Active defense method, device, equipment and medium aiming at influence of typhoon weather on power system

Abstract

The invention provides an active defense method, device and equipment for the influence of typhoon weather on an electric power system and a medium, and relates to the technical field of electric power system maintenance, and the method comprises the steps: obtaining a prediction position and a prediction speed based on a path similarity method and a Holland model according to a track parameter and a movement parameter of a to-be-predicted typhoon; constructing a Markov state topology network based on the Markov theory according to the risk fault element determined by the prediction position, and calculating the fault transition probability; constructing an objective function according to a load loss function, a load penalty function and a Markov state probability function, constructing an optimization model of the Markov state topology network by taking the active defense strategy in each decision period as a variable and taking the power system performance constraint as a constraint condition, and calculating an optimal solution, according to the obtained target active defense strategy, the corresponding target active defense strategy is executed before the next decision period, the load loss is greatly reduced on the whole, and the elasticity of the power system under the typhoon weather event is effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of power system maintenance, and specifically to an active defense method, device, equipment and medium against the impact of typhoon weather on the power system. Background Art

[0002] In recent decades, with the cyclical changes in the global climate, all parts of the world are facing increasing risks of climate change, and the frequency of typhoon weather is also increasing. With the increase in typhoon weather events, the high risk of typhoon weather events has posed new challenges to the safety, stability and economic operation of the power system, and the construction of power system resilience is particularly important.

[0003] The resilience of the power system refers to the system's resistance to external interference and its ability to recover quickly after a failure. Typhoon weather events are low-probability, high-risk events. To deal with typhoon weather events, the dispatching department must take preventive measures before the typhoon weather event arrives, take control measures during the typhoon weather event, and quickly restore power supply after the typhoon weather event ends to reduce the impact and losses caused by the typhoon weather event. This ability to resist typhoon weather events and reduce their impact is the basic ability that a resilient power system needs to have. In order to better respond to typhoon weather events and reduce their impact, power research institutions in various countries have increased their research on the safe and stable operation of power systems under typhoon weather events.

[0004] In traditional power system reliability analysis, the expected fault table is usually used to directly specify the expected faults of the power system and then calculate these expected faults offline. However, the expected fault table does not take into account the impact of the actual environment of the power system on the failure rate of power system components. In addition, in order to more fully understand the consequences of various power system component failures and the measures that need to be taken, it is necessary to increase the number of expected faults in the expected fault table, which will cause the dimensionality disaster of large power systems. At the same time, the expected dimensionality disaster causes the expected accident table to usually be unable to consider group failures, but group failures often occur in typhoon weather events. In addition, the traditional emergency control measures are to identify the faulty components after the system fails, and then take relevant operations to minimize the load loss. This passive defense strategy lacks prevention and integrity, and the minimum load loss pursued is a local minimum.

[0005] In summary, in order to study the impact of typhoon weather on the power system, it is urgent to propose a method to predict the typhoon trajectory in advance and implement active defense strategies for the power system components that are expected to be affected by the typhoon. Summary of the invention

[0006] The purpose of this application is to address the above problems and provide an active defense method, device, equipment and medium against the impact of typhoon weather on the power system, so as to reduce more load losses as a whole and improve the resilience of the power system under typhoon weather events.

[0007] In a first aspect, the present application provides an active defense method against the impact of typhoon weather on the power system, comprising:

[0008] According to the trajectory parameters and current movement parameters of the typhoon to be predicted, the predicted positions corresponding to a plurality of consecutive decision cycles of the typhoon to be predicted are obtained based on the path similarity method, and the predicted speed of the typhoon to be predicted in each of the decision cycles is calculated based on the Holland model and the predicted positions;

[0009] Acquire risk fault components according to the predicted positions and the distribution positions of the power components, and construct a Markov state topology network of the power system according to the risk fault components based on Markov theory to determine the Markov state of the power system;

[0010] Calculating the failure probability of each risk failure component according to the predicted speed and the universal vulnerability curve of the power component, and calculating the failure transfer probability of the power system in each Markov state according to the failure probability;

[0011] An objective function is constructed according to a load loss function, a load penalty function and a Markov state probability function, and an active defense strategy in each decision cycle is used as a variable and a power system performance constraint is used as a constraint condition to construct an optimization model of the Markov state topology network and calculate the optimal solution to obtain a target active defense strategy in each decision cycle; wherein, the load loss function is determined according to the fault transfer probability and the load loss of the defense strategy of the power system in each Markov state, the load penalty function is determined according to the power supply and power demand of the power system in each Markov state, and the Markov state probability function is determined according to the Markov state of the power system; the power system performance constraints include power flow balance constraints, branch active and reactive power flow constraints, generator output active and reactive power constraints, node voltage constraints, load reduction constraints, and generator set climbing rate constraints;

[0012] According to the target active defense strategy, a corresponding target active defense strategy is executed before the to-be-forecasted typhoon enters the next decision cycle.

[0013] According to the technical solution provided by the present application, the method of obtaining the predicted position corresponding to multiple consecutive decision cycles of the typhoon to be predicted based on the path similarity method according to the trajectory parameters of the typhoon to be predicted and the current movement parameters includes:

[0014] According to the trajectory parameters of the typhoon to be predicted and the trajectory parameters of historical typhoons, screening geographically similar historical typhoons corresponding to the typhoon to be predicted;

[0015] According to the trajectory parameters of the typhoon to be predicted, the trajectory parameters of the geographically similar historical typhoons, and the decision cycle, the similarity of the direction and speed of the geographically similar historical typhoons and the typhoon to be predicted is calculated, the geographically similar historical typhoons whose direction and speed similarity are greater than a preset threshold are selected as target similar historical typhoons, and the similarity weight of the target similar historical typhoons is calculated;

[0016] According to the current movement parameters, the similarity weight, and the trajectory parameters of the target similar historical typhoon, the predicted movement direction and moving speed of the next trajectory point of the typhoon to be predicted is calculated, and according to the trajectory parameters of the typhoon to be predicted and the predicted movement direction and moving speed, the predicted position of the next trajectory point of the typhoon to be predicted is calculated.

