Power distribution network fault evolution path prediction method and device, electronic equipment and medium

Abstract

The application discloses a power distribution network fault evolution path prediction method and device, electronic equipment and medium, which are used to solve the technical problem that the research object is single in the prior art, the influence of multi-energy coupling on the operation of the active power distribution network is not considered, and the fault evolution mechanism is affected. The method comprises the following steps: acquiring meteorological information of extreme weather and energy network structure information of the multi-energy coupling active power distribution network; performing fault calculation according to the meteorological information and the energy network structure information to obtain an initial fault of the multi-energy coupling active power distribution network; based on optimal power flow operation constraints, performing multi-round fault evolution according to the initial fault, the meteorological information and the energy network structure information to obtain a fault evolution path result; and selecting a fault evolution path with the maximum fault probability from the fault evolution path result as a target fault evolution path of the multi-energy coupling active power distribution network under extreme weather.

Description

Technical Field

[0001] This invention relates to the field of power systems and their automation technology, and in particular to a method, device, electronic equipment and medium for predicting the evolution path of faults in a distribution network. Background Technology

[0002] Active distribution networks are often located in low-lying areas and have a high proportion of overhead lines, making them more susceptible to the impact of typhoons and heavy rains. Areas along typhoon paths frequently experience pole collapses, line breaks, and other accidents, which can lead to widespread power outages and severely disrupt people's lives.

[0003] In multi-energy coupled active distribution networks, the occurrence and development of major blackouts is essentially a process in which an initial grid fault triggers a chain reaction of new faults, and this process is repeated until catastrophic consequences are ultimately caused. Building upon the initial fault caused by extreme weather, the coupling of multiple energy sources makes the operation of the active distribution network more complex, resulting in subsequent faults being influenced by both the operational status of the multi-energy coupled active distribution network and extreme weather conditions.

[0004] However, existing studies often treat distribution networks as a single research object, rarely considering the impact of multi-energy coupling on the operation of active distribution networks, and thus affecting the fault evolution mechanism. Summary of the Invention

[0005] This invention provides a method, device, electronic device, and storage medium for predicting the fault evolution path of a distribution network. It is used to solve or partially solve the technical problem that existing related technologies focus on a single research object and do not consider the impact of multi-energy coupling on the operation of active distribution networks, thereby affecting the fault evolution mechanism.

[0006] This invention provides a method for predicting fault evolution paths in distribution networks, applicable to multi-energy coupled active distribution networks. The method includes:

[0007] Acquire meteorological information on extreme weather events, as well as energy network structure information of the multi-energy coupled active distribution network;

[0008] Based on the meteorological information and the energy network structure information, fault calculation is performed to obtain the initial fault of the multi-energy coupled active distribution network;

[0009] Based on the optimal power flow operation constraints, multiple rounds of fault evolution are performed according to the initial fault, the meteorological information, and the energy network structure information to obtain the fault evolution path results;

[0010] The fault evolution path with the highest fault probability is selected from the fault evolution path results and used as the target fault evolution path for the multi-energy coupled active distribution network under the extreme weather conditions.

[0011] Optionally, the step of calculating faults based on the meteorological information and the energy network structure information to obtain the initial faults of the multi-energy coupled active distribution network includes:

[0012] Based on the meteorological information and the energy network structure information, calculate the component stress of overhead lines and towers in the multi-energy coupled active distribution network under the extreme weather conditions;

[0013] Calculate the strength of overhead line components and tower components based on the stress of the components;

[0014] Based on the stress of the components, the strength of the overhead line components, and the strength of the tower components, the initial component failure rate of each component in the multi-energy coupled active distribution network is calculated.

[0015] Components whose initial failure rate meets the preset failure conditions are regarded as initial failure components, thereby determining the initial failure of the multi-energy coupled active distribution network under the extreme weather.

[0016] Optionally, the initial fault includes the initial component failure rate and component operating status, and the energy network structure information includes the operating parameters of the multi-energy coupled active distribution network. Based on optimal power flow constraints, the steps of fault evolution according to the initial fault, the meteorological information, and the energy network structure information include:

[0017] Step S301: Based on Monte Carlo simulation, determine the operating state of the components to obtain the next fault scenario of the multi-energy coupled active distribution network;

[0018] Step S302: Based on the initial component failure rate and the meteorological information, calculate the extreme weather line failure rate of each transmission line in the multi-energy coupled active distribution network;

[0019] Step S303: Based on the operating parameters and the fault scenario at the next moment, perform power flow optimization calculations in conjunction with the optimal power flow operation constraints to obtain the line load rate of each of the transmission lines;

[0020] Step S304: For each of the transmission lines, determine whether the line overload protection has been activated, and calculate the line overload fault rate of the transmission line based on the determination result and the line load rate.

[0021] Step S305: Calculate the overall line failure rate at the next moment based on the line overload failure rate and the line failure rate under extreme weather conditions;

[0022] Step S306: Determine the operating status of the transmission line at the next moment based on the comprehensive fault rate of the line at the next moment;

[0023] Step S307: Determine whether the extreme weather event has ended. If yes, output the fault evolution path of this fault evolution and record the corresponding fault probability. If no, repeat steps S301 to S306.

[0024] Optionally, determining the operating status of the transmission line at the next moment based on the comprehensive line fault rate at the next moment includes:

[0025] If the overall failure rate of the line at the next moment meets the preset failure conditions, then the transmission line will be regarded as a faulty transmission line, and the operating state of the faulty transmission line at the next moment will be regarded as a fault state.

[0026] If the overall failure rate of the line at the next moment does not meet the preset failure conditions, the transmission line will be regarded as a normal transmission line, and the operating status of the normal transmission line at the next moment will be regarded as a normal operating status.

[0027] Optionally, the method further includes:

[0028] The optimal power flow operation constraints of the multi-energy coupled active distribution network are formed by constructing power flow model constraints, distributed power unit output constraints, distribution network topology constraints, natural gas system node flow balance equations, natural gas system and power distribution system coupling element constraints, and thermal balance constraints.

[0029] The objective function is constructed with minimizing line load as the operation optimization objective of the multi-energy coupled active distribution network.

[0030] Optionally, the multi-energy coupled active distribution network includes an active distribution network, a gas supply network, and a heating network, and the method further includes:

[0031] For the aforementioned active distribution network, node power balance constraints are constructed;

[0032] For the gas supply network, mass conservation equations and momentum conservation equations are constructed;

[0033] For the aforementioned heating network, hydraulic and thermal equations are constructed.

[0034] The node power balance constraints, the mass conservation equation, the momentum conservation equation, the hydraulic equation, and the thermodynamic equation are integrated into the energy transmission model of the multi-energy coupled active distribution network.

[0035] Optionally, the extreme weather includes typhoon weather and rainstorm weather, and the method further includes:

[0036] In response to the aforementioned typhoon weather, stress equations for components are constructed based on the force effects of typhoon weather on overhead lines and towers.

[0037] In response to the aforementioned rainstorm weather, a rainstorm flood disaster risk index equation is constructed based on the impact of rainstorm risks.

[0038] The component stress equation and the rainstorm and flood disaster risk index equation are integrated into the extreme weather model of the multi-energy coupled active distribution network.

[0039] This invention also provides a distribution network fault evolution path prediction device, applied to a multi-energy coupled active distribution network, the device comprising:

[0040] The pending information acquisition module is used to acquire meteorological information on extreme weather and energy network structure information of the multi-energy coupled active distribution network;

[0041] An initial fault calculation module is used to perform fault calculations based on the meteorological information and the energy network structure information to obtain the initial faults of the multi-energy coupled active distribution network.

[0042] The fault evolution module is used to perform multiple rounds of fault evolution based on the optimal power flow operation constraints, the initial fault, the meteorological information, and the energy network structure information to obtain the fault evolution path results.

[0043] The target fault evolution path determination module is used to select the fault evolution path with the highest fault probability from the fault evolution path results, and use it as the target fault evolution path of the multi-energy coupled active distribution network under the extreme weather.

[0044] The present invention also provides an electronic device, the device comprising a processor and a memory:

[0045] The memory is used to store program code and transmit the program code to the processor;

[0046] The processor is used to execute the distribution network fault evolution path prediction method as described above, according to the instructions in the program code.

[0047] The present invention also provides a computer-readable storage medium for storing program code for executing the distribution network fault evolution path prediction method as described in any of the preceding claims.

[0048] As can be seen from the above technical solutions, the present invention has the following advantages:

[0049] This paper presents a method for predicting fault evolution paths in distribution networks. First, it acquires meteorological information on extreme weather conditions and energy network structure information of multi-energy coupled active distribution networks. Then, it calculates faults based on the meteorological and energy network structure information to obtain the initial faults of the multi-energy coupled active distribution network. Next, based on optimal power flow constraints, it performs multiple rounds of fault evolution according to the initial faults, meteorological information, and energy network structure information to obtain fault evolution path results. Finally, it selects the fault evolution path with the highest probability from the results as the target fault evolution path for the multi-energy coupled active distribution network under extreme weather conditions. This allows for fault evolution path prediction for multi-energy coupled active distribution networks under extreme weather conditions based on existing multi-energy coupled distribution network structure information and extreme weather meteorological information. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A flowchart illustrating the steps of a method for predicting the evolution path of a power distribution network fault, as provided in an embodiment of the present invention.

[0052] Figure 2 A schematic diagram of natural gas pipeline distribution parameters provided in an embodiment of the present invention;

[0053] Figure 3 A schematic diagram of the thermal path for distributed parameters of a heat pipe is provided in an embodiment of the present invention;

[0054] Figure 4 A schematic diagram of an algorithm combining an OPA model and optimal power flow provided in an embodiment of the present invention;

[0055] Figure 5 A schematic diagram of an improved IEEE-33 node provided in an embodiment of the present invention;

[0056] Figure 6 A schematic diagram of an improved 32-node heat distribution network in Bali provided in an embodiment of the present invention;

[0057] Figure 7 A schematic diagram of an improved 7-node natural gas system provided in an embodiment of the present invention;

[0058] Figure 8 This is a schematic diagram of a fault evolution path provided by an embodiment of the present invention;

[0059] Figure 9 This invention provides a schematic diagram of active distribution network optimization during fault evolution.

[0060] Figure 10 This invention provides a schematic diagram of load changes in an active distribution network during fault evolution.

[0061] Figure 11 This is a structural block diagram of a power distribution network fault evolution path prediction device provided in an embodiment of the present invention. Detailed Implementation

[0062] This invention provides a method, device, electronic device, and medium for predicting the fault evolution path of a distribution network, which addresses or partially addresses the technical problem in existing related technologies that focus on a single research object and do not consider the impact of multi-energy coupling on the operation of the active distribution network, thereby affecting the fault evolution mechanism.

[0063] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0064] As an example, in a multi-energy coupled active distribution network, the occurrence and development of a major blackout is essentially a process in which an initial fault in the power grid induces new faults in a chain reaction, and this process is repeated until it ultimately leads to catastrophic consequences. Building upon the initial fault caused by extreme weather, the coupling of multiple energy sources makes the operation of the active distribution network more complex, resulting in subsequent faults being influenced by both the operating status of the multi-energy coupled active distribution network and extreme weather conditions.

[0065] However, existing studies often treat distribution networks as a single research object, rarely considering the impact of multi-energy coupling on the operation of active distribution networks, and thus affecting the fault evolution mechanism.

[0066] Meanwhile, existing research mainly focuses on the impact of extreme weather on active distribution network components, without considering the process of fault recovery of multi-energy coupled active distribution networks through network reconfiguration, multi-energy coordination and optimized operation after extreme events. Therefore, it is difficult to consider the impact of optimized operation of multi-energy coupled active distribution networks on fault evolution mechanisms.

[0067] Therefore, one of the core inventive points of this invention is: to address the above-mentioned problems, a method for predicting the fault evolution path of a multi-energy coupled active distribution network based on OPA (ORNL-Pserc-Alaska, a power grid cascading fault model) and optimal power flow of the power system under extreme weather conditions is proposed. First, mathematical models of multiple networks, including active distribution networks, heating networks, and gas supply networks, as well as extreme weather models, are used to accurately describe the multi-energy coupling effect and the impact of extreme weather on multi-energy coupled active distribution networks. Then, based on component strength and the severity of extreme events, the initial fault of the multi-energy coupled active distribution network is calculated. Next, based on the initial fault scenario, an OPA model considering both optimal power flow and the impact of extreme weather is used to construct a fault evolution path prediction model for the multi-energy coupled active distribution network. The line load rate is calculated based on the optimized operation results of the multi-energy coupled active distribution network. Taking into account line overload protection and the impact of extreme weather on component failure rates, the overall line failure rate at the next moment is calculated, and fault state prediction is performed. Finally, by simulating the operation state of the multi-energy coupled active distribution network during the duration of extreme weather, the fault evolution path prediction results are obtained, thus achieving fault evolution path prediction for multi-energy coupled active distribution networks under extreme weather conditions. By adopting the technical solution of this invention, based on existing multi-energy coupled distribution network structure information and extreme weather meteorological information, and comprehensively considering the fault evolution paths of active distribution networks, heating networks, and gas supply networks under different types and severity of extreme weather, the cascading fault evolution path of multi-energy coupled active distribution networks under the influence of such extreme weather can be obtained. This can be used to reveal the fault propagation mechanism and evolution law of subsequent multi-energy coupled active distribution networks, and provide a research foundation for subsequent research on fault evolution mechanisms and fault mitigation and recovery schemes.

