Electric power system rapid toughness evaluation method considering disaster evolution process

Abstract

The invention discloses an electric power system rapid toughness evaluation method considering a disaster evolution process, and relates to the field of electric power systems and toughness evaluation, and the method comprises the following steps: constructing a toughness evaluation index, and enumerating a potential electric power system low-order fault state through an influence increment enumeration method; constructing an optimal load reduction model of the power system, and calculating a potential optimal load reduction value of the power system; constructing an extreme ice and snow disaster model, and calculating the fault probability of a line in the power system in real time based on remote sensing prediction; according to the toughness evaluation index, the optimal load reduction value of the power system and the fault probability of the line in the power system, guiding operation scheduling of the power system; the time-consuming calculation process is transferred to be carried out before the disaster, multiplexing influencing increment is carried out in the extreme disaster period, time-consuming calculation of the optimal load reduction amount of the system in the extreme disaster period is avoided, and the operation state of the system can be rapidly evaluated in the extreme disaster evolution period.

Description

Technical Field

[0001] This invention relates to the field of power systems and resilience assessment, and in particular to a method for rapid resilience assessment of power systems that takes into account the evolution of disasters. Background Technology

[0002] The frequency of extreme typhoon disasters has increased significantly globally, causing severe impacts on power systems. The effects of extreme natural disasters on power systems have spatiotemporal evolution characteristics. For example, ice storms, typhoon movement, and earthquake aftershocks can all cause successive failures of components in the system.

[0003] In existing technologies, power system resilience assessment methods typically include analytical methods and simulation methods. Analytical methods have high computational accuracy, but their computational complexity is high. As the system scale increases, such as the number of nodes and the number of fault scenarios, the computational load increases exponentially with the system scale, leading to the dimensionality curse. In addition, analytical methods are difficult to handle highly uncertain extreme events and cannot cope with the spatiotemporal randomness of natural disasters.

[0004] Simulation methods simulate the future operation of a power system by sampling possible system states. The accuracy of the calculation depends on the number of samples, and a large number of simulations are required to converge to a stable result. At the same time, high-quality training data is required, otherwise it may lead to overfitting or poor generalization ability.

[0005] Therefore, a fast resilience assessment method for power systems that considers the evolution of disasters is provided to address the above problems. Summary of the Invention

[0006] The purpose of this invention is to provide a method for rapid resilience assessment of power systems that considers the evolution of disasters, effectively selects potential system states during extreme disasters, and constructs and calculates resilience assessment indices in conjunction with real-time failure probabilities during the evolution of extreme disasters.

[0007] To achieve the above objectives, the present invention provides a method for rapid resilience assessment of power systems that considers the evolution of disasters, comprising the following steps: S1: Construct resilience assessment indicators by enumerating potential low-order power system fault states through the influence incremental enumeration method; S2: Construct an optimal load reduction model for the power system and calculate the potential optimal load reduction value for the power system; S3: Construct an extreme ice and snow disaster model, and calculate the failure probability of power lines in the power system in real time based on remote sensing prediction; S4: Based on resilience assessment indicators, optimal load reduction values ​​for the power system, and the probability of line failures in the power system, guide the operation and dispatch of the power system.

[0008] Preferably, step S1 specifically includes the following steps: S11: Constructing System-Level Resilience Assessment Metrics R System-level resilience assessment indicators R Specifically set as follows: in, s Ω represents the system state, and Ω represents the set of system states. P ( s ) represents the probability of the system state. I ( s This represents the optimal load reduction for the system state. S12: Calculate the increment of the impact of the system state. Incremental impact of system state Specifically set as follows: in, n s The number of faulty transmission branches indicating the system status. I s This represents the increment of the system state, i.e., the load loss. u Indicates the line unavailability rate. This indicates the incremental impact of the faulty line. Indicates system state n Subset of order; S13: Including M In a power system with multiple transmission branches, the expected value of load loss is calculated using a state enumeration method based on the influence increment. R M Expected load loss R M Specifically set as follows: in, N The highest order representing the fault state. express M One transmission branch n Subset of order, P s This represents the probability of a system failure. m Indicates the first m One power transmission branch, P m Indicates the first m The probability of failure of a transmission branch. s This indicates the system fault state that needs to be analyzed. This represents the probability of a corrected failure in the system state. Indicates system fault status s The incremental impact.

[0009] Preferably, in step S12, the system state is... n Rank subset Specifically set as follows: Among them, Card ( u ) indicates a fault state u The number of faulty transmission branches included; when n When =0, ; in, This indicates a scenario where no faults occur in the system.

[0010] Preferably, step S2 specifically includes the following steps: S21: Construct the objective function min of the optimal load reduction model f LC objective function min f LC Specifically set as follows: ; S22: Constraints for constructing the optimal load reduction model. The specific constraints are set as follows: in, G b This represents the complex power balance constraint at the nodes. V Representing the complex voltage of the node n b ×1 vector, S g Represents the generator's complex power n b ×1 vector, S LC Indicates load reduction n b ×1 vector, Y bus Indicates nodal admittance. Representing the complex voltage of the node n b The conjugate vector of a ×1 vector C g Indicates generator connection. S d Indicates complex load n b ×1 vector, Y fl Indicates branch admittance, F max Indicates the maximum complex power of the line. U Indicates the magnitude of node voltage nb ×1 vector, U min The smallest vector representing the magnitude of node voltage. U max The largest vector representing the node voltage magnitude. θ Represents the phase angle of the node voltage. n b ×1 vector, θ min The smallest vector representing the phase angle of the node voltage. θ max The vector representing the maximum phase angle of the node voltage. P g Indicates the active power of the generator. P gmin This represents the minimum active power of the generator. P gmax This indicates the maximum active power of the generator. Q g Indicates the reactive power of the generator. Q gmin This represents the minimum reactive power of the generator. Q gmax This indicates the maximum reactive power of the generator. P LC This indicates the amount of active power load reduction at each node. P d This represents the active power load value of each node. Q LC This indicates the amount of reactive power reduction at each node. Q d This represents the reactive load value of each node; S23: Based on the objective function min f LC Given the constraints, calculate the potential optimal load reduction value for the power system.

[0011] Preferably, in step S22, the load reduction is... n b ×1 vector S LC Specifically set as follows: in, j Represents the imaginary unit; Complex load n b ×1 vector S d Specifically set as follows: ; Generator power nb ×1 vector S g Specifically set as follows: ; Minimum active power of generator P gmin and minimum reactive power of generator Q gmin satisfy: in, S gmin This represents the minimum complex power of the system; Maximum active power of generator P gmax and the maximum reactive power of the generator Q gmax satisfy: in, S gmax This represents the maximum complex power of the system.

[0012] Preferably, step S3 specifically includes the following steps: S31: Construct an extreme ice and snow disaster model using the Jones model to calculate the ice and wind load per unit length of the line. L IW ( t ); S32: Based on ice and wind load L IW ( t ), calculate the line length per unit length in t The probability of a disconnection failure occurring at any time P f ( t The probability of a wire breakage fault. P f ( t Specifically set as follows: in, e Represents the natural constant. a IW This indicates the design load for the line's ability to withstand icing. b IW This indicates the ultimate load that the line can withstand to prevent icing. S33: Calculation length is l The probability of line failure during ice storms P l ( t ), length is lThe probability of line failure during ice storms P l ( t Specifically set as follows: .

[0013] Preferably, step S31 specifically includes the following steps: Step 1: Calculation t Ice thickness on the time line D ice ( t Ice thickness D ice ( t Specifically set as follows: in, express t The amount of freezing rain at any given moment. This indicates the density of ice. This indicates the density of water. v w This indicates the wind speed at the transmission line. W ( t This indicates the water content in the ambient air; Step 2: According to t Ice thickness on the time line D ice ( t ), calculate the ice load per unit length of the line. L I ( t Ice load L I ( t Specifically set as follows: in, d Indicates the diameter of the line; Step 3: Calculation t Wind speed at the location of the current line v W ( t Wind speed v W ( t Specifically set as follows: in, v max This indicates the maximum wind speed. Indicates the polar radius of the line from the center of the ice storm. Indicates the polar angle from the line to the center of the ice storm. This represents the polar radius from the point of maximum wind speed to the center of the ice storm. This represents the polar angle from the point of maximum wind speed to the center of the ice storm. k Indicates the attenuation coefficient. Indicates the angle between the wind direction and the line; Step 4: Based on wind speed v W ( t ), calculate the wind load per unit length of the line. L W ( t Wind load L W ( t Specifically set as follows: in, C It is a constant, a constant C The specific value is set to 6.964 × 10. -3 , S Indicates the span factor; Step 5: Calculate the icing wind load per unit length of the line. L IW ( t Ice wind load L IW ( t Specifically set as follows: .

[0014] Preferably, in step 4, the span factor S Specifically set as follows: .

[0015] Therefore, the present invention employs the above-mentioned method for rapid resilience assessment of power systems that considers the evolution of disasters, and has the following beneficial effects: (1) This scheme adopts the state enumeration method based on impact increment to simulate potential failure scenarios in the pre-disaster stage. By increasing the proportion of low-order indicators in the resilience assessment indicators, it can accurately assess the system resilience by only enumerating low-order failures. (2) This scheme combines the failure rate that changes in real time during the evolution of extreme disasters to realize the rapid update of the system resilience level, solves the problem of slow calculation speed of resilience assessment during the disaster stage, helps to discover potential problems in the power system in advance, thereby improving the reliability and stability of the power system and ensuring that people's lives and work are not affected by power outages.

[0016] The method of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0017] Figure 1 This is a flowchart of a method for rapid resilience assessment of power systems that considers the evolution of disasters, according to the present invention. Figure 2 This is a schematic diagram of the algorithm principle of a fast resilience assessment method for power systems that considers the evolution process of disasters, according to the present invention. Figure 3 This is a schematic diagram illustrating the impact of ice storms on the transmission line of this invention. Figure 4 This is a schematic diagram illustrating the impact of wind disasters on the transmission line of this invention. Figure 5 This is a geographical wiring diagram of the RTS-79 node testing system of the present invention. Detailed Implementation

[0018] The method of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Unless otherwise defined, the methodological or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0020] The terms "comprising" or "including" as used in this invention mean that the element preceding the term encompasses the element listed after the term, and do not exclude the possibility of encompassing other elements. Terms such as "inner," "outer," "upper," and "lower" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. In this invention, unless otherwise explicitly specified and limited, the term "attached" and similar terms should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral part; it can refer to a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication of two elements or the interaction relationship between two elements. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0021] Example like Figure 1 As shown, this invention provides a method for rapid resilience assessment of power systems that considers the evolution of disasters, comprising the following steps: S1: In order to achieve rapid calculation and accurate solution of system resilience level, a resilience assessment index is constructed, and potential low-order fault states of power system are enumerated by the influence incremental enumeration method. Step S1 specifically includes the following steps: S11: Constructing System-Level Resilience Assessment Metrics R The expected value of load loss under each scenario characterizes the potential impact on the system; this is a system-level resilience assessment index. R The larger the value, the greater the likelihood of system failure and the lower the system resilience level. This is a key indicator for system-level resilience assessment. R Specifically set as follows: in, s Ω represents the system state, and Ω represents the set of system states. P ( s ) represents the probability of the system state. I ( s This represents the optimal load reduction for the system state. S12: Calculate the increment of the impact of the system state. Incremental impact of system state Specifically set as follows: in, n s The number of faulty transmission branches indicating the system status. I s The increment representing the system state, i.e., the load loss, can be obtained through optimal power flow calculation based on the AC power flow optimal load reduction model. u Indicates the line unavailability rate. This indicates the incremental impact of the faulty line. Indicates system state n Subset of order; In step S12, the system state n Rank subset Specifically set as follows: Among them, Card ( u ) indicates a fault state u The number of faulty transmission branches included; when n When =0, ; in, This indicates a scenario where no faults occur in the system.

[0022] S13: Traditional enumeration methods sequentially enumerate the states of each fault, then combine them to form possible system states. However, as the enumeration order increases, combinatorial explosion occurs, severely impacting the computational speed of resilience assessment. Furthermore, the enumeration method often neglects higher-order faults, leading to inaccurate resilience assessment calculations. Therefore, in situations involving... MIn a power system with multiple transmission branches, the expected value of load loss is calculated using a state enumeration method based on the influence increment. R M ,like Figure 2 As shown, S 1 indicates the impact of a single fault in system line 1. S 2 indicates the impact of a single fault in system line 2. S 12 This indicates the impact of simultaneous failures in system line 1 and system line 2. I Indicates the influence of state. This indicates the increment of the state's influence; This method decomposes the influence of a large number of high-order fault states into two parts by influencing the increment: the influence of each low-order fault state that makes up the high-order state and the influence increment corresponding to the high-order state itself. Then, the influence of the high-order state is converted into a smaller number of low-order states, so that a single fault state analysis can cover multiple fault states, avoiding the analysis of high-order system states, and improving the evaluation accuracy and computational efficiency. Expected load loss R M Specifically set as follows: in, N The highest order representing the fault state. express M One transmission branch n Subset of order, P s This represents the probability of a system failure. m Indicates the first m One power transmission branch, P m Indicates the first m The probability of failure of a transmission branch. s This indicates the system fault state that needs to be analyzed. This represents the probability of a corrected failure in the system state. Indicates system fault status s The incremental impact.

