Cellular automaton-based urban power distribution network fault defense method under flood disaster

Abstract

The invention discloses an urban power distribution network fault defense method under flood disasters based on a cellular automaton, relates to the field of power systems, and aims to accurately predict the failure probability of power distribution network equipment by simulating the dynamic evolution process of accumulated water under the flood disasters and combine emergency resource configuration and deployment of pre-disaster defense. Accurate defense of power distribution network fault conditions under flood disasters is realized. According to the method, complex physical calculation and high-precision data requirements are avoided by utilizing the spatial evolution characteristics of CA, so that the calculation efficiency is improved, the adaptability to large-scale urban areas is enhanced, the toughness of the power distribution network under extreme disasters can be remarkably improved, resource allocation can be optimized, economic losses caused by the disasters can be reduced, and the economic benefits of the power distribution network are improved. And a powerful guarantee is provided for safe and reliable operation of an urban power system.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and particularly to a method for fault defense of urban distribution networks under flood disasters based on cellular automata. Background Art

[0002] As the end link of the power system, the urban distribution network is directly related to the power supply reliability of users. However, with the intensification of global climate change, the frequency of extreme weather events has increased significantly, seriously threatening the safe and stable operation of urban distribution networks. For example, flood disasters are one of the common extreme disasters, with characteristics such as strong suddenness, great destructiveness, and wide influence range, seriously affecting the power restoration resource scheduling. The pre-disaster deployment of emergency resources can improve the system resource layout in advance, but this process needs to fully consider the impact of flood disasters on urban distribution networks. Therefore, it is crucial to fully consider the configuration and deployment of pre-disaster emergency resources and study the method to minimize the load loss of the distribution network under flood disasters.

[0003] Traditional fault defense methods often rely on historical data and empirical judgment, and are difficult to cope with the complexity and uncertainty under extreme disasters. For example, existing technologies usually adopt static resource configuration schemes and cannot adjust defense measures in real time according to the dynamic evolution of disasters, resulting in low resource utilization and poor defense effects. In addition, traditional methods also have limitations in terms of fault prediction accuracy and calculation efficiency, and are difficult to meet the real-time defense needs of large-scale urban distribution networks. The existing technologies have the following deficiencies in the fault defense of urban distribution networks:

[0004] 1. Insufficient prediction accuracy: Existing methods mainly based on hydrodynamic models fail to fully consider the actual impact of flood disasters on the operation state of the distribution network, resulting in low fault prediction accuracy.

[0005] 2. Inflexible resource configuration: Traditional methods mostly adopt static resource configuration schemes and cannot adjust defense measures in real time according to the dynamic evolution of disasters, resulting in low resource utilization.

[0006] 3. Low calculation efficiency: Existing methods have high computational complexity when dealing with large-scale urban distribution networks and are difficult to meet the needs of real-time defense.

[0007] 4. Lack of systematicness: Existing research mainly focuses on post-disaster recovery and lacks systematic research on pre-disaster defense measures, especially in terms of obvious deficiencies in fault defense strategies and resource configuration optimization.

[0008] Therefore, it is necessary to provide a method for fault defense of urban distribution networks under flood disasters based on cellular automata to solve the above problems. Summary of the Invention

[0009] To solve the above problems, the present invention provides a method for defending against faults in urban distribution networks under flood disasters based on cellular automata. By simulating the dynamic evolution process of water accumulation under flood disasters, the failure probability of distribution network equipment is accurately predicted, and combined with the emergency resource allocation and deployment for pre-disaster defense, accurate defense against faults in the distribution network under flood disasters is achieved. This can not only significantly improve the resilience of the distribution network under extreme disasters, but also optimize resource allocation, reduce economic losses caused by disasters, and provide a strong guarantee for the safe and reliable operation of the urban power system.

[0010] To achieve the above object, the present invention provides a method for defending against faults in urban distribution networks under flood disasters based on cellular automata, which specifically includes the following steps:

[0011] Step S1: Use the cellular automata CA method to grid the urban area, construct a square cellular space using elevation data and land use data. Each cell includes a static state and a dynamic state, and the state evolution of the cell is affected by itself and neighboring cells. The neighborhood adopts the Moore type structure, which consists of the target cell and its surrounding 8 cells;

[0012] Step S2: Use the Monte Carlo method to generate n groups of rainfall scenarios with different probabilities. Each group of rainfall scenarios includes rainfall intensity, duration, and spatial distribution data. Based on cellular automata, simulate the evolution process of urban water accumulation depth under different rainfall scenarios, establish an urban waterlogging model under flood scenarios, and analyze the water accumulation depth.

