Power grid flood disaster early warning dynamic deduction system and method

Abstract

The invention belongs to the technical field of flood disaster early warning and deduction, and relates to a power grid flood disaster early warning dynamic deduction system and method. According to the method, the physical relevance of power grid damage probability analysis is improved by accurately simulating the flood spreading path and the submerging parameters of the sub-regions of the flood spreading path, a multi-dimensional analysis basis is provided for the equipment damage probability by quantifying the risk level, and the misjudgment rate is reduced; an upstream power failure area and a downstream influence range of threatened equipment are identified through dynamic deduction, a load transfer path is optimized in real time, a power supply gap is reduced, time-space dynamic prediction and management of power failure influence are realized, and power supply continuity of a key area is guaranteed; by displaying the flood inundation animation, the equipment state icon and the first-aid repair progress bar in real time, high-risk equipment can be quickly positioned, resources can be scheduled, the first-aid repair response time is shortened, a whole-process closed loop of'monitoring-early warning-response-feedback 'is formed, and the emergency efficiency is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of flood disaster early warning deduction, and relates to a power grid flood disaster early warning dynamic deduction system and method. Background Art

[0002] As climate change intensifies, floods and waterlogging pose an increasingly significant threat to the safe operation of power grids. Traditional early warning systems rely on a single data source or static model, and it is difficult to dynamically integrate multi-dimensional data such as power grids, hydrology, and meteorology, resulting in insufficient disaster prediction accuracy and a lack of ability to deduce the impact of cascading power outages. In addition, existing technologies often separate early warning information from emergency response, and lack visual display and real-time decision support.

[0003] In the prior art, there are also some related solutions involving flood disasters in power grids. For example, a risk warning method and system for power grid equipment under flood disasters is disclosed in Chinese Patent Publication No. CN113869804B. The method mainly calculates the flood disaster risk value within a certain grid based on the exposure of power grid equipment and the vulnerability of power grid equipment, and determines whether the flood disaster risk value is greater than the risk threshold, so as to judge the risk degree of power grid equipment under flood disasters based on the flood disaster risk value.

[0004] Another Chinese patent publication number is CN116911613A, which is a flood risk monitoring system and method for power equipment and facilities. It mainly divides the grid to predict the flood inundation range, inundation water level and flood disaster level of the grid. If the warning value is reached, an early warning will be issued, and the power outage area prediction module is used to predict whether the grid will have a power outage, thereby improving the accuracy of the prediction.

[0005] Although the above schemes have proposed some solutions to power grid flood disasters, they still have certain limitations: (1) Some existing technologies can judge the risk level of power grid equipment under flood disasters based on the flood disaster risk value, but their risk value calculation relies on the static equipment exposure and vulnerability, and does not introduce the power grid topology structure to analyze the chain blackout effect. It is impossible to simulate the power supply gap and load transfer path caused by the threatened equipment in real time, and thus it is impossible to achieve the spatiotemporal dynamic prediction of the impact of power outages.

[0006] (2) Some existing technologies make predictions by dividing the grid, but their model coupling is limited to a single prediction method. They lack deep coupling of multi-dimensional models such as hydrology, meteorology, and the power grid itself. They are also unable to accurately calculate the flooding depth and duration of flooding by combining parameters such as terrain and drainage rate, and are unable to improve the physical relevance of the probability analysis of power grid damage.

[0007] (3) Existing technologies have not achieved closed-loop management of early warning signals and the execution of emergency measures, and lack the use of GIS to dynamically display flood spread, equipment status, and material allocation progress, forming a closed loop of the entire process of "monitoring-early warning-response-feedback" to improve emergency efficiency. Summary of the invention

[0008] In view of this, in order to solve the problems raised in the above background technology, a power grid flood disaster early warning dynamic simulation system and method are proposed.

[0009] The purpose of the present invention can be achieved through the following technical solutions: The first aspect of the present invention provides a power grid flood disaster warning dynamic simulation system, including: a data acquisition module, which uses the collected power grid data, hydrological data and meteorological data of the target power grid area as flood disaster monitoring indicators.

[0010] The disaster prediction module predicts the flood spreading path of the target power grid area and the flooding depth and duration of each sub-area of ​​the target power grid based on flood disaster monitoring indicators, and divides the risk level according to the fuzzy hierarchical method.

[0011] The disaster loss assessment module combines historical flood disaster data and flood disaster monitoring indicators to analyze the damage probability of each power grid equipment in each risk level area, and identifies the threatened equipment based on the dynamic threshold judgment method.

[0012] The disaster impact assessment module predicts the area of ​​​​chain power outages caused by the threatened equipment based on flood disaster monitoring indicators, and analyzes the power supply gap and load transfer path through power flow calculation.

[0013] The early warning response and visualization module generates multi-level disaster early warning signals and corresponding emergency response measures based on the risk level of each sub-area, the damage probability and the chain blackout area, and displays them in a visual interface.

[0014] A second aspect of the present invention provides a method for dynamic deduction of flood disaster warning for power grid, comprising: S1, data collection: using the collected power grid data, hydrological data and meteorological data of the target power grid area as flood disaster monitoring indicators.

[0015] S2. Disaster prediction: Based on flood disaster monitoring indicators, the flood spreading path of the target power grid area and the flooding depth and duration of each sub-area of ​​the target power grid are predicted, and the risk level is divided according to the fuzzy hierarchical method.

[0016] S3. Disaster loss assessment: Combine historical flood disaster data and flood disaster monitoring indicators to analyze the probability of damage to each power grid equipment in each risk level area, and identify threatened equipment based on the dynamic threshold judgment method.

[0017] S4. Disaster impact assessment: predict the area of ​​cascading power outages caused by the threatened equipment based on flood disaster monitoring indicators, and analyze the power supply gap and load transfer path through power flow calculation.

[0018] S5. Warning response and visualization: Based on the risk level of each sub-area, the damage probability and the chain blackout area, a multi-level disaster warning signal and corresponding emergency response measures are generated, and a visual interface is displayed.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The present invention accurately simulates the flood spreading path and the inundation parameters of its sub-areas through the spatiotemporal alignment and model coupling of power grid data, hydrological data and meteorological data, improves the physical correlation of the probability analysis of power grid damage, and combines the fuzzy hierarchical method to quantify the risk level, providing a multi-dimensional analysis basis for the probability of equipment damage, which helps to reduce the misjudgment rate.

[0020] 2. Based on the grid node connection relationship diagram, the present invention identifies the upstream power failure area and downstream impact range of the threatened equipment through dynamic deduction, and optimizes the load transfer path in real time according to the flow calculation, thereby minimizing the power supply gap, realizing the spatiotemporal dynamic prediction and management of the impact of power outages, and ensuring the continuity of power supply in key areas.

[0021] 3. The present invention can display flood inundation animations, equipment status icons and repair progress bars in real time by deeply binding with graded warning signals and GIS visualization, supporting the command center to quickly locate high-risk equipment and dispatch resources, shortening the repair response time, forming a "monitoring-warning-response-feedback" full-process closed loop, and improving emergency efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0023] Figure 1 It is a schematic diagram of system module connection of the present invention.

[0024] Figure 2 The present invention is a schematic diagram of the implementation process of the method.

[0025] Figure 3 This is a process diagram for predicting the flood spreading path of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Embodiment 1

[0028] See also Figure 1 As shown, the present invention provides a power grid flood disaster early warning dynamic deduction system, and the specific modules are distributed as follows: data acquisition module, disaster prediction module, disaster loss assessment module, disaster impact assessment module and early warning response and visualization module. Among them, the connection mode between the modules is: the data acquisition module is connected to the disaster prediction module, the disaster prediction module is connected to the disaster loss assessment module, the disaster loss assessment module is connected to the disaster impact assessment module, and the disaster impact assessment module is connected to the early warning response and visualization module.

[0029] The data acquisition module uses the collected power grid data, hydrological data and meteorological data of the target power grid area as flood disaster monitoring indicators.

[0030] It should be further explained that the power grid data includes but is not limited to power grid operation data, operation data of each power grid device and power grid topology data.

[0031] The grid operation data includes but is not limited to the voltage, current and power factor of each node of the target grid.

[0032] It should be noted that the specific method for obtaining the voltage, current and power factor of each node of the target power grid is: using a deployed multimeter to directly detect the voltage, current and power factor of each node of the target power grid.

[0033] The operating data of each power grid equipment includes but is not limited to voltage, current and power factor, as well as altitude, service life and number of failures.

[0034] It should be noted that the specific method of obtaining the voltage, current and power factor of each power grid device as well as the altitude, service life and number of failures is: using the deployed multimeter to directly detect the voltage, current and power factor of each power grid device.

[0035] The altitude, service life and number of failures of each power grid equipment are directly extracted from the power grid database.

[0036] The power grid topology data includes but is not limited to the connection relationship and layout of each power grid device in the target power grid.

[0037] It should be noted that the specific method for obtaining the connection relationship and layout of each power grid device in the target power grid is: directly extracting the connection relationship and layout of each power grid device in the target power grid from the power grid database.

[0038] Hydrological data include but are not limited to water level, flow and flow rate of each monitored river.

[0039] It should be noted that the specific method of obtaining the water level, flow rate and flow rate of each monitored river is: all rivers in the target power grid area are numbered and recorded as the monitored rivers in the target power grid area, and the water level, flow rate and flow rate of each monitored river are obtained by real-time detection of the monitored rivers in the target power grid area through remote sensing technology.

[0040] Meteorological data includes, but is not limited to, rainfall amount and precipitation intensity.

[0041] It should be noted that the specific method for obtaining the rainfall amount and precipitation intensity is: directly extracting the rainfall amount and precipitation intensity from the meteorological center of the target power grid area.

[0042] The disaster prediction module predicts the flood spreading path of the target power grid area and the flooding depth and duration of each sub-area of ​​the target power grid based on flood disaster monitoring indicators, and divides the risk level areas according to the fuzzy hierarchical method.

[0043] It should be further explained that the risk level includes but is not limited to a low risk level, a medium risk level and a high risk level.

[0044] like Figure 3 The flood spread path prediction process diagram shown is an optimal feasibility example. The specific prediction method of the flood spread path in the target power grid area includes: A1. Data preprocessing: preprocessing the power grid data, hydrological data and meteorological data of the target power grid area by data cleaning, data standardization and time-space alignment.

[0045] Specifically, data cleaning refers to removing outliers, missing values, and duplicate values ​​from the data. Data standardization refers to unifying different types of data into the same dimension and value range for subsequent model calculations. For example, data such as voltage and current are normalized so that their values ​​are in the interval [0,1]. Spatiotemporal alignment refers to aligning data from different sources according to the same time step (such as every 15 minutes or every hour), and mapping the data to the same spatial coordinate system based on the geographic information system (GIS) to ensure the consistency of power grid, hydrological, and meteorological data in time and space.

[0046] A2. Multi-dimensional modeling: Build a power grid model, a hydrological model, and a meteorological model based on the pre-processed power grid data, hydrological data, and meteorological data of the target power grid area.

[0047] It needs to be further explained that the specific construction method of the power grid model is: select a suitable power grid model, perform power grid modeling of the target power grid area in the power grid model according to the power grid data, and record the obtained model as the power grid model.

[0048] Specifically, a grid wiring diagram is drawn based on the grid topology data in the grid data to clarify the electrical connections and power transmission paths between various devices. Then, the grid operation data and the operation data of each grid device in the grid data are used as technical parameters to construct a grid model for the target grid area.

[0049] It should be noted that for steady-state analysis of power systems, commonly used models include power flow calculation models, such as the Newton-Raphson method, the PQ decomposition method, etc.; for transient analysis, electromagnetic transient models or electromechanical transient models may be used, such as the models used by software such as ATP-EMTP, PSCAD / EMTDC, etc.

[0050] It should be explained that the verified and calibrated power grid model can be used in the planning and design, operation and dispatch, fault analysis and other aspects of the power grid. For example, the power distribution and voltage stability of the power grid can be analyzed through power flow calculation to provide a basis for the expansion and transformation of the power grid; the fault analysis model can be used to study the current and voltage changes of the power grid when short circuits, line breaks and other faults occur, and formulate reasonable protection strategies.

[0051] The specific construction method of the hydrological model is: selecting a suitable hydrological model, performing hydrological modeling of the target power grid area in the hydrological model according to the hydrological data, and recording the obtained model as the hydrological model.

[0052] Specifically, the topography, soil type, vegetation coverage, water system distribution and other characteristics of the target power grid area are taken as the basis of the hydrological model, and then rainfall, evaporation, runoff, water level and other parameters are used as technical parameters to construct the hydrological modeling of the target power grid area.

[0053] It should be noted that common hydrological models include the Xin'anjiang model and the SWAT model. Different models are suitable for different watershed types and hydrological conditions. For example, the Xin'anjiang model is suitable for watersheds in humid areas, while the SWAT model can be used for complex watershed systems and can take into account the impact of multiple factors such as land use and soil type on hydrological processes.

[0054] It should be explained that verified hydrological models can be used in flood forecasting, water resources management, river basin planning, etc. For example, based on the rainfall information from the meteorological forecast, the hydrological model can be used to predict the runoff and water level changes in the river basin, providing decision support for flood prevention and disaster reduction.

[0055] The specific construction method of the meteorological model is: selecting a suitable meteorological model, performing meteorological modeling of the target power grid area in the meteorological model according to meteorological data, and recording the obtained model as the meteorological model.

[0056] Specifically, meteorological data is used as the basic input of the meteorological model, and then the selected model is parameterized, including microphysical processes, radiation processes, boundary layer parameterization, etc., to provide the model with initial meteorological field conditions, such as the initial temperature, humidity, air pressure and other distributions. At the same time, the boundary conditions of the model are set, including lateral boundary conditions (such as the input of atmospheric circulation) and lower boundary conditions (such as surface characteristics, topography, etc.).

[0057] It should be noted that meteorological models include but are not limited to WRF, etc. These models are usually based on basic physical principles such as fluid mechanics and thermodynamics, and use numerical calculations to simulate changes in atmospheric movement and meteorological elements.

[0058] It should be explained that the verified meteorological models can be used for meteorological forecasting and related research, for example, to forecast weather conditions for a period of time in the future, or to analyze climate characteristics and changing trends under specific meteorological conditions.

[0059] A3. Multi-dimensional model coupling: The power grid model and the hydrological model are coupled to obtain a hydrological-power grid model, and the meteorological module is coupled to the hydrological model to obtain a meteorological-hydrological module, and the coupled multi-dimensional model is recorded as a multi-dimensional dynamic deduction model of flood.

[0060] Specifically, meteorological-hydrological coupling: the rainfall results predicted by the meteorological model are used as the input of the hydrological model to drive the flood evolution process. By establishing a rainfall-runoff relationship model, the rainfall is converted into runoff, which in turn affects the occurrence and development of floods.

[0061] Hydrology-grid coupling: Combine the flood inundation range, water level, flow rate and other information obtained from the hydrological model simulation with the grid model. Analyze the impact of floods on grid equipment. For example, if the tower foundation is washed away and causes the tower to tilt or collapse, the mechanical model can be used to calculate the stress of the tower under the action of the flood, and then determine the stability of the tower. If water accumulation causes short circuits in electrical equipment, the electrical insulation model can be used to analyze the changes in the insulation performance of the equipment under different water accumulation depths and water quality conditions.

[0062] Based on the real-time updated power grid data, hydrological data and meteorological data of the target power grid area, the parameters of the multi-dimensional dynamic flood simulation model are continuously adjusted and real-time simulation is carried out. The model is deduced forward at a certain time step (such as every hour) to predict the development trend of flood disasters and their impact on the power grid, including the flood inundation range at different times, the damage to power grid equipment, and changes in the operating status of the power grid.

[0063] A4. Flood spread path prediction: In the multi-dimensional dynamic simulation model of floods, the main rivers, tributaries and drainage ditches in the target power grid area are used as references for the natural spread of floods. Based on the principle of "water flows to low places", the water flow path is simulated starting from the rainfall concentration point along the direction of the maximum slope. At the same time, combined with the direction of the river, the overlapping part of the path and the natural waterway is adjusted (such as floods preferentially spread along the riverbed), and the flood spread path of the target power grid area is obtained. If it is found that the simulated path is inconsistent with the actual terrain (such as the simulated path passes through the ridge), the elevation value in the model is adjusted according to the field survey data. Compare the actual flooding range of historical floods with the simulation results, and calibrate the model parameters (such as the water flow resistance coefficient).

[0064] As a preferred feasibility example, the specific construction process of the flooding depth and duration of each sub-area of ​​the target power grid includes: extracting the cumulative rainfall, runoff coefficient, terrain water storage capacity, drainage rate and drainage duration of each sub-area of ​​the target power grid from the flood disaster monitoring indicators, and substituting them into the standard calculation formula for the flooding depth to directly calculate the flooding depth of each sub-area of ​​the target power grid.

[0065] It should be noted that the standard calculation formula for submergence depth is the prior art in this field.

[0066] It should be explained that the cumulative rainfall is obtained by adding up the rainfall in the meteorological data during the detection process from the detection starting point to the current time point; the runoff coefficient is dimensionless, reflecting the proportion of rainfall converted into surface runoff; the terrain water storage capacity refers to the amount of water naturally stored in the area, and the drainage rate represents the drainage capacity of the drainage system, which are directly extracted from the power grid database; the drainage duration is the interval from the start time of drainage of the drainage system to the current time point.

[0067] The terrain water storage after the rain stops in each sub-area of ​​the target power grid is extracted from the flood disaster monitoring indicators, and the ratio is added to the drainage rate of the corresponding sub-area and the drainage time of the corresponding sub-area to obtain the duration of each sub-area of ​​the target power grid. This formula realizes the dynamic calculation of the duration of waterlogging by integrating the terrain water storage after the rain stops, the drainage rate and the rainfall duration, which solves the problem that the traditional static assessment cannot reflect the time accumulation effect and breaks through the limitation of a single data source. For example, even if the flooding depth of a certain area is the same, if the drainage rate is different, the duration of waterlogging may differ by several times.

[0068] As a specific example, an example of calculating the flooding depth and duration of each sub-area of ​​the target power grid is shown in Table 1.

[0069] Table 1 Calculation example of flooding depth and duration of target power grid sub-area

[0070] Among them, Group 4 had the largest rainfall (200 mm) and insufficient drainage capacity, resulting in the deepest flooding depth (1.44 meters); Group 2 had the longest waterlogging duration (≈4.57 hours) due to its low drainage rate (25 mm / h); and Group 3 had no waterlogging because the terrain water storage capacity (60 mm) exceeded the runoff (40 mm).

[0071] As a preferred feasibility example, the specific division process of the risk level area includes: B1, selecting the membership function type, and determining the central value corresponding to the membership peak of the risk level and the width of the membership coverage, wherein the membership function type includes a triangle, trapezoid or Gaussian function.

[0072] It is necessary to further explain that the specific method for determining the central value corresponding to the membership peak value of the risk level and the width of the membership coverage is: to obtain the distribution characteristics of the inundation depth / duration in historical flood events by statistically analyzing the distribution characteristics of the inundation depth / duration in historical flood events, wherein the typical value of the corresponding risk level in the historical flood time (such as the average value of historical data for medium risk) is taken as the central value corresponding to the membership peak value of the risk level, and the width of the membership coverage of the risk level is determined according to the fluctuation range of the indicator (such as 2 times the standard deviation of historical data for medium risk). For example, the central value corresponding to the membership peak value of the high risk level is 1.5 meters (based on the average value of 1.5 meters of historical data); the width of the membership coverage of the low risk level is 0.5 meters (based on 2 times the standard deviation of 0.25 meters of historical data).

[0073] B2. Calculate the degree of membership of each risk level corresponding to the depth and duration of flooding in each sub-area of ​​the target power grid according to the depth and duration of flooding in each sub-area of ​​the target power grid, and select the maximum risk level membership as the degree of membership corresponding to the depth and duration of flooding in each sub-area of ​​the target power grid.

[0074] A specific example, if the triangular membership function type is used to calculate the membership, it can be calculated according to the standard formula Calculated, where is the depth or duration of submergence, They are the left endpoint, center point and right endpoint of the risk level range respectively. If the flooding depth of a sub-area of ​​the target power grid is 1.2 meters, the specific calculation example of the membership degree of each risk level corresponding to the sub-area of ​​the target power grid is shown in Table 2.

[0075] Table 2 Example of calculation of membership degree of corresponding risk level of target power grid sub-area

[0076] B3. The corresponding membership of the flooding depth and duration of each sub-area of ​​the target power grid is weighted and summed according to the preset corresponding weights to obtain the fuzzy comprehensive evaluation of each sub-area of ​​the target power grid, and it is compared with the fuzzy comprehensive evaluation range corresponding to each risk level to obtain the risk level of each sub-area of ​​the target power grid.

[0077] As a specific example, the preset corresponding weights of the corresponding membership of the submergence depth and duration can be set to 0.6 and 0.4 respectively. The submergence depth directly determines the risk of equipment insulation failure (for example, a water depth of 1 meter can cause a short circuit in the switch cabinet), so it accounts for a large proportion, and its preset corresponding weight is assigned to 0.6. The duration affects the durability of the equipment material (for example, the corrosion rate of a metal pole tower increases by 3 times after being immersed for 48 hours), so its preset corresponding weight is assigned to 0.4.

[0078] The disaster loss assessment module combines historical flood disaster data and flood disaster monitoring indicators to analyze the damage probability of each power grid equipment in each risk level area, and identifies the threatened equipment based on the dynamic threshold judgment method.

[0079] As a preferred feasibility example, the specific analysis method of the damage probability of each power grid equipment in each risk level area includes: extracting the altitude, service life and number of failures of each power grid equipment in each risk level area from the power grid data of the target power grid area in the flood disaster monitoring index, and obtaining the relative safety factor, usage score and relative failure assessment coefficient of each power grid equipment in each risk level area according to normalization processing.

[0080] The relative safety factor, usage score and relative fault assessment coefficient of each power grid equipment in each risk level area are weighted and summed according to the preset corresponding weights to obtain the damage probability of each power grid equipment in each risk level area, and then compared with the set damage probability threshold. If the damage probability of a power grid equipment in a certain risk level area is greater than or equal to the set damage probability threshold, the power grid equipment in the risk level area is recorded as a threatened device, and then the threatened devices are obtained.

[0081] As a preferred feasibility example, the specific process of obtaining the relative safety factor, usage score and relative fault assessment coefficient of each power grid equipment in each risk level area based on normalization processing includes: comparing the altitude of each power grid equipment in each risk level area with the historical highest flood level of the corresponding area in the historical flood disaster data, and the result is recorded as the relative safety factor of each power grid equipment in each risk level area.

[0082] A specific example is that if the historical highest flood level is 50 meters above sea level, and the altitude of a certain power grid equipment in a certain risk level area is 60 meters, then the relative safety height of the power grid equipment in this risk level area is 60 / 50=1.2; if the altitude of a certain power grid equipment in a certain risk level area is 55 meters, then the relative safety height of the power grid equipment in this risk level area is 60 / 55=1.09.

[0083] The service life of each power grid equipment in each risk level area is compared according to the age scoring table to obtain the service score of each power grid equipment in each risk level area.

[0084] As a specific example, the years of experience scoring table is shown in Table 3.

[0085] Table 3 Example of Year-Based Rating Table

[0086] The number of failures of each power grid equipment in each risk level area is compared with the average number of failures of historical equipment in the corresponding area in historical flood disaster data, and the result is recorded as the relative failure assessment coefficient of each power grid equipment in each risk level area.

[0087] The present invention accurately simulates the flood spreading path and the inundation parameters of its sub-areas through the spatiotemporal alignment and model coupling of power grid data, hydrological data and meteorological data, improves the physical correlation of the probability analysis of power grid damage, and combines the fuzzy hierarchical method to quantify the risk level, providing a multi-dimensional analysis basis for the probability of equipment damage, which helps to reduce the misjudgment rate.

[0088] The disaster impact assessment module predicts the impact of cascading power outages that may be caused by the threatened equipment based on flood disaster monitoring indicators, and analyzes power supply gaps and load transfer paths through power flow calculations.

[0089] As a preferred feasibility example, the specific prediction method of the impact of the chain power outage that may be caused by the threatened equipment includes: constructing a grid node (substation, transmission line) connection relationship diagram based on the grid topology data belonging to the grid data of the target grid area in the flood disaster monitoring indicators, and identifying the upstream and downstream power supply areas corresponding to each threatened equipment.

[0090] Specifically, the specific construction process of the power grid node connection relationship diagram includes: using tools such as ArcGIS / QGIS, superimposing power grid vector data and terrain base map, classifying symbols according to voltage levels (such as circles for substations and triangles for towers), drawing connecting lines according to the direction of the line, setting the start-end point attributes, and adding parameters such as conductor model, current carrying capacity, impedance, etc., to obtain the power grid node connection relationship diagram.

[0091] The specific operation of identifying the upstream and downstream power supply areas corresponding to each threatened device includes: according to the current direction of each threatened device, the power supply area where the current direction ends at each threatened device is recorded as the upstream power supply area corresponding to each threatened device, and the power supply area where the current axis starts from each threatened device is recorded as the downstream power supply area corresponding to each threatened device.

[0092] According to the grid node connection relationship diagram, the upstream power failure area, downstream direct power outage area and downstream indirect impact area of ​​each threatened device are determined, which are collectively referred to as the chain power outage area caused by the threatened equipment.

[0093] Specifically, the steps for determining the upstream voltage failure area are as follows: (1) Reverse tracing of the power supply path: Starting from the threatened equipment (such as a flooded substation), search for the power supply source in the reverse direction of power transmission. For example: a 10kV distribution tower fails → trace its upper 35kV substation → then trace the 110kV power supply point of the substation. (2) Voltage stability analysis: Check the load rate of each upstream node: If the load rate is greater than 80%, there is an overload risk, and it will be recorded as a voltage failure area.

[0094] The steps for determining the downstream direct power outage area are as follows: (1) Forward tracing of the load path: Starting from the threatened equipment, find all directly connected distribution lines and users along the power transmission direction. For example: 110kV substation failure → its connected 35kV lines A, B, C → distribution transformers at the end of each line → corresponding residential area. (2) Quantification of the power outage scope: Obtain a list of users associated with the faulty equipment through the power consumption information collection system.

[0095] The steps for determining the downstream indirect impact area are as follows: applying the power grid dynamic adjustment model, simulating the post-fault power dispatching strategy, faulty equipment load → adjacent substation → backup line → load transfer, identifying potentially overloaded lines (current > rated value 85%) and substations (load rate > 90%).

[0096] As a preferred feasibility example, the specific analysis process of the power supply gap and load transfer path includes: recording the upstream power failure area of ​​each dangerous equipment, the downstream direct power outage area and the downstream indirect impact area as power supply gap areas, and counting the total amount of electricity required for each power supply gap area.

[0097] Specifically, the historical average hourly electricity usage of each power supply gap area is directly extracted from the target power grid database, and it is multiplied by the corresponding duration to obtain the total electricity required for each power supply gap area (for example, "Area A usually requires 1000 kWh of electricity per hour, and the duration is 3 hours, then the total electricity required for Area A is 3000 kWh).

[0098] According to the grid node connection diagram, the available power supply locations and available power corresponding to each power supply gap area are listed, and the specific load transfer lines corresponding to each power supply gap area are determined.

[0099] Specifically, if there is a backup line in a power supply gap area, the backup line will be started. If there is no backup line in a power supply gap area, the nearest substation will be prioritized. If its available power can meet the total power required by the power supply gap area, the connection line between the power supply gap area and the substation will be used as the specific load transfer line corresponding to the power supply gap area. Otherwise, the second closest substation will be searched. Similarly, the specific load transfer line corresponding to the power supply gap area can be determined until the specific load transfer line corresponding to the power supply gap area is determined.

[0100] Based on the power grid node connection relationship diagram, the present invention identifies the upstream power failure area and downstream impact range of the threatened equipment through dynamic deduction, and optimizes the load transfer path in real time according to the flow calculation, thereby minimizing the power supply gap, realizing the spatiotemporal dynamic prediction and management of the impact of power outages, and ensuring the continuity of power supply in key areas.

[0101] The early warning response and visualization module generates multi-level disaster early warning signals and corresponding emergency response measures based on the risk level of each sub-area, the damage probability and the chain blackout area, and displays them in a visual interface.

[0102] As a preferred feasibility example, the specific contents of the early warning and emergency response module include: determining the triggering thresholds of multi-level disaster early warning signals and corresponding emergency response measures according to the risk levels of each sub-area of ​​the target power grid, the probability of damage to each power grid equipment in each risk level area, and the chain power outage area caused by the equipment.

[0103] Specifically, the trigger thresholds of the multi-level disaster warning signals are shown in Table 4.

[0104] Table 4 Examples of trigger thresholds for multi-level disaster warning signals

[0105] Among them, the blue warning indicates that general flood disasters may occur. For example, when the regional risk level is low risk and the probability of damage and the number of chained power outage areas are both less than 5, the affected area is small. The yellow warning corresponds to larger flood disasters. For example, when the regional risk level is medium risk and the probability of damage and the chained power outage area are (5, 10) and (5, 15) respectively, some areas are affected. The orange warning is for severe flood disasters. For example, when the regional risk level is medium risk and the probability of damage and the chained power outage area are (10, 15) and (15, 30) respectively, the affected area is large. The red warning is for extremely severe flood disasters. For example, when the regional risk level is medium risk and the probability of damage and the chained power outage area are greater than 15 and greater than 30 respectively, large-scale power outages and other serious consequences may occur.

[0106] Specifically, (1) Emergency response measures for blue warning response include: activating the emergency duty mechanism and arranging special personnel to closely monitor the flood dynamics; preparing necessary emergency materials, such as sandbags and pumps; and notifying relevant maintenance personnel to make emergency preparations. (2) Emergency response measures for yellow warning response include: increasing the number of emergency duty personnel and strengthening the inspection of power grid equipment in key areas; taking temporary protective measures for substations, towers and other equipment that may be affected, such as reinforcing the tower foundation and setting up water retaining cofferdams; coordinating emergency power sources such as power generators and preparing them for use at any time. (3) Emergency response measures for orange warning response control: activating the emergency command center and coordinating resources from all parties; transferring important equipment and materials in advance; adjusting the power grid operation mode according to the load transfer strategy to give priority to power supply to important users; organizing emergency repair teams and being on standby to carry out repair work at any time. (4) Emergency response measures for red warning response include: fully devoting themselves to disaster relief and ensuring the safety of personnel; implementing emergency response to large-scale power outages in accordance with pre-made plans; and timely releasing power outage information and power supply restoration plans.

[0107] Using geographic information system technology, the spread process and early warning information of flood disasters in the target power grid area can be dynamically displayed.

[0108] Specifically, data interface technology is used to obtain the dynamic simulation results of flood disasters in real time and convert them into a format that can be recognized by the visualization interface. Geographic Information System (GIS) technology is used to display the spread of floods, and the flooding range and depth changes of floods at different times are dynamically displayed through animation. The power grid equipment is superimposed in the form of icons to intuitively present the affected equipment. For example, when a tower is submerged by floods, the tower icon changes to a flashing red state.

[0109] An early warning information bar is set up in a prominent position on the visual interface, which displays the current warning level, warning area and estimated impact range in eye-catching colors and fonts. At the same time, the warning release time and estimated release time are updated in real time.

[0110] It should be noted that the implementation of emergency response measures can be displayed in the form of charts or progress bars. For example, the storage and allocation progress of emergency materials, the dispatch of repair teams and the progress of work, etc. Establish a real-time data update mechanism to obtain the latest flood disaster monitoring and prediction data, power grid operation data and emergency response data from the data source regularly (such as every 5-10 minutes). Update the information on the visualization interface in a timely manner to ensure that the displayed content is synchronized with the actual situation. Provide a user interaction interface that allows operators to zoom in and out of the map to view detailed information on a specific area. Click on the device icon to view the specific parameters, fault status and historical records of the device. Set up a query function to facilitate users to query the disaster simulation process and warning information based on time, location and other conditions.

[0111] The present invention can display flood inundation animations, equipment status icons, and emergency repair progress bars in real time by deeply binding graded warning signals with GIS visualization, supporting the command center to quickly locate high-risk equipment and dispatch resources, shortening the emergency repair response time, forming a full-process closed loop of "monitoring-warning-response-feedback", and improving emergency efficiency.

[0112] Embodiment 2

[0113] See also Figure 2 As shown, the present invention provides a dynamic deduction method for early warning of flood disasters in power grids, and the specific steps are as follows: S1, data collection: the collected power grid data, hydrological data and meteorological data of the target power grid area are used as flood disaster monitoring indicators.

[0114] S2. Disaster prediction: Based on flood disaster monitoring indicators, the flood spreading path of the target power grid area and the flooding depth and duration of each sub-area of ​​the target power grid are predicted, and the risk level is divided according to the fuzzy hierarchical method.

[0115] S3. Disaster loss assessment: Combine historical flood disaster data and flood disaster monitoring indicators to analyze the probability of damage to each power grid equipment in each risk level area, and identify threatened equipment based on the dynamic threshold judgment method.

[0116] S4. Disaster impact assessment: predict the area of ​​cascading power outages caused by the threatened equipment based on flood disaster monitoring indicators, and analyze the power supply gap and load transfer path through power flow calculation.

[0117] S5. Warning response and visualization: Based on the risk level of each sub-area, the damage probability and the chain blackout area, a multi-level disaster warning signal and corresponding emergency response measures are generated, and a visual interface is displayed.

[0118] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A power grid flood disaster early warning dynamic simulation system, characterized by: include: The data collection module uses the collected power grid data, hydrological data and meteorological data of the target power grid area as flood disaster monitoring indicators; The disaster prediction module predicts the flood spreading path of the target power grid area and the flooding depth and duration of each sub-area of ​​the target power grid based on flood disaster monitoring indicators, and divides the risk level according to the fuzzy hierarchical method; The disaster loss assessment module combines historical flood disaster data and flood disaster monitoring indicators to analyze the damage probability of each power grid equipment in each risk level area, and identifies the threatened equipment based on the dynamic threshold judgment method; The disaster impact assessment module predicts the cascading power outage area caused by the threatened equipment based on flood disaster monitoring indicators, and analyzes the power supply gap and load transfer path through power flow calculation; The early warning response and visualization module generates multi-level disaster early warning signals and corresponding emergency response measures based on the risk level of each sub-area, the damage probability and the chain blackout area, and displays them in a visual interface.

2. A power grid flood disaster early warning dynamic deduction system according to claim 1, characterized in that: The specific prediction method of the flood spreading path of the target power grid area includes: A1. Data preprocessing: preprocess the power grid data, hydrological data and meteorological data of the target power grid area by data cleaning, data standardization and time-space alignment; A2. Multi-dimensional modeling: Build a power grid model, a hydrological module, and a meteorological model based on the pre-processed power grid data, hydrological data, and meteorological data of the target power grid area; A3. Multi-dimensional model coupling: the power grid model and the hydrological model are coupled to obtain a hydrological-power grid model, and the meteorological module is coupled to the hydrological model to obtain a meteorological-hydrological module, and the coupled multi-dimensional model is recorded as a flood multi-dimensional dynamic deduction model; A4. Flood spread path prediction: In the multi-dimensional dynamic simulation model of floods, the main rivers, tributaries and drainage ditches in the target power grid area are used as reference paths for the natural spread of floods. Based on the principle of "water flows to low places", the water flow path is simulated starting from the rainfall concentration point along the direction of the maximum slope. At the same time, the overlapping part of the path with the natural waterway is adjusted in combination with the river direction, so as to obtain the flood spread path of the target power grid area.

3. A power grid flood disaster early warning dynamic deduction system according to claim 2, characterized in that: The specific construction process of the flooding depth and duration of each sub-area of ​​the target power grid includes: The cumulative rainfall, runoff coefficient, terrain water storage, drainage rate and drainage time of each sub-area of ​​the target power grid are extracted from the flood disaster monitoring indicators, and the flood depth of each sub-area of ​​the target power grid is directly calculated by substituting them into the standard calculation formula of flood depth; The topographic water storage capacity of each sub-area of ​​the target power grid after the rain stops is extracted from the flood disaster monitoring indicators, and the ratio is added to the drainage rate of the corresponding sub-area and the drainage time of the corresponding sub-area to obtain the duration of each sub-area of ​​the target power grid.

4. A power grid flood disaster early warning dynamic deduction system according to claim 1, characterized in that: The specific classification process of the risk level includes: B1. Select the membership function type and determine the central value corresponding to the membership peak of the risk level and the width of the membership coverage; B2. Calculate the membership of each risk level corresponding to the flooding depth and duration of each sub-area of ​​the target power grid according to the flooding depth and duration of each sub-area of ​​the target power grid, and select the maximum risk level membership as the membership corresponding to the flooding depth and duration of each sub-area of ​​the target power grid; B3. The corresponding membership of the flooding depth and duration of each sub-area of ​​the target power grid is weighted and summed according to the preset corresponding weights to obtain the fuzzy comprehensive evaluation of each sub-area of ​​the target power grid, and it is compared with the fuzzy comprehensive evaluation range corresponding to each risk level to obtain the risk level of each sub-area of ​​the target power grid.

5. The power grid flood disaster early warning dynamic deduction system according to claim 1 is characterized by: The specific analysis method of the damage probability of each power grid equipment in each risk level area includes: The altitude, service life and number of failures of each power grid equipment in each risk level area are extracted from the power grid data of the target power grid area in the flood disaster monitoring index, and the relative safety factor, usage score and relative failure assessment coefficient of each power grid equipment in each risk level area are obtained according to normalization processing; The relative safety factor, usage score and relative fault assessment coefficient of each power grid equipment in each risk level area are weighted and summed according to the preset corresponding weights to obtain the damage probability of each power grid equipment in each risk level area, and then compared with the set damage probability threshold. If the damage probability of a power grid equipment in a certain risk level area is greater than or equal to the set damage probability threshold, the power grid equipment in the risk level area is recorded as a threatened device, and then the threatened devices are obtained.

6. A power grid flood disaster early warning dynamic deduction system according to claim 5, characterized in that: The specific process of obtaining the relative safety factor, usage score and relative fault assessment coefficient of each power grid equipment in each risk level area according to the normalization process includes: The altitude of each power grid equipment in each risk level area is compared with the historical highest flood level of the corresponding area in the historical flood disaster data, and the result is recorded as the relative safety height of each power grid equipment in each risk level area; Compare the service life of each power grid equipment in each risk level area according to the service life score table to obtain the service score of each power grid equipment in each risk level area; The number of failures of each power grid equipment in each risk level area is compared with the average number of failures of historical equipment in the corresponding area in historical flood disaster data, and the result is recorded as the relative failure assessment coefficient of each power grid equipment in each risk level area.

7. The power grid flood disaster early warning dynamic deduction system according to claim 1 is characterized by: The specific prediction methods of the chain blackout area caused by the threatened equipment include: Based on the grid topology data of the target grid area in the flood disaster monitoring indicators, a grid node connection diagram is constructed to identify the upstream and downstream power supply areas corresponding to each threatened device; According to the grid node connection relationship diagram, the upstream power failure area, downstream direct power outage area and downstream indirect impact area of ​​each threatened device are determined, which are collectively referred to as the chain power outage area caused by the threatened equipment.

8. A power grid flood disaster early warning dynamic deduction system according to claim 7, characterized in that: The specific analysis process of the power supply gap and load transfer path includes: The upstream power failure area of ​​each dangerous equipment, the downstream direct power outage area and the downstream indirect impact area are recorded as power supply gap areas, and the total power required for each power supply gap area is calculated; According to the grid node connection diagram, the available power supply locations and available power corresponding to each power supply gap area are listed, and the specific load transfer lines corresponding to each power supply gap area are determined.

9. The power grid flood disaster early warning dynamic deduction system according to claim 1, characterized in that: The specific contents of the early warning and emergency response module include: Determine the trigger threshold of multi-level disaster warning signals and corresponding emergency response measures based on the risk level of each sub-area of ​​the target power grid, the probability of damage to each power grid equipment in each risk level area, and the chain blackout area caused by the equipment; Using geographic information system technology, the spread process and early warning information of flood disasters in the target power grid area can be dynamically displayed.

10. A method for dynamic simulation of flood disaster warning in power grid, characterized by: include: S1. Data collection: The collected power grid data, hydrological data and meteorological data of the target power grid area are used as flood disaster monitoring indicators; S2. Disaster prediction: Based on flood disaster monitoring indicators, the flood spreading path of the target power grid area and the flooding depth and duration of each sub-area of ​​the target power grid are predicted, and the risk level is divided according to the fuzzy hierarchical method; S3. Disaster loss assessment: Combine historical flood disaster data and flood disaster monitoring indicators to analyze the damage probability of each power grid equipment in each risk level area, and identify the threatened equipment based on the dynamic threshold judgment method; S4. Disaster impact assessment: predict the cascading power outage area caused by the threatened equipment based on flood disaster monitoring indicators, and analyze the power supply gap and load transfer path through power flow calculation; S5. Warning response and visualization: Based on the risk level of each sub-area, the damage probability and the chain blackout area, a multi-level disaster warning signal and corresponding emergency response measures are generated, and a visual interface is displayed.

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