[0023] S2: Construct an optimal load reduction model for the power system and calculate the potential optimal load reduction value for the power system; Step S2 specifically includes the following steps: S21: Potential faults in the power system may seriously threaten the reliability of power supply. Dispatch optimization based on optimal load reduction simulation provides an effective means to solve this problem. By simulating the dispatch process of the system under different potential fault states through the optimal load reduction model, the optimal load reduction calculation is performed one by one according to the topology of the system and the source load level, with the goal of minimizing load reduction. This can maintain the stability of power supply to the maximum extent when a fault occurs, and reduce economic losses and social impact.

[0024] The objective function min for constructing the optimal load reduction model f LC objective function min f LC Specifically set as follows: ; S22: Constraints for constructing the optimal load reduction model. The specific constraints are set as follows: in, G b This represents the complex power balance constraint at the nodes. V Representing the complex voltage of the node n b ×1 vector, S g Represents the generator's complex power n b ×1 vector, S LC Indicates load reduction n b ×1 vector, Y bus Indicates nodal admittance. Representing the complex voltage of the node n b The conjugate vector of a ×1 vector C g Indicates generator connection. S d Indicates complex load n b ×1 vector, Y fl Indicates branch admittance, F max Indicates the maximum complex power of the line. U Indicates the magnitude of node voltage n b ×1 vector, U min The smallest vector representing the magnitude of node voltage. U max The largest vector representing the node voltage magnitude. θRepresents the phase angle of the node voltage. n b ×1 vector, θ min The smallest vector representing the phase angle of the node voltage. θ max The vector representing the maximum phase angle of the node voltage. P g Indicates the active power of the generator. P gmin This represents the minimum active power of the generator. P gmax This indicates the maximum active power of the generator. Q g Indicates the reactive power of the generator. Q gmin This represents the minimum reactive power of the generator. Q gmax This indicates the maximum reactive power of the generator. P LC This indicates the amount of active power load reduction at each node. P d This represents the active power load value of each node. Q LC This indicates the amount of reactive power reduction at each node. Q d This represents the reactive load value of each node; In step S22, the load reduction n b ×1 vector S LC Specifically set as follows: in, j Represents the imaginary unit; Complex load n b ×1 vector S d Specifically set as follows: ; Generator power n b ×1 vector S g Specifically set as follows: ; Minimum active power of generator P gmin and minimum reactive power of generator Q gmin satisfy: in,S gmin This represents the minimum complex power of the system; Maximum active power of generator P gmax and the maximum reactive power of the generator Q gmax satisfy: in, S gmax This represents the maximum complex power of the system.

[0025] S23: Based on the objective function min f LC Given the constraints, calculate the potential optimal load reduction value for the power system.

[0026] S3: Construct an extreme ice and snow disaster model, and calculate the failure probability of power lines in the power system in real time based on remote sensing prediction; Step S3 specifically includes the following steps: S31: Construct an extreme ice and snow disaster model using the Jones model to calculate the ice and wind load per unit length of the line. L IW ( t ); Step S31 specifically includes the following steps: Step 1: Extreme ice and snow disasters cause damage to the power system as they move, exhibiting evolutionary characteristics. Under the influence of ice disasters, due to the large distances covered by the transmission system, not all lines are usually affected during an ice disaster. Figure 3 As shown, only the parts of the line affected by ice storms will accumulate ice, such as the parts of lines AB and AC affected by ice storms. The remaining parts unaffected by ice storms will not accumulate ice, such as line BC.

[0027] To study the impact of ice storms on power transmission systems, this embodiment constructs a mathematical model of ice storm scenarios based on the spatiotemporal characteristics of extreme disasters, and calculates the changes in line ice thickness by combining weather forecasting and other methods.