[0017] According to the technical solution provided by the present application, the calculation of the prediction speed of the typhoon to be predicted in each decision cycle according to the Holland model and the prediction position includes:

[0018] According to the formula:

[0019]

[0020] Calculate the predicted speed of the typhoon to be predicted in each decision cycle; wherein v(r) is the predicted speed of the next trajectory point with a distance r from the current trajectory point; p n is the average air pressure outside the typhoon; p c is the typhoon center pressure; B is the shape coefficient; ρ a is the air density; R max is the maximum wind speed radius; r is the distance between the predicted position of the next trajectory point and the current trajectory point; ψ is the predicted latitude of the next trajectory point.

[0021] According to the technical solution provided by the present application, the risk fault component is obtained according to the predicted position and the distribution position of the power component, and the Markov state topology network of the power system is constructed according to the risk fault component based on the Markov theory to determine the Markov state of the power system, including:

[0022] According to the predicted position and the distribution position of the power components, selecting the power components that the track of the to-be-predicted typhoon passes through as risk failure components;

[0023] Determine all expected states of the risk fault components based on Markov theory, take all expected operating states of each risk fault component as topological nodes, connect them in sequence according to the decision cycle, and form a Markov state topological network of the power system;

[0024] According to the Markov state topology network, a plurality of Markov states of the power system are obtained; wherein one of the Markov states is a sequence of operating states of risk fault components arranged in sequence according to a decision cycle.

[0025] According to the technical solution provided by the present application, the fault transfer probability of the power system is calculated according to the fault probability, including:

[0026] According to the formula:

[0027]

[0028] Calculate the failure transfer probability of each risk failure component; k,t ,s k,t+1 They represent the operating status of the risk fault component k at time t and time t+1 respectively, 1 represents the normal operation of the risk fault component k, and 0 represents the failure of the risk fault component k; λ is the failure probability of the risk fault component;

[0029] According to the formula:

[0030] Pr(S i,t ,S j,t+1 )=Πpr(s k,t ,s k,t+1 )

[0031] Calculate the fault transfer probability of the power system; where Pr(S i,t ,S j,t+1 ) is the fault transfer probability of the power system; pr(s k,t ,s k,t+1 ) is the failure transfer probability of risk fault component k from time t to time t+1.

[0032] According to the technical solution provided by the present application, before constructing the objective function according to the load loss function, the load penalty function and the Markov state probability function, the method further includes:

[0033] Based on the failover probability and defense strategy load loss, according to the formula:

[0034]

[0035] Construct the load loss function; where C(S i,t , A i，t ) is the i-th Markov state in A at time t i，t Load loss under active defense strategy; A i，t is the active defense strategy of the i-th Markov state at time t; ΔL1(S i，t , Ai,t ) is the current load loss after the corresponding active defense strategy is implemented in the i-th Markov state at time t; C(S j，t+1 , A j，t+1 ) is the load loss after the corresponding active defense strategy is implemented in the next state of the power system state; Pr(S i，t , S j，t+1 ) is the fault transfer probability of the power system;

[0036] Based on the power supply and power demand of the power system under different Markov states, according to the formula:

[0037] C penalty (i, t) = k·max(0, P load (i, t)-P Supply (i, t))

[0038] Construct the load penalty function; where C penalty (i, t) is the penalty cost of the ith Markov state under the active defense strategy at time t; k is the penalty coefficient; P load (i, t) represents the load demand of the i-th Markov state at time t; P Supply (i, t) represents the power supply of the i-th Markov state at time t;

[0039] Based on the Markov state of the risk failure component, according to the formula:

[0040]

[0041] Construct the Markov state probability function; where N T is the number of decision cycles; P(j, t-1) is the probability of existence of the Markov state corresponding to the system topology; P(i, t) is the probability of existence of the Markov state of the power system; Pr(S j，t-1 , S i,t ) is the fault transfer probability of the power system.

[0042] According to the technical solution provided by the present application, the objective function is constructed according to the load loss function, the load penalty function and the Markov state probability function, including:

[0043] Based on the load loss function, load penalty function and Markov state probability function, according to the formula:

[0044]

[0045] Construct the objective function; wherein OF1 is the objective function; P(i, t) is the probability of existence of the Markov state of the power system; C(S i，t, A i,t ) is the i-th Markov state in A at time t i，t Load loss under active defense strategy; C penalty (i, t) is the penalty cost of the i-th Markov state at time t under the active defense strategy.

[0046] In a second aspect, the present application provides an active defense device against the impact of typhoon weather on the power system, comprising:

[0047] A prediction module, used for obtaining the predicted positions corresponding to a plurality of consecutive decision cycles of the typhoon to be predicted based on the path similarity method according to the trajectory parameters and current movement parameters of the typhoon to be predicted, and calculating the predicted speed of the typhoon to be predicted in each of the decision cycles according to the Holland model and the predicted positions;

[0048] A construction module is used to obtain risk fault components according to the predicted position and the distribution position of the power components, and to construct a Markov state topology network of the power system according to the risk fault components based on the Markov theory to determine the Markov state of the power system;

[0049] A calculation module, used to calculate the failure probability of each risk failure element according to the predicted speed and the universal vulnerability curve of the power element, and calculate the failure transfer probability of the power system under each Markov state according to the failure probability;

[0050] A processing module, used to construct an objective function according to a load loss function, a load penalty function and a Markov state probability function, take the active defense strategy in each decision cycle as a variable, take the power system performance constraint as a constraint condition, construct an optimization model of the Markov state topology network and calculate the optimal solution, and obtain the target active defense strategy in each decision cycle; wherein the load loss function is determined according to the fault transfer probability and defense strategy load loss of the power system in each Markov state, the load penalty function is determined according to the power supply and power demand of the power system in each Markov state, and the Markov state probability function is determined according to the Markov state of the power system; the power system performance constraints include power flow balance constraints, branch active and reactive power flow constraints, generator output active and reactive power constraints, node voltage constraints, load reduction constraints, and generator set climbing rate constraints;

[0051] The control module is used to execute the corresponding target active defense strategy according to the target active defense strategy before the typhoon to be predicted enters the next decision cycle.

[0052] In a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described above when executing the computer program.

[0053] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and the computer program implements the method described above when executed by a processor.

[0054] Compared with the prior art, the beneficial effects of the present application are as follows: the active defense method, device, equipment and medium for the impact of typhoon weather on the power system provided by the present application, based on the path similarity method and the Holland model, obtains the predicted position and predicted speed according to the trajectory parameters and movement parameters of the typhoon to be predicted; based on the Markov theory, a Markov state topological network is constructed according to the risk fault element determined by the predicted position and the fault transfer probability is calculated; the objective function is constructed according to the load loss function, the load penalty function and the Markov state probability function, and the active defense strategy in each decision cycle is used as a variable, and the power system performance constraint is used as a constraint condition, and the optimization model of the Markov state topological network is constructed and the optimal solution is calculated, and the corresponding target active defense strategy is executed before the next decision cycle according to the obtained target active defense strategy. The active defense strategy provided by the present application takes into account the impact of the typhoon movement state and the typhoon on the probability of component failure, and well solves the contradiction between "dimensionality curse" and "sufficiency", and takes into account the mass failures caused by typhoon events, and will not miss potential failures with high harm but low probability of occurrence, and can pre-generate the optimal active output of the power plant under all expected states of the system before the typhoon arrives. At the same time, the wind speed distribution of the typhoon is taken into account, and different observation points have different impacts on the power system components, so the impact of the typhoon on the power system components can be predicted more accurately, thereby taking into account all expected fault scenarios of the system. The optimal active output of the power plant corresponding to each system state comprehensively considers the load loss and load satisfaction of the current state, so that the total load loss of the next state expected from the current state is minimized, and the load satisfaction is maximized. Compared with the passive operation strategy of taking measures after a fault, it can reduce more load losses overall, improve load satisfaction, and effectively improve the resilience of the power system under typhoon weather events. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solution in this embodiment, the drawings required for use in the description of the embodiment will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1A flowchart of an active defense method for the impact of typhoon weather on the power system provided in an embodiment of the present application;

[0057] Figure 2 A schematic diagram of Markov state transition provided for an embodiment of the present application;

[0058] Figure 3 A schematic diagram of an active defense device for the impact of typhoon weather on a power system provided in an embodiment of the present application;

[0059] Figure 4 A schematic diagram of a computer system of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] In order to enable those skilled in the art to better understand the technical solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings. The description in this section is only exemplary and explanatory and should not have any limiting effect on the protection scope of the present application. Specifically, the described embodiments are only embodiments of a part of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work should fall within the scope of protection of the present application.

[0061] It should be noted that similar reference numerals and letters represent similar items in the following figures, so once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0062] In order to make the technical solution of the present application clearer and easier to understand, the active defense method against the impact of typhoon weather on the power system provided by the embodiment of the present application is introduced below.

[0063] like Figure 1 As shown in FIG. 1 , the figure is a flow chart of an active defense method for the impact of typhoon weather on the power system provided by this embodiment, and the method includes the following steps:

[0064] S101, according to the trajectory parameters and current movement parameters of the typhoon to be predicted, obtaining the predicted positions corresponding to multiple consecutive decision cycles of the typhoon to be predicted based on the path similarity method, and calculating the predicted speed of the typhoon to be predicted in each decision cycle according to the Holland model and the predicted positions;

[0065] Specifically, the typhoon to be predicted is any typhoon that is currently on track, and the first trajectory parameter includes the positions (longitude and latitude) of multiple trajectory points that the typhoon to be predicted has passed before the current trajectory point and the position (longitude and latitude) of the current trajectory point; the current moving parameter includes the moving speed and moving angle of the current trajectory point of the typhoon to be predicted. It should be noted that the moving speed is the total speed of the typhoon to be predicted; of course, the first trajectory parameter and the current moving parameter may also include other parameters, which can be set and adjusted according to actual conditions, and no specific limitation is made this time. Obtaining the predicted positions corresponding to multiple consecutive decision cycles of the typhoon to be predicted based on the path similarity method specifically includes: selecting geographically similar historical typhoons corresponding to the typhoon to be predicted according to the trajectory parameters of the typhoon to be predicted and the trajectory parameters of historical typhoons, and then calculating the similarity of movement direction and speed between the geographically similar historical typhoons and the typhoon to be predicted according to the trajectory parameters of the typhoon to be predicted, the trajectory parameters of the geographically similar historical typhoons and the decision cycle, selecting geographically similar historical typhoons with movement direction and speed similarity greater than a preset threshold as target similar historical typhoons, calculating the similarity weight of the target similar historical typhoons, and then calculating the predicted movement direction and speed of the next trajectory point of the typhoon to be predicted according to the current movement parameters, the similarity weight, and the trajectory parameters of the target similar historical typhoon, and calculating the predicted position of the next trajectory point of the typhoon to be predicted according to the trajectory parameters of the typhoon to be predicted and the predicted movement direction and speed, and then calculating the predicted speed of the typhoon to be predicted in each decision cycle according to the Holland model and the predicted position. Among them, the decision cycle is 6 hours, and can also be set and adjusted to other durations according to actual conditions, and is not specifically limited here.

[0066] S102, acquiring risk fault components according to the predicted positions and the distribution positions of the power components, and constructing a Markov state topology network of the power system according to the risk fault components based on the Markov theory to determine the Markov state of the power system;

[0067] Specifically, according to the predicted position of the next trajectory point of the to-be-predicted typhoon and the distribution position of the power elements, the power elements that the trajectory of the to-be-predicted typhoon passes through are screened as risk fault elements, and then all expected states of the risk fault elements are determined based on the Markov theory, and all expected operating states of each of the risk fault elements are used as topological nodes, which are connected in sequence according to a decision cycle to form a Markov state topological network of the power system, and then based on the Markov state topological network, multiple Markov states of the power system are obtained; wherein, one Markov state is a sequence of operating states of risk fault elements arranged in sequence according to the decision cycle.

[0068] S103, calculating the failure probability of each risk failure component according to the predicted speed and the universal vulnerability curve of the power component, and calculating the failure transfer probability of the power system in each Markov state according to the failure probability;

[0069] Specifically, the universal vulnerability curve of the basic components of the power system shows that each basic component of the power system has a critical bearing capacity when facing extreme natural disasters. When the dangerous force caused by extreme natural disasters is greater than this critical bearing capacity, the failure probability of the component is greatly increased, as expressed by the above formula. Taking the impact of typhoon weather events on the power system transmission lines as an example, the transmission lines are designed with a maximum allowable wind speed, that is, the power components can withstand the critical speed v1. It is generally believed that when the typhoon wind speed is less than v1, the failure rate of the transmission line is 0, and when the typhoon wind speed is greater than twice the maximum allowable wind speed, the transmission line will definitely fail. Therefore, according to the formula:

[0070]

[0071] Calculate the failure probability of each risk failure component; wherein λ is the failure probability of the risk failure component, v is the predicted speed, v1 is the critical speed that the power component can withstand, a is a preset parameter, and parameter a=0.6931.

[0072] S104. Construct an objective function according to a load loss function, a load penalty function and a Markov state probability function, take the active defense strategy in each decision cycle as a variable, take the power system performance constraint as a constraint condition, construct an optimization model of the Markov state topology network and calculate the optimal solution, and obtain the target active defense strategy in each decision cycle; wherein the load loss function is determined according to the fault transfer probability and the defense strategy load loss of the power system in each Markov state, the load penalty function is determined according to the power supply and power demand of the power system in each Markov state, and the Markov state probability function is determined according to the Markov state of the power system; the power system performance constraints include power flow balance constraints, branch active and reactive power flow constraints, generator output active and reactive power constraints, node voltage constraints, load reduction constraints, and generator set climbing rate constraints;

[0073] Specifically, the load loss function is constructed based on the fault transfer probability and the load loss of the defense strategy of the power system in each Markov state, and then the load penalty function is constructed based on the power supply and power demand of the power system in each Markov state, and then the Markov state probability function is constructed based on the Markov state of the power system, and then the objective function is constructed based on the load loss function, the load penalty function and the Markov state probability function. Taking the active defense strategy in each decision cycle as a variable and the power system performance constraint as a constraint condition, the optimization model of the Markov state topology network is constructed and the optimal solution is calculated to obtain the target active defense strategy in each decision cycle. The active defense strategy includes adjusting the active power output of the generator, and can also include other defense strategies, which can be set and adjusted according to the actual situation, and are not specifically limited here. Among them, the power system performance constraints of the active defense strategy include power flow balance constraints, branch active and reactive power flow constraints, generator output active and reactive power constraints, node voltage constraints, load reduction constraints, and generator set climbing rate constraints.

[0074] The power flow balance constraints include:

[0075] Active power balance constraints:

[0076] P i =V i ∑V j (G ij cosθ ij +B ij sinθ ij )

[0077] Reactive power balance constraints:

[0078] Q i =V i ∑V j (G ij sinθ ij -B ij cosθ ij )

[0079] In the risk assessment of power generation and transmission systems, in order to reduce the amount of calculation, the optimal power flow model based on DC power flow is often used. The DC power flow model only considers active power and is expressed as:

[0080] P=B′θ

[0081] Among them, P i is the active injection power of node i; Q i is the reactive injection power of node i; V i and V j are the voltages of nodes i and j respectively; G ijand B ij are the real and imaginary parts of the node admittance matrix respectively; θ ij is the voltage phase difference between the two ends of the branch ij; P is the active power column vector injected into the node.

[0082] The diagonal elements of B′ satisfy:

[0083]

[0084] The off-diagonal elements of B′ satisfy:

[0085] B ij '=-B ij

[0086] The branch active and reactive power flow constraints (if the DC power flow model is used, only the branch active power flow constraints) include:

[0087] The active power flow constraint is expressed as:

[0088]

[0089] The reactive power flow constraint is expressed as:

[0090]

[0091] Among them, P ij is the active power of branch ij; is the maximum allowable value of active power of branch ij; Q ij is the reactive power of branch ij; is the maximum allowable value of reactive power in branch ij.

[0092] Usually there are multiple generators in a power plant that can adjust active and reactive power. The active and reactive output of each generator has a minimum value and a maximum value. Therefore, the active and reactive power constraints of the generator (if the DC power flow model is used, there are only upper and lower limits of the active power of the generator) are expressed as:

[0093] Pi∈[Pimin,Pimax]

[0094] Qi∈[Qimin,Qimax]

[0095] Among them, P gi and Q gi are the active power and reactive power of generator i respectively; and are the maximum active power and maximum reactive power of generator i respectively; and are the minimum active power and minimum reactive power of generator i respectively.

[0096] The node voltage constraints (if the DC power flow model is used, there is no node voltage constraint) should satisfy:

[0097] Vi∈[V i min , V i max ]

[0098] in, is the voltage lower limit of node i; is the voltage upper limit of node i; V i is the voltage at node i.

[0099] In addition, the lost load is not greater than the original load, so the load reduction constraint is expressed as:

[0100]

[0101] Where, ΔL i，s is the load loss of node i in state s; is the initial load of node i in state s.

[0102] In addition, the active power change of the generator within a certain period of time cannot exceed a fixed value. The generator set ramp rate constraint is expressed as:

[0103]

[0104] in, represents the active power of the generator at node i at time t1; represents the active power of the generator at node i at time t2; PC is the generator ramp rate.

[0105] S105. Execute a corresponding target active defense strategy according to the target active defense strategy before the to-be-forecasted typhoon enters a next decision cycle.

[0106] Specifically, before the predicted typhoon enters the next decision cycle, the Markov state of the power system is determined, and the active defense strategy corresponding to the corresponding Markov state is found in the Markov state topological network as the target active defense strategy, and the generator output active power is adjusted.

[0107] Further on the basis of the above embodiment, the step of obtaining the predicted position corresponding to a plurality of consecutive decision cycles of the typhoon to be predicted based on the path similarity method according to the trajectory parameters of the typhoon to be predicted and the current movement parameters includes:

[0108] According to the trajectory parameters of the typhoon to be predicted and the trajectory parameters of historical typhoons, screening geographically similar historical typhoons corresponding to the typhoon to be predicted;

[0109] Specifically, according to the longitudes and latitudes of the multiple track points that the to-be-forecasted typhoon has passed through, and the longitudes and latitudes of the track points included in each historical typhoon, obtain the following:

[0110] as the geographically similar historical typhoon; wherein la(i, j) is the latitude of the jth trajectory point of the ith historical typhoon; lap(j) is the latitude of the jth trajectory point that the typhoon to be predicted has passed; lo(i, j) is the longitude of the jth trajectory point of the ith historical typhoon; lop(j) is the longitude of the jth trajectory point that the typhoon to be predicted has passed; maxdla and maxdlo are the edge latitude and edge longitude corresponding to the preset judgment area.

[0111] According to the trajectory parameters of the typhoon to be predicted, the trajectory parameters of the geographically similar historical typhoons, and the decision cycle, the similarity of the direction and speed of the geographically similar historical typhoons and the typhoon to be predicted is calculated, the geographically similar historical typhoons whose direction and speed similarity are greater than a preset threshold are selected as target similar historical typhoons, and the similarity weight of the target similar historical typhoons is calculated;

[0112] Specifically, according to the trajectory parameters of the typhoon to be predicted and the decision cycle, the first moving speed and the second moving speed of the typhoon to be predicted are calculated; then, according to the trajectory parameters of the geographically similar historical typhoons and the decision cycle, the third moving speed and the fourth moving speed of the geographically similar historical typhoon are calculated; then, according to the first moving speed and the third moving speed, the first average speed difference between the typhoon to be predicted and the geographically similar historical typhoons is calculated; then, according to the second moving speed and the fourth moving speed, the second average speed difference between the typhoon to be predicted and the geographically similar historical typhoons is calculated; then, the first average speed difference and the second average speed difference are normalized respectively to obtain the first similarity and the second similarity between the geographically similar historical typhoon and the typhoon to be predicted. Among them, the first moving speed is the speed of the typhoon to be predicted perpendicular to the latitude direction, the second moving speed is the speed of the typhoon to be predicted perpendicular to the longitude direction; the third moving speed is the speed of the geographically similar historical typhoon perpendicular to the latitude direction, and the fourth moving speed is the speed of the geographically similar historical typhoon perpendicular to the longitude direction; the first average speed difference is the average speed difference perpendicular to the latitude direction; the second average speed difference is the average speed difference perpendicular to the longitude direction.

[0113] Then, geographically similar historical typhoons whose first similarity and second similarity are both less than a first preset threshold (valued at 0.5) are selected as the target similar historical typhoons, and the trajectory parameters corresponding to the target similar historical typhoons are obtained; according to the formula:

[0114]

[0115] Calculate the first similarity weight and second similarity weight of each target similar historical typhoon respectively; wherein, kva(i) is the first similarity weight of the i-th target similar historical typhoon; VASI(i) is the first similarity of the i-th target similar historical typhoon; length(VASI) is the number of first similarities corresponding to the target similar historical typhoons; kvo(i) is the second similarity weight of the i-th target similar historical typhoon; VOSI(i) is the second similarity of the i-th target similar historical typhoon; length(VOSI) is the number of second similarities corresponding to the target similar historical typhoons.

[0116] According to the current movement parameters, the similarity weight, and the trajectory parameters of the target similar historical typhoon, the predicted movement direction and moving speed of the next trajectory point of the typhoon to be predicted is calculated, and according to the trajectory parameters of the typhoon to be predicted and the predicted movement direction and moving speed, the predicted position of the next trajectory point of the typhoon to be predicted is calculated.

[0117] Specifically, according to the moving speed and the moving angle of the current track point of the typhoon to be predicted, the first moving current speed and the second moving current speed of the typhoon to be predicted are calculated; according to the longitude and latitude of the multiple track points included in the target similar historical typhoon and the decision cycle, the first moving average speed and the second moving average speed of each target similar historical typhoon are calculated; according to the formula:

[0118]

[0119] Calculate the predicted moving speed of the next track point of the typhoon to be predicted (including the first moving speed predicted in the direction perpendicular to the latitude and the second moving speed predicted in the direction perpendicular to the longitude); wherein, vap1 is the first moving speed predicted in the next track point of the typhoon to be predicted; kva(i) is the first similarity weight of the i-th target similar historical typhoon; va(i, length(lap)) is the first moving average speed of the i-th target similar historical typhoon, and λ is the weight factor; va0 is the current speed of the first moving direction of the typhoon to be predicted; vop1 is the second moving speed predicted in the next track point of the typhoon to be predicted; kvo(i) is the first similarity weight of the i-th target similar historical typhoon; vo(i, length(lap)) is the second moving average speed of the i-th target similar historical typhoon; voO is the current speed of the second moving direction of the typhoon to be predicted; the first moving average speed and the second moving average speed are the average speed perpendicular to the latitude and the average speed perpendicular to the longitude, respectively. According to the formula:

[0120] lap1=lapO+vap1*dt

[0121] lop1=lopO+vop1*dt

[0122] Calculate the predicted position (including predicted latitude and predicted longitude) of the next track point of the typhoon to be predicted; wherein, lap1 is the predicted latitude of the typhoon to be predicted, lapO is the latitude of the current track point of the typhoon to be predicted; vap1 is the first moving direction predicted speed of the next track point of the typhoon to be predicted; dt is the decision cycle; lop1 is the predicted longitude of the typhoon to be predicted, lopO is the longitude of the current track point of the typhoon to be predicted; vop1 is the second moving direction predicted speed of the next track point of the typhoon to be predicted.

[0123] Further on the basis of the above embodiment, the calculation of the prediction speed of the typhoon to be predicted in each decision cycle according to the Holland model and the prediction position includes:

[0124] According to the formula:

[0125]

[0126] Calculate the predicted speed of the typhoon to be predicted in each decision cycle; wherein v(r) is the predicted speed of the next trajectory point with a distance r from the current trajectory point; p n is the average air pressure outside the typhoon; p c is the typhoon center pressure; B is the shape coefficient; ρ a is the air density; R max is the maximum wind speed radius; r is the distance between the predicted position of the next trajectory point and the current trajectory point; ψ is the predicted latitude of the next trajectory point.

[0127] Specifically, according to the Holland model, the gradient wind speed expression in the typhoon wind field is:

[0128]

[0129] Where v(r) is the predicted velocity of the next track point with a distance r from the current track point; the average air pressure outside the typhoon p n Take 1010hPa; typhoon center pressure p c It can be observed from the weather station; the shape coefficient B is used to describe the shape of the pressure profile; the air density ρ a Take 1.2kg / m 3 ; Maximum wind speed radius R max is the radius where the wind speed reaches the maximum value; r is the distance between the predicted position of the next trajectory point and the current trajectory point. Compared with other pressure models, the Holland model needs to determine two key parameters: Rmax and B, which are related to the typhoon’s central air pressure, typhoon’s location, etc.

