Active defense method, device, equipment and medium for the impact of typhoon weather on power system

Abstract

The present application provides an active defense method, device, equipment and medium for the impact of typhoon weather on the power system, relating to the field of power system maintenance technology. The method includes obtaining a predicted position and a predicted speed according to the trajectory parameters and movement parameters of the typhoon to be predicted based on the path similarity method and the Holland model; constructing a Markov state topology network based on the risk fault elements determined by the predicted position based on the Markov theory and calculating the fault transfer probability; constructing an objective function based on the load loss function, the load penalty function and the Markov state probability function, constructing an optimization model of the Markov state topology network and calculating the optