Electric power emergency repair quick response method and system based on disaster early warning

Abstract

The invention relates to the technical field of electric power, and discloses an electric power emergency repair quick response method and system based on disaster early warning, and the key points of the technical scheme are as follows: multi-source disaster early warning data acquisition and power grid vulnerable area evaluation, repair resource preset planning and plan making, and post-disaster dynamic response and priority repair. By means of full-process linkage of early warning, presetting, response and first-aid repair, passive first-aid repair is changed into active preset response, first-aid repair resources are arranged in advance, plans are made in a targeted mode, response is quickly activated after disasters, important load power supply is preferentially recovered, and the power emergency first-aid repair efficiency and the disaster resistance toughness of a power grid are improved.

Description

Technical Field

[0001] This invention relates to the field of power technology, and more specifically, to a rapid response method and system for power emergency repair based on disaster early warning. Background Technology

[0002] In recent years, extreme natural disasters (such as typhoons, rainstorms, and earthquakes) have occurred frequently, causing severe damage to the power system, leading to large-scale power outages, and seriously affecting social production, daily life, and public safety. Existing power emergency repair models are mostly reactive responses after disasters occur, which have many problems: First, there is a lack of advance preparation, relying on assessing damage and allocating resources only after a disaster occurs, resulting in slow repair initiation; second, there is insufficient repair capacity in key areas, and resource allocation lacks focus, failing to prioritize the restoration of power to important loads such as hospitals and transportation hubs; third, there is insufficient linkage between emergency repair and disaster early warning, failing to fully utilize early warning data for advance resource deployment, leading to low emergency response efficiency.

[0003] While existing research has made some progress in areas such as power emergency management systems, intelligent early warning technologies, and resource scheduling, limitations still exist. For example, some studies focus on the construction of post-disaster emergency response systems without addressing pre-disaster resource pre-positioning; some studies introduce big data and AI technologies for disaster risk assessment but fail to deeply integrate them with the dynamic allocation of repair resources and the formulation of contingency plans; and some studies focus on the coordinated recovery of multi-energy systems but have not designed for an "active pre-positioning response" model that can provide early warnings of disasters.

[0004] Therefore, there is an urgent need for a rapid response method and system that integrates multi-source data assessment, advance resource pre-positioning, and dynamic response and repair, with disaster early warning linkage as the core, to solve the problems of delayed response and insufficient preparation in existing solutions. Summary of the Invention

[0005] This disclosure aims to address the shortcomings of existing technologies by providing a rapid response method and system for power emergency repair based on disaster early warning. The invention transforms "passive repair" into "proactive pre-positioned response" through the linkage of the entire process of "early warning-pre-positioning-response-repair". This enables the advance deployment of repair resources, the targeted formulation of contingency plans, and the rapid activation of response after a disaster. It prioritizes the restoration of power supply to important loads, thereby improving the efficiency of power emergency repair and the resilience of the power grid against disasters.

[0006] The above-mentioned technical objective of the present invention is achieved through the following technical solution: a rapid response method and system for power emergency repair based on disaster early warning, comprising the following steps:

[0007] S1. Multi-source disaster early warning data collection and power grid vulnerable area assessment: Collect multi-source disaster early warning data, power grid basic data and historical data, and classify vulnerable areas based on line and substation fault probability models;

[0008] S2. Emergency Repair Resource Pre-positioning Planning and Contingency Plan Formulation: Based on the assessment results of vulnerable areas, calculate the emergency repair resource requirements, select key pre-positioning points, and formulate targeted emergency repair contingency plans;

[0009] S3. Post-disaster dynamic response and priority repair: Real-time collection of power grid damage data, activation of pre-set resources and plans, dynamic adjustment of repair forces, and priority repair of power supply lines for important loads.

[0010] As a preferred embodiment of the present invention, the line fault probability model in step 1 includes a conductor fault probability model, a tower fault probability model, and an overall line fault probability model. The conductor fault probability model P line (v) is:

[0011]

[0012] Tower failure probability model P tow (v) is:

[0013]

[0014] Line overall fault probability model P l,ij for:

[0015]

[0016] Where v is the typhoon wind speed, v line,1 The wind speed threshold at which the probability of a sudden increase in conductor failure, v line,2 To determine the fault wind speed threshold for the conductor, β1, P is the fitting parameter. line,min Let Φ(·) be the baseline fault probability of the conductor, and v be the standard normal distribution function. tow,1 The wind speed threshold at which the probability of tower failure increases sharply, v tow,2 To determine the wind speed threshold for tower faults, β2, For the fitting parameters, m i Let P be the number of towers for line ij. line,ij Let P be the conductor fault probability of line ij. tow,ij,k Let be the failure probability of the k-th tower of line ij.

