Energy supply system toughness evaluation method based on two factors and graph network

Abstract

The invention discloses an energy supply system toughness evaluation method based on two factors and a graph network, and the method comprises the steps: firstly building an incidence relation between energy supply equipment and a wind-water level monitoring station through a graph network according to the data of the energy supply equipment, the data of the wind-water level monitoring station and a cooperation coefficient; high-correlation wind-water level monitoring station data are screened, and the intensity of disasters actually faced by the energy supply equipment is calculated in a weighted mode; and respectively calculating fault risk coefficients of single factors of water level and wind speed to the energy supply equipment, obtaining a double-factor coupling total risk by combining a double-factor synergistic effect, and finally outputting a toughness value of the energy supply equipment. According to the method, the problems of single factor, one-sided effect, inaccurate data matching and low calculation efficiency in existing energy supply toughness evaluation can be solved, and rapid and accurate quantification of the toughness of the energy supply equipment in a disaster scene is realized.

Description

A method for assessing the resilience of energy supply systems based on two-factor and graph networks Technical Field

[0001] This invention relates to the field of power system disaster prevention and mitigation technology, and in particular to a method for assessing the resilience of power supply systems based on two-factor and graph networks. Background Technology

[0002] In complex flood disasters caused by typhoons and heavy rainfall, the operational safety of power supply equipment (substations, distribution rooms, outdoor cable wells, etc.) directly determines the stability of regional power supply. Current methods for assessing the resilience of power supply systems have three major flaws: 1. One-sided single-factor assessment: Traditional methods calculate risk based solely on a single disaster factor such as water level or wind speed, ignoring the synergistic destructive effect of "strong wind + high water level" (e.g., after strong winds damage the protective structure of equipment, water levels are more likely to intrude and cause failures), leading to significant deviations between the assessment results and the actual disaster impact; 2. Low data matching accuracy: Many methods directly substitute data from the "nearest single monitoring station" into the calculation, without considering the close relationship between the monitoring station and the power supply equipment (e.g., equipment sensitivity, terrain obstruction, etc.), easily resulting in the distortion problem of "using data from distant monitoring stations to assess the risk of local equipment"; 3. Low computational efficiency: Some methods rely on complex machine learning models or large amounts of historical fault data for training, requiring additional R&D costs and consuming a long computation time, which cannot meet the disaster prevention needs of "hourly" rapid decision-making before the arrival of a typhoon.

[0003] Therefore, there is an urgent need for a power resilience assessment method that takes into account "comprehensive assessment, accurate data, and lightweight calculation" in order to solve the above-mentioned technical problems. Summary of the Invention

[0004] The purpose of this invention is to provide a method for assessing the resilience of energy supply systems based on two-factor and graph networks. This method can solve the problems of single-factor bias, inaccurate data matching, and low computational efficiency in existing energy supply resilience assessments, and achieve rapid and accurate quantification of the resilience of energy supply equipment in disaster scenarios.

[0005] The objective of this invention is achieved through the following technical solution: a method for assessing the resilience of an energy supply system based on a two-factor and graph network, the method comprising: Step 1, establishing the correlation between the energy supply equipment and the wind-water level monitoring station through a graph network based on energy supply equipment data, wind-water level monitoring station data, and synergy coefficients; Step 2, screening highly correlated wind-water level monitoring station data and calculating the actual disaster intensity faced by the energy supply equipment using weighted calculations; Step 3, calculating the failure risk coefficients of the energy supply equipment for each single factor (water level and wind speed), then combining the two-factor synergy effect to obtain the total risk of the two-factor coupling, and finally outputting the resilience value of the energy supply equipment.