[0013] Step S3: According to the dynamic change of the water accumulation depth, use the exponential function fitting method to establish the relationship between the failure rate of distribution equipment and the water accumulation height, and construct a failure probability model;

[0014] Step S4: Construct a total objective function, consider the occurrence probabilities of different rainfall scenarios, and according to the uncertainty of the disaster scenario, pre-position the deployment locations of inflatable dams and energy storage to minimize the investment cost and load loss cost;

[0015] Step S5: Establish an operation model of the distribution network, dynamically evaluate the load loss amount, and ensure the stable operation of the distribution network under disasters.

[0016] Preferably, in step S2, based on step S1, define precipitation rules and confluence rules to realize the simulation of the evolution of urban water accumulation depth;

[0017] The precipitation rule is used to simulate the rain interception and runoff formation during the rainfall process, and the calculation is as follows:

[0018]

[0019] V i,drain = qΨF i

[0020] In the formula, P i,t is the rainfall of different cells at different times, S is the potential maximum retention, Q i,t is the rainfall volume of different cells at different times, CN is different road utilization types, and urban residential areas, commercial areas and industrial areas are set to 70 - 90, V i,drain is the drainage volume of different cells, q is the rainstorm intensity, Ψ is the comprehensive runoff coefficient, F i is the catchment area;

[0021] The confluence rule is used to calculate the water flow distribution between cells, including water flow determination, water flow direction and water volume distribution;

[0022] Water flow determination: If the water depth h of a cell is greater than 0 and there are cells in its neighborhood with a water level lower than that of this cell, then water flow exchange occurs;

[0023] Water flow direction: The water flow preferentially flows to the neighboring cell with the lowest water level. If there are multiple lowest cells, the water volume is distributed proportionally;

[0024] Water volume distribution: The water volume distribution is restricted by the water level elevation difference and flow velocity between cells. The calculation formula is as follows:

[0025]

[0026] In the formula, q i,t is the confluence flow, l i,t is the flow distance, R is the distance between neighboring cells;

[0027] Based on the Manning formula, the water flow velocity is calculated and the urban environment is simplified:

[0028]

[0029] In the formula, v i,t is the water flow velocity, i i,t is the average rainfall intensity, j i is the slope, and n is the Manning roughness coefficient;

[0030] q, as a dynamic attribute of the cell, is calculated for the inflow or outflow of the cell in the current time interval according to the previous time interval, V i,t and rainfall The self - storage flow of the cell is calculated, and the accumulated water depth h in the current time interval is calculated through the catchment area F i , and the calculation formula is as follows: i,t

[0031]

[0032] ​Preferably, in step S3, when a disaster occurs, the failure rate of the power distribution equipment is equivalent to that under normal weather conditions. As the accumulated water continues to increase, when the water level in the area where the power distribution equipment is located exceeds the flood prevention height, the failure rate of the power distribution equipment rises rapidly. According to the grid division, an exponential function fitting method is used to establish the relationship between the failure rate of the power distribution equipment and the dynamically changing water level. The failure rate of the power distribution equipment at time t is as follows:

[0033]

[0034] In the formula, h i,t is the water accumulation degree of the grid where equipment w is located at time t; D i is the designed flood prevention height of the substation or box-type substation; D Bw is the ground elevation; ζ is the attenuation coefficient; γ is the damping coefficient;

[0035] The [0, T] time period is composed of several time periods with a time length of Δt, and the failure probability within [0, T] is calculated using the series model:

[0036]

[0037] The state is obtained through the failure probability and the threshold of the power distribution equipment, which is expressed as:

[0038]

[0039] In the formula, η is the threshold of the power grid node, K i is the state of the power grid node, 1 represents the normal state, and 0 represents the fault state.

[0040] Preferably, in step S4, during the planning and configuration stage, the specifications and quantity constraints for installing the inflatable dam are as follows:

[0041]

[0042]

[0043] In the formula, indicates whether the inflatable dam l is configured, is the length of the inflatable dam l, and are the upper and lower limits of the length of the inflatable dam l, L is the total number of mobile power sources, and L max is the total length of the inflatable dam;

[0044] During the pre-disaster defense stage, according to the uncertainty of the disaster scenario, the inflatable dam for the pre-positioning planning stage is determined. One node can only be intelligently connected to one inflatable dam, which is expressed as:

[0045]

[0046] An inflatable dam intelligence is represented at a node as:

[0047]

[0048] The pre-disaster layout of the inflatable dam is affected by the configuration stage and is represented as:

[0049]

[0050] Where x l,i,s is whether the inflatable dam is arranged in the pre-disaster stage scenario s;

[0051] The fault situation after defense:

[0052] Analysis of the fault situation of power distribution equipment after taking defense measures. The configured inflatable dam will affect the subsequent power outage fault situation:

[0053]

[0054] Where is the water accumulation depth at node i in scenario s, is the fault state at node i in scenario s, 0 indicates a fault, and 1 indicates no fault.

[0055] Preferably, in step S4, the total objective function is to minimize the investment cost and load loss:

[0056]

[0057] Where C f is the cost required per unit length of configuring the inflatable dam, L i is the length of the configured inflatable dam, C P is the load loss cost, p s is the probability of scenario s occurring, is the weight of the load at node i, is the load loss amount at node i in scenario s.

[0058] Preferably, in step S5, the distribution network operation model needs to model the active power output, line power flow, and node voltage constraints in the distribution network;

[0059] During the operation of the distribution network, distributed power sources are used to provide power support for important loads:

[0060]

[0061] Where and are the active and reactive power dispatch outputs of the distributed power source in scenario s, and are the maximum output ranges of the units;

[0062] Among them, a power supply path starting from a substation or a distributed power source is established within the power supply area to provide power support for important loads and reduce power outage losses;

[0063]

[0064] In the formula, f i,i,t,s is the starting point of the power supply path in scenario s, Ω S is the set of substation nodes, Ω DG is the set of distributed power source nodes;

[0065] There can be at most one power supply path on a closed line:

[0066]

[0067] In the formula, f i,j,t,s is the power supply path from node i to node j in scenario s, and N is the total number of nodes in the distribution network;

[0068] When the distribution network supplies power, it is necessary to maintain a tree structure, and through radial constraints, it is ensured that each node can have at most one upstream node of the power supply path:

[0069]

[0070] For the line power flow, the power cannot exceed the line power flow capacity:

[0071]

[0072] In the formula, P i,j,t,s and Q i,j,t,s are the active and reactive power flows on line i-j in scenario s, respectively, and are the maximum line capacities;

[0073] During the fault process of the distribution network, for line nodes, it is necessary to ensure active and reactive power balance:

[0074]

[0075] In the formula, k i,t is the node load power factor, is the node load demand;

[0076] During the fault process of the distribution network, for line nodes without faults, it is necessary to ensure voltage balance and the voltage must be within the deviation range:

[0077]

[0078] In the formula, U i,t,sis the node voltage in scenario s, R i,j and X i,j respectively represent the line resistance reactance coefficients, U i and respectively represent the minimum and maximum ranges of the voltage.

[0079] Therefore, the present invention adopts the above-mentioned method for defending against faults in urban distribution networks under flood disasters based on cellular automata, and has the following beneficial effects:

[0080] (1) By integrating technical means such as cellular automata simulation, fault probability prediction, and resource optimization allocation, the present invention realizes the rapid location and defense of distribution network faults.

[0081] (2) The present invention can not only significantly improve the resilience of the distribution network under extreme disasters, but also optimize resource allocation, reduce economic losses caused by disasters, and provide a strong guarantee for the safe and reliable operation of the urban power system.

[0082] (3) By combining urban waterlogging simulation, the present invention more accurately evaluates the risk of equipment damage; by combining pre-disaster defense measures, it reduces the fault risk and optimizes the allocation strategy; by adopting the MILP optimization model, it ensures the optimality and computational efficiency of the solution; it is applicable to different flood disaster scenarios and has good adaptability and popularization.

[0083] The technical solution of the present invention will be further described in detail below through the accompanying drawings and embodiments. Description of the Drawings

[0084] Figure 1 is the algorithm flowchart of a method for defending against faults in urban distribution networks under flood disasters based on cellular automata according to the present invention;

[0085] Figure 2 is the topology diagram of the test system in the embodiment of the present invention;

[0086] Figure 3 is the water depth map of different nodes in the embodiment of the present invention;

[0087] Figure 4 is the comparison diagram of the disaster situation of the inflatable dam urban distribution network in the embodiment of the present invention. Detailed Embodiments

[0088] The technical solution of the present invention will be further described below through the accompanying drawings and embodiments.

[0089] Unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the ordinary meaning understood by those of ordinary skill in the art to which the present invention belongs.

[0090] In the present invention, words such as "including" or "comprising" mean that the elements before this word cover the elements listed after this word, and do not exclude the possibility of also covering other elements. The orientation or positional relationship indicated by terms such as "inside", "outside", "above", "below", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation of the present invention. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly. In the present invention, unless otherwise clearly specified and limited, terms such as "attachment" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be directly connected, or indirectly connected through an intermediate medium, and can be the communication inside two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0091] A method for fault defense of urban distribution network under flood disasters based on cellular automata specifically includes the following steps, as Figure 1 shown:

[0092] Step S1: Use the cellular automata CA method to perform grid processing on the urban area, construct a square cellular space using elevation data and land use data. Each cell includes a static state and a dynamic state, and the state evolution of the cell is affected by itself and neighboring cells. The neighborhood adopts the Moore type structure, which consists of the target cell and its surrounding 8 cells;

[0093] Step S2: Use the Monte Carlo method to generate n groups of rainfall scenarios with different probabilities. Each group of rainfall scenarios includes rainfall intensity, duration, and spatial distribution data. Based on cellular automata, simulate the evolution process of urban waterlogging depth under different rainfall scenarios, establish an urban waterlogging model under flood scenarios, and analyze the waterlogging depth.

[0094] In step S2, define precipitation rules and confluence rules based on step S1 to achieve the simulation of the evolution of urban waterlogging depth;

[0095] The precipitation rule is used to simulate the rain interception and runoff formation during the rainfall process, and the calculation is as follows:

[0096]

[0097] V i,drain = qΨF i

[0098] In the formula, P i,t is the rainfall of different cells at different times, S is the potential maximum retention, Q i,tis the rainfall of different cells at different times, CN is different road utilization types, and urban residential areas, commercial areas, and industrial areas are set to 70-90, V i,drain is the drainage volume of different cells, q is the rainstorm intensity, Ψ is the comprehensive runoff coefficient, F i is the catchment area;

[0099] The confluence rule is used to calculate the water flow distribution between cells, including water flow determination, water flow direction, and water volume distribution;

[0100] Water flow determination: If the water depth h of a cell is greater than 0 and there are cells in its neighborhood with a water level lower than that of this cell, then water flow exchange occurs;

[0101] Water flow direction: The water flow preferentially flows to the neighboring cell with the lowest water level. If there are multiple lowest cells, the water volume is distributed proportionally;

[0102] Water volume distribution: The water volume distribution is restricted by the water level elevation difference and flow velocity between cells. The calculation formula is as follows:

[0103]

[0104] In the formula, q i,t is the confluence flow, l i,t is the flow distance, R is the distance to the neighboring cell;

[0105] Calculate the water flow velocity based on the Manning formula and simplify the urban environment:

[0106]

[0107] In the formula, v i,t is the water flow velocity, i i,t is the average rainfall intensity, j i is the slope, and n is the Manning roughness coefficient;

[0108] q, as a dynamic attribute of the cell, is calculated for the inflow or outflow of the cell in the current time interval based on the previous time interval V i,t and rainfall The self-stored flow of the cell is calculated, and the waterlogging depth h in the current time interval is calculated through the catchment area F i , and the calculation formula is as follows: i,t

[0109]

[0110] Step S3: According to the dynamic change of the waterlogging depth, adopt the exponential function fitting method to establish the relationship between the failure rate of power distribution equipment and the waterlogging height, and construct a failure probability model;

[0111] ​In step S3, during a disaster, the failure rate of the power distribution equipment is equivalent to that under normal weather conditions. As the accumulated water continues to rise, when the water level in the area where the power distribution equipment is located exceeds the flood prevention height, the failure rate of the power distribution equipment increases rapidly. According to the grid division, an exponential function fitting method is used to establish the relationship between the failure rate of the power distribution equipment and the dynamically changing water level. The failure rate of the power distribution equipment at time t is as follows:

[0112]

[0113] where h i,t is the water accumulation level in the grid where equipment w is located at time t; D i is the designed flood prevention height of the substation or box-type substation; D Bw is the ground elevation; ζ is the attenuation coefficient; γ is the damping coefficient;

[0114] The time period [0, T] is composed of several time periods with a length of Δt, and the series model is used to calculate the failure probability within [0, T]:

[0115]

[0116] The state is obtained through the failure probability and the threshold of the power distribution equipment, expressed as:

[0117]

[0118] where η is the threshold of the distribution network node, K i is the state of the distribution network node, 1 represents the normal state, and 0 represents the faulty state.

[0119] Step S4: Construct the total objective function. Considering the occurrence probabilities of different rainfall scenarios, and based on the uncertainty of the disaster scenarios, pre-locate the deployment positions of the inflatable dams and energy storage to minimize the investment cost and the load loss cost;

[0120] In step S4, during the planning and configuration stage, the specifications and quantity constraints for installing the inflatable dams are as follows:

[0121]

[0122] where indicates whether the inflatable dam l is configured, is the length of the inflatable dam l, and are the upper and lower limits of the length of the inflatable dam l. L is the total number of mobile power sources, and L max is the total length of the inflatable dams;

[0123] During the pre-disaster defense stage, according to the uncertainty of the disaster scenarios, pre-locate the inflatable dams in the pre-location planning stage. It is expressed that one node can only be intelligently connected to one inflatable dam as:

[0124]

[0125] An inflatable dam intelligence is represented at a node as:

[0126]

[0127] The pre-disaster layout of the inflatable dam is affected by the configuration stage and is represented as:

[0128]

[0129] where x l,i,s is whether the inflatable dam is arranged in the pre-disaster stage scenario s;

[0130] The fault situation after defense:

[0131] Analysis of the fault situation of the power distribution equipment after taking defense measures. The configured inflatable dam will affect the subsequent power outage fault situation:

[0132]

[0133] where is the water depth at node i in scenario s, is the fault state of node i in scenario s, 0 indicates a fault, and 1 indicates no fault.

[0134] In step S4, the total objective function is to minimize the investment cost and load loss:

[0135]

[0136] where C f is the cost required per unit length of configuring the inflatable dam, L i is the length of the configured inflatable dam, C P is the load loss cost, p s is the probability of the occurrence of scenario s, is the weight of the load at node i, is the load loss amount at node i in scenario s.

[0137] Step S5: Establish a power distribution network operation model to dynamically evaluate the load loss amount and ensure the stable operation of the power distribution network under disasters. In step S5, the power distribution network operation model needs to model the distributed power generation output, line power flow, and node voltage constraints in the power distribution network;

[0138] During the operation of the power distribution network, distributed power sources are used to provide power support for important loads:

[0139]

[0140] where and are the active and reactive power dispatch outputs of the distributed power sources in scenario s, and is the maximum output range of the unit;

[0141] Among them, a power supply path starting from a substation or a distributed power source is established within the power supply area to provide power support for important loads and reduce power outage losses;

[0142]

[0143] In the formula, f i,i,t,s is the starting point of the power supply path in scenario s, Ω S is the set of substation nodes, Ω DG is the set of distributed power source nodes;

[0144] There can be at most one power supply path on the closed line:

[0145]

[0146] In the formula, f i,j,t,s is the power supply path from node i to node j in scenario s, and N is the total number of nodes in the distribution network;

[0147] When the distribution network supplies power, it is necessary to maintain a tree structure, and each node can have at most one upstream node of the power supply path through radial constraints:

[0148]

[0149] For the line power flow, the power cannot exceed the line power flow capacity:

[0150]

[0151] In the formula, P i,j,t,s and Q i,j,t,s are the active and reactive power flows on line i-j in scenario s respectively, and is the maximum capacity of the line;

[0152] During the distribution network fault process, for the line nodes, it is necessary to ensure the balance of active and reactive power:

[0153]

[0154] In the formula, k i,t is the node load power factor, is the node load demand;

[0155] During the distribution network fault process, for the non-faulty line nodes, it is necessary to ensure voltage balance and the voltage must be within the deviation range:

[0156]

[0157] Wherein, U i,t,s is the node voltage in scenario s, and R i,j and X i,j respectively represent the line resistance reactance coefficients, U i and respectively represent the minimum and maximum ranges of the voltage.