[0017] As a preferred embodiment of the present invention, the substation fault probability model in step 1 is as follows:

[0018]

[0019] Where D is the water depth of the substation, D bus,1 D is the water depth threshold that causes a sharp increase in the probability of substation failure. bus,2To determine the water depth threshold for faults in substations, β3 and D are fitting parameters.

[0020] As a preferred embodiment of the present invention, the formula for calculating the demand for the emergency repair team in step 2 is as follows:

[0021]

[0022] The formula for calculating the demand for emergency power generation vehicles is:

[0023]

[0024] Among them, L high For the length of the line in the high-risk area, N sub,high The number of substations in high-risk areas is represented by L0 and N0, which are historical emergency repair efficiency parameters. P is the floor function. load,imp P represents the total power of critical loads in high-risk areas. gen,rate This refers to the rated power of the emergency power generator vehicle.

[0025] As a preferred embodiment of the present invention, the preset point selection in step 2 adopts an improved particle swarm optimization algorithm, and the objective function is:

[0026]

[0027] In the formula:

[0028]

[0029] x ki ∈{0,1}

[0030] Where M is the number of fault points in the high-risk area, and N... pre X represents the number of candidate preset points. ki =1 indicates that the k-th fault point is covered by the i-th preset point, d ki This is the distance from the k-th fault point to the i-th preset point.

[0031] As a preferred embodiment of the present invention, the topology reconstruction scheme in step 2 adopts the following constraints:

[0032]

[0033] Where, ε ij,t γ ij,t ρ is the state variable of the line power flow. lij,t L is the variable representing the normal state of the line. nor Let y1 and y2 be the branch set of unbalanced nodes, t be the time interval, and T be the time set.

[0034] As a preferred embodiment of the present invention, the dynamic adjustment of emergency repair forces in step 3 adopts a multi-objective optimization model, with the objective function being:

[0035]

[0036] In the formula:

[0037]

[0038] Among them, Ω imp For the set of important loads, γ i,t T is the state variable for critical load recovery. repair This refers to the actual repair time. For the expected repair time, C res For actual resource consumption, For budgeted resource consumption, ω1, ω2, and ω3 are weighting coefficients, and x ij,t Assign variables to the repair team, N crew,j To maximize the workload of the repair team, Ω fault Ω represents the set of fault points. crew The repair team is assembled.

[0039] As a preferred embodiment of the present invention, the emergency repair process in step 3 adopts the DistFlow model and voltage constraints. The DistFlow model is as follows:

[0040]

[0041] in, For the active and reactive power of distributed power sources, For the charging and discharging power of energy storage,

[0042] For node load, To cut off the load, p lt q lt For line power, v jt ε represents the node voltage, and ε is the allowable voltage deviation.

[0043] A rapid response system for power emergency repair based on disaster early warning includes a multi-source data acquisition module, a disaster early warning and risk assessment module, a pre-planning module for repair resources, a post-disaster dynamic response and repair module, and a data storage and interaction module. The modules work together to achieve the functions of the above-mentioned method.

[0044] As a preferred technical solution of the present invention, an edge-cloud collaborative architecture is adopted. The edge layer is deployed at pre-positioned points and substations, responsible for real-time data acquisition and local resource activation; the cloud layer is deployed at the dispatch center, responsible for model calculation and data storage; the terminal layer includes handheld terminals and monitoring terminals, used for command interaction and progress display.

[0045] In summary, the present invention has the following beneficial effects:

[0046] Firstly, this invention uses disaster early warning as its core trigger point, assessing vulnerable areas in advance through multi-source data (meteorological, geological, power grid topology, historical damage data), and combining this with a line fault probability model (such as conductor P). line (v), Tower P tow (v) Line overall P l,ij Model) and substation fault probability model (P) bus (D) Model) accurately delineates high, medium, and low-risk areas; then, it pre-positions repair teams, emergency power generation vehicles, and spare parts at key pre-positioning points, while simultaneously developing contingency plans for topology reconfiguration and resource scheduling. After a disaster occurs, pre-positioned resources and plans can be activated directly, eliminating the need to start the assessment and allocation process from scratch. Verified in actual typhoon disaster scenarios in coastal cities, this approach significantly shortens the repair initiation time compared to existing solutions, effectively addressing the pain point of "delayed response."