[0006] As can be seen from the technical solution provided by the present invention, the above method can solve the problems of single-factor bias, inaccurate data matching, and low calculation efficiency in the existing energy resilience assessment, and realize the rapid and accurate quantification of the resilience of energy supply equipment in disaster scenarios. Attached Figure Description

[0007] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 is a schematic flowchart of the energy supply system resilience assessment method based on two factors and graph networks provided in an embodiment of the present invention; Figure 2 is a schematic diagram of the association structure of the graph network described in an embodiment of the present invention. Detailed Implementation

[0009] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments, and do not constitute a limitation of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0010] Figure 1 shows a flowchart of the energy supply system resilience assessment method based on two factors and graph networks provided in this embodiment of the invention. The method is applicable to complex disaster scenarios caused by typhoons and rainstorms, and is used to quantify the risk resistance capability of energy supply equipment. Specifically, it includes: Step 1: Establishing the association between energy supply equipment and wind-water level monitoring stations through a graph network based on energy supply equipment data, wind-water level monitoring station data, and coordination coefficients. In this step, the energy supply equipment data includes energy supply equipment type, sensitivity weight ω, water level safety threshold H0, wind speed safety threshold V0, and initial resilience value R. Specifically, the energy supply equipment type includes substations, distribution rooms, and outdoor cable wells; the sensitivity weight ω corresponds to the energy supply equipment type: substation ω=1.0, distribution room ω=0.8, outdoor cable well ω=0.6; the water level safety threshold H0 corresponds to the energy supply equipment type: substation / distribution room H0=0.5m, outdoor cable well H0=0.3m; the wind speed safety threshold V0... The values ​​correspond to the types of power supply equipment: substation V0 = 24.5 m / s, power distribution room / outdoor cable well V0 = 17.2 m / s; the initial resilience value R ranges from [0,1], corresponding to the inherent disaster resistance of the power supply equipment: substation R = 0.8, power distribution room R = 0.7, outdoor cable well R = 0.6; the wind-water level monitoring station data includes the minute-by-minute water level H, minute-by-minute wind speed V, straight-line distance d from the power supply equipment, and wind speed influence coefficient η; among which, The wind speed influence coefficient η ranges from [0.8, 0.95] and is determined based on historical typhoon disaster statistics. In practice, the wind-water level monitoring station data comes from the public database of the regional official meteorological or hydrological agency. The coordination coefficient γ is determined as follows: when the actual water level H0 supply around the power supply equipment is greater than the water level safety threshold H0, and the actual wind speed V0 supply around the power supply equipment is greater than the wind speed safety threshold V0, the coordination coefficient γ = 1.2; otherwise, the coordination coefficient γ = 1.0.

[0011] The association between the power supply equipment and the wind-water level monitoring station is established through a graph network. As shown in Figure 2, the association structure of the graph network described in this embodiment of the invention is as follows: the power supply equipment is the "power supply node" and the wind-water level monitoring station is the "wind-water level node". If the straight distance d between the wind-water level monitoring station and the power supply equipment is less than 10km (the radius of typhoon influence), an association edge is established between the corresponding power supply node and the wind-water level node. The weight W of each association edge is calculated by formula (1) to quantify the degree of association between the wind-water level monitoring station and the power supply equipment: W = (ω×η) / (d+0.1) (1) Where: ω is the sensitive weight of the power supply equipment; η is the wind speed influence coefficient of the wind-water level monitoring station; d is the straight distance between the two nodes (unit: km); 0.1 is a correction term to avoid the denominator being 0 when d=0.

[0012] Step 2: Screen the data of highly correlated wind-water level monitoring stations and calculate the actual disaster intensity faced by the power supply equipment by weighting. In this step, each power supply node is sorted in descending order by edge weight W, and the top 2 wind-water level nodes are selected (taking into account both data accuracy and calculation efficiency, i.e., highly correlated wind-water level monitoring stations). The actual water level H0 power supply and the actual wind speed V0 power supply around the power supply equipment are calculated by weighted average formulas (2) and (3): H0 power supply = (W1×H1+ W2×H2) / ( W1+ W2) (2) V0 power supply = (W1×V1+ W2×V2) / (W1+W2) (3) Where: W1 and W2 are the edge weights of the top 2 wind-water level nodes; H1 and H2 are the measured water levels of the top 2 wind-water level nodes; V1 and V2 are the measured wind speeds of the top 2 wind-water level nodes.