[0068] Reference Figure 1 This document illustrates a flowchart of a method for predicting the fault evolution path in a distribution network according to an embodiment of the present invention. The method is applied to a multi-energy coupled active distribution network and may specifically include the following steps:

[0069] Step 101: Obtain meteorological information on extreme weather and energy network structure information of the multi-energy coupled active distribution network;

[0070] Before executing the fault evolution handling process, relevant mathematical calculation models and extreme weather models of multi-energy coupled active distribution networks can be constructed to perform fault analysis and other related calculations under extreme weather conditions in subsequent processes.

[0071] Specifically, regarding the classification of multi-energy networks, the multi-energy coupled active distribution network studied in this embodiment of the invention mainly includes an active distribution network, a gas supply network, and a heating network. Therefore, corresponding energy transmission sub-models can be established for these three types of energy networks respectively, and the various energy transmission sub-models can be integrated as the overall energy transmission model of the multi-energy coupled active distribution network.

[0072] For active distribution networks, node power balance constraints need to be satisfied during energy transmission. Therefore, node power balance constraints can be constructed based on the following formula:

[0073]

[0074] In the formula, P i,i,t Q i,i,t These represent the active power and reactive power injected into node i, respectively; V i,t V j,t Let G represent the voltage magnitudes at nodes i and j at time t, respectively; C(i) is the set of nodes connected to node i; ij B ij δ represents the conductance and susceptance of branch ij, respectively; ij,t Let be the voltage phase angle difference between node i and node j at time t.

[0075] For gas supply networks, the flow process of natural gas in pipelines needs to be determined by both the mass conservation equation and the momentum conservation equation. Therefore, the mass conservation equation and the momentum conservation equation can be constructed using the following two formulas:

[0076]

[0077]

[0078] In the formula, ρ, p, and v represent the density, pressure, and flow velocity of natural gas, respectively; λ, θ, and D are the friction coefficient, inclination angle, and inner diameter of the pipeline, respectively; and g is the acceleration due to gravity. The product of density ρ and flow velocity v equals momentum density.

[0079] For example, refer to Figure 2 The diagram illustrates a distribution parameter diagram of a natural gas pipeline provided by an embodiment of the present invention.

[0080] Figure 2 In the context, p, p0, dp, p i Let G, G0, dG, and G represent the air pressure, the initial air pressure at the start of the pipeline, the change in air pressure, and the air pressure at the end of the pipeline, respectively; i These represent the airflow, the initial airflow at the start of the pipe, the change in airflow, and the airflow at the end of the pipe, respectively; L g Indicates equivalent gas sensation; R g Indicates equivalent air resistance; Cg Indicates equivalent gas volume; k g dx represents the equivalent gas conductance; dx indicates the variable to be solved (as shown in the schematic diagram of the pipe distribution structure indicated by the arrow outside the dashed box).

[0081] For heating networks, the heating medium is often water; therefore, heating networks are a combination of hydraulic and thermal processes. Thus, hydraulic and thermal equations can be constructed separately for each heating network.

[0082] For the hydraulic processes in heating networks, the one-dimensional process of water flowing in pipes can also be constructed using a mass conservation equation similar to that used for gas supply networks. However, unlike natural gas, the density ρ of water... W Constant, therefore ρ W The following equation (hydraulic equation) must be satisfied:

[0083]

[0084] For the thermal processes of the heating network, based on the energy conservation equation, the time-domain thermal circuit formula for distributed parameters can be obtained as follows (thermal equation):

[0085]

[0086] In the formula, c is the specific heat of water; T is the excess temperature characterized by the difference between water temperature and ambient temperature; μ is the heat dissipation coefficient of the pipe; G is the mass flow rate of water; and A is the cross-sectional area of ​​the pipe.

[0087] For example, refer to Figure 3 The diagram shows a thermal path schematic of distributed parameters for a thermal pipeline provided by an embodiment of the present invention.

[0088] Figure 3 In this context, T, T0, dT, and T1 represent temperature, the initial temperature at the start of the pipe, the temperature change, and the temperature at the end of the pipe, respectively; h represents the equivalent heat flux; L t Indicates equivalent thermal sensation; R t Indicates equivalent thermal resistance; C t G represents the equivalent heat capacity. t This represents the equivalent thermal conductivity.

[0089] Then, the nodal power balance constraints of the active distribution network, the mass conservation equation and momentum conservation equation of the gas supply network, and the hydraulic equation and thermal equation of the heating network can be integrated into an energy transmission model of a multi-energy coupled active distribution network.

[0090] Therefore, by considering the balance constraints of the gas supply network and the heating network in a multi-energy coupled active distribution network, as well as the impact of coupling elements on the operating state of the active distribution network, a mathematical model is established for a multi-energy coupled active distribution network, including the active distribution network, the heating network, and the gas supply network. This model is used to quantitatively describe the energy flow process in the energy network and the process of energy conversion between energy networks through coupling elements. This allows for a precise description of the changes in the operating state of the distribution network under the coupling of multiple energy networks, enabling simultaneous consideration of multiple energy coupling scenarios under extreme events and improving the accuracy of fault evolution path prediction.

[0091] The present invention is based on the actual situation of coastal cities in southern my country for the establishment of extreme weather models, and focuses on typhoon weather and rainstorm weather. Therefore, extreme weather can include typhoon weather and rainstorm weather.

[0092] In response to typhoon weather, stress equations for components can be constructed based on the forces exerted by typhoons on overhead lines and towers. Specifically, the forces exerted by typhoons on overhead lines and towers can be derived using the Batts model (a numerical model based on typhoon gradient equilibrium equations) (component stress equations):

[0093]

[0094] M T =M1+M2

[0095]

[0096] ||M2||=||P p ||Z

[0097] In the formula, T l The total tension in the tangential direction at the conductor suspension point; T3 represents the horizontal tension at the lowest point of the sag; ||P T || represents the combined load per unit length of the conductor; β is the elevation difference angle; P p Z is the wind load on the pole; Z is the height of the tower center; P 1k Let k be the horizontal wind load component of the conductor; l ld h is the distance from the suspension point to the lowest point. k n is the vertical height of the conductor k above the ground. d The number of conductors suspended on the tower; l d M1 represents the pole span; M2 represents the bending moment caused by wind load on the pole body; M3 represents the bending moment indirectly caused by horizontal wind load on the conductor on the pole. T This indicates the combined bending moment caused by wind load on the pole.

[0098] In response to rainstorm weather, this invention describes the severity of rainstorm weather based on the Flood Disaster Risk Index (FDRI), that is, based on the impact of rainstorm risk, a rainstorm flood disaster risk index equation is constructed:

[0099] FDRI=Hω H +Vω V +Eω E

[0100] In the formula, FDRI represents the rainstorm and flood disaster risk index, H represents the disaster hazard index; V represents the disaster-bearing body exposure index; E represents the disaster-bearing body vulnerability index; ω H ω V ω E Indicates the weight.

[0101] Next, the component stress equation and the rainstorm and flood disaster risk index equation can be integrated into an extreme weather model of a multi-energy coupled active distribution network for subsequent calculations related to extreme weather. It should be noted that the typhoon weather and rainstorm weather referred to in the embodiments of this invention are only examples. Those skilled in the art can set extreme weather according to actual conditions, such as actual geographical location. For example, extreme weather can be set as sandstorm weather or heavy snow weather, etc., and corresponding constraints or calculation models can be constructed based on the actual extreme weather settings. It is understood that this invention does not limit this.

[0102] By providing physical models for different extreme weather conditions, the corresponding component failure rate models will also differ. This allows for a quantitative representation of the failure rate of different energy network components under extreme weather conditions during subsequent fault evolution analysis, thereby obtaining the initial faults and fault evolution path influencing factors of multi-energy coupled active distribution networks under extreme weather conditions.

[0103] Before conducting fault evolution analysis, we can first obtain energy network structure information of multi-energy coupled active distribution networks, as well as meteorological information of extreme weather events to be studied.

[0104] The energy network structure information can mainly include the grid structure (such as node location, branch length, etc.) and operating parameters (such as source load parameters, component operating status, etc.).

[0105] Meteorological information for extreme weather can be mainly divided into two parts. One part is the meteorological information corresponding to typhoon weather, such as elevation difference angle, wind load on poles or towers, pole or tower center height, and horizontal wind load components on conductors. Using this information, combined with the component stress equations constructed in the preceding steps, the component stresses on overhead lines and towers in multi-energy coupled active distribution networks under extreme weather conditions can be calculated. The other part is the meteorological information corresponding to rainstorm weather, such as disaster hazard index, disaster-bearing body exposure index, and disaster-bearing body vulnerability index. Using this information, combined with the rainstorm and flood disaster risk index equations constructed in the preceding steps, the severity of rainstorm weather under extreme weather conditions can be obtained.

[0106] Step 102: Perform fault calculation based on the meteorological information and the energy network structure information to obtain the initial fault of the multi-energy coupled active distribution network;

[0107] Next, based on the energy transmission model and extreme weather model of the multi-energy coupled active distribution network constructed in the aforementioned steps, fault calculations can be performed according to the acquired meteorological information and energy network structure information to obtain the initial fault of the multi-energy coupled active distribution network.

[0108] In practical implementation, fault calculations are performed based on meteorological information and energy network structure information to obtain the initial fault handling process for a multi-energy coupled active distribution network, which may include the following sub-steps:

[0109] Step S1021: Based on meteorological information and energy network structure information, calculate the component stress of overhead lines and towers in a multi-energy coupled active distribution network under extreme weather conditions;

[0110] Based on extreme weather meteorological information and energy network structure information, combined with the energy transmission model and extreme weather model constructed in the preceding steps, the component stress of overhead lines and towers in a multi-energy coupled active distribution network under extreme weather conditions can be calculated. The component stress can be specifically expressed as the conductor stress of overhead lines and the tower bending moment of towers.

[0111] Step S1022: Calculate the strength of overhead line components and tower components based on component stress;

[0112] The formula for calculating the strength of overhead line components is as follows:

[0113]

[0114] In the formula, μ l δ l These represent the mean and standard deviation of the conductor's tensile strength, respectively; σ1 represents the stress experienced by the overhead line; f R(σ1) represents the strength of the overhead line component when the stress on the overhead line is σ1, and can also be understood as the fault probability density of the overhead line.

[0115] The formula for calculating the strength of tower components is as follows:

[0116]

[0117] In the formula, μ p δ represents the average bending strength of the tower. p M represents the standard deviation of the tower's bending strength. P f represents the bending moment experienced by the tower; R (M P This indicates that when the bending moment on the tower is M... P The strength of tower components at a given time can also be understood as the failure probability density of the tower.

[0118] Step S1023: Calculate the initial component failure rate of each component in the multi-energy coupled active distribution network by combining component stress, overhead line component strength, and tower component strength;

[0119] Among them, the component failure rate P fault The calculation formula is as follows:

[0120]

[0121] In the formula, R represents the component strength, i.e., the ability of the conductor and tower to withstand loads; S is the load effect, i.e., the component stress; f R (r) represents the component strength when the stress on the component is r; P r This represents the load on a component when the stress on the component is r.

[0122] Step S1024: Components whose initial component failure rate meets the preset failure conditions are regarded as initial faulty components, thereby determining the initial fault of the multi-energy coupled active distribution network under extreme weather conditions.

[0123] In the initial fault scenario, assuming reasonable power flow distribution across the lines of the multi-energy coupled active distribution network, and disregarding the impact of line overload protection, the initial fault scenario is determined solely by the component failure rate caused by extreme weather. Therefore, by iterating through all components in the multi-energy coupled active distribution network, a component is considered to be in a fault state in the initial stage when its failure rate satisfies the following formula:

[0124] P fault.i ≥δ i=1,2,3,...,n

[0125] In the formula, P fault.i Let be the component failure rate of the i-th component; δ is a random number in the range [0,1].

[0126] Step 103: Based on the optimal power flow operation constraints, perform multiple rounds of fault evolution according to the initial fault, the meteorological information, and the energy network structure information to obtain the fault evolution path results;

[0127] In this step, based on the initial fault of the multi-energy coupled active distribution network, combined with meteorological information of extreme weather and energy network structure information, a multi-energy coupled active distribution network OPA model is established based on the multi-energy coordinated fault recovery method of the optimal power flow of the power system. The OPA model is then used to simulate the fault evolution path in multiple rounds to obtain the fault evolution path results, so as to characterize the process of fault self-healing and fault recovery of the active distribution network after being subjected to extreme weather.

[0128] In the specific implementation, we can first construct power flow model constraints, distributed power unit output constraints, distribution network topology constraints, natural gas system node flow balance equations, natural gas system and power distribution system coupling element constraints, and thermal balance constraints to form the optimal power flow operation constraints of the multi-energy coupled active distribution network; at the same time, we can construct the objective function with the minimization of line load as the operation optimization objective of the multi-energy coupled active distribution network.

[0129] Specifically, with the aim of achieving rapid fault recovery in multi-energy coupled active distribution networks under extreme weather conditions through network reconfiguration, multi-energy coordination, and optimized operation, an optimal power flow model can be constructed by using the energy power of generators, natural gas sources, and heat sources within the multi-energy coupled active distribution network, the distribution network structure, and the load output of controllable loads as optimization variables, and including the following model constraints:

[0130] (1) Constraints of the DistFlow power flow model:

[0131] V i -V j =z ij I ij

[0132]

[0133]

[0134] In the formula, V i V represents the voltage at node i; j Represents the voltage at node j; z ij I represents the impedance of branch ij; ij I represents the current flowing through branch ij; ij * indicates complex current I ij The conjugate of complex numbers; S ij S represents the power injected from node i into node j; jk S represents the power injected from node j into node k;j This represents the net complex power injected into node j from the outside.

[0135] DistFlow is a distributed power flow calculation method in power systems based on voltage phase angle differences. Its core idea is to estimate the power flow distribution by calculating the voltage phase angle differences between nodes. Compared with traditional power flow calculation methods, DistFlow has higher computational efficiency and better accuracy.

[0136] (2) Output constraints of distributed power generation units:

[0137] P DG.jmin ≤P DG.j ≤P DG.jmax

[0138] Q DG.jmin ≤Q DG.j ≤Q DG.jmax

[0139] In the formula, P DG.j P DG.jmax P DG.jmin Q represents the active power and its upper and lower limits of the distributed generator set connected to node j; DG.j Q DG.jmax Q DG.jmin These represent the reactive power of the distributed generator set connected to node j and its upper and lower limits, respectively.

[0140] (3) Distribution network topology constraints:

[0141] ∑y ij =N-1

[0142] α ij +α ji =y ij

[0143]

[0144]

[0145] In the formula, y ij Indicates the working status of line ij, y ij =1 indicates a closed circuit; N represents the number of nodes in the distribution network; α ij =1 indicates that node i is the parent node of node j; α ji =1 indicates that node j is the parent node of node i.

[0146] (4) Natural gas system node flow balance equations:

[0147]

[0148] In the formula, W GW,m,t For the natural gas supply at node m at time t; W E,m,t W represents the gas load at node m at time t. shed,m,t W represents the amount of gas load reduced at node m at time t. DG,m,t F represents the gas turbine gas consumption at node m at time t. mn,t F represents the gas flow rate from node m to node n. pm,t Let be the gas flow rate from node p to node m.

[0149] (5) Constraints on coupling components between the natural gas system and the power distribution system:

[0150] W DG.m.t =B i P DG.i.t

[0151] P com.i.t =C z F z.t

[0152] In the formula, B i P is the energy conversion coefficient. DG,i,t P represents the gas turbine electrical power at node m at time t. com.i.t C represents the power consumption of the compressor at time t. z F is the energy conversion coefficient. z,t Let be the gas flow rate of the compressor branch at time t.

[0153] (6) Thermal energy balance constraint:

[0154]

[0155] In the formula, Q CHP j.t Q represents the heating capacity of cogeneration unit j at time t; k.t Q represents the heating power of the boiler at time t; t This represents the heat energy demand at time t.

[0156] Specifically, the objective functions for the multi-energy coupled active distribution network can be constructed as follows: minimizing branch transmission power exceeding limits and minimizing fault loads.

[0157] minf=ω1|P load.i -P load.i.ref |+ω2|P i -P i.ref |+ω3|y ij -y ij.ref |

[0158] In the formula, P load.i P load.i.refP represents the load at node i and its rated value, respectively; i P i.ref Let y represent the transmission power and rated power of the i-th transmission line, respectively; ij y ij.ref ω1, ω2, and ω3 represent the switching state (operating state) and initial switching state (initial operating state) of line ij, respectively; ω1, ω2, and ω3 represent the weights.

[0159] In this embodiment of the invention, for the multi-energy coupled active distribution network under study, an optimal power flow calculation model considering network reconfiguration, multi-energy coordination, and optimized operation is established to form an OPA model for the multi-energy coupled active distribution network. The OPA model is then solved using MATLAB-Yalmip-Cplex software to obtain the load rate of each line and other relevant parameters. A schematic diagram of the algorithm combining the OPA model and optimal power flow can be found in [the following text is missing from the original] Figure 4 Compare and contrast to understand.

[0160] Yalmip is a MATLAB optimization toolkit used for modeling and solving optimization problems. Cplex is a mathematical optimization software that can be integrated with Yalmip to solve optimization problems such as linear programming, integer programming, and quadratic programming. In practical applications, combining MATLAB, Yalmip, and Cplex makes it easier to model and solve optimization problems.

[0161] Based on the foregoing, the initial fault can at least include the initial component failure rate and component operating status, and the energy network structure information can at least include the operating parameters of the multi-energy coupled active distribution network. Therefore, in the specific implementation, the steps for fault evolution based on optimal power flow constraints, initial fault, meteorological information, and energy network structure information can include the following sub-steps:

[0162] Step S301: Based on Monte Carlo simulation, determine the operating status of the components to obtain the next fault scenario of the multi-energy coupled active distribution network;

[0163] First, the current operating status of components in the multi-energy coupled active distribution network can be statistically analyzed to determine whether each line is in a closed or open state. Simultaneously, to better recreate the fault scenario, the current operating status of each line and component can be integrated to determine whether each line and component is currently in a fault state or a normal operating state. Then, based on Monte Carlo simulation, predictive analysis is performed on the integrated relevant data to obtain the fault scenario of the multi-energy coupled active distribution network at the next moment.

[0164] Step S302: Based on the initial component failure rate and meteorological information, calculate the extreme weather line failure rate of each transmission line in the multi-energy coupled active distribution network;

[0165] Regarding the extreme weather line failure rate calculation process in step S302, at the corresponding moment of the extreme event, such as the moment corresponding to this calculation, the calculation can be performed by referring to the overhead line component strength calculation formula, tower component strength calculation formula, and component failure rate calculation formula introduced in the previous steps. It will not be elaborated here.

[0166] Step S303: Based on the operating parameters and the fault scenario at the next moment, perform power flow optimization calculations in conjunction with the optimal power flow operation constraints to obtain the line load rate of each transmission line;

[0167] In the process of power flow optimization calculation, it is necessary to combine the fault scenario at the next moment, and calculate based on the operating parameters, the various constraints constructed in the previous steps (i.e. the optimal power flow operation constraints of multi-energy coupled active distribution network), and the objective function with the minimization of line load as the operation optimization objective.

[0168] The formula for calculating the line load factor of a transmission line is as follows:

[0169]

[0170] In the formula, η i This represents the line load rate of the i-th transmission line.

[0171] Step S304: For each transmission line, determine whether the line overload protection has been activated, and calculate the line overload fault rate of the transmission line based on the determination result and the line load rate.

[0172] Based on the line load rate calculated in the preceding steps, the line overload fault rate of the transmission line under the influence of line overload protection can be further obtained as follows:

[0173]

[0174] In the formula, P ol.i η represents the line overload fault rate of the i-th transmission line under the influence of line overload protection; i This represents the line load rate of the i-th transmission line.

[0175] Step S305: Calculate the overall line fault rate at the next moment based on the line overload fault rate and the line fault rate under extreme weather conditions;

[0176] Taking into account the impact of extreme weather on the failure rate of components and lines, as well as the effect of line overload protection disconnecting overloaded lines, the overall line failure rate at the next moment is calculated according to the following formula:

[0177] P Fault.j =1-(1-P) w.j (1-P) ol.j )

[0178] In the formula, P Fault.j P represents the overall fault rate of the j-th transmission line at the next moment; w.j This represents the failure rate of the j-th transmission line under the influence of extreme weather.

[0179] Step S306: Determine the operating status of the transmission line at the next moment based on the overall line fault rate at the next moment;

[0180] The line comprehensive fault rate P at the next moment calculated in step S305 Fault.j By traversing all transmission lines in a multi-energy coupled active distribution network, the overall line fault rate P at the next moment is calculated. Fault.j When the fault state judgment formula mentioned in step S1024 is satisfied, the operating state of the transmission line at the next moment is regarded as a fault state.

[0181] In practical implementation, determining the operating status of the transmission line at the next moment based on the overall line fault rate can be done as follows:

[0182] If the overall failure rate of the line meets the preset failure conditions at the next moment, the transmission line will be regarded as a faulty transmission line, and the operating state of the faulty transmission line at the next moment will be regarded as a fault state.

[0183] If the overall fault rate of the line does not meet the preset fault conditions at the next moment, the transmission line will be regarded as a normal transmission line, and the operating status of the normal transmission line at the next moment will be regarded as a normal operating status.

[0184] Step S307: Determine whether the extreme weather event has ended. If yes, output the fault evolution path of this fault evolution and record the corresponding fault probability. If no, repeat steps S301 to S306.

[0185] Those skilled in the art will recognize that extreme weather events can last for a period of time, such as typhoons which can last all day. Therefore, for a single fault evolution, it is necessary to combine the duration of the extreme weather event and conduct multiple fault state prediction processes at different times until the extreme event ends, so as to form the corresponding fault evolution path for this fault evolution.

[0186] Step 104: Select the fault evolution path with the highest fault probability from the fault evolution path results, and use it as the target fault evolution path of the multi-energy coupled active distribution network under the extreme weather.

[0187] Combining the aforementioned steps, multiple OPA simulations can be performed on the multi-energy coupled active distribution network under extreme weather conditions. The fault evolution paths with the most prominent fault probabilities in multiple simulations can be statistically analyzed, and the fault evolution path with the highest fault probability can be taken as the most likely fault evolution path of the multi-energy coupled active distribution network under this extreme weather condition.

[0188] This invention provides a method for predicting the fault evolution path of a multi-energy coupled active distribution network under extreme weather conditions, based on the OPA model and optimal power flow of the power system. By employing this invention, based on existing multi-energy coupled distribution network structure information and extreme weather meteorological information, and comprehensively considering the fault evolution paths of the active distribution network, heating network, and gas supply network under different types and severity of extreme weather, the cascading fault evolution path of the multi-energy coupled active distribution network under the influence of the extreme weather can be obtained. This method reveals the subsequent fault propagation mechanism and evolution law of the multi-energy coupled active distribution network, providing a research foundation for subsequent research on fault evolution mechanisms and fault mitigation and recovery schemes.

[0189] For ease of understanding, the following description uses a specific example to illustrate an embodiment of the present invention.

[0190] In this example, a multi-energy coupled active distribution network fault evolution path prediction method based on OPA model and optimal power flow under extreme weather conditions, as provided in the foregoing embodiments, is used. Figures 5 to 7 The simulator failure evolution paths shown are for the improved IEEE-33 node, the improved Bali 32-node heating network, and the improved 7-node natural gas system when subjected to typhoon weather.

[0191] Step 1: First, input the operating parameters such as the network structure, load information, and power supply information of the improved IEEE-33 node, the improved Bali 32 node heat distribution network, and the improved 7-node natural gas system into the MATLAB software. Simultaneously, input the meteorological information of the extreme weather event to be studied into the MATLAB software. In this example, the extreme weather event studied is typhoon weather, and its relevant meteorological information is shown in Table 1 below:

[0192] Extreme weather parameters numerical values <![CDATA[Maximum influence radius R max > 50km <![CDATA[Typhoon wind speed V max > 40m / s

[0193] Table 1: Relevant Parameters for Typhoon Meteorological Information

[0194] Step 2: Based on typhoon weather information and grid structure information, analyze the component failure rate of overhead lines and towers to obtain the initial faults of the multi-energy coupled active distribution network.

[0195] Step 2.1: Calculate the effects of typhoon weather on overhead lines and towers (component stress), including the tension on overhead lines (conductor stress) and the bending moment on towers.

[0196] Step 2.2: Calculate the strength of overhead line components and tower components based on component stress.

[0197] Step 2.3: Calculate the initial component failure rate for each component by combining the component stress, the strength of overhead line components, and the strength of tower components.

[0198] Step 2.4: In MATLAB, iterate through all components, generating a random number in the range [0,1] for each component. If the random number is less than the initial component failure rate, the component is considered faulty. This yields the initial failure rate caused by the typhoon.

[0199] Step 3: Establish a multi-energy coupled active distribution network OPA model and use the OPA model to simulate the fault evolution path.

[0200] Step 3.1: Input the energy network structure information to be studied, extreme weather meteorological information, and initial faults into the OPA model.

[0201] Step 3.2: For the initial fault, solve the optimal power flow model that considers network reconstruction, multi-energy cooperation, and optimized operation.

[0202] Step 3.2.1: Linearize and input the operating constraints of the multi-energy coupled active distribution network in the MATLAB program, including the optimal power flow operating constraints and the operating objective function.

[0203] Step 3.2.2: Using the analytic hierarchy process (AHP), determine the objective functions ω1, ω2, and ω3 of the multi-energy coupled active distribution network as [0.648, 0.230, 0.122].

[0204] Step 3.3: Calculate the line load rate of each transmission line, determine whether the line overload protection has been activated, and obtain the line overload fault rate under the influence of the line overload protection.

[0205] Step 3.4: Calculate the typhoon's meteorological information for the next moment (mainly focusing on the movement of the typhoon center). Calculate the line failure rate under the typhoon's influence based on the new meteorological information. Then, comprehensively consider the impact of the typhoon and the line's heavy load protection to calculate the overall line failure rate for the next moment.

[0206] Step 3.5: In MATLAB, iterate through all components, generating a random number in the range [0,1] for each component. When the random number is less than the overall line fault rate at the next time step, the component is considered faulty, and the corresponding transmission line's operating state at the next time step is considered faulty. This allows us to determine faults caused by both typhoon weather and line overload.

[0207] Step 3.6: Set the typhoon duration to 24 hours, repeat the above steps 24 times, and output the fault evolution path results for this round.