[0047] Secondly, this invention achieves precise resource allocation through two major technical means: on the one hand, it calculates resource requirements based on the weight of critical loads, for example, through N... crew N gen Formulas are used to accurately match the number of repair teams and emergency power generation vehicles, avoiding resource redundancy or shortage; on the other hand, an improved particle swarm optimization algorithm is used to select pre-positioned points, minimizing the average distance from the pre-positioned points to high-risk areas. Ensure resources are rapidly delivered to critical load power lines. Simultaneously, post-disaster, a multi-objective optimization model (maxf) dynamically adjusts repair efforts, prioritizing the restoration of critical load lines. In typhoon disaster examples in coastal cities, the power restoration time for critical loads such as hospitals was significantly shortened compared to existing solutions. Attached Figure Description

[0048] Figure 1 A flowchart of a rapid response method for power emergency repair based on disaster early warning, provided for an embodiment of the present invention. Detailed Implementation

[0049] The present application will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application. These all fall within the protection scope of the present application.

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0051] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0052] Furthermore, the technical features involved in the various embodiments of this application described below can be combined with each other as long as they do not conflict with each other.

[0053] This invention aims to address the problems of existing power emergency repairs being largely reactive after disasters, lacking advance preparation leading to slow repair initiation, insufficient repair resources in critical areas, and low system response efficiency. Therefore, this invention proposes a rapid response method and system for power emergency repairs based on disaster early warning, to improve the efficiency of power system emergency repairs in the face of predictable natural disasters and ensure rapid restoration of power to critical loads. This method utilizes multi-source data acquisition and fusion technology, combined with configurable risk assessment models, resource pre-positioning strategies, and dynamic repair adjustment mechanisms. It constructs a differentiated "early warning-pre-positioning-response-repair" rapid response system tailored to different disaster scenarios such as typhoons and rainstorms, and the grid characteristics of different regions such as urban, rural, and remote areas. This transforms "passive repair" into "proactive pre-positioning response," reduces system complexity and operating costs, and enhances the grid's disaster resilience.

[0054] Please refer to Figure 1 , Figure 1 A flowchart of a rapid response method for power emergency repair based on disaster early warning, as described in an embodiment of this disclosure, is shown. The overall process mainly includes the following three steps:

[0055] Step 1: Collect multi-source disaster early warning data and assess vulnerable areas of the power grid.

[0056] Step 1.1 Multi-source data acquisition: The following data is acquired synchronously through the multi-source data acquisition module:

[0057] Disaster warning data: warning parameters such as typhoon wind speed (v), heavy rainfall, and earthquake magnitude issued by meteorological departments; and warning area data for geological disasters (such as landslides and debris flows) issued by geological departments.

[0058] Basic power grid data: power grid topology (node ​​and line connections), line parameters (resistance r) ij Reactance x ij ), Substation location and capacity, load distribution and importance classification (important load weight ω) i such as hospitals i =100, Transportation hub ω i =80, ordinary residential load ω i =10).

[0059] Historical data: Records of damage to power grid lines and substations under similar historical disasters, including failure probability, location of damage, and repair time.

[0060] Step 1.2 Power Grid Vulnerability Assessment: Combining disaster early warning parameters and power grid data, the following model is used to assess the failure probability of power grid components (lines, substations) and determine vulnerable areas:

[0061] Line fault probability model: For typhoon disasters, the fault probability of lines (including conductors and towers) is calculated using a piecewise function:

[0062] Wire failure probability P line (v):

[0063]

[0064] Where v is the typhoon wind speed, v line,1 The wind speed threshold at which the probability of a sudden increase in conductor failure, v line,2 To determine the fault wind speed threshold for the conductor, β1, P is the fitting parameter. line,min The baseline fault probability for the conductor (taken as 1×10) -2 ), where Φ(·) is the standard normal distribution function.

[0065] Tower failure probability P tow (v):

[0066]

[0067] Among them, v tow,1 The wind speed threshold at which the probability of tower failure increases sharply, v tow,2 To determine the wind speed threshold for tower faults, β2, These are the fitting parameters.