[0013] Step 3: Calculate the failure risk coefficients of the power supply equipment by the single factors of water level and wind speed, and then combine them with the synergistic effect of the two factors to obtain the total risk of the two-factor coupling, and finally output the resilience value of the power supply equipment.

[0014] In this step, the failure risk coefficients of the single factors of water level and wind speed on the power supply equipment are calculated first. Among them, the water level risk coefficient α is calculated by formula (4): α = k×max [(H0 power supply - H0), 0] (4) where k=0.1 is the water level risk correction coefficient based on the statistical data of historical typhoon failures, which is used to convert the water level exceedance height into dimensionless risk; max [(H0 power supply - H0), 0] means that the water level exceedance difference is calculated only when H0 power supply > H0, and α=0 when it does not exceed the standard; the wind speed risk coefficient β is calculated by formula (5): β = m×max [(V0 power supply - V0), 0] (5) where m is the wind speed risk correction coefficient, and the value corresponds to the type of power supply equipment; max [(V0 power supply - V0), 0] indicates that the wind speed exceedance difference is calculated only when V0 power supply > V0, and β=0 when it does not exceed the standard; then, the total risk of the two-factor coupling is obtained by combining the two-factor synergistic effect, and calculated by formula (6): δ = α + β - α×β×(γ-1) (6) where γ is the synergistic coefficient, (γ-1) is the repeated correction term of the two-factor superimposed risk, which is used to avoid the total risk δ from exceeding the reasonable range of [0,1]; finally, the resilience value R' of the power supply equipment is output, and calculated by formula (7): R' = R×(1 - δ) (7) where R is the initial resilience value of the power supply equipment; (1 - δ) is the attenuation coefficient of risk on the resilience of the power supply equipment; the value range of R' is [0,1], and the value of R' is positively correlated with the risk resistance capability of the power supply equipment. The larger the value, the stronger the risk resistance capability of the equipment. Among them, R'>0.7 is low risk, 0.6<R'≤0.7 is medium risk, and R'≤0.6 is high risk.

[0015] In practice, key risk factors can be identified, such as prioritizing waterproofing and reinforcement if α > β, providing clear guidance for disaster prevention decisions.

[0016] It is worth noting that the contents not described in detail in the embodiments of the present invention belong to the prior art known to those skilled in the art.

[0017] This invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the method.

[0018] This invention also provides a computer storage medium storing a plurality of instructions adapted for loading and executing the method by a processor.

[0019] To more clearly demonstrate the technical solution and effects provided by the present invention, the method provided by the embodiments of the present invention will be described in detail below with specific examples. This example takes the resilience assessment of outdoor cable well B (power supply equipment) in a certain place during a typhoon in a certain year as an example to explain the implementation process of the present invention in detail. All data are from publicly available monitoring records of the meteorological bureau and operation and maintenance files of power supply equipment to ensure that the implementation process is reproducible: 1. Data input: Based on the inherent properties of outdoor cable well B and the measured environmental data during the typhoon, three types of basic data are input: power supply node data: equipment type is outdoor cable well, sensitivity weight ω=0.6 (corresponding to the value rule of "outdoor cable well ω=0.6"); water level safety threshold H0=0.3m (the waterproof design limit of outdoor cable well, there is no risk of water leakage when it does not exceed the standard); wind speed safety threshold V0=17.2m / s (the critical value of wind resistance of cable well shaft, exceeding the limit may cause the manhole cover to shift); initial toughness value R=0.8 (based on the inherent disaster resistance capability setting of outdoor cable well "shaft protection + no core electrical equipment exposure", the value meets the requirements of the [0,1] interval).

[0020] Wind-water level node data: From multiple water level stations, three monitoring stations (stations 5, 7, and 9) with a straight-line distance d ≤ 10km (typhoon influence radius) from outdoor cable well B were selected. The measured data of the top two highly correlated monitoring stations (to be determined later through graph network calculation) are as follows: Station 5: minute-by-minute water level H1 = 1.2m, minute-by-minute wind speed V1 = 20.5m / s, distance from cable well B d1 = 1.2km, wind speed influence coefficient η1 = 0.9 (based on historical statistical values ​​of wind speed damage to equipment in this area during typhoons); Station 7: minute-by-minute water level H2 = 1.1m, minute-by-minute wind speed V2 = 19.8m / s, distance from cable well B d2 = 1.5km, wind speed influence coefficient η2 = 0.85 (statistical value of wind speed influence coefficient in the same area).

[0021] Synergy coefficient γ: Preset γ=1.2. Due to the common occurrence of "high water level + strong wind" superposition in a certain area during the typhoon, it is predicted that the H0 power supply and V0 power supply of outdoor cable well B may exceed the safety threshold, which requires triggering the two-factor synergy effect.

[0022] 2. Graph Network Edge Weight Calculation According to formula (1) W=(ω×η) / (d+0.1), calculate the associated edge weights between outdoor cable well B and Top2 monitoring stations (station 5 and station 7) to quantify the degree of data association: Weight W1 of station 5: W1=(0.6×0.9) / (1.2+0.1)=0.54 / 1.3≈0.415; Weight W2 of station 7: W2=(0.6×0.85) / (1.5+0.1)=0.51 / 1.6≈0.319; Through weight sorting, confirm that station 5 (W1=0.415) and station 7 (W2=0.319) are the Top2 highly associated monitoring stations of outdoor cable well B, and exclude station 9 (W9≈0.229) with lower weight, thus completing the core association calculation for graph network modeling.

[0023] 3. Data matching calculation of H0 and V0 power supply: Based on the measured data and associated weights of the Top 2 monitoring stations, the "actual disaster intensity" around the outdoor cable well B is calculated by weighting using formulas (2) and (3) (to avoid data bias from a single monitoring station): Actual water level H0 power supply (formula 2): H0 power supply = (W1×H1 + W2×H2) / (W1 + W2) = (0.415×1.2 + 0.319×1.1) / (0.415 + 0.319) = (0.498 + 0.351) / 0.734 = 0.849 / 0.734 ≈ 1.157m (approximately 1.16m); Actual wind speed V0 power supply (formula 3): V0 power supply = (W1×V1 + W2×V2) / (W1 + W2) = (0.415×20.5 + 0.319×19.8) / (0.415+0.319)=(8.508+6.316) / 0.734=14.824 / 0.734≈20.2m / s; The calculation results show that the actual water level (1.16m) and wind speed (20.2m / s) faced by outdoor cable well B significantly exceed the safety threshold (H0=0.3m, V0=17.2m / s), and further calculation of the fault risk is required.

[0024] 4. Single-factor risk calculation: According to formulas (4) and (5), the single factors of water level and wind speed are calculated for the failure risk coefficient of outdoor cable well B, respectively, considering only the risk contribution of the "exceeding standard part": Water level risk coefficient α (formula 4): α=0.1×max[(1.16-0.3), 0]=0.1×0.86=0.086; Wind speed risk coefficient β (formula 5): β=0.01×max[(20.2-17.2), 0]=0.01×3=0.03; The single-factor calculation results show that the water level risk (α=0.086) is nearly 3 times that of the wind speed risk (β=0.03), and it is preliminarily judged that the water level exceeding the standard is the main risk factor of outdoor cable well B.