[0158] Embodiment

[0159] An optimization method for fault defense of urban distribution networks under flood disasters is proposed to enhance the resistance of the distribution network. As Figure 2 shown, it is tested on the improved IEEE-33 system. The system topology schematic diagram is as Figure 1 shown, where 101-104 are substation buses, K1-K3 are line switches, and three distributed power sources are located at nodes 5, 22, and 31 respectively.

[0160] A large number of flood scenarios are generated by the Monte Carlo method, and the water accumulation depths of substations S3, S4 and nodes N7, N11 at different times under one flood scenario are selected as Figure 3 shown.

[0161] Through the simulation of a large number of water accumulation scenarios, the time-varying failure rates of each node device are calculated through the fault probability model as shown in Table 1, and the failure probabilities are shown in Table 2. Therefore, inflatable dams are respectively deployed at substations S3, S4 and node N7. The flooded conditions of the urban distribution network with and without the deployment of inflatable dams are Figure 4 shown.

[0162] Table 1

[0163]

[0164]

[0165] Table 2

[0166]

[0167] (1) Simulation environment and hardware configuration

[0168] The operating system of the simulation environment is Win10 64bit, the hardware environment is Intel i7-9700HQ CPU 3.00GHz, 16GB RAM, and the used version is MATLAB R2016a.

[0169] (2) Urban waterlogging modeling and water accumulation analysis

[0170] 1. Rainfall scenario generation:

[0171] Use the Monte Carlo method to generate n groups of rainfall scenarios with different probabilities. Each group of scenarios includes rainfall intensity, duration, and spatial distribution data.

[0172] Combine historical rainfall data with meteorological prediction models to ensure the diversity and representativeness of rainfall scenarios.

[0173] 2. Construction of cellular automata model:

[0174] Based on the cellular automata method, divide the urban surface into several cells. Each cell contains attributes such as terrain elevation, surface type, and drainage capacity.

[0175] Simulate the process of rainfall reaching the ground, calculate the average rainfall intensity, runoff, and confluence flow, and dynamically update the ponding depth of each cell.

[0176] 3. Calculation of ponding depth:

[0177] Consider the drainage capacity of urban rainwater wells, and combine the number of drainage wells, drainage coefficient, and cross-sectional area to calculate the ponding depth of each cell.

[0178] Output the spatio-temporal distribution map of ponding depth under different rainfall scenarios to provide data support for subsequent fault prediction.

[0179] (3) Fault probability model

[0180] Calculation of equipment failure probability:

[0181] According to the dynamic change of ponding depth, adopt the exponential function fitting method to establish the relationship between the failure rate of power distribution equipment and the ponding height.

[0182] Calculate the failure probability of power distribution equipment in the time period [0, T], and evaluate the fault status of power distribution network nodes by combining the series model.

[0183] (4) Pre-disaster defense and optimization of resource allocation

[0184] 1. Construction of the total objective function:

[0185] Aim at minimizing the investment cost and load loss cost, and optimize the configuration of inflatable dams.

[0186] Consider the occurrence probabilities of different rainfall scenarios to ensure the economy and efficiency of resource allocation.

[0187] 2. Constraints on resource allocation:

[0188] Set constraints on the specifications and quantities of inflatable dams.

[0189] According to the uncertainty of disaster scenarios, pre-position the deployment locations of inflatable dams.

[0190] 3. Pre-disaster Defense Plan:

[0191] Combine the distribution network topology with the disaster risk distribution to formulate a pre-disaster resource defense plan.

[0192] Ensure the efficient utilization of emergency resources during disasters.

[0193] (5) Evaluation of Distribution Network Operating Status

[0194] 1. Construction of Distribution Network Operating Model:

[0195] Establish a distribution network operating model, considering the output of distributed power sources, line power flow constraints, and node voltage balance.

[0196] Dynamically evaluate the load loss to ensure the minimum loss of the distribution network under disasters.

[0197] 2. Optimization of Load Loss:

[0198] With the goal of minimizing the load loss, construct a pre-disaster defense model.

[0199] Convert the model into a mixed-integer linear programming (MILP) form and solve the optimal solution through a CPLEX solver.