[0068] Overall line fault probability P l,ij (Line ij includes m) i (each tower)

[0069]

[0070] Substation failure probability model: For rainstorm and waterlogging disasters, the failure probability P of the substation node bus (D) Calculated based on the waterlogging depth D:

[0071]

[0072] where D is the waterlogging depth of the substation, D bus,1 is the waterlogging depth threshold at which the failure probability of the substation suddenly increases, D bus,2 is the waterlogging depth threshold for determining the failure of the substation, and β3, D are fitting parameters.

[0073] Vulnerable area division: According to the failure probabilities of lines and substations, combined with the power grid topology, divide vulnerable areas into high-risk (failure probability ≥ 0.7), medium-risk (0.3 < P < 0.7), and low-risk (P ≤ 0.3) areas, and mark the power supply lines of important loads in high-risk areas.

[0074] Step 2: Pre-positioning planning and plan formulation of emergency repair resources.

[0075] Step 2.1 Calculation of emergency repair resource requirements Calculate the emergency repair resource requirements according to the power grid scale (line length, number of substations) and load importance in the vulnerable area:

[0076] Requirement for emergency repair teams: Based on the line length L in high-risk areas high and the number of substations N sub,high , combined with the historical emergency repair efficiency (such as each team can repair 5 km of lines and 2 substations per day on average), calculate the required number of emergency repair teams N crew :

[0077]

[0078] where is the ceiling function.

[0079] Requirement for emergency power generation vehicles: For the important load P in high-risk areas load,imp (total power), considering the rated power P of the emergency power generation vehicle gen,rate , calculate the required number of emergency power generation vehicles N gen :

[0080]

[0081] where 0.8 is the load rate coefficient of the emergency power generation vehicle.

[0082] Requirement for spare parts: Based on the line models in high-risk areas and the types of substation equipment, combined with the historical damage rate, determine the quantity of spare parts such as conductors, poles, and transformer accessories. For example, the requirement for conductors:

[0083] Qcable =L high ×(1+P line,ang )

[0084] Among them, P line,avg (This represents the average failure probability of lines in high-risk areas).

[0085] Step 2.2 Selection of Pre-positioned Repair Resource Points: An improved particle swarm optimization algorithm is used to select key pre-positioned points (such as power supply stations and material warehouses around the disaster area). The goal is to minimize the average distance between the pre-positioned points and high-risk areas.

[0086]

[0087] Where M is the number of fault points in the high-risk area, and N... pre X represents the number of candidate preset points. ki =1 indicates that the k-th fault point is covered by the i-th preset point, d ki This is the distance from the k-th fault point to the i-th preset point.

[0088] Step 2.3 Develop targeted emergency repair plans. For critical load power supply lines in high-risk areas, develop emergency repair plans, including:

[0089] Priority repair order: based on the weight of critical load ω i Determine the priority of emergency repairs; the higher the weight, the higher the priority.

[0090] Topology reconfiguration contingency plan: A pre-defined fault line isolation scheme is implemented, and the power grid topology is adjusted via a remote control switch (RCS), such as:

[0091]

[0092] Where, ε ij,t γ ij,t Let ρ represent the forward and reverse power flow states (0-1 variables) of line ij at time t. lij,t Let L represent the normal state of line ij at time t (0-1 variable). nor For the set of branches of unbalanced nodes, y1 and y2 are variables indicating the branch flow direction;

[0093] Resource dispatch plan: Clarify the dispatch rules for emergency repair teams and emergency power generation vehicles at each pre-positioned point, such as route planning when providing support across pre-positioned points (based on a traffic network model to avoid waterlogged and congested road sections).

[0094] Step 3: Post-disaster dynamic response and priority repair.

[0095] Step 3.1 Real-time Data Collection of Power Grid Damage After a disaster occurs, real-time damage data is collected using the following methods:

[0096] Intelligent monitoring equipment: Utilizing drones, satellite remote sensing, and intelligent sensors, it collects images and data on damage such as broken strands in power lines, tilted towers, and water accumulation in substations;

[0097] Fault location system: Based on SCADA system and WAMS system data, combined with fault location algorithm, the fault location (x,y) and fault type (such as short circuit, open circuit) are determined, and the fault location accuracy meets ±50m;

[0098] Manual inspection supplement: For remote areas with weak signals, the damaged data is supplemented through the initial inspection by the pre-positioned emergency repair team.

[0099] Step 3.2 Activation of Pre-set Resources and Contingency Plans: Based on real-time damage data, activate the resources and contingency plans for the corresponding pre-set points:

[0100] Resource activation: Send activation command to the pre-positioned point, the emergency repair team sets off with spare parts, and the emergency power generation vehicle goes to the location of the critical load;

[0101] Contingency plan matching: Based on the fault type and location, match the emergency repair plan formulated in step 2.3. For example, match the conductor replacement plan for a line break fault, and match the drainage and equipment maintenance plan for a substation water accumulation fault.

[0102] Step 3.3 Dynamic Adjustment of Repair Forces: Based on real-time damage data updates, the allocation of repair forces is dynamically adjusted using a multi-objective optimization model. The objective function is:

[0103]

[0104] In the formula:

[0105]

[0106] Among them, Ω imp For the set of important loads, γ i,t Let T be the recovery state of critical load i at time t (0-1 variable). repair This refers to the actual repair time. For the expected repair time, C res For actual resource consumption, For budgeted resource consumption, ω1, ω2, and ω3 are weighting coefficients, and x ij,t Let N be the time interval t and let N be the time interval t. (Note: The text contains some inconsistencies and unclear grammatical structures. A more accurate translation would require the full context.) crew,j This represents the maximum workload of the j-th emergency repair team.

[0107] Step 3.4 Priority repairs will be carried out according to the following rules:

[0108] Prioritize the repair of power supply lines for critical loads, such as those to hospitals and transportation hubs, and adopt the principle of "connect first, then restore," first ensuring temporary power supply through emergency generators, and then repairing the faulty lines.

[0109] During the emergency repair process, the power grid's operating status is monitored in real time, and the DistFlow model is used to ensure that the power grid meets safety constraints after the repair.

[0110]

[0111] in, For the active and reactive power of distributed power source i, For the charging and discharging power of energy storage, For the active and reactive loads of node j, For the load shedding at node j, p lt q lt

[0112] V represents the active and reactive power of line l. jt Let ε be the voltage at node j (per unit value), and ε be the allowable voltage deviation (taken as 0.05).

[0113] After the emergency repairs were completed, the restored power lines and substations were put into trial operation and monitoring to ensure that there were no secondary faults.

[0114] The embodiments of this application disclose a rapid response system for power emergency repair based on disaster early warning. The overall system architecture mainly includes: a multi-source data acquisition module, a disaster early warning and risk assessment module, a pre-planning module for repair resources, a post-disaster dynamic response and repair module, and a data storage and interaction module. The modules work together to achieve rapid response throughout the entire process.

[0115] Source data acquisition module: Used to receive multi-source disaster early warning data such as typhoons, rainstorms, and earthquakes issued by meteorological and geological departments, and to collect power grid topology data, historical disaster damage data, real-time power grid operation data (voltage, current, power, etc.), emergency repair resource data (number and skills of emergency repair teams, parameters of emergency power generation vehicles, and inventory of spare parts), and load importance classification data (location and power supply demand of important loads such as hospitals and transportation hubs).

[0116] Disaster early warning and risk assessment module: Based on multi-source data, assess the vulnerable areas and risk levels of the power grid corresponding to the disaster early warning level.

[0117] Emergency repair resource pre-positioning planning module: Based on the assessment results of vulnerable areas, plan emergency repair resource pre-positioning schemes and targeted emergency repair plans.

[0118] Post-disaster dynamic response and emergency repair module: After a disaster occurs, real-time data on power grid damage is collected, pre-set resources and plans are activated, emergency repair forces are dynamically adjusted, and priority repairs are carried out.

[0119] Data storage and interaction module: Stores data from each module, enables data interaction and sharing between modules, and supports data interface with external systems (such as meteorological departments and power grid dispatching systems).

[0120] This system is implemented based on a collaborative architecture of edge computing and cloud platform:

[0121] Edge layer: Deployed at pre-positioned points and substations, responsible for real-time collection of multi-source data (disaster early warning, power grid operation, damage data), executing local resource activation commands, and using edge computing nodes to process data with high real-time requirements (such as fault location, voltage monitoring) to reduce data transmission latency;

[0122] Cloud layer: Deployed in the power company's dispatch center, it is responsible for running the disaster risk assessment model, resource pre-positioning optimization model, and dynamic emergency repair adjustment model, storing historical data and contingency plans, and realizing data interaction and sharing among various pre-positioning points and modules through the cloud platform;

[0123] Terminal layer: includes handheld terminals for repair personnel and monitoring terminals in the dispatch center, used to display damage data, repair progress, and resource status, and to support the issuance and feedback of repair instructions.

[0124] Example: Taking a coastal city's response to typhoon disasters as an example, the city's power grid includes a 33-node distribution network and 5 220kV substations. Important loads include 3 hospitals and 2 transportation hubs.

[0125] The specific implementation steps are as follows:

[0126] 2.1 Step 1: Multi-source data acquisition and assessment of vulnerable areas

[0127] The meteorological department issued a typhoon warning: maximum wind speed v = 12 m / s, expected landfall time 48 hours later;

[0128] Collect power grid data: 33-node topology, line parameters (such as resistance r in line 1-2) 12 =0.1Ω, reactance x 12 =0.3Ω), important load data (total power P of 3 hospitals) load,imp =2MW);

[0129] Calculate the probability of failure: according to the formula P line (v), P tow (v), P l,ijThe results show that the failure probability of lines in coastal areas (such as lines 1-2 and 6-7) is P = 0.8 (high risk), and the failure probability of lines in inland areas is P = 0.2 (low risk); the failure probability of substations 1 and 3 (coastal) is P = 0.75 (high risk).

[0130] Vulnerable areas are delineated: the high-risk areas are the three coastal power lines and two substations, and the hospital's power supply lines (such as lines 3-4 and 7-8) are marked.

[0131] 2.2 Step 2: Emergency Repair Resource Pre-positioning Planning and Contingency Plan Formulation

[0132] Computational resource requirements: Line length L in high-risk areas high =15km, number of substations N sub,high =2, number of emergency repair teams required Support; Important load P load,imp =2MW, rated power P of emergency generator vehicle gen,rate =500kW, required emergency power generation vehicle Taiwan; conductor demand Q cable =15×(1+0.8)=27km;

[0133] Selection of Pre-set Points: The candidate pre-set points are three power supply stations along the coast. Using the improved particle swarm optimization algorithm, two pre-set points (power supply stations A and B) are selected with an average distance d = 3 km.

[0134] Contingency plan: Prioritize emergency repairs of the hospital's power supply lines, and pre-set the operation schemes for remote control switches RCS1 and RCS2 (disconnect lines 1-2 and close RCS1 to achieve topology reconfiguration in case of a fault).

[0135] 2.3 Step 3: Post-disaster dynamic response and priority repair

[0136] Real-time data collection of damage: After the typhoon made landfall, drones were used to discover that line 1-2 was broken and substation 1 was flooded. The fault location was located at (x=118.5°, y=25.3°).

[0137] Activate resources and contingency plans: Activate 4 emergency repair teams and 5 emergency power generation vehicles at pre-positioned points A and B, and match the line break repair contingency plan and the substation drainage contingency plan;

[0138] Dynamically adjust emergency repair forces: 2 teams to repair lines 1-2, 1 team to drain water and repair substation 1, and 1 team on standby; 2 emergency generator vehicles to hospitals 1 and 2, and 3 on standby.

[0139] Priority repairs: Emergency power generators will arrive at the hospital within 30 minutes to ensure temporary power supply; repair time for lines 1-2 is 4 hours, restoration time for substation 1 is 3 hours, and restoration time for permanent power supply to the hospital is 5 hours, which is significantly shorter than the existing plan.

[0140] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A rapid response method for power emergency repair based on disaster early warning, characterized in that, Includes the following steps: S1. Multi-source disaster early warning data collection and power grid vulnerable area assessment: Collect multi-source disaster early warning data, power grid basic data and historical data, and classify vulnerable areas based on line and substation fault probability models; S2. Emergency Repair Resource Pre-positioning Planning and Contingency Plan Formulation: Based on the assessment results of vulnerable areas, calculate the emergency repair resource requirements, select key pre-positioning points, and formulate targeted emergency repair contingency plans; S3. Post-disaster dynamic response and priority repair: Real-time collection of power grid damage data, activation of pre-set resources and plans, dynamic adjustment of repair forces, and priority repair of power supply lines for important loads.