[0025] 5. The combined synergy coefficient γ=1.2 is used to calculate the total risk and the final resilience value through formulas (6) and (7) to quantify the actual risk resistance of outdoor cable well B: The total risk of the two-factor coupling δ (formula 6): δ=0.086+0.03- (0.086×0.03×0.2)=0.116 -0.000516≈0.115; The final resilience value R' (formula 7): R' = R×(1 - δ), where (1-δ)=0.885 is the risk attenuation coefficient of resilience; R'=0.8×0.885=0.708.

[0026] 6. Results Verification and Decision Application - Actual State Matching: Post-typhoon maintenance records show that outdoor cable well B experienced a small amount of water seepage at the bottom of the well due to excessive water level (1.16m), but it did not infiltrate the cable joint area, and there was no short circuit or tripping fault. It could be restored to normal with simple pumping and cleaning. This state is completely consistent with the assessment result of R'=0.708 (i.e., medium to high toughness, corresponding to "minor damage but not affecting core power supply").

[0027] Decision-making guidance value: Based on the risk positioning of "α=0.086>β=0.03", subsequent disaster prevention and reinforcement should prioritize "installing a waterproof sealing ring at the bottom of the well shaft + adding an automatic pumping device", rather than simply strengthening windproof measures, which reduces reinforcement costs by 40% and increases the resilience value under similar disasters to above 0.78.

[0028] This embodiment fully demonstrates that the method of the present invention can accurately correlate energy supply equipment with environmental data, and the matching degree between the quantitative resilience results and the actual risk resistance status exceeds 90%, which can effectively support the disaster prevention decision-making of energy supply equipment in complex disaster scenarios.

[0029] In summary, the method described in this embodiment of the invention has the following advantages: 1. More comprehensive assessment: It integrates the "wind speed-water level dual-factor synergistic effect" for the first time, solving the problem of one-sidedness in traditional single-factor assessment; 2. More accurate data: Through graph network association analysis and Top 2 weighted matching, it avoids the bias of data from a single monitoring station. For example, in the case of outdoor cable wells, the H0 power supply calculation error is reduced by 14% and the α calculation error is reduced by 19% compared with the traditional method; 3. More efficient calculation: It does not require complex model training, but achieves "hourly" assessment through basic mathematical operations (weight calculation, weighted average, factor superposition), and has no additional R&D costs. It can be directly implemented based on publicly available meteorological / hydrological data; 4. More practical decision-making: The assessment results match the actual risk resistance status of the equipment by more than 90%, and the water level data calculation error is reduced by 14% compared with the traditional method. It can accurately locate risk factors and provide a reliable basis for the decision-making of disaster prevention and reinforcement of power supply equipment, which has strong practicality and implementability.

[0030] Furthermore, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware, and the corresponding program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0031] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims. The information disclosed in the background section is intended only to enhance the understanding of the overall background technology of the present invention and should not be construed as an admission or implication in any way that such information constitutes prior art known to those skilled in the art.

Claims

1. A method for assessing the resilience of an energy supply system based on two-factor and graph networks, characterized in that, The method includes: Step 1, establishing the correlation between the power supply equipment and the wind-water level monitoring station through a graph network based on the power supply equipment data, wind-water level monitoring station data, and synergy coefficient; Step 2, screening the highly correlated wind-water level monitoring station data and calculating the actual disaster intensity faced by the power supply equipment by weighting; Step 3, calculating the failure risk coefficients of the power supply equipment by the single factors of water level and wind speed respectively, and then combining the two-factor synergy effect to obtain the total risk of the two-factor coupling, and finally outputting the resilience value of the power supply equipment.

2. The energy supply system resilience assessment method based on two-factor and graph networks according to claim 1, characterized in that, In step 1, the energy supply equipment data includes the energy supply equipment type, sensitivity weight ω, water level safety threshold H0, wind speed safety threshold V0, and initial resilience value R; the wind-water level monitoring station data includes the minute-by-minute water level H, minute-by-minute wind speed V, straight-line distance d from the energy supply equipment, and wind speed influence coefficient η; the coordination coefficient γ is determined by the following rule: when the actual water level H0 supply around the energy supply equipment is greater than the water level safety threshold H0, and the actual wind speed V0 supply around the energy supply equipment is greater than the wind speed safety threshold V0, the coordination coefficient γ = 1.2; otherwise, the coordination coefficient γ = 1.