[0028] calculate t Ice thickness on the time line D ice ( t Ice thickness D ice ( t Specifically set as follows: in, express t The amount of freezing rain at any given moment. This indicates the density of ice. This indicates the density of water. v w This indicates the wind speed at the transmission line. W ( t This indicates the water content in the ambient air; Step 2: When ice accumulates on the power line, the load on the line is the icing load generated in the vertical direction due to the weight of the ice. L I and horizontal wind load L W The synthesis, according to t Ice thickness on the time line D ice ( t ), calculate the ice load per unit length of the line. L I ( t Ice load L I ( t Specifically set as follows: in, d Indicates the diameter of the line; Step 3: As Figure 4 As shown, considering the spatiotemporal characteristics of ice storms, the maximum impact radius of ice storms, and the actual geographical locations of the power lines and the center of the ice storm, calculations are performed. t Wind speed at the location of the current line v W ( t Wind speed v W ( t Specifically set as follows: in, v max This indicates the maximum wind speed. Indicates the polar radius of the line from the center of the ice storm. Indicates the polar angle from the line to the center of the ice storm. This represents the polar radius from the point of maximum wind speed to the center of the ice storm. This represents the polar angle from the point of maximum wind speed to the center of the ice storm. k Indicates the attenuation coefficient. Indicates the angle between the wind direction and the line; Step 4: Based on wind speed v W ( t ), calculate the wind load per unit length of the line.L W ( t Wind load L W ( t Specifically set as follows: in, C It is a constant, a constant C The specific value is set to 6.964 × 10. -3 , S Indicates the span factor; In step 4, span factor S Specifically set as follows: .

[0029] Step 5: Calculate the icing wind load per unit length of the line. L IW ( t Ice wind load L IW ( t Specifically set as follows: .

[0030] S32: Based on ice and wind load L IW ( t ), calculate the line length per unit length in t The probability of a disconnection failure occurring at any time P f ( t The probability of a wire breakage fault. P f ( t Specifically set as follows: in, e Represents the natural constant. a IW This indicates the design load for the line's ability to withstand icing. b IW This indicates the ultimate load that the line can withstand to prevent icing. S33: Calculation length is l The probability of line failure during ice storms P l ( t ), length is l The probability of line failure during ice storms P l ( t Specifically set as follows: .

[0031] S4: Based on resilience assessment indicators, optimal load reduction values ​​for the power system, and the probability of line failures in the power system, guide the operation and dispatch of the power system.

[0032] In this embodiment, the impact increment of each fault state is pre-solved and stored in the database. Therefore, as the extreme disaster evolves, the previously calculated impact can be called up and the system's resilience level can be calculated in real time. After avoiding the time-consuming calculation of optimal load reduction, the calculation of the system-level resilience assessment index is reduced to only basic addition and multiplication operations.

[0033] like Figure 5 As shown, this embodiment takes the IEEE RTS-79 node system as an example for simulation analysis and verification. The IEEE RTS-79 node system includes 32 generators, 33 transmission lines, 5 transformers, 17 load nodes, with a total installed capacity of 3405MW and a maximum system load of 2850MW. All simulations are performed on the Intel hardware environment, and extreme ice and snow disaster simulation and resilience assessment calculations are carried out in Matlab. Assuming the ice storm makes landfall at the origin (0,0) km, moves from southwest to northeast at a 30° angle to due east, the average freezing rainfall in the ice storm model is 3 mm / h, the maximum radius of influence of the freezing rainfall is 105 km, the freezing rainfall speed is 12.5 km / h, the attenuation coefficient is 10000, the design value of the line icing thickness is 15 mm, the warning value of the icing thickness is 12 mm, and the simulated ice storm duration is 48 h; Taking the RTS-79 node system at the 96th moment of an ice storm as the research target, a resilience assessment is conducted, which will affect the maximum enumeration order of the incremental method and the enumeration method. N order Set to 3, using the Monte Carlo method MCS as the baseline value, compare the impact of the incremental enumeration method IISE with the traditional enumeration method SE, and analyze the effectiveness of the impact of the incremental enumeration method IISE. As shown in Table 1, in the RTS-79 node system, when the IISE index is calculated to the third order, the error affecting the incremental enumeration method IISE is much smaller than that of the traditional enumeration method SE, and the calculation result of the incremental enumeration method IISE is similar to that of the Monte Carlo method MCS. This is because the IISE method can transfer the influence of higher-order fault states to related lower-order fault states, increase the proportion of the influence of lower-order faults in the index, and thus reduce the impact of ignoring higher-order fault states on the calculation accuracy.