[0200] (7) Simulation Experiments and Result Analysis

[0201] 1. Experimental Setup:

[0202] Conduct simulation experiments based on the improved IEEE-33 node distribution network, setting different rainfall scenarios and disaster intensities.

[0203] Compare the performance of the traditional method and the method proposed in the present invention in terms of waterlogging simulation, pre-disaster defense, and resource utilization rate.

[0204] 2. Result Analysis:

[0205] The experimental results show that the method proposed in the present invention can effectively reduce the load loss and achieve effective defense of the distribution network under flood disasters.

[0206] Compared with the traditional method, the present invention has significant advantages in disaster defense, resource allocation optimization, and reducing the load loss.

[0207] Therefore, the present invention adopts the above-mentioned method for fault defense of urban distribution networks under flood disasters based on cellular automata, using a negative dispersion ytterbium-doped fiber as the gain medium of a high-repetition-rate fiber laser to simplify dispersion management, reduce the threshold, optimize nonlinear effect control, and improve the repetition rate performance, and finally obtain a low-threshold mode-locked pulse output with a repetition rate of hundreds of GHz.

[0208] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and such modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for fault prevention of urban distribution network under flood disasters based on cellular automata, characterized in that: Specifically, it includes the following steps: Step S1: Use the cellular automaton CA method to grid the urban area, construct a square cellular space using elevation data and land use data. A single cell contains static and dynamic states, and the state evolution of the cell is affected by itself and neighboring cells. The neighborhood adopts the Moore type structure, which consists of the target cell and its surrounding 8 cells; Step S2: Use the Monte Carlo method to generate n groups of rainfall scenarios with different probabilities. Each group of rainfall scenarios includes rainfall intensity, duration, and spatial distribution data. Based on the cellular automaton, simulate the evolution process of urban waterlogging depth under different rainfall scenarios, establish an urban waterlogging model under flood scenarios, and analyze the waterlogging depth; Step S3: According to the dynamic change of the waterlogging depth, use the exponential function fitting method to establish the relationship between the failure rate of power distribution equipment and the waterlogging height, and construct a failure probability model; Step S4: Construct the total objective function, consider the occurrence probabilities of different rainfall scenarios, and according to the uncertainty of the disaster scenario, pre-locate the deployment positions of the inflatable dam and energy storage to minimize the investment cost and load loss cost; Step S5: Establish a power distribution network operation model to dynamically evaluate the load loss amount and ensure the stable operation of the power distribution network under disasters.

2. The method for fault prevention of urban distribution network under flood disaster based on cellular automata according to claim 1, wherein: In step S2, based on step S1, define the precipitation rule and the confluence rule to realize the simulation of the evolution of urban waterlogging depth; The precipitation rule is used to simulate the rainwater interception and runoff formation during the rainfall process, and the calculation is as follows: V i,drain = qΨF i Wherein, P i,t is the rainfall of different cells at different times, S is the potential maximum retention, Q i,t is the rainfall of different cells at different times, CN is different road utilization types, and urban residential areas, commercial areas and industrial areas are set to 70-90, V i,drain is the drainage volume of different cells, q is the rainstorm intensity, Ψ is the comprehensive runoff coefficient, F i is the catchment area; The confluence rule is used to calculate the water flow distribution between cells, including water flow determination, water flow direction, and water volume distribution; Water flow determination: If the water depth h of the cell is greater than 0 and there are cells in its neighborhood with a water level lower than that of this cell, then water flow exchange occurs; Water flow direction: The water flow preferentially flows to the neighboring cell with the lowest water level. If there are multiple lowest cells, the water volume is distributed proportionally; Water volume distribution: The water volume distribution is restricted by the water level elevation difference and flow velocity between cells, and the calculation formula is as follows: where q i,t is the runoff, l i,t is the flow distance, and R is the distance to the neighboring cell; Calculate the water flow velocity based on the Manning formula and simplify the urban environment: where, v i,t is the water flow velocity, i i,t is the average rainfall intensity, j i is the slope, and n is the Manning roughness coefficient; q, as a dynamic property of the cell, is calculated based on the previous time interval to calculate the inflow or outflow of the cell V in the current time interval i,t , rainfall the flow stored in the cell itself Calculated through the catchment area F i to calculate the water depth h in the current time interval i,t , and the calculation formula is as follows:

3. A method for fault defense of urban distribution network under flood disasters based on cellular automata according to claim 2, characterized in that: In step S3, during the occurrence of a disaster, the failure rate of the power distribution equipment is equivalent to that under normal weather. As the waterlogging continuously accumulates, when the waterlogging height in the area where the power distribution equipment is located exceeds the flood prevention height, the failure rate of the power distribution equipment rises rapidly. According to the grid division, use the exponential function fitting method to establish the relationship between the failure rate of the power distribution equipment and the dynamically changing waterlogging height. The failure rate of the power distribution equipment at time t is: where h i,t is the water accumulation degree of the grid where the device w is located at time t; D i is the designed flood prevention height of the substation or box-type substation; D Bw is the elevation above the ground; ζ is the attenuation coefficient; γ is the damping coefficient; The time period [0, T] is composed of several time periods with a length of Δt, and the series model is used to calculate the failure probability within [0, T]; The state is obtained through the failure probability and the threshold of the power distribution equipment, which is expressed as: where η is the threshold of the distribution network node, and K i is the state of the distribution network node, 1 represents the normal state, and 0 represents the fault state.

4. A method for fault defense of urban distribution network under flood disaster based on cellular automata according to claim 3, characterized in that: In step S4, during the planning and configuration stage, the specifications and quantity constraints for installing the inflatable dam: In the formula, indicates whether the inflatable dam l is configured, is the length of the inflatable dam l, and are the upper and lower limits of the length of the inflatable dam l, L is the total number of mobile power supplies, and L max is the total length of the inflatable dam; During the pre-disaster defense stage, pre-locate the inflatable dam in the pre-location planning stage according to the uncertainty of the disaster scenario. One node can only be intelligently connected to one inflatable dam, which is expressed as: One inflatable dam can only be located at one node, which is expressed as: The pre-disaster layout of the inflatable dam is affected by the configuration stage, which is expressed as: where x l,i,s is whether the inflatable dam is arranged in the scenario s during the pre-disaster stage; The fault situation after defense: Analysis of the fault situation of the power distribution equipment after taking defense measures. The configured inflatable dam will affect the subsequent power outage fault situation: Wherein, is the accumulated water depth of node i in scenario s, is the fault state of node i in scenario s, where 0 indicates a fault and 1 indicates no fault.

5. A method for fault defense of urban distribution network under flood disaster based on cellular automata according to claim 4, characterized in that: In step S4, the total objective function is to minimize the investment cost and load loss: where C f is the cost required for configuring the inflatable dam per unit length, L i is the length of the configured inflatable dam, C P is the load loss cost, p s is the probability of the occurrence of scenario s, is the weight of the load at node i, is the load loss amount at node i in scenario s.

6. A method for fault prevention of urban distribution network under flood disaster based on cellular automata according to claim 5, characterized in that: In step S5, the distribution network operation model needs to model the output of distributed power sources, line power flow, and node voltage constraints in the distribution network; During the operation of the distribution network, distributed power sources are used to provide power support for important loads: In the formula, and are the active and reactive power dispatch outputs of the distributed power source in scenario s, and are the maximum output ranges of the units; Among them, a power supply path starting from a substation or a distributed power source is established within the power supply area to provide power support for important loads and reduce power outage losses; where f i,i,t,s is the starting point of the power supply path in scenario s, Ω S is the set of substation nodes, Ω DG is the set of distributed power source nodes; There can be at most one power supply path on a closed line: where f i,j,t,s is the power supply path from node i to node j in scenario s, and N is the total number of nodes in the distribution network; When the distribution network is powered, a tree structure needs to be maintained, and radial constraints are used to ensure that each node can have at most one upstream node of the power supply path: For the line power flow, the power cannot exceed the line power flow capacity: Where P i,j,t,s and Q i,j,t,s are the active and reactive power flows on line i-j in scenario s respectively, and are the maximum capacities of the line; During the fault process of the distribution network, for line nodes, it is necessary to ensure the balance of active and reactive power: where k i,t is the node load power factor, and is the node load demand; During the fault process of the distribution network, for line nodes without faults, it is necessary to ensure voltage balance and the voltage must be within the deviation range: Where, U i,t,s is the node voltage in scenario s, R i,j and X i,j respectively represent the line resistance and reactance coefficients, U i and respectively represent the minimum and maximum ranges of the voltage.

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