[0130] Further on the basis of the above embodiment, the risk fault component is obtained according to the predicted position and the distribution position of the power component, and the Markov state topology network of the power system is constructed according to the risk fault component based on the Markov theory to determine the Markov state of the power system, including:

[0131] According to the predicted position and the distribution position of the power components, selecting the power components that the track of the to-be-predicted typhoon passes through as risk failure components;

[0132] Specifically, since not all power system equipment will be affected by typhoons, and the reliability of basic power system components is usually high enough, in order to reduce the number of Markov states, it is assumed that power system components that have not been passed by the typhoon are operating reliably during the entire weather event. In this way, the power components that are expected to fail along the trajectory of the predicted typhoon are screened out based on the predicted position and the distribution position of the power components, and are treated as risk failure components.

[0133] Determine all expected states of the risk fault components based on Markov theory, take all expected operating states of each risk fault component as topological nodes, connect them in sequence according to the decision cycle, and form a Markov state topological network of the power system;

[0134] Specifically, based on the Markov theory, under typhoon weather events, it is considered that the topological state of the power system is uniquely determined by the state of the power system components. A basic component of the power system has two states: normal operation and offline (including components that are planned to be deactivated and faulty components). A decision cycle is 6 hours. In each decision cycle, the components of the power system have only one expected state. It is considered that the components have been in a faulty state after the typhoon hits and causes the component to fail. Then the number of Markov states of the power system is 2. n , n is the number of risk failure components. For example, assuming that the Markov process is divided into three decision cycles, the state in the first decision cycle is S 11 , the number of risk faulty components in the second decision cycle is 1, then the expected state of the faulty components in the second decision cycle is offline or normal (i.e., S 21 and S 22 ), the number of risky faulty components in the third decision cycle is 2, and the expected state of the faulty components in the second decision cycle is offline (S 31 , S 32 ) or normal (S 33 , S 34 ), it can be seen that the Markov state topology network is Figure 2shown.

[0135] According to the Markov state topology network, a plurality of Markov states of the power system are obtained; wherein one of the Markov states is a sequence of operating states of risk fault components arranged in sequence according to a decision cycle.

[0136] Specifically, in each decision cycle, the current state of the power system and the next expected state are considered simultaneously, thereby converting the uncertain sequence of the power system state caused by extreme events into a discrete Markov process.

[0137] Further on the basis of the above embodiment, calculating the fault transfer probability of the power system according to the fault probability includes:

[0138] According to the formula:

[0139]

[0140] Calculate the failure transfer probability of each risk failure component; k,t ,s k,t+1 They represent the operating status of the risk fault component k at time t and time t+1 respectively, 1 represents the normal operation of the risk fault component k, and 0 represents the failure of the risk fault component k; λ is the failure probability of the risk fault component;

[0141] Specifically, for a basic power element of a power system law, during two adjacent decision cycles, the power element is expected to change from one state to another state, and the above formula is used to represent the fault transfer probability of each risk fault element.

[0142] According to the formula:

[0143]

[0144] Calculate the fault transfer probability of the power system; where Pr(S i,t ,S j,t+1 ) is the fault transfer probability of the power system; pr(s k,t ,s k,t+1 ) is the failure transfer probability of risk fault component k from time t to time t+1.

[0145] Specifically, the Markov state of the power system is uniquely determined by the state of the risk fault component, so the transition probability of different Markov states can be expressed by the above formula.

[0146] On the basis of the above embodiment, an objective function is further constructed according to the load loss function, the load penalty function and the Markov state probability function, including:

[0147] Based on the failover probability and defense strategy load loss, according to the formula:

[0148]

[0149] Construct the load loss function; where C(S i，t , A i，t ) is the i-th Markov state in A at time t i，t Load loss under active defense strategy; A i，t is the active defense strategy of the i-th Markov state at time t; ΔL1(S i，t , A i，t ) is the current load loss after the corresponding active defense strategy is implemented in the i-th Markov state at time t; C(S j，t+1 , A j，t+1 ) is the load loss after the corresponding active defense strategy is implemented in the next state of the power system state; Pr(S i，t , S j，t+1 ) is the fault transfer probability of the power system;

[0150] Based on the power supply and power demand of the power system under different Markov states, according to the formula:

[0151] C penalty (i, t) = k·max(0, P load (i, t)-P Supply (i, t))

[0152] Construct the load penalty function; where C penalty (i, t) is the penalty cost of the ith Markov state under the active defense strategy at time t; k is the penalty coefficient; P load (i, t) represents the load demand of the i-th Markov state at time t; P Supply (i, t) represents the power supply of the i-th Markov state at time t;

[0153] Based on the Markov state of the risk failure component, according to the formula:

[0154]

[0155] Construct the Markov state probability function; where N T is the number of decision cycles; P(j, t-1) is the probability of existence of the Markov state corresponding to the system topology; P(i, t) is the probability of existence of the Markov state of the power system; Pr(S j，t-1 , S i，t ) is the fault transfer probability of the power system;

[0156] Based on the load loss function, load penalty function and Markov state probability function, according to the formula:

[0157]

[0158] Construct the objective function; wherein OF1 is the objective function; P(i, t) is the probability of existence of the Markov state of the power system; C(S i,t ,A i,t ) is the i-th Markov state in A at time t i,t Load loss under active defense strategy; C penalty (i,t) is the penalty cost of the i-th Markov state at time t under the active defense strategy.

[0159] Specifically, during typhoon weather events, the power system dispatch should be based on the current state of each decision cycle of the power system, and consider the expected state of the next decision cycle, and actively adjust the generator output to minimize the overall loss caused by the typhoon weather event. Typhoon weather events often lead to large-scale power outages, and because of the arrival of the typhoon, the power of the power components must be adjusted, and there will definitely be load losses. However, if the power drops to a certain level, it is expected to cause an imbalance between power supply and demand. If the power supply is still sufficient even after adjusting the power of the components, then the penalty cost is 0. If the power supply is less than the demand, there will definitely be a penalty cost. In short, the goal of the active defense strategy is to minimize the load loss of the power system and maximize the load satisfaction. Therefore, the load loss function is constructed based on the fault transfer probability and the load loss of the defense strategy to characterize the load loss of the power system; the load penalty function is constructed based on the power supply and power demand of the power system under different Markov states to characterize the load penalty cost of the system caused by the active defense strategy; in addition, the linear scaling method is used to transform multiple objectives into a single objective, and the existence probability of each Markov state is used as the weight of the objective function, and then the Markov state probability function is constructed based on the Markov state of the risk fault element to characterize the existence probability of the Markov state corresponding to the Markov state topology network of the power system.

[0160] Combination of the above Figure 1-2 The active defense method against the impact of typhoon weather on the power system provided in the embodiment of the present application is introduced in detail. The active defense device, electronic device and computer-readable storage medium against the impact of typhoon weather on the power system provided in the embodiment of the present application will be introduced in conjunction with the accompanying drawings.

[0161] like Figure 3 As shown in the figure, this figure is a schematic diagram of an active defense device for the impact of typhoon weather on the power system provided by the present application, and the device includes:

[0162] The prediction module 201 is used to obtain the predicted positions corresponding to a plurality of consecutive decision cycles of the typhoon to be predicted based on the path similarity method according to the trajectory parameters and current movement parameters of the typhoon to be predicted, and calculate the predicted speed of the typhoon to be predicted in each decision cycle according to the Holland model and the predicted positions;

[0163] A construction module 202 is used to obtain risk fault components according to the predicted position and the distribution position of the power components, and to construct a Markov state topology network of the power system according to the risk fault components based on the Markov theory to determine the Markov state of the power system;

[0164] A calculation module 203, configured to calculate the failure probability of each risk failure component according to the predicted speed and the universal vulnerability curve of the power component, and calculate the failure transfer probability of the power system in each Markov state according to the failure probability;

[0165] The processing module 204 is used to construct an objective function according to the load loss function, the load penalty function and the Markov state probability function, take the active defense strategy in each decision cycle as a variable, take the power system performance constraint as a constraint condition, construct an optimization model of the Markov state topology network and calculate the optimal solution, and obtain the target active defense strategy in each decision cycle; wherein the load loss function is determined according to the fault transfer probability and the defense strategy load loss of the power system in each Markov state, the load penalty function is determined according to the power supply and power demand of the power system in each Markov state, and the Markov state probability function is determined according to the Markov state of the power system; the power system performance constraints include power flow balance constraints, branch active and reactive power flow constraints, generator output active and reactive power constraints, node voltage constraints, load reduction constraints, and generator set climbing rate constraints;

[0166] The control module 205 is used to execute the corresponding target active defense strategy according to the target active defense strategy before the typhoon to be predicted enters the next decision cycle.

[0167] The active defense device for the impact of typhoon weather on the power system provided in the embodiment of the present application can correspond to the active defense method for the impact of typhoon weather on the power system described in the embodiment of the present application, and the above functions of each module of the device correspond to the implementation of Figure 1 For the sake of brevity, the corresponding process of the method shown will not be repeated here.

[0168] An embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the active defense method against the impact of typhoon weather on the power system as described in the above embodiment is implemented.

[0169] like Figure 4 As shown, the computer system 300 of the electronic device includes a CPU 301, which can perform various appropriate actions and processes according to the program stored in the ROM 302 or the program loaded from the storage part 308 to the RAM 303. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, the ROM 302 and the RAM 303 are connected to each other through a bus 304. The I / O interface 305 is also connected to the bus 304. Among them, CPU 301 represents a central processing unit, ROM 302 represents a read-only memory, RAM 303 represents a random access memory, and I / O represents input / output.

[0170] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section 307 including a cathode ray tube, a liquid crystal display, etc., and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed, so that a computer program read therefrom is installed into the storage section 308 as needed.

[0171] In particular, the process of the active defense method for the impact of typhoon weather on the power system described in the above embodiment can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains program code for executing the active defense method for the impact of typhoon weather on the power system described in the above embodiment. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 309, and / or installed from the removable medium 311. When the computer program is executed by CPU301, the above functions defined in the present computer system 300 are executed.

[0172] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the active defense method against the impact of typhoon weather on the power system as described in the above embodiment is implemented.

[0173] Specifically, the computer-readable storage medium may be included in the electronic device described in the above embodiment; or it may exist independently without being assembled into the electronic device. The above computer-readable storage medium carries one or more programs. When the above one or more programs are executed by an electronic device, the electronic device implements the active defense method against the impact of typhoon weather on the power system as described in the above embodiment.

[0174] It should be noted that the computer-readable storage medium shown in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above.

[0175] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and its core ideas of this application. The above is only the preferred implementation method of this application. It should be pointed out that due to the limitations of textual expression and the objective existence of infinite specific structures, ordinary technicians in this technical field can make several improvements, modifications or changes without departing from the principles of the present invention, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the scope of protection of this application.

Claims

1. An active defense method for the impact of typhoon weather on power systems, characterized in that: include: According to the trajectory parameters and current movement parameters of the typhoon to be predicted, the predicted positions corresponding to a plurality of consecutive decision cycles of the typhoon to be predicted are obtained based on the path similarity method, and the predicted speed of the typhoon to be predicted in each of the decision cycles is calculated based on the Holland model and the predicted positions; Acquire risk fault components according to the predicted positions and the distribution positions of the power components, and construct a Markov state topology network of the power system according to the risk fault components based on Markov theory to determine the Markov state of the power system; Calculating the failure probability of each risk failure component according to the predicted speed and the universal vulnerability curve of the power component, and calculating the failure transfer probability of the power system in each Markov state according to the failure probability; An objective function is constructed according to a load loss function, a load penalty function and a Markov state probability function, and an active defense strategy in each decision cycle is used as a variable and a power system performance constraint is used as a constraint condition to construct an optimization model of the Markov state topology network and calculate the optimal solution to obtain a target active defense strategy in each decision cycle; wherein, the load loss function is determined according to the fault transfer probability and the load loss of the defense strategy of the power system in each Markov state, the load penalty function is determined according to the power supply and power demand of the power system in each Markov state, and the Markov state probability function is determined according to the Markov state of the power system; the power system performance constraints include power flow balance constraints, branch active and reactive power flow constraints, generator output active and reactive power constraints, node voltage constraints, load reduction constraints, and generator set climbing rate constraints; According to the target active defense strategy, a corresponding target active defense strategy is executed before the to-be-forecasted typhoon enters the next decision cycle.