[0208] Step 4: Run the OPA model 100 times to output the failure evolution path with the highest probability of failure (highest frequency of occurrence), as shown below. Figure 8 As shown. Figure 9 , Figure 10 The diagrams show the optimized operation and load changes of the multi-energy coupled active distribution network during the fault evolution process.

[0209] Reference Figure 11 This diagram illustrates a structural block diagram of a distribution network fault evolution path prediction device provided by an embodiment of the present invention. The device is applied to a multi-energy coupled active distribution network and may specifically include:

[0210] The pending information acquisition module 1101 is used to acquire meteorological information of extreme weather and energy network structure information of the multi-energy coupled active distribution network;

[0211] The initial fault calculation module 1102 is used to perform fault calculation based on the meteorological information and the energy network structure information to obtain the initial fault of the multi-energy coupled active distribution network.

[0212] The fault evolution module 1103 is used to perform multiple rounds of fault evolution based on the optimal power flow operation constraints, the initial fault, the meteorological information, and the energy network structure information to obtain the fault evolution path results.

[0213] The target fault evolution path determination module 1104 is used to select the fault evolution path with the highest fault probability from the fault evolution path results, as the target fault evolution path of the multi-energy coupled active distribution network under the extreme weather.

[0214] In one optional embodiment, the initial fault calculation module 1102 includes:

[0215] The component stress calculation module is used to calculate the component stress of overhead lines and towers in the multi-energy coupled active distribution network under extreme weather conditions, based on the meteorological information and the energy network structure information.

[0216] The component strength calculation module is used to calculate the strength of overhead line components and tower components based on the stress of the components.

[0217] The initial component failure rate calculation module is used to calculate the initial component failure rate of each component in the multi-energy coupled active distribution network by combining the component stress, the strength of the overhead line component, and the strength of the tower component.

[0218] The initial fault determination module is used to regard components whose initial component failure rate meets the preset fault conditions as initial fault components, thereby determining the initial fault of the multi-energy coupled active distribution network under the extreme weather.

[0219] In one optional embodiment, the initial fault includes an initial component failure rate and component operating status, the energy network structure information includes the operating parameters of the multi-energy coupled active distribution network, and the fault evolution module 1103 includes:

[0220] The next moment fault scenario calculation module is used to execute step S301: based on Monte Carlo simulation, determine the working state of the component and obtain the next moment fault scenario of the multi-energy coupled active distribution network;

[0221] The extreme weather line failure rate calculation module is used to execute step S302: calculate the extreme weather line failure rate of each transmission line in the multi-energy coupled active distribution network based on the initial component failure rate and the meteorological information;

[0222] The line load rate calculation module is used to execute step S303: based on the operating parameters and the fault scenario at the next moment, combined with the optimal power flow operation constraints, perform power flow optimization calculation to obtain the line load rate of each of the transmission lines.

[0223] The line overload fault rate calculation module is used to execute step S304: for each of the transmission lines, determine whether the line overload protection has been activated, and calculate the line overload fault rate of the transmission line based on the determination result and the line load rate.

[0224] The next moment line comprehensive failure rate calculation module is used to execute step S305: calculate the next moment line comprehensive failure rate based on the line overload failure rate and the extreme weather line failure rate;

[0225] The next moment operation status judgment module is used to execute step S306: judge the operation status of the transmission line in the next moment based on the comprehensive line fault rate in the next moment;

[0226] The fault evolution path output module is used to execute step S307: determine whether the extreme weather event has ended. If yes, output the fault evolution path of this fault evolution and record the corresponding fault probability. If no, repeat steps S301 to S306.

[0227] In one optional embodiment, the next-moment running state determination module is specifically used for:

[0228] If the overall failure rate of the line at the next moment meets the preset failure conditions, then the transmission line will be regarded as a faulty transmission line, and the operating state of the faulty transmission line at the next moment will be regarded as a fault state.

[0229] If the overall failure rate of the line at the next moment does not meet the preset failure conditions, the transmission line will be regarded as a normal transmission line, and the operating status of the normal transmission line at the next moment will be regarded as a normal operating status.

[0230] In one alternative embodiment, the device further includes:

[0231] The optimal power flow operation constraint construction module is used to construct power flow model constraints, distributed power generation unit output constraints, distribution network topology constraints, natural gas system node flow balance equations, natural gas system and distribution system coupling element constraints, and thermal balance constraints, so as to form the optimal power flow operation constraints of the multi-energy coupled active distribution network.

[0232] The objective function construction module is used to construct an objective function with the minimization of line load as the operation optimization objective of the multi-energy coupled active distribution network.

[0233] In one optional embodiment, the multi-energy coupled active distribution network includes an active distribution network, a gas supply network, and a heating network. The device further includes an energy transmission model construction module, which is specifically used for:

[0234] For the aforementioned active distribution network, node power balance constraints are constructed;

[0235] For the gas supply network, mass conservation equations and momentum conservation equations are constructed;

[0236] For the aforementioned heating network, hydraulic and thermal equations are constructed.

[0237] The node power balance constraints, the mass conservation equation, the momentum conservation equation, the hydraulic equation, and the thermodynamic equation are integrated into the energy transmission model of the multi-energy coupled active distribution network.

[0238] In one optional embodiment, the extreme weather includes typhoon weather and rainstorm weather, and the device further includes an extreme weather model construction module, which is specifically used for:

[0239] In response to the aforementioned typhoon weather, stress equations for components are constructed based on the force effects of typhoon weather on overhead lines and towers.

[0240] In response to the aforementioned rainstorm weather, a rainstorm flood disaster risk index equation is constructed based on the impact of rainstorm risks.

[0241] The component stress equation and the rainstorm and flood disaster risk index equation are integrated into the extreme weather model of the multi-energy coupled active distribution network.

[0242] As the device embodiment is basically similar to the method embodiment, it is described in a relatively simple way. For relevant details, please refer to the description of the method embodiment above.

[0243] This invention also provides an electronic device, which includes a processor and a memory:

[0244] The memory is used to store program code and transfer the program code to the processor;

[0245] The processor is used to execute the power grid fault evolution path prediction method of any embodiment of the present invention according to the instructions in the program code.

[0246] This invention also provides a computer-readable storage medium for storing program code for executing the power grid fault evolution path prediction method of any embodiment of this invention.

[0247] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0248] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0249] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0250] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0251] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0252] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the evolution path of faults in a distribution network, characterized in that, The method is applied to a multi-energy coupled active distribution network, which includes an active distribution network, a gas supply network, and a heating network; the method includes: Acquire meteorological information on extreme weather events, as well as energy network structure information of the multi-energy coupled active distribution network; Based on the meteorological information and the energy network structure information, fault calculation is performed to obtain the initial fault of the multi-energy coupled active distribution network; Based on the optimal power flow operation constraints, multiple rounds of fault evolution are performed according to the initial fault, the meteorological information, and the energy network structure information to obtain the fault evolution path results; The fault evolution path with the highest fault probability is selected from the fault evolution path results and used as the target fault evolution path of the multi-energy coupled active distribution network under the extreme weather conditions. The initial fault includes the initial component failure rate and component operating status; the energy network structure information includes the operating parameters of the multi-energy coupled active distribution network; the steps of fault evolution based on optimal power flow operation constraints, according to the initial fault, the meteorological information, and the energy network structure information, include: Step S301: Based on Monte Carlo simulation, determine the operating state of the components to obtain the next fault scenario of the multi-energy coupled active distribution network; Step S302: Based on the initial component failure rate and the meteorological information, calculate the extreme weather line failure rate of each transmission line in the multi-energy coupled active distribution network; Step S303: Based on the operating parameters and the fault scenario at the next moment, perform power flow optimization calculations in conjunction with the optimal power flow operation constraints to obtain the line load rate of each of the transmission lines; Step S304: For each of the transmission lines, determine whether the line overload protection has been activated, and calculate the line overload fault rate of the transmission line based on the determination result and the line load rate. Step S305: Calculate the overall line failure rate at the next moment based on the line overload failure rate and the line failure rate under extreme weather conditions; Step S306: Determine the operating status of the transmission line at the next moment based on the comprehensive fault rate of the line at the next moment; Step S307: Determine whether the extreme weather event has ended. If yes, output the fault evolution path of this fault evolution and record the corresponding fault probability. If no, repeat steps S301 to S306.

2. The method for predicting the evolution path of a distribution network fault according to claim 1, characterized in that, The step of calculating faults based on the meteorological information and the energy network structure information to obtain the initial faults of the multi-energy coupled active distribution network includes: Based on the meteorological information and the energy network structure information, calculate the component stress of overhead lines and towers in the multi-energy coupled active distribution network under the extreme weather conditions; Calculate the strength of overhead line components and tower components based on the stress of the components; Based on the stress of the components, the strength of the overhead line components, and the strength of the tower components, the initial component failure rate of each component in the multi-energy coupled active distribution network is calculated. Components whose initial failure rate meets the preset failure conditions are regarded as initial failure components, thereby determining the initial failure of the multi-energy coupled active distribution network under the extreme weather.

3. The method for predicting the evolution path of a distribution network fault according to claim 1, characterized in that, The step of determining the operating status of the transmission line at the next moment based on the comprehensive fault rate of the line at the next moment includes: If the overall failure rate of the line at the next moment meets the preset failure conditions, then the transmission line will be regarded as a faulty transmission line, and the operating state of the faulty transmission line at the next moment will be regarded as a fault state. If the overall failure rate of the line at the next moment does not meet the preset failure conditions, the transmission line will be regarded as a normal transmission line, and the operating status of the normal transmission line at the next moment will be regarded as a normal operating status.

4. The method for predicting the fault evolution path of a distribution network according to claim 1, characterized in that, Also includes: The optimal power flow operation constraints of the multi-energy coupled active distribution network are formed by constructing power flow model constraints, distributed power unit output constraints, distribution network topology constraints, natural gas system node flow balance equations, natural gas system and power distribution system coupling element constraints, and thermal balance constraints. The objective function is constructed with minimizing line load as the operation optimization objective of the multi-energy coupled active distribution network.

5. The method for predicting the evolution path of a distribution network fault according to any one of claims 1 to 4, characterized in that, The method further includes: For the aforementioned active distribution network, node power balance constraints are constructed; For the gas supply network, mass conservation equations and momentum conservation equations are constructed; For the aforementioned heating network, hydraulic and thermal equations are constructed. The node power balance constraints, the mass conservation equation, the momentum conservation equation, the hydraulic equation, and the thermodynamic equation are integrated into the energy transmission model of the multi-energy coupled active distribution network.

6. The method for predicting the evolution path of a distribution network fault according to any one of claims 1 to 4, characterized in that, The extreme weather includes typhoon weather and rainstorm weather, and the method further includes: In response to the aforementioned typhoon weather, stress equations for components are constructed based on the force effects of typhoon weather on overhead lines and towers. In response to the aforementioned rainstorm weather, a rainstorm flood disaster risk index equation is constructed based on the impact of rainstorm risks. The component stress equation and the rainstorm and flood disaster risk index equation are integrated into the extreme weather model of the multi-energy coupled active distribution network.

7. A distribution network fault evolution path prediction device, characterized in that, The device is applied to a multi-energy coupled active distribution network, which includes an active distribution network, a gas supply network, and a heating network; the device includes: The pending information acquisition module is used to acquire meteorological information on extreme weather and energy network structure information of the multi-energy coupled active distribution network; An initial fault calculation module is used to perform fault calculations based on the meteorological information and the energy network structure information to obtain the initial faults of the multi-energy coupled active distribution network. The fault evolution module is used to perform multiple rounds of fault evolution based on the optimal power flow operation constraints, the initial fault, the meteorological information, and the energy network structure information to obtain the fault evolution path results. The target fault evolution path determination module is used to select the fault evolution path with the highest fault probability from the fault evolution path results, and use it as the target fault evolution path of the multi-energy coupled active distribution network under the extreme weather. The initial fault includes the initial component failure rate and component operating status; the energy network structure information includes the operating parameters of the multi-energy coupled active distribution network; the fault evolution module includes: The next moment fault scenario calculation module is used to execute step S301: based on Monte Carlo simulation, determine the working state of the component and obtain the next moment fault scenario of the multi-energy coupled active distribution network; The extreme weather line failure rate calculation module is used to execute step S302: calculate the extreme weather line failure rate of each transmission line in the multi-energy coupled active distribution network based on the initial component failure rate and the meteorological information; The line load rate calculation module is used to execute step S303: based on the operating parameters and the fault scenario at the next moment, combined with the optimal power flow operation constraints, perform power flow optimization calculation to obtain the line load rate of each of the transmission lines. The line overload fault rate calculation module is used to execute step S304: for each of the transmission lines, determine whether the line overload protection has been activated, and calculate the line overload fault rate of the transmission line based on the determination result and the line load rate. The next moment line comprehensive failure rate calculation module is used to execute step S305: calculate the next moment line comprehensive failure rate based on the line overload failure rate and the extreme weather line failure rate; The next moment operation status judgment module is used to execute step S306: judge the operation status of the transmission line in the next moment based on the comprehensive line fault rate in the next moment; The fault evolution path output module is used to execute step S307: determine whether the extreme weather event has ended. If yes, output the fault evolution path of this fault evolution and record the corresponding fault probability. If no, repeat steps S301 to S306.

8. An electronic device, characterized in that, The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the power distribution network fault evolution path prediction method according to any one of claims 1-6 according to the instructions in the program code.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for executing the distribution network fault evolution path prediction method according to any one of claims 1-6.

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