Large-scale flood risk dynamic assessment method for flood-related area of power grid equipment

Abstract

The invention discloses a large-scale flood risk dynamic assessment method for a power grid equipment flood-related area, and aims to solve the problems of risk assessment result distortion under complex hydrology, insufficient pertinence of power grid equipment flood risk assessment, insufficient risk assessment dynamic updating capability and the like in a traditional flood risk assessment method. According to the method, basic data such as meteorology, hydrology, terrain, remote sensing images and power grid historical flood conditions are integrated, and flood-prone areas are analyzed and determined; the method comprises the following steps: aiming at a flood-related area of power grid equipment, constructing a district distributed flood forecasting model based on a three-water-source Xinanjiang model, taking a hydrological forecasting model result as input, constructing a refined one-two-dimensional coupling flood routing analysis model, and simulating a flood inundation risk evolution process of the power grid equipment in different scenes; and constructing a power grid equipment multi-index flood inundation risk grading system to clarify the risk grade, constructing a large-scale flood risk assessment model, and outputting a dynamic risk assessment result by associating real-time data.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of flood prevention, and in particular to a large-scale flood risk dynamic assessment method for flood-involved areas of power grid equipment. BACKGROUND

[0002] Under the influence of climate change, the frequency of global extreme precipitation events has increased significantly, and the severe fluctuations in hydrological conditions have brought severe challenges to the existing flood control system. In high-risk flood areas, power grid facilities, as a key component of urban and rural infrastructure, are directly related to the sustained and stable operation of social production, the normal development of economic activities, and the basic guarantee of residents' lives. Once affected by floods, the damage to power grid facilities may trigger a wide range of chain reactions, causing energy supply disruptions, public service disruptions, and other problems. In this context, the flood risk faced by power grid facilities has been increasingly valued by government departments, research institutions, and power companies. How to build a scientific and effective risk assessment method to improve the ability of power grids to cope with flood disasters has become an important issue to ensure social operation and development. However, traditional flood risk assessment methods, while able to identify flood risks to some extent, still lack targeted assessment of power grid equipment under complex weather conditions. The core bottleneck lies in the following three aspects:

[0003] First, the assessment accuracy is insufficient under complex hydrological processes. Traditional methods rely on historical flood data and static terrain analysis, based on empirical statistics and simplified physical models, and fail to fully consider the interactions of complex hydrological processes, climate change, and factors such as terrain and land use within the region, resulting in systematic biases in the assessment results that cannot match the actual temporal and spatial heterogeneity of flood risk, causing systematic biases. Such biases not only fail to provide reliable basis for risk prevention and control, but may even mislead flood control engineering planning and emergency response decisions.

[0004] Second, there is a lack of specific risk assessment for power grid equipment. Existing research focuses on the direct damage to houses, agriculture, industry, water conservancy projects, transportation, water supply, and other disaster-bearing bodies within the flooded area, as well as the social and ecological environmental impacts of personnel casualties and drinking water source pollution. The study of flood disaster mechanisms for lifeline projects such as power grids is insufficient. At the same time, such lifeline projects have characteristics such as wide distribution, complex structure, and serious consequences of failure, so once a flood disaster occurs, it is easy to trigger a chain reaction, resulting in a flood impact area much larger than the actual flooded area, and the proportion of indirect losses increases significantly. The characteristics and disaster characteristics of power supply facilities affected by floods are significantly different from other types of disaster-bearing bodies, and the current flood risk analysis and assessment work for power supply equipment is lacking. Traditional methods cannot accurately depict the flooding response and chain effect, resulting in underestimation of indirect losses.

[0005] Third, the dynamic simulation and real-time updating capability is poor. The traditional evaluation results are mainly presented in the form of static maps and fixed reports, and the data basis often relies on historical observation values at a specific period (such as multi-year average precipitation and fixed underlying surface conditions), and the scene setting is highly fixed and cannot be dynamically adjusted with external environmental changes. With the rapid changes of climate and land use types, the old data used by traditional static evaluation cannot match the current situation, especially in the real-time evolution process of flood, the key parameters such as water level rise and flow rate change are in a dynamic updating state, and the traditional method is difficult to capture these dynamic information and adjust the evaluation conclusion in real time, which finally leads to the lag of the evaluation results behind the actual risk, and it is difficult to support the real-time optimization of flood control decision.

[0006] In the face of the above problems, how to provide fine flood risk assessment for power grid equipment in complex river basins, there is currently no complete and efficient method. SUMMARY

[0007] To solve the above problems, the present application provides a large-scale flood risk dynamic evaluation method for flood-related areas of power grid equipment, by constructing a hydrology-hydrodynamics coupled model, inputting differential model parameters, simulating the flood evolution process under different precipitation and basin conditions, combining real-time power grid data for analysis, and establishing an integrated technical framework of "coupled model flood simulation-power grid equipment risk assessment", so as to accurately identify the risk area of power grid equipment, and through mutual verification with the monitoring indexes extracted in real time by the space-time-ground integrated disaster monitoring platform, dynamically correcting the model parameters of flood impact calculation, realizing real-time updating of the evaluation results, and providing scientific basis for flood control design, emergency management and risk mitigation of power grid.

[0008] The technical scheme of the present application is as follows, a large-scale flood risk dynamic evaluation method for flood-related areas of power grid equipment, comprising the following steps:

[0009] S1 Collect and sort out the basic data of the target basin, such as meteorological, hydrological, topographic, remote sensing image, and historical flood situation of power grid, and delineate the flood-prone area;

[0010] S2 For the flood-related area of power grid equipment, a distributed flood forecasting model based on the three-source Xinanjiang model is constructed;

[0011] S3 For the flood-related area of power grid equipment, a fine one-two-dimensional coupled flood evolution analysis model is constructed with the hydrological forecasting model results as input, to simulate the flood inundation risk evolution process of power grid equipment under different scenarios;

[0012] S4 A multi-index flood inundation risk grading system for power grid equipment is constructed to determine the risk level;

[0013] S5 Construct a large-scale flood risk dynamic assessment model for the flood area of power grid equipment, input real-time data and parameter correction, and output risk assessment results.

[0014] Further, step S1 is specifically:

[0015] S101 adopt ANUSPLIN interpolation method to process station meteorological data, and generate 1km×1km grid data;

[0016] S102 obtain long sequence high-precision hydrological observation data, and adopt "linear interpolation + regression extension" method to process missing or abnormal hydrological data;

[0017] S103 construct a digital elevation model of the basin based on high-resolution geospatial data;

[0018] S104 obtain the geographic location information of power grid equipment and the distribution data of power lines through remote sensing images, and determine the scope of the flood area of power grid equipment.

[0019] Further, step S2 is specifically:

[0020] S201 adopt the Thiessen polygon method to divide sub-basins, and calculate the Thiessen weight and underlying surface parameters of each sub-basin;

[0021] S202 calculate the long series of meteorological observation processes of each unit according to the spatial relationship between the calculation unit and the rain gauge;

[0022] S203 establish a distributed Xin'anjiang model with the area as a unit, and perform calculation of evapotranspiration, runoff generation, water source division, and confluence, etc., to extract flood forecasting results;

[0023] S204 adopt NSGA-II multi-objective optimization algorithm to perform model parameter calibration according to the measured and simulated runoff processes of each key section;

[0024] S205 adopt flood peak error, flood volume error, and Nash coefficient to evaluate the flood forecasting results.