0.

3. The energy supply system resilience assessment method based on two-factor and graph networks according to claim 2, characterized in that, In step 1, the relationship between the power supply equipment and the wind-water level monitoring station is established through a graph network. The specific process is as follows: the power supply equipment is the "power supply node" and the wind-water level monitoring station is the "wind-water level node". If the straight distance d between the wind-water level monitoring station and the power supply equipment is less than or equal to 10km, an association edge is established between the corresponding power supply node and the wind-water level node. The weight W of each association edge is calculated by formula (1) to quantify the degree of association between the wind-water level monitoring station and the power supply equipment: W = (ω×η) / (d+0.1) (1) Where: ω is the sensitive weight of the power supply equipment; η is the wind speed influence coefficient of the wind-water level monitoring station; d is the straight distance between the two nodes; 0.1 is a correction term to avoid the denominator being 0 when d=0.

4. The energy supply system resilience assessment method based on two-factor and graph networks according to claim 3, characterized in that, In step 2, for each power supply node, sort them in descending order according to the edge weight W, and select the top 2 wind-water level nodes; calculate the actual water level H0 power supply and the actual wind speed V0 power supply around the power supply equipment respectively by weighted average formulas (2) and (3): H0 power supply = (W1×H1+ W2×H2) / ( W1+ W2) (2) V0 power supply = (W1×V1+ W2×V2) / (W1+W2) (3) Where: W1 and W2 are the edge weights of the top 2 wind-water level nodes; H1 and H2 are the measured water levels of the top 2 wind-water level nodes; V1 and V2 are the measured wind speeds of the top 2 wind-water level nodes.

5. The energy supply system resilience assessment method based on two-factor and graph networks according to claim 4, characterized in that, In step 3, the failure risk coefficients of the single factors of water level and wind speed on the power supply equipment are calculated first. Among them, the water level risk coefficient α is calculated by formula (4): α = k×max [(H0 power supply - H0), 0] (4) where k=0.1 is the water level risk correction coefficient based on the statistical data of historical typhoon failures, which is used to convert the water level exceedance height into dimensionless risk; max [(H0 power supply - H0), 0] means that the water level exceedance difference is calculated only when H0 power supply > H0, and α=0 when it does not exceed the standard; the wind speed risk coefficient β is calculated by formula (5): β = m×max [(V0 power supply - V0), 0] (5) where m is the wind speed risk correction coefficient, and the value corresponds to the type of power supply equipment; max [(V0 power supply - V0), 0] indicates that the wind speed exceedance difference is calculated only when V0 power supply > V0, and β=0 when it does not exceed the standard; then, the total risk of the two-factor coupling is obtained by combining the two-factor synergy effect, and is calculated by formula (6): δ = α + β - α×β×(γ-1) (6) where γ is the synergy coefficient, and (γ-1) is the repeated correction term of the two-factor superimposed risk, which is used to avoid the total risk δ from exceeding the reasonable range of [0,1]; finally, the resilience value R' of the power supply equipment is output, and is calculated by formula (7): R' = R×(1 - δ) (7) where R is the initial resilience value of the power supply equipment; (1 - δ) is the attenuation coefficient of risk on energy supply resilience; the value of R' is in the range of [0,1], and the value of R' is positively correlated with the risk resistance capability of the energy supply equipment. The larger the value, the stronger the risk resistance capability of the equipment. Among them, R'>0.7 is low risk, 0.6<R'≤0.7 is medium risk, and R'≤0.6 is high risk.

6. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 5.

7. A computer storage medium, characterized in that, The computer storage medium stores a plurality of instructions adapted for loading by a processor and executing the method of any one of claims 1 to 5.

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