2. The method according to claim 1, characterized in that The step of obtaining the predicted positions corresponding to a plurality of consecutive decision cycles of the typhoon to be predicted based on the path similarity method according to the trajectory parameters of the typhoon to be predicted and the current movement parameters includes: According to the trajectory parameters of the typhoon to be predicted and the trajectory parameters of historical typhoons, screening geographically similar historical typhoons corresponding to the typhoon to be predicted; According to the trajectory parameters of the typhoon to be predicted, the trajectory parameters of the geographically similar historical typhoons, and the decision cycle, the similarity of the direction and speed of the geographically similar historical typhoons and the typhoon to be predicted is calculated, the geographically similar historical typhoons whose direction and speed similarity are greater than a preset threshold are selected as target similar historical typhoons, and the similarity weight of the target similar historical typhoons is calculated; According to the current movement parameters, the similarity weight, and the trajectory parameters of the target similar historical typhoon, the predicted movement direction and moving speed of the next trajectory point of the typhoon to be predicted is calculated, and according to the trajectory parameters of the typhoon to be predicted and the predicted movement direction and moving speed, the predicted position of the next trajectory point of the typhoon to be predicted is calculated.

3. The method according to claim 1, characterized in that The calculating the prediction speed of the typhoon to be predicted in each decision cycle according to the Holland model and the prediction position includes: According to the formula: Calculate the predicted speed of the typhoon to be predicted in each decision cycle; wherein v(r) is the predicted speed of the next trajectory point with a distance r from the current trajectory point; p n is the average air pressure outside the typhoon; p c is the typhoon center pressure; B is the shape coefficient; ρ a is the air density; R max is the maximum wind speed radius; r is the distance between the predicted position of the next trajectory point and the current trajectory point; ψ is the predicted latitude of the next trajectory point.

4. The method according to claim 1, characterized in that: The step of acquiring risk fault components according to the predicted positions and the distribution positions of the power components, and constructing a Markov state topology network of the power system according to the risk fault components based on the Markov theory to determine the Markov state of the power system includes: According to the predicted position and the distribution position of the power components, selecting the power components that the track of the to-be-predicted typhoon passes through as risk failure components; Determine all expected states of the risk fault components based on Markov theory, take all expected operating states of each risk fault component as topological nodes, connect them in sequence according to the decision cycle, and form a Markov state topological network of the power system; According to the Markov state topology network, a plurality of Markov states of the power system are obtained; wherein one of the Markov states is a sequence of operating states of risk fault components arranged in sequence according to a decision cycle.

5. The method according to claim 1, characterized in that Calculating the fault transfer probability of the power system according to the fault probability includes: According to the formula: Calculate the failure transfer probability of each risk failure component; k,t ,S k,t+1 They represent the operating status of the risk fault component k at time t and time t+1 respectively, 1 represents the normal operation of the risk fault component k, and 0 represents the failure of the risk fault component k; λ is the failure probability of the risk fault component; According to the formula: Pr(S i,t ,S j,t+1) =Πpr(s k,t ,s k,t+1 ) Calculate the fault transfer probability of the power system; where Pr(S i,t ,S j,t+1 ) is the fault transfer probability of the power system; pr(s k,t ,s k,t+1 ) is the failure transfer probability of risk fault component k from time t to time t+1.

6. The method according to claim 1, characterized in that Before constructing the objective function according to the load loss function, the load penalty function and the Markov state probability function, the method further includes: Based on the failover probability and defense strategy load loss, according to the formula: Construct the load loss function; where C(S i,t ,A i,t ) is the i-th Markov state in A at time t i,t Load loss under active defense strategy; A i,t is the active defense strategy of the i-th Markov state at time t; ΔL1(S i,t ,A i,t ) is the current load loss after the corresponding active defense strategy is implemented in the i-th Markov state at time t; C(S j,t+1 ,A j,t+1 ) is the load loss after the corresponding active defense strategy is implemented in the next state of the power system state; Pr(S i,t ,S j,t+1 ) is the fault transfer probability of the power system; Based on the power supply and power demand of the power system under different Markov states, according to the formula: C penalty (i,t)=k·max(0,P load (i,t)-P Supply (i,t)) Construct the load penalty function; where C penalty (i, t) is the penalty cost of the i-th Markov state under the active defense strategy at time t; k is the penalty coefficient; P load (i, t) represents the load demand of the i-th Markov state at time t; P Supply (i,t) represents the power supply of the i-th Markov state at time t; Based on the Markov state of the risk failure component, according to the formula: Construct the Markov state probability function; where N T is the number of decision cycles; P(j,t-1) is the probability of existence of the Markov state corresponding to the system topology; P(i,t) is the probability of existence of the Markov state of the power system; Pr(S j,t-1 ,S i,t ) is the fault transfer probability of the power system.

7. The method according to claim 6, characterized in that The objective function is constructed according to the load loss function, the load penalty function and the Markov state probability function, including: Based on the load loss function, load penalty function and Markov state probability function, according to the formula: Construct the objective function; wherein OF1 is the objective function; P(i, t) is the probability of existence of the Markov state of the power system; C(S i,t ,A i,t ) is the i-th Markov state in A at time t i,t Load loss under active defense strategy; C penalty (i,t) is the penalty cost of the i-th Markov state at time t under the active defense strategy.

8. An active defense device for the impact of typhoon weather on the power system, characterized in that: include: A prediction module, used for obtaining the predicted positions corresponding to a plurality of consecutive decision cycles of the typhoon to be predicted based on the path similarity method according to the trajectory parameters and current movement parameters of the typhoon to be predicted, and calculating the predicted speed of the typhoon to be predicted in each of the decision cycles according to the Holland model and the predicted positions; A construction module is used to obtain risk fault components according to the predicted position and the distribution position of the power components, and to construct a Markov state topology network of the power system according to the risk fault components based on Markov theory to determine the Markov state of the power system; A calculation module, used to calculate the failure probability of each risk failure element according to the predicted speed and the universal vulnerability curve of the power element, and calculate the failure transfer probability of the power system under each Markov state according to the failure probability; A processing module, used to construct an objective function according to a load loss function, a load penalty function and a Markov state probability function, take the active defense strategy in each decision cycle as a variable, take the power system performance constraint as a constraint condition, construct an optimization model of the Markov state topology network and calculate the optimal solution, and obtain the target active defense strategy in each decision cycle; wherein the load loss function is determined according to the fault transfer probability and defense strategy load loss of the power system in each Markov state, the load penalty function is determined according to the power supply and power demand of the power system in each Markov state, and the Markov state probability function is determined according to the Markov state of the power system; the power system performance constraints include power flow balance constraints, branch active and reactive power flow constraints, generator output active and reactive power constraints, node voltage constraints, load reduction constraints, and generator set climbing rate constraints; The control module is used to execute the corresponding target active defense strategy according to the target active defense strategy before the typhoon to be predicted enters the next decision cycle.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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