[0034] The speed of the impact incremental enumeration method IISE was verified. The resilience assessment results at time 96 demonstrate that the impact incremental enumeration method IISE can meet the needs of rapid resilience assessment during extreme disasters.

[0035] Table 1: Results of toughness assessment indicators and error statistics

[0036] As shown in Table 2, when using the Monte Carlo method (MCS) to assess the resilience at a certain moment in the RTS-79 node system, the time consumption is significant due to the need to consider a large number of sampling processes. In contrast, the incremental enumeration method (IISE) and the traditional enumeration method (SE) consider the same enumeration termination, and therefore have similar time consumption.

[0037] Table 2: Statistical Results of Resilience Assessment Efficiency

[0038] In summary, the Incremental Impact Enumeration (IISE) method provides more accurate resilience assessment results compared to the traditional enumeration method (SE). The Monte Carlo method (MCS) calculation is significantly time-consuming due to the consideration of a large number of sampling processes. Therefore, the Incremental Impact Enumeration (IISE) method used in this embodiment satisfies both accuracy and speed, and can be applied to the rapid resilience assessment of power systems during extreme disaster evolution processes.

[0039] Therefore, this invention adopts the above-mentioned method for rapid resilience assessment of power systems that considers the evolution of disasters. It uses a state enumeration method based on the impact increment to simulate potential fault scenarios in the pre-disaster stage. By increasing the proportion of low-order indicators in the resilience assessment indicators, it can accurately assess system resilience by only enumerating low-order faults. At the same time, by combining the fault rate that changes in real time during the evolution of extreme disasters, it can realize the rapid updating of the system resilience level and solve the problem of slow calculation speed of resilience assessment in the disaster stage.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the method of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the method of the present invention, and these modifications or equivalent substitutions should not cause the modified method to deviate from the spirit and scope of the method of the present invention.

Claims

1. A method for rapid resilience assessment of power systems considering the evolution of disasters, characterized in that, Includes the following steps: S1: Construct resilience assessment indicators by enumerating potential low-order power system fault states through the influence incremental enumeration method; S2: Construct an optimal load reduction model for the power system and calculate the potential optimal load reduction value for the power system; S3: Construct an extreme ice and snow disaster model, and calculate the failure probability of power lines in the power system in real time based on remote sensing prediction; S4: Based on resilience assessment indicators, optimal load reduction values ​​for the power system, and the probability of line failures in the power system, guide the operation and dispatch of the power system.

2. The method for rapid resilience assessment of power systems considering disaster evolution as described in claim 1, characterized in that, Step S1 specifically includes the following steps: S11: Constructing System-Level Resilience Assessment Metrics R System-level resilience assessment indicators R Specifically set as follows: in, s Ω represents the system state, and Ω represents the set of system states. P ( s ) represents the probability of the system state. I ( s This represents the optimal load reduction for the system state. S12: Calculate the increment of the impact of the system state. Incremental impact of system state Specifically set as follows: in, n s The number of faulty transmission branches indicating the system status. I s This represents the increment of the system state, i.e., the load loss. u Indicates the line unavailability rate. This indicates the incremental impact of the faulty line. Indicates system state n Subset of order; S13: Including M In a power system with multiple transmission branches, the expected value of load loss is calculated using a state enumeration method based on the influence increment. R M Expected load loss R M Specifically set as follows: in, N The highest order representing the fault state. express M One transmission branch n Subset of order, P s This represents the probability of a system failure. m Indicates the first m One power transmission branch, P m Indicates the first m The probability of failure of a transmission branch. s This indicates the system fault state that needs to be analyzed. This represents the probability of a corrected failure in the system state. Indicates system fault status s The incremental impact.

3. The method for rapid resilience assessment of power systems considering disaster evolution as described in claim 2, characterized in that, In step S12, the system state n Rank subset Specifically set as follows: Among them, Card ( u ) indicates a fault state u The number of faulty transmission branches included; when n When =0, ; in, This indicates a scenario where no faults occur in the system.

4. The method for rapid resilience assessment of power systems considering disaster evolution as described in claim 1, characterized in that, Step S2 specifically includes the following steps: S21: Construct the objective function min of the optimal load reduction model f LC objective function min f LC Specifically set as follows: ； S22: Constraints for constructing the optimal load reduction model. The specific constraints are set as follows: in, G b This represents the complex power balance constraint at the nodes. V Representing the complex voltage of the node n b ×1 vector, S g Represents the generator's complex power n b ×1 vector, S LC Indicates load reduction n b ×1 vector, Y bus Indicates nodal admittance. Representing the complex voltage of the node n b The conjugate vector of a ×1 vector C g Indicates generator connection. S d Indicates complex load n b ×1 vector, Y fl Indicates branch admittance, F max Indicates the maximum complex power of the line. U Indicates the magnitude of node voltage n b ×1 vector, U min The smallest vector representing the magnitude of node voltage. U max The largest vector representing the node voltage magnitude. θ Represents the phase angle of the node voltage. n b ×1 vector, θ min The smallest vector representing the phase angle of the node voltage. θ max The vector representing the maximum phase angle of the node voltage. P g Indicates the active power of the generator. P gmin This represents the minimum active power of the generator. P gmax This indicates the maximum active power of the generator. Q g Indicates the reactive power of the generator. Q gmin This represents the minimum reactive power of the generator. Q gmax This indicates the maximum reactive power of the generator. P LC This indicates the amount of active power load reduction at each node. P d This represents the active power load value of each node. Q LC This indicates the amount of reactive power reduction at each node. Q d This represents the reactive load value of each node; S23: Based on the objective function min f LC Given the constraints, calculate the potential optimal load reduction value for the power system.

5. A method for rapid resilience assessment of power systems considering disaster evolution as described in claim 4, characterized in that, In step S22, the load reduction n b ×1 vector S LC Specifically set as follows: in, j Represents the imaginary unit; Complex load n b ×1 vector S d Specifically set as follows: ； Generator power n b ×1 vector S g Specifically set as follows: ； Minimum active power of generator P gmin and minimum reactive power of generator Q gmin satisfy: in, S gmin This represents the minimum complex power of the system; Maximum active power of generator P gmax and the maximum reactive power of the generator Q gmax satisfy: in, S gmax This represents the maximum complex power of the system.

6. The method for rapid resilience assessment of power systems considering disaster evolution as described in claim 1, characterized in that, Step S3 specifically includes the following steps: S31: Construct an extreme ice and snow disaster model using the Jones model to calculate the ice and wind load per unit length of the line. L IW ( t ); S32: Based on ice and wind load L IW ( t ), calculate the line length per unit length in t The probability of a disconnection failure occurring at any time P f ( t The probability of a wire breakage fault. P f ( t Specifically set as follows: in, e Represents the natural constant. a IW This indicates the design load for the line's ability to withstand icing. b IW This indicates the ultimate load that the line can withstand to prevent icing. S33: Calculation length is l The probability of line failure during ice storms P l ( t ), length is l The probability of line failure during ice storms P l ( t Specifically set as follows: 。 7. A method for rapid resilience assessment of power systems considering disaster evolution as described in claim 6, characterized in that, Step S31 specifically includes the following steps: Step 1: Calculation t Ice thickness on the time line D ice ( t Ice thickness D ice ( t Specifically set as follows: in, express t The amount of freezing rain at any given moment. This indicates the density of ice. This indicates the density of water. v w This indicates the wind speed at the transmission line. W ( t This indicates the water content in the ambient air; Step 2: According to t Ice thickness on the time line D ice ( t ), calculate the ice load per unit length of the line. L I ( t Ice load L I ( t Specifically set as follows: in, d Indicates the diameter of the line; Step 3: Calculation t Wind speed at the location of the current line v W ( t Wind speed v W ( t Specifically set as follows: in, v max This indicates the maximum wind speed. Indicates the polar radius of the line from the center of the ice storm. Indicates the polar angle from the line to the center of the ice storm. This represents the polar radius from the point of maximum wind speed to the center of the ice storm. This represents the polar angle from the point of maximum wind speed to the center of the ice storm. k Indicates the attenuation coefficient. Indicates the angle between the wind direction and the line; Step 4: Based on wind speed v W ( t ), calculate the wind load per unit length of the line. L W ( t Wind load L W ( t Specifically set as follows: in, C It is a constant, a constant C The specific value is set to 6.964 × 10. -3 , S Indicates the span factor; Step 5: Calculate the icing wind load per unit length of the line. L IW ( t Ice wind load L IW ( t Specifically set as follows: 。 8. A method for rapid resilience assessment of power systems considering disaster evolution as described in claim 7, characterized in that, In step 4, span factor S Specifically set as follows: 。

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