessment method according to any one of claims 1 to 9.

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