2. The rapid response method for power emergency repair based on disaster early warning as described in claim 1, characterized in that, The line fault probability model in step 1 includes the conductor fault probability model, the tower fault probability model, and the overall line fault probability model. The conductor fault probability model P line (v) is: Tower failure probability model P tow (v) is: Line overall fault probability model P l,ij for: Where v is the typhoon wind speed, v line,1 The wind speed threshold at which the probability of a sudden increase in conductor failure, v line,2 To determine the fault wind speed threshold for the conductor, β1, P is the fitting parameter. line,min Let Φ(·) be the baseline fault probability of the conductor, and v be the standard normal distribution function. tow,1 The wind speed threshold at which the probability of tower failure increases sharply, v tow,2 To determine the fault wind speed threshold for the tower, β2, For the fitting parameters, m i Let P be the number of towers for line ij. line,ij Let P be the conductor fault probability of line ij. tow,ij,k Let be the failure probability of the k-th tower of line ij.

3. The rapid response method for power emergency repair based on disaster early warning as described in claim 1, characterized in that, The substation fault probability model in step 1 is as follows: Where D is the water depth of the substation, D bus,1 D is the water depth threshold that causes a sharp increase in the probability of substation failure. bus,2 To determine the water depth threshold for faults in substations, β3 and D are fitting parameters.

4. The rapid response method for power emergency repair based on disaster early warning as described in claim 1, characterized in that, The formula for calculating the demand for the emergency repair team in step 2 is as follows: The formula for calculating the demand for emergency power generation vehicles is: Among them, L high For the length of the line in the high-risk area, N sub,high The number of substations in high-risk areas is represented by L0 and N0, which are historical emergency repair efficiency parameters. P is the floor function. load,imp P represents the total power of critical loads in high-risk areas. gen,rate This refers to the rated power of the emergency power generator vehicle.

5. The rapid response method for power emergency repair based on disaster early warning as described in claim 1, characterized in that, In step 2, the selection of preset points adopts an improved particle swarm optimization algorithm, and the objective function is: In the formula: x ki ∈{0，1} Where M is the number of fault points in the high-risk area, and N... pre X represents the number of candidate preset points. ki =1 indicates that the k-th fault point is covered by the i-th preset point, d ki This is the distance from the k-th fault point to the i-th preset point.

6. The rapid response method for power emergency repair based on disaster early warning as described in claim 1, characterized in that, The topology reconfiguration plan in step 2 adopts the following constraints: Where, ε ij,t γ ij,t ρ is the state variable of the line power flow. lij , t L is the variable representing the normal state of the line. nor Let y1 and y2 be the branch set of unbalanced nodes, t be the time interval, and T be the time set.

7. The rapid response method for power emergency repair based on disaster early warning as described in claim 1, characterized in that, Step 3, which dynamically adjusts the emergency repair force, employs a multi-objective optimization model. The objective function is: In the formula: Among them, Ω imp For the set of important loads, γ i,t T is the state variable for critical load recovery. repair This refers to the actual repair time. For the expected repair time, C res For actual resource consumption, For budgeted resource consumption, ω1, ω2, and ω3 are weighting coefficients, and x ij,t Assign variables to the repair team, N crew,j To maximize the workload of the repair team, Ω fault Ω represents the set of fault points. crew The repair team is assembled.

8. A rapid response method for power emergency repair based on disaster early warning, as described in claim 1, is characterized in that, Step 3 of the emergency repair process uses the DistFlow model and voltage constraints. The DistFlow model is as follows: in, For the active and reactive power of distributed power sources, For the charging and discharging power of energy storage, For node load, To cut off the load, p lt q lt For line power, v jt ε represents the node voltage, and ε is the allowable voltage deviation.

9. A rapid response system for power emergency repair based on disaster early warning, characterized in that, It includes a multi-source data acquisition module, a disaster early warning and risk assessment module, a pre-planning module for emergency repair resources, a post-disaster dynamic response and emergency repair module, and a data storage and interaction module. The modules work together to achieve the function of the method described in any one of claims 1-8.

10. A rapid response system for power emergency repair based on disaster early warning as described in claim 9, characterized in that, An edge-cloud collaborative architecture is adopted, with the edge layer deployed at pre-positioned points and substations, responsible for real-time data acquisition and local resource activation; the cloud layer deployed at the dispatch center, responsible for model calculation and data storage; and the terminal layer including handheld terminals and monitoring terminals, used for command interaction and progress display.

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