[0025] Further, step 3 is specifically:

[0026] S301 construct a one-dimensional river model;

[0027] Adopt Godunov format finite volume method to solve one-dimensional Saint-Venant equation set, and the calculation formula is as follows:

[0028] Continuity equation:

[0029]

[0030] Momentum equation:

[0031]

[0032] where A is the cross-sectional area of the river, Q is the discharge, u is the lateral inflow velocity in the river direction, t is time, x is the horizontal coordinate along the flow direction, q is the lateral inflow, a is the momentum correction coefficient, g is the gravity acceleration, y is the water level, S f is the friction slope, n is the river roughness, R is the hydraulic radius, and P is the wetted perimeter.

[0033] The boundary conditions of the river model are defined.

[0034] The tributary inflow is considered as a concentrated inflow, and the calculation formula is as follows:

[0035]

[0036] where q(t) is the lateral inflow at a certain time, Q 支 (t) is the measured / predicted discharge of the tributary at a certain time, L 汇入段 is the length of the calculation section of the one-dimensional river at the tributary inflow point, ensuring uniform distribution of the inflow to the river section.

[0037] The GPU heterogeneous parallel algorithm is used to accelerate the one-dimensional hydrodynamic model.

[0038] S302 constructs a two-dimensional hydrodynamic model

[0039] The Godunov-type finite volume method is used to numerically discretize the two-dimensional shallow water equation, and the calculation formula is as follows:

[0040] U t +E(U) x +H(U) y =S0+S

[0041]

[0042] where h is the water depth, u is the flow velocity in the x direction, v is the flow velocity in the y direction, p a is the atmospheric pressure on the water surface, z b is the bed elevation, τ ax , τ ay are the wind loads, ρ a is the air density, is the wind speed at 10 m above the water surface, C Ds is the drag coefficient, τ bx , τ by are the bed shear stresses, and n is the two-dimensional area roughness.

[0043] The HLLC format approximate Riemann solver is used to calculate the flux on the control unit interface, and the calculation formula is as follows in the x direction:

[0044]

[0045] where u * is the intermediate wave speed, U L , U R are the left and right interface vectors of the conserved variable U = [h, hu, hv] T , c is the sound speed, and p is the pressure.

[0046] The second-order MUSCL numerical reconstruction method is used for spatial interpolation of the variables to avoid numerical oscillation, and the calculation formula is as follows:

[0047]

[0048] Δu i = u i+1 - u i

[0049]

[0050] where u i is the average value of u at i, Δu i is the cell slope, θ i is the slope ratio, and φ(θ) is the minmod limiter φ(θ) = max(0, min(1, θ)), which ensures that the reconstructed variable satisfies the "monotone conservation";

[0051] The GPU parallel computing is used to realize high-speed operation without reducing the accuracy.

[0052] S303 realizes the coupling of the one-dimensional and two-dimensional models by using lateral connection, and uses the weir formula method to calculate the exchange water quantity of the two models at the coupling boundary, and the calculation formula is as follows:

[0053]

[0054] C = ε·C0

[0055] h = max(y1-y2, 0)

[0056] where C is the weir coefficient, b is the connection width, h is the water head difference of the coupling boundary, ε is the side contraction coefficient, C0 is the basic weir coefficient, y1 is the water level of the one-dimensional river coupling section, and y2 is the water level of the two-dimensional coupling boundary grid, which ensures that the flow exchanges only from the high water level to the low water level.

[0057] S304 uses the DAMBRK method of the United States National Weather Service to calculate the dike breach flow, and the calculation formula is as follows:

[0058]

[0059] wherein Q is the instantaneous breach flow, b is the instantaneous breach base width, g is the acceleration of gravity, h is the upstream water level, h b is the instantaneous breach base elevation, S is the breach slope, c v is the approach velocity coefficient, k s is the submergence coefficient, W b is the dam length, h b,term is the final breach base elevation, h ds is the downstream water level;

[0060] S305 performs model calibration and verification to ensure water balance and reasonable flow field distribution;

[0061] S306 specifies the format of model input and output data.

[0062] Further, step 4 is specifically:

[0063] S401 selects indexes based on the three dimensions of “hazard, importance and urgency”, uses VIF and CV to screen indexes, uses variance inflation factor VIF to test the multicollinearity between indexes, and screens the redundancy of indexes, and the calculation formula is as follows:

[0064]

[0065] wherein is the multiple correlation coefficient of the jth index to other indexes, and the screening standard is VIF j ≤10;

[0066] Use the coefficient of variation CV to determine the discrimination of indexes, and screen the discrimination of indexes, and the calculation formula is as follows:

[0067]

[0068] wherein σ j is the standard deviation of the jth index, is the mean value, and the screening standard is CV j ≥0.2;

[0069] S402 uses the analytic hierarchy process to determine the weight of each index, constructs a judgment matrix and solves the characteristic vector;

[0070] S403 establishes a risk grade classification system.

[0071] Further, step S402 is specifically: hierarchical division is carried out in the target layer, the criterion layer and the index layer, and the division is as follows:

[0072] Target layer O: power grid equipment flood risk level evaluation;

[0073] Criteria layer C: C1 is risk, C2 is ground element importance, C3 is emergency disposal urgency;

[0074] Index layer U: U 11 , U 12 …U 17 ; U 21 …U 24 ; U 31 …U 35 ;

[0075] The judgment matrix A (3x3) of the criteria layer, the judgment matrix B1 (7x7), B2 (4x4), B3 (5x5) of the index layer are constructed;

[0076] The maximum eigenvalue λ max of the judgment matrix is solved, and the corresponding eigenvector is normalized to obtain the weight, and the calculation formula is as follows:

[0077]

[0078] Further, in step S401, the index screening is specifically:

[0079] The risk index of the power grid equipment is determined, the distance from the river L, the elevation difference ΔH, the waterproof grade W, the flood frequency F, the flood peak flow Q, the submergence depth h, and the submergence duration t;

[0080] The importance index of the ground element is determined, the population density P, the economic density E, the number of important facilities C, and the power supply load grade L0;

[0081] The urgency index of the emergency disposal is determined, the dam firmness K, the flood discharge capacity Q0, the plan completeness rate R, the emergency team size N0, and the early warning advance Δt;

[0082] Index screening is performed to eliminate redundant and low discriminant indexes.

[0083] Further, step S5 is specifically:

[0084] S501 inputs data to the model;

[0085] S502 performs real-time dynamic simulation of the flood to calculate parameters such as the inundation area, water depth, and duration;

[0086] One-dimensional model is used for long river flood simulation;

[0087] Two-dimensional fast model is used for flood simulation of local key river sections and flood storage areas;

[0088] Considering the boundary relationship of each model, one-dimensional and two-dimensional longitudinal and transverse coupling is used in local river sections and flood storage areas.

[0089] S503 accurate assessment of flood risk is realized according to the flood risk index and the consequence influence index;

[0090] S504 the flood impact calculation model parameters are dynamically corrected in real time through mutual verification with the monitoring indexes extracted in real time by the space-time integrated disaster monitoring platform;

[0091] S505 the flood risk zoning map of the power grid equipment is drawn by using the positioning chart method, and the disaster loss assessment result is dynamically visualized in real time.

[0092] The beneficial effects of the present application are:

[0093] The method can provide accurate risk assessment results, provide accurate support for power grid equipment risk identification and real-time early warning, and help efficient emergency protection. In step 2, a distributed flood forecasting model based on a three-source Xin'anjiang model is constructed for the flood area of the power grid equipment, the parameters are calibrated through an NSGA-II multi-objective optimization algorithm, and the hydrological prediction result accuracy is ensured; in step 3, the high-precision hydrological prediction result output in step 2 is input to construct a refined one-two-dimensional flood evolution analysis model, which is verified through water balance, flow field and DEM elevation matching, etc., to accurately simulate the flood inundation risk evolution process of the power grid equipment under different scenarios, greatly reducing the systematic deviation of traditional evaluation, providing high-quality data support for accurate identification and real-time evaluation of high-risk areas of the power grid equipment, and effectively avoiding the misleading of flood control engineering planning and emergency response decision-making, providing a scientific basis for power grid flood control design, risk mitigation and emergency disposal.

[0094] The method can deeply couple hydrology and power grid equipment data, make the risk assessment closely match the power grid operation and maintenance needs, and improve the practical value of the evaluation result for power grid flood control. In step 3, the flood area of the power grid equipment is designed, and the distributed flood forecasting result based on the three-source Xin'anjiang model in step 2 is used as the core input, rather than generalized hydrological data. The refined one-two-dimensional flood evolution analysis model constructed by it specially simulates the flood inundation risk evolution process of the power grid equipment under different scenarios, focuses on calculating key parameters such as the inundation range, water depth and flow rate around the power grid equipment, and subsequently associates these hydrological simulation results with data such as the type, quantity and geographical location of the power grid equipment, forming a special risk assessment result centered on the power grid equipment, avoiding the problem of "generalized evaluation" of traditional methods and the disconnection between the power grid operation and maintenance, and providing targeted support for power grid flood reinforcement and risk control.

[0095] The method can realize dynamic visualization of risk assessment, and provide technical support for safe and reliable operation of power grid. In step 3, the refined one-two-dimensional coupled flood evolution analysis model continuously outputs the dynamic evolution data of the flood submergence risk of power grid equipment under different scenarios in the simulation process, and clearly specifies the input and output data formats of the one-dimensional model and the coupled model, ensuring the standardization and callability of the dynamic data; in step 5, through mutual verification with the monitoring indexes extracted in real time by the space-time integrated disaster monitoring platform, the dynamic data input for visualization is ensured to be highly consistent with the actual flood situation, avoiding the lag problem of static simulation data, and through updating the parameters, the visualization result has dynamic and accurate characteristics, which provides intuitive support for power grid operation and maintenance personnel to master the risk situation and adjust the emergency strategy in real time. BRIEF DESCRIPTION OF DRAWINGS

[0096] Figure 1 It is a unit line schematic diagram.

[0097] Figure 2 It is a unit line push flow schematic diagram.

[0098] Figure 3 It is a Thiessen polygon division schematic diagram.

[0099] Figure 4 It is a flow chart of the Xin'anjiang model of three water sources.

[0100] Figure 5 It is a general flow chart of the NSGA-II algorithm.

[0101] Figure 6 It is a schematic diagram of one-dimensional and two-dimensional model lateral connection.

[0102] Figure 7 Technical flow chart of large-scale flood risk assessment model of power grid equipment typical flood-involved area

[0103] Figure 8 It is a water body monitoring and flood submergence distribution diagram of power transmission pole equipment in Jilin Province (Changchun City) in 2024.

[0104] Figure 9 It is a water body monitoring and flood submergence distribution diagram of power transmission pole equipment in Jilin Province (Baicheng City) in 2024.

[0105] Figure 10 It is a unit division diagram of Lalin River Basin area.

[0106] Figure 11 It is the flood simulation result of the Xin'anjiang model of important nodes (Hulan-Lalin).

[0107] Figure 12 It is a schematic diagram of water dynamic modeling range of Lalin River Basin (in the green frame in the figure).

[0108] Figure 13 To draw the DEM data range map of Lalin River Basin.

[0109] Figure 14 To draw the water level-flow relationship of Caijia Gully Station of Lalin River. DETAILED DESCRIPTION

[0110] The technical scheme adopted by the present application is: a large-scale flood risk dynamic evaluation method for typical flood-involved areas of power grid equipment, comprising the following steps:

[0111] Step 1: Collect and sort out the basic data of the target basin, such as meteorological, hydrological, topographic, remote sensing image, and historical flood situation of power grid, and delineate the flood-prone area;

[0112] Step 2: For the flood-involved area of power grid equipment, a distributed flood forecasting model based on the three-source Xinanjiang model is constructed for the sub-area;

[0113] Step 3: For the flood-involved area of power grid equipment, a refined one-two-dimensional coupled flood evolution analysis model is constructed with the hydrological forecasting model results as input, to simulate the evolution process of flood submergence risk of power grid equipment under different scenarios;

[0114] Step 4: A multi-index flood submergence risk grading system for power grid equipment is constructed to determine the risk level;

[0115] Step 5: A large-scale flood risk dynamic evaluation model for the flood-involved area of power grid equipment is constructed, real-time data and parameters are input and corrected, and the risk evaluation results are output.

[0116] The collection and sorting out of the basic data of the target basin, such as meteorological, hydrological, topographic, remote sensing image, and historical flood situation of power grid, and the delineation of the flood-prone area in step 1 are calculated as follows:

[0117] (1) The ANUSPLIN interpolation method is used to process the station meteorological data to generate 1km×1km grid data, and the calculation formula is as follows:

[0118]

[0119] In the formula, Z(x,y) is the meteorological element value (such as precipitation, air temperature) at the grid (x,y), φ i (x,y)=||(x,y)-(x i ,y i )| 3 is a cubic thin plate spline basis function, (x i ,y i ) is the coordinate of the meteorological station, ψ j (x,y) is the trend item (such as linear trend ψ0=1, ψ1=x, ψ2=y), β i , γj The to-be-sought coefficient is solved by minimizing the interpolation error and the curvature penalty term.

[0120] (2) Obtain long sequence high-precision hydrological observation data, and use the "linear interpolation + regression extension" method to process missing or abnormal hydrological data. The calculation formula is as follows:

[0121] ① Linear interpolation (missing time length ≤3 days):

[0122]

[0123] In the formula, Q t is the missing flow at time t, Q t-1 and Q t+k are adjacent measured flows, and k is the missing interval in days.

[0124] ② Regression extension (missing time length > 3 days):

[0125] Based on the correlation of flows of adjacent stations (such as upstream and downstream of the control section), a linear regression model is established, and the calculation formula is as follows:

[0126] Q A = aQ B +b#(3)

[0127]

[0128] Accuracy requirements:

[0129]

[0130] In the formula, Q A is the missing flow of the target station, Q B is the measured flow of the adjacent station, a and b are regression coefficients (fitted by historical data at the same period), is the fitted value, is the historical average flow of the target station.

[0131] (3) Construct the digital elevation model (DEM) of the basin based on high-resolution geospatial data

[0132] ① The data is unified in the national 2000 geodetic coordinate system, and the DEM is constructed using the 1985 national elevation datum;

[0133] ② On the basis of obtaining high-precision DSM data in some key areas, the river bottom topography is corrected in elevation to obtain corrected DEM data, and the correction formula is as follows:

[0134]

[0135] In the formula, Z corr(x, y) is the corrected DEM river bottom elevation, Z sec (x i , y i ) is the river bottom elevation of the i-th measured river section (obtained from section measurement data), d i is the distance from the grid to the section point, w is the weight index (usually 2, which ensures that the near-section points have higher weights), and p is the number of section points participating in interpolation (at least ≥ 3).

[0136] (4) Obtain geographic location information of power grid equipment, power line distribution and other data through remote sensing images to determine the flood-involved area range of the power grid

[0137] ① Query and count the target basin power grid equipment through the remote sensing monitoring platform;

[0138] ② Screen the flood-involved area range of the power grid through the remote sensing monitoring platform to determine the flood control weak point area of the power grid equipment.

[0139] For the flood-involved area of the power grid equipment, a distributed flood forecasting model based on the three-source Xin'anjiang model is constructed in step 2, and the calculation method is as follows:

[0140] (1) Divide the sub-basin by the Thiessen polygon method, and calculate the Thiessen weight and underlying surface parameters of each sub-basin

[0141] ① Construct a Thiessen polygon, each polygon corresponding to a control sub-basin of a rainfall station;

[0142] ② Calculate the Thiessen weight of the sub-basin (i.e. the proportion of the area of the basin to the total area of the basin), and the calculation formula is as follows:

[0143]

[0144] In the formula, A j is the area of a sub-basin j, and A 总 is the total area of the target basin.

[0145] ③ Quantify the underlying surface key parameters such as basin slope (S), vegetation coverage (C v ), soil water storage capacity (W M ), etc., and the calculation formula is as follows:

[0146]

[0147] W M = W M0 ×(1+0.2C v )#(10)

[0148] In the formula, C v ∈[0,1], W M0The maximum water storage capacity of bare land soil (sandy loam W M0 = 80-100 mm, loam W M0 = 100-120 mm).

[0149] (2) According to the spatial relationship between the calculation unit and the rainfall station, the long series of meteorological (rainfall) observation processes of each unit are calculated, and the calculation formula is as follows:

[0150]

[0151] In the formula, w j,i is the Thiessen weight of the sub-basin j to the i-th rainfall station (w If the rainfall station i does not control the sub-basin j, then w j，i = 0), P i，t is the measured rainfall of the i-th rainfall station at time t, and t is the time step.

[0152] (3) A distributed Xin'anjiang model is established for each area as a unit to calculate evapotranspiration, runoff, water source division, and confluence, etc., and the flood forecasting results are extracted

[0153] ① For each sub-basin unit, a distributed Xin'anjiang model is constructed, and rainfall and evaporation data are input for evapotranspiration, runoff, water source division, and confluence process calculation;

[0154] ② The flood process at the outlet of each sub-basin unit is calculated through river confluence to the outlet station, and the flow process of each unit to the outlet station is obtained through linear superposition to get the flow process of the outlet station;

[0155] ③ For the junction of the main stream and the tributary, the Xin'anjiang model and the Muskingum model are used to realize the flood process forecasting of the control station and the junction station of the main stream and the tributary in the area.

[0156] (4) The NSGA-II multi-objective optimization algorithm is used to calibrate the model parameters according to the measured and simulated runoff processes of each key section, and the objective function calculation formula is as follows:

[0157]

[0158]

[0159] In the formula, NSE is the Nash efficiency coefficient, RE is the water balance error, RPE is the relative error of flood peak flow, PTE is the error of flood peak occurrence time, DC is the single flood certainty coefficient, RRD is the relative error of runoff depth, Q t，obs , Q t，sim are the measured and simulated flow at time t, respectively, is the average of the measured flow, T is the total time step, and N is the number of single flood in the calibration period. respectively are the predicted and measured peak flood flow values of the i-th flood, respectively are the simulated and measured time period of the peak flood occurrence of the i-th flood, ij，sim ij，obs respectively are the simulated and measured flow values of the j-th time period of the i-th flood, is the average measured flow value of the i-th flood, i，sim i,obs respectively are the simulated and measured runoff depth of the i-th flood.

[0160] (5) The flood forecasting results are evaluated by using the peak error, flood volume error and Nash coefficient, and the evaluation standards are as follows:

[0161] Table 1 Model parameter evaluation standard table

[0162]

[0163] The flood area of the power grid equipment is constructed in step 3, and the hydrological forecasting model results are used as input to build a refined one-two-dimensional flood evolution analysis model to simulate the evolution process of the flood submergence risk of the power grid equipment under different scenarios, and the calculation method is as follows:

[0164] (1) Construct a one-dimensional river model

[0165] ① The Godunov format finite volume method is used to solve the one-dimensional Saint-Venant equation set, and the calculation formula is as follows:

[0166] Continuity equation:

[0167]

[0168] Momentum equation:

[0169]

[0170] In the formula, A is the river cross-sectional area, Q is the flow, u is the lateral inflow velocity in the river direction, t is the time, x is the horizontal coordinate along the flow direction, q is the lateral inflow of the river, a is the momentum correction coefficient, g is the gravitational acceleration, y is the water level, S f is the friction slope, n is the river roughness, R is the hydraulic radius, and P is the wet perimeter.

[0171] ② Define the boundary conditions of the river model;

[0172] a. Define the upper boundary condition

[0173] When there is no control project on the upstream, the actual flood flow process or the design flood flow process of the river upstream of the calculation area is used;

[0174] ​​When there is a control project upstream, the designed discharge process is used.

[0175] b. Define the lower boundary condition

[0176] When there is a downstream water level-discharge relationship or control water conservancy project scheduling rules, use the content of the data to define it;

[0177] When there is no data above, the Manning formula is used to calculate the lower boundary condition at least five sections away from the downstream river channel, and the calculation formula is as follows:

[0178]

[0179] In the formula, v is the flow rate, n is the roughness, and R is the hydraulic radius.

[0180] ③ The form of concentrated inflow is used to consider the confluence of tributary flood, and the calculation formula is as follows:

[0181]

[0182] In the formula, q(t) is the lateral inflow at a certain time, Q 支 (t) is the measured / predicted flow of the tributary at a certain time, L 汇入段 is the length of the calculation section of the one-dimensional river channel at the confluence of the tributary, ensuring uniform distribution of inflow to the river section.

[0183] ④ Use GPU heterogeneous parallel algorithm to realize the acceleration of one-dimensional hydrodynamic model.

[0184] (2) Construct a two-dimensional hydrodynamic model

[0185] ① The finite volume method of Godunov type is used to numerically disperse the two-dimensional shallow water equation, and the calculation formula is as follows:

[0186] U t +E(U) x +H(U) y =S0+S#(23)

[0187]

[0188] In the formula, h is the water depth, u is the flow rate in the x direction, v is the flow rate in the y direction, p a is the atmospheric pressure on the water surface, z b is the bed elevation, τ ax , τ ay are the forces of wind load, ρ a is the air density, is the wind speed at 10m above the water surface, C Ds is the drag coefficient, τ bx , τby is the bed shear stress, n is the two-dimensional area roughness.

[0189] ②The flux calculation on the control unit interface is carried out by the HLLC format approximate Riemann solver, and the calculation formula is as follows by taking the x direction as an example:

[0190]

[0191]

[0192] In the formula, u * is the intermediate wave speed (dividing the flow region), U L and U R are the conservative variable vectors on the left and right sides of the interface respectively (U = [h, hu, hv] T ), c is the sound speed, and p is the pressure.

[0193] ③The second-order MUSCL numerical reconstruction method is used for spatial interpolation of the variables to avoid numerical oscillation, and the calculation formula is as follows:

[0194]

[0195] Δu i = u i+1 - u i #(31)

[0196]

[0197] In the formula, u i is the average value of u of i, Δu i is the unit slope, θ i is the slope ratio, φ(θ) is the minmod limiter (φ(θ) = max(0, min(1, θ))), and the reconstructed variables are ensured to satisfy the "monotone conservation".

[0198] ④The GPU parallel computing is used to realize high-speed operation without reducing the accuracy.

[0199] (3) The lateral connection is used to realize the coupling of the surface one-dimensional and two-dimensional models, and the weir formula method is used to calculate the exchange water quantity of the two models at the coupling boundary, and the calculation formula is as follows:

[0200]

[0201] C = ε · C0#(34)

[0202] h = max (y1-y2, 0)#(35)

[0203] Where C is the weir flow coefficient, b is the connection width, h is the coupled boundary water head difference, ε is the side contraction coefficient (ε = 0.8-0.9 for the dike overflow, and ε = 0.95-1.0 for the rectangular breach), C0 is the basic weir flow coefficient (C0 = 0.385 for the free outflow, and C0 = 0.35-0.38 for the submerged outflow), y1 is the water level of the one-dimensional river channel coupled section, and y2 is the water level of the two-dimensional coupled boundary grid, ensuring that the water flow exchanges only from the high water level to the low water level.

[0204] (4) The DAMBRK method of the U.S. National Weather Service is used to calculate the dike breach flow, and the calculation formula is as follows:

[0205]

[0206] Where Q is the instantaneous breach flow, b is the instantaneous breach bottom width, g is the acceleration of gravity, h is the upstream water level, h b is the instantaneous breach bottom elevation, S is the breach slope, c v is the approach flow velocity coefficient, k s is the submergence coefficient, W b is the dam length, h b，term is the bottom elevation of the final breach, and h ds is the downstream water level.

[0207] (5) The model is calibrated and verified to ensure water balance and reasonable flow field distribution

[0208] ① The flood flow of each scheme is calculated, and the water quantity in the model in the calculation area at the last moment is compared to ensure water balance;

[0209] ② For the entire calculation area, the flow field distribution shown by the calculation results should be consistent with the overall elevation distribution of the DEM, and the trend of the flood flow should follow the principle of from high to low, and then it is considered that the simulation results of the scheme are more reasonable;

[0210] ③ The maximum flood flow velocity, the maximum flood submergence water depth, and the flood front arrival time of different calculation schemes are compared respectively.

[0211] The flood magnitude is positively correlated with the maximum flood flow velocity, the maximum flood submergence water depth, and the flood front arrival time, and then it is considered that the calculation results of the scheme are more reasonable;

[0212] ④ Combined with the DEM data, the maximum flood flow velocity, the maximum flood submergence water depth, and the flood front arrival time of the same calculation scheme are compared, and the terrain elevation is negatively correlated with the maximum flood flow velocity, the maximum flood submergence water depth, and the flood front arrival time, and then it is considered that the calculation results of the scheme are more reasonable.

[0213] (6) The format of the model input and output data is specified, and the format requirements are as follows:

[0214] Table 2 One-dimensional and two-dimensional model input and output data format requirements

[0215] Data type Format type River reach data Excel file or text file Cross-section data Excel file Time series data .txt,.xls,.xlsx,.series, etc. Mesh partition data Text file and shapefile

[0216] The multi-index flood inundation risk classification system of power grid equipment is constructed in step 4, and the risk level is determined, and the calculation method is as follows:

[0217] (1) Based on the three-dimensional selection of "risk, importance and urgency", VIF and CV are used for index screening

[0218] ① Determine the risk index of power grid equipment, such as distance from river (L), elevation difference (ΔH), waterproof grade (W), flood frequency (F), flood peak flow (Q), inundation depth (h), and inundation duration (t) etc.

[0219] ② Determine the importance index of ground elements, such as population density (P), economic density (E), number of important facilities (C), power supply load grade (L0) etc.

[0220] ③ Determine the urgency index of emergency disposal, such as dam firmness (K), flood discharge capacity (Q0), plan completeness rate (R), emergency team size (N0), early warning lead time (Δt) etc.

[0221] ④ Perform index screening to eliminate redundant and low discriminant indexes.

[0222] a. Use variance inflation factor (VIF) to test the multicollinearity between indexes, and screen the redundancy of indexes, the calculation formula is as follows:

[0223]

[0224] In the formula, is the multiple correlation coefficient of the jth index to other indexes, and the screening standard is VIF j ≤ 10 (no serious multicollinearity).

[0225] b. Use the coefficient of variation (CV) to judge the discriminant degree of the index, and screen the discriminant degree of the index, the calculation formula is as follows:

[0226]

[0227] In the formula, σ j is the standard deviation of the jth index, is the mean value, and the screening standard is CV j ≥ 0.2 (with sufficient discriminant degree).

[0228] (2) The weight of each index is determined by using the analytic hierarchy process (AHP), the judgment matrix is constructed and the characteristic vector is solved

[0229] ① Hierarchical division is carried out in target layer, criterion layer and index layer, and the division is as follows:

[0230] Target layer (O): power grid equipment flood risk level evaluation;

[0231] Criterion layer (C): C1 is danger, C2 is ground element importance, and C3 is emergency disposal urgency;

[0232] Index layer (U): U 11 (L), U 12 (ΔH)…U 17 (t) (7 items); U 21 (P)…U 24 (L0) (4 items); U 31 (K)…U 35 (Δt) (5 items).

[0233] ② The criterion layer judgment matrix A (3x3), the index layer judgment matrix B1 (7x7), B2 (4x4) and B3 (5x5) are constructed, such as the criterion layer judgment matrix:

[0234]

[0235] In the formula, a ij is the score of experts on C i and C j importance (1: equal importance; 9: extreme importance).

[0236] ③ The maximum eigenvalue λ max of the judgment matrix is solved, and the corresponding eigenvector is obtained, and the weight is obtained after normalization, and the calculation formula is as follows:

[0237]

[0238] In the formula, M is the judgment matrix (such as A, B1, B2 and B3), v is the eigenvector, w i ' is the converged eigenvector element, and m is the matrix dimension.

[0239] (3) Establishing a risk level division system

[0240] ① Quantify each index;

[0241] a. The extreme difference method is used for quantitative index standardization, and the calculation formula is as follows:

[0242] Positive index (the greater the value, the lower the risk):

[0243]

[0244] Negative indicators (the greater the value, the higher the risk):

[0245]

[0246] where: x ij is the original value of the jth indicator of the ith device; x max , x min is the maximum value and minimum value of the indicator; y ij is the standardized value (y ij ∈ [0, 1]).

[0247] b. Qualitative indicators are quantified using the hierarchical assignment method, and the hierarchical standards are as follows:

[0248] Table 3 Qualitative quantification table of part of the indicators

[0249]

[0250] ② The comprehensive risk value is calculated using the weighted summation method, and the calculation formula is as follows:

[0251]

[0252] where R is the comprehensive risk value of the device (R ∈ [0, 1]), w Ck is the weight of the kth dimension of the criterion layer (k = 1, 2, 3; w C1 + w C2 + w C3 = 1), n k is the number of indicators of the kth dimension of the criterion layer (n1 = 7, n2 = 4, n3 = 5), is the weight of the jth indicator of the kth dimension of the indicator layer, is the quantification score of the jth indicator of the kth dimension of the indicator layer.

[0253] ③ Based on the R value of the historical sample, the K-means clustering is used to determine the grade threshold;

[0254] a. Randomly select K = 3 (K is the number of risk grades, when K = 3, there are low, medium and high risks) R values of historical samples as initial centers

[0255] b. Calculate the distance of each sample R p (p = 1, 2,..., n) to the three centers, and assign it to the nearest category, and the calculation formula is as follows:

[0256]

[0257] where d(p, i) is the Euclidean distance, and since R is a single variable, it can be simplified as |R p - μ iIf d(p, i) = min{d(p, 1), d(p, 2), d(p, 3)}, then R p ∈S i (S i is the i-th sample set).

[0258] c. Calculate the R mean value of each sample as the new center, the calculation formula is as follows:

[0259]

[0260] In the formula, |S i | is the number of i-th samples, and t is the iteration number.

[0261] d. If Or the clustering error sum of squares J no longer changes, stop iteration, and the clustering error sum of squares calculation formula is as follows:

[0262]

[0263] In the formula, when J is minimum, the clustering is optimal.

[0264] e. The μ1 and μ2 (μ1 < μ2) after iteration convergence are the risk level threshold values.

[0265] (4) Determine the final division, and the division criteria are as follows:

[0266] Low risk: R ∈ [0, μ1];

[0267] Medium risk: R ∈ [μ1, μ2];

[0268] High risk: R ∈ [μ2, 1].

[0269] The construction of the large-scale flood risk dynamic assessment model of the typical flood area of the power grid equipment in step 5 inputs real-time data and parameter correction, and outputs risk assessment results, and the calculation method is as follows:

[0270] (1) Input data to the model, and the input data types are as follows:

[0271] Table 4 Input data type table of large-scale flood risk assessment model of typical flood area of power grid equipment

[0272]

[0273]

[0274] (2) Perform real-time dynamic simulation of flood, and calculate parameters such as inundation area, water depth, and duration

[0275] ① Use one-dimensional model to simulate long river flood;

[0276] ② Flood simulation of key river sections and flood diversion and storage areas is performed using a two-dimensional fast model. A simplified finite volume model under an unstructured grid system and its accelerated algorithm are employed to calculate the inundation range (A) of floods at different frequencies. 淹 ) and flood inundation depth (h), inundation duration (t) 淹 The calculation formula is as follows:

[0277]

[0278] In the formula, A i Let be the area of ​​the grid where h > 0 at time t, and p be the number of submerged grids.

[0279] h = max(yz) b ,0) (52)

[0280] In the formula, y represents the simulated water level, and z represents the simulated water level. b h represents the bed elevation, and h>0 represents the flooded area.

[0281]

[0282] In the formula, Δt k h is the time step where h > 0, and m is the number of flooding periods.

[0283] ③ Considering the boundary relationships of each model, one-dimensional and two-dimensional longitudinal and lateral coupling is adopted in local river sections and flood diversion and storage areas.

[0284] a. Along the river channel direction, a one-dimensional main channel is coupled with segmented two-dimensional tense longitudinal coupling, with continuous flow at the coupling cross-section. The calculation formula is as follows:

[0285]

[0286] In the formula, Q 1D (x=t) represents the flow rate of the one-dimensional river channel at time t at the coupling section x, Q 2D，j (x=t) represents the outflow of the j-th grid connected to the cross section in the two-dimensional region, and q represents the number of coupled grids.

[0287] b. Couple the one-dimensional and two-dimensional floodplains along both banks of the river channel. The floodplain flow is included in the one-dimensional lateral inflow, and the calculation formula is as follows:

[0288]

[0289] In the formula, Q 漫，j (t) represents the flow rate at time t of the j-th overflow point, r represents the number of overflow points, and L 耦合段 The length of the two-dimensional coupled section of the river channel.

[0290] (3) Accurate assessment of flood risk based on flood hazard indicators and consequence impact indicators.

[0291] ① The flood risk index is obtained through hydrological statistics and one- and two-dimensional hydrodynamic model simulation, and the specific obtaining method is as follows:

[0292] Table 5 Obtaining method of flood risk index

[0293] Specific results Unit Determination method Rainfall frequency / Hydrological statistics Flood frequency / Hydrological statistics Submerged area km 2 ]] Numerical model simulation Submerged water depth m Numerical model simulation Submerged duration h Numerical model simulation Water level m Numerical model simulation Flow velocity m / s Numerical model simulation

[0294] ② The consequence impact index is obtained through spatial superposition, data statistics, and surface comprehensive loss method, and the specific obtaining method is as follows:

[0295] Table 6 Obtaining method of consequence impact index

[0296]

[0297] The parameters are specifically calculated according to the spatial superposition method as follows:

[0298]

[0299] In the formula, U is the target calculation parameter, n is the total number of units of the flood inundation range layer (such as the number of inundation grids or the number of inundation sub-basins), m is the total number of units of the attribute layer (such as the population density layer or the equipment distribution layer), A ij is the spatial intersection area of the i th inundation unit and the j th attribute unit, p i is the attribute density / attribute value (such as population density, equipment density, or GDP density) of the i th attribute unit, d ij is the spatial correlation coefficient (d i,j = 1 indicates that the i th inundation unit and the j th attribute unit have an intersection, and d i,j = 0 indicates no intersection, which is used to screen effective superposition units).

[0300] The parameters are specifically calculated according to the surface comprehensive loss method as follows:

[0301]

[0302] In the formula, L 总 is the total flood loss of the region (such as direct economic loss or indirect economic loss), n is the total number of sub-regions divided according to the flood inundation intensity, L k is the flood loss of the k th inundation sub-region, A k is the area of the k th inundation sub-region, E k is the economic density of the k th inundation sub-region, and a k is the loss rate of the k th inundation sub-region.

[0303] ③ The comprehensive per capita loss value (L 地，历 ) of historical floods is calculated according to historical flood data人，历 ), considering the asset growth factor, loss rate change and price, etc. to adjust the actual comprehensive average (L 地，现 ) / per capita loss value (L 人，现 ), calculate the flood disaster loss (L 总，现(地) / L 总，现(人) ), the calculation formula is as follows:

[0304]

[0305] L 地，现 =L 地，历 ·K A ·K α ·K P , L 人，现 =L 人，历 ·K A ·K α ·K P (60)

[0306] L 总，现(地) =L 地，现 ·A 淹，现 , L 总，现 (people) = L 人，现 ·P 受，现 (62)

[0307] In the formula, L 总,历 is the total loss of historical flood, A 淹,历 is the historical flood submerged area, P 受,历 is the affected population of historical flood, K A is the asset growth coefficient, V 现 , V 历 are the total assets of the present and history respectively, K α is the loss rate correction coefficient, α 现 , α 历 are the present and historical flood loss rate respectively, K P is the price index, CPI 现 , CPI 历 are the resident consumer price index of the present and historical flood year respectively, A 淹,现 is the present flood simulation submerged area, P 受,现 is the present flood affected population.

[0308] (4) through the monitoring index extracted by the space-time integrated disaster monitoring platform in real time, the flood impact calculation model parameters in step (2) are verified and dynamically corrected in real time, the correction calculation formula is as follows:

[0309] x 新 (t) = x旧 (t) + K(t) · (y 监测 (t) - y 模拟 (t|x 旧 (t))) (63)

[0310]

[0311] where x 旧 is the parameter to be corrected, K(t) is the Kalman gain, y 监测 (t) is the measured index of the space-air-ground integrated platform at time t, y 模拟 (t|x 旧 (t)) is the simulated index calculated based on the old parameter x 旧 (t) at time t, P 预测 (t) is the predicted covariance of the parameter at time t, H(t) is the sensitivity coefficient at time t, R(t) is the monitoring error variance at time t, and Δx is the small disturbance of the parameter.

[0312] (5) The positioning chart method is used to draw the flood risk zoning map of power grid equipment, and the disaster loss assessment results are dynamically visualized in real time

[0313] ①The flood water level process and flood flow process of the river section are displayed in the form of charts, and the river water level is classified and displayed in thematic maps according to normal (y 警 ), above warning (y 警 < y 保 ), and above guarantee (y>y 保 ). The water level calculation formulas are as follows:

[0314]

[0315] y 保 = y T＝100 (67)

[0316] where y 警 is the warning water level, y 保 is the guarantee water level, y y is the average flood level in the past 30 years, σ T＝100 is the water level standard deviation, and y GDP is the 100-year flood level.

[0317] ②The submerged area, submerged water depth, submerged duration, and flood flow velocity are displayed in the form of charts, and the change of the submerged range and submerged water depth with time is dynamically displayed in the form of a map.

[0318] ③The positioning chart method is used to display the GDP of the submerged area in a thematic map, and the change of the affected population and affected GDP with time during the submerged process is dynamically displayed in the form of a chart. The specific calculation formulas are as follows:

[0319]

[0320] where P(t) is the population affected over time, L GDP (t) is the GDP affected over time.

[0321] Embodiments

[0322] The method for dynamically evaluating large-scale flood risk of power grid equipment in typical flood area provided by the embodiments includes the following steps:

[0323] Step 1. Collect and sort out the basic data of the target basin, such as meteorological, hydrological, terrain, remote sensing image, and historical flood situation of power grid, and delineate the flood-prone area. Taking Jilin Province in 2024 as an example, meteorological data, hydrological data, basic topographic map data, river section data, and water conservancy engineering data are obtained by going to the meteorological bureau, hydrological bureau, and actual measurement, to construct a digital elevation model of the basin, and then landsat satellite is used to obtain remote sensing images of Jilin Province in the past 30 years, flood period and flood period are extracted by water body algorithm, flood disasters of power grid equipment in the past 30 years are analyzed, and the flood area of power grid equipment is determined.

[0324] Step 2. For the flood area of power grid equipment, a distributed flood forecasting model based on the three-source Xin'anjiang model is constructed. Taking the Lalinhe River area as an example, according to the distribution of basin system and station, 13 sub-basin units are divided by the Thiessen polygon method (see Figure 10 ), and the weight of each rainfall station in the unit is allocated (see Table 7). According to the collected flood data, the 9 flow stations of the whole basin controlled by Caijiagou in Lalinhe River area are all lack of concurrent flood, so NSGA-II multi-objective optimization algorithm is used to calibrate the model parameters according to "upstream first and downstream later". After determining the model parameters of each unit, in addition to the flood simulation results of the hydrological station, the flood process of the main tributaries is further extracted (see Figure 11 ) as the calculation boundary of the hydrodynamic model. According to "Hydrological Information Forecasting Specification" (GB / T22482-2008), the flood forecasting results of Xin'anjiang model are evaluated by flood peak error, flood volume error and Nash coefficient (see Table 8).

[0325] Table 7 Rainfall station section scheme configuration table of Lalinhe River basin area

[0326]

[0327] Table 8 Accuracy evaluation table of part of the station prediction scheme

[0328]

[0329] Step 3. For the flood-prone areas of power grid equipment, a refined one-dimensional and two-dimensional coupled flood evolution analysis model is constructed with the results of the hydrological forecasting model as the input to simulate the evolution process of the flood inundation risk of power grid equipment under different scenarios. Taking the Lalin River area as an example, based on the review results of the "23·8" flood process of the Lalin River, combined with the positions of the forecast cross-sections and the distribution of power grid equipment, the modeling scope of the hydrodynamic model is preliminarily determined (as shown by the green wireframe area in Figure 11 ), the flood out of the embankment is modeled by a two-dimensional hydrodynamic model accelerated by GPU, the river channel is calculated by a one-dimensional Godunov format model, and the coupling is achieved through lateral connection. According to the flood processes provided by the hydrological model for Laojieji, Chongheqiao, Hulan, Ping'an, Yushu, and the upper reaches of the Lalin River (see Figure 13 ), they are used as the inflow boundary conditions of the hydrodynamic model. The inflow in the interval is calculated using the results of the flood forecasting model, and the lower boundary uses the water level-flow relationship results of Caijiagou Station (see Figure 14 ).

[0330] Step 4. Construct a multi-index flood inundation risk grading system for power grid equipment to determine the risk levels. Taking the Lalin River area as an example, first select indicators from the three dimensions of "hazard - importance - urgency": for hazard, select the distance from the river channel, inundation depth, etc.; for importance, select the substation density, power supply load; for urgency, refer to the data of 150 breaches in the "23·8" flood to determine the embankment firmness, etc. Use VIF and CV to screen out redundant indicators, and use AHP to determine the weights (the weight of the hazard layer in the criterion layer is 0.45, importance is 0.35, and urgency is 0.2). Quantify the indicators (such as using the range method for the inundation depth, 1.72m corresponds to 0.1), sum them up after weighting to get the R value, and use K-means clustering to obtain the thresholds μ1 = 0.3 and μ2 = 0.6. The results show that the downstream of Caijiagou and the Kacha-Lalin node are high-risk areas (R > 0.6), the surrounding area of Ping'an Station is medium-risk area (0.3 < R ≤ 0.6), and the upstream of Laojieji is low-risk area (R ≤ 0.3).

[0331] Step 5. Construct a large-scale flood risk dynamic assessment model for the flood-prone areas of power grid equipment, input real-time data and parameter correction, and output risk assessment results. Taking the right-bank protected area of the Nenjiang River as an example, first input the basic data (12.5m resolution DEM data of Areas A and B of the protected area, hourly rainfall from 2010 to 2020), real-time water level / flow data, spatial distribution data of poles and substations, and historical breach data. Use a one-dimensional model to calculate the flood evolution of the main stream, and a two-dimensional model to simulate the inundated area of the breach. Obtain the inundated area and the maximum water depth through longitudinal and transverse coupling. At the same time, obtain the data of affected power grid equipment through spatial superposition, and combine with the measured data of the space-air-ground integrated platform to correct the dynamic parameters in real time and output the dynamic visualization power grid flood risk map.

Claims

1. A large-scale flood risk dynamic assessment method for flood-involved areas of power grid equipment, characterized in that, The method comprises the following steps: S1 Collect and sort the basic data of the target basin, such as meteorological data, hydrological data, terrain data, remote sensing image data, and historical flood data of the power grid, and delineate the flood-prone area; S2 For the flood-prone area of the power grid equipment, a distributed flood forecasting model based on the three-source Xin'anjiang model is constructed for the sub-area; S3 For the flood-prone area of the power grid equipment, a fine one-dimensional and two-dimensional coupled flood evolution analysis model is constructed with the hydrological forecasting model results as input, and the evolution process of the flood submergence risk of the power grid equipment under different scenarios is simulated; S4 A multi-index flood submergence risk grading system for power grid equipment is constructed, and the risk level is determined; S5 A large-scale flood risk dynamic assessment model for the flood-prone area of the power grid equipment is constructed, real-time data and parameters are input and corrected, and the risk assessment results are output.

2. The method of claim 1, wherein the power grid device is a power grid device in a flood-prone area. Step S1 is specifically: S101 The ANUSPLIN interpolation method is used to process the station meteorological data to generate 1km×1km grid data; S102 Obtain long sequence high precision hydrological observation data, and use the "linear interpolation + regression extension" method to process the missing or abnormal hydrological data; S103 Based on high-resolution geographic spatial data, a digital elevation model of the basin is constructed; S104 Obtain the geographic location information and power line distribution data of the power grid equipment through remote sensing image, and determine the scope of the flood-prone area of the power grid. 3.The method of claim 1, wherein the method further comprises: determining a flood risk level of each grid device based on the flood risk index and the flood risk level of each grid device. Step S2 is specifically: S201 The Thiessen polygon method is used to divide the sub-basin, and the Thiessen weight and underlying surface parameters of each sub-basin are calculated; S202 According to the spatial relationship between the calculation unit and the rain gauge station, the long sequence meteorological observation process of each unit is calculated; S203 A distributed Xin'anjiang model is established for each area as a unit, and evaporation, runoff, water source, and confluence are calculated to extract the flood forecasting results; S204 The NSGA-II multi-objective optimization algorithm is used to calibrate the model parameters according to the measured and simulated runoff processes of each key section; S205 The flood forecasting results are evaluated by using the flood peak error, flood volume error, and Nash coefficient.

4. The method of claim 1, wherein the method is a method of dynamic large-scale flood risk assessment of a power grid equipment flood-prone area, characterized in that, Step 3 is specifically: S301 Construct a one-dimensional river model; Adopting Goduno v The one-dimensional Saint-Venant equations are solved by using the finite volume method. The calculation formula is as follows: Continuity equation: Momentum equation: where A is the cross-sectional area of the river, Q is the discharge, u is the velocity of the lateral inflow in the direction of the river, t is time, x is the horizontal coordinate in the direction of the flow, q is the lateral inflow to the river, a is a momentum correction factor, g is the acceleration due to gravity, y is the water level, S f is the friction slope, n is the roughness of the river, R is the hydraulic radius, and P is the wetted perimeter. Define the boundary conditions of the river model; The inflow of the tributary flood is considered in the form of concentrated inflow, and the calculation formula is as follows: where q(t) is the lateral inflow at time t, Q 支 (t) is the measured / predicted flow of the tributary at time t, L 汇入段 is the length of the calculated section of the one-dimensional river channel at the confluence of the tributary, ensuring uniform distribution of the inflow to this river section; Use GPU heterogeneous parallel algorithm to realize the acceleration of one-dimensional water dynamic model; S302 Construct a two-dimensional water dynamic model The finite volume method of Godunov type is used to numerically disperse the two-dimensional shallow water equation, and the calculation formula is as follows: U t +E(U) x +H(U) y =S o +S In the formula, h is the water depth, u is the flow velocity in the x-direction, v is the flow velocity in the y-direction, and p a For atmospheric pressure at the water surface, z b The elevation of the bed bottom is τ. ax τ ay For the force of wind load, ρ a air density, C represents the wind speed at a point 10 meters above the water surface. Ds τ is the drag coefficient. bx τ by Let n be the shear stress on the bed surface, and n be the roughness of the two-dimensional region. The HLLC format approximate Riemann solver is used to calculate the flux on the interface of the control unit, and the calculation formula is as follows: where u * is the intermediate wave speed, U L , U R are the left and right interface conservative variable vectors, respectively, U = [h, hu, hv] T , c is the sound speed, and p is the pressure. The second-order MUSCL numerical reconstruction method is used for spatial interpolation of variables to avoid numerical oscillation, and the calculation formula is as follows: Δu i = u i+1 - u i where u i is the average value of u over i, Δu i is the unit slope, θ i is the slope ratio, φ(θ) is a minmod limiter φ(θ) = max(0, min(l, θ)) that ensures that the reconstructed variable satisfies "monotonicity conservation"; GPU parallel computing is used to realize high-speed operation without reducing accuracy; S303 The lateral connection is used to realize the coupling of the one-dimensional and two-dimensional models, and the weir formula method is used to calculate the exchange of water at the coupling boundary of the two models, and the calculation formula is as follows: C=ε·C0 h=max(y1-y2,0) where C is the weir flow coefficient, b is the connection width, h is the coupled boundary water head difference, ε is the side contraction coefficient, ε = 0.8-0.9 for a dike, ε = 0.95-1.0 for a rectangular breach, C0is the basic weir flow coefficient, C0= 0.385 for free outflow, C0= 0.35-0.38 for submerged outflow, y1is the water level of the one-dimensional river channel coupled section, and y2is the water level of the two-dimensional coupled boundary grid, ensuring that the water flow exchanges only from the high water level to the low water level ； S304 The dam break flow is calculated by the DAMBRK method of the U.S. National Weather Service, and the calculation formula is as follows: where Q is the instantaneous breach flow, b is the instantaneous breach base width, g is the acceleration of gravity, h is the upstream water level, h b is the instantaneous breach base elevation, S is the breach slope, c v is the velocity of approach factor, k s is the submergence factor, W b is the dam length, h b,term is the final breach base elevation, h ds is the downstream water level; S305 performs model calibration and verification to ensure water balance and reasonable flow field distribution; S306 specifies the format of model input and output data.

5. The method of claim 1, wherein the power grid device is a power grid device in a flood-prone area. Step 4 is specifically: S401 selects indicators based on the "hazard-importance-urgency" three-dimensional selection, uses VIF and CV for index screening; uses variance inflation factor VIF to test the multicollinearity between indicators, and screens the redundancy of indicators, the calculation formula is as follows: wherein is the multiple correlation coefficient of the jth indicator to other indicators, and the screening criterion is VIF j ≤ 10; Use the coefficient of variation CV to judge the discrimination of indicators, and screen the discriminative power of indicators, the calculation formula is as follows: wherein σ j is the standard deviation of the jth metric, is the mean, and the screening criterion is CV j ≥ 0.2; S402 uses the analytic hierarchy process to determine the weight of each indicator, constructs the judgment matrix and solves the characteristic vector; S403 establishes a risk grade classification system.

6. The method of claim 5, wherein the power grid device is a power grid device in a flood-prone area. In step S401, the index screening is specifically: Determine the hazard index of power grid equipment, the distance from the river L, the elevation difference ΔH, the waterproof grade W, the flood frequency F, the flood peak flow Q, the submergence depth h, and the submergence duration t; Determine the importance index of ground elements, population density P, economic density E, number of important facilities C, power supply load grade L0; Determine the urgency index of emergency disposal, dam firmness K, flood discharge capacity Q0, plan completeness rate R, emergency team size N0, and early warning advance Δt; Perform index screening to eliminate redundant and low-discriminative indicators.

7. The method of claim 5, wherein the method further comprises: Step S402 is specifically divided into target layer, criterion layer, and index layer, and the division is as follows: Target layer O: power grid equipment flood risk level evaluation; Criteria layer C: C1 for hazard, C2 for ground element importance, and C3 for emergency disposal urgency; Index layer U: U 11 , U 12 ... U 17 ; U 21 ... U 24 ; U 31 ... U 35 ; Build criterion layer judgment matrix A(3x3), index layer judgment matrix B1(7x7), B2(4x4), and B3(5x5); Solving the largest eigenvalue of judgment matrix λ max The corresponding eigenvector, after normalization, the weight is obtained, the calculation formula is as follows:

8. The method for large-scale dynamic assessment of flood risk in flood-affected areas of power grid equipment according to claim 1, characterized in that, Step S5 is specifically: S501 inputs data to the model; S502 performs real-time dynamic simulation of flood, calculates parameters such as inundation area, water depth, and duration; Use one-dimensional model to simulate long river flood; Use two-dimensional fast model to simulate local key river sections and flood storage areas; Consider the boundary relationship of each model, and use one-dimensional and two-dimensional longitudinal and transverse coupling in local river sections and flood storage areas; S503 realizes accurate assessment of flood risk according to flood hazard indicators and consequence impact indicators; S504 verifies the monitoring indicators extracted in real time by the space-time integrated disaster monitoring platform, and dynamically corrects the parameters of the flood impact calculation model in real time; S505 uses the positioning chart method to draw the power grid equipment flood risk zoning map, and dynamically visualizes the disaster loss assessment results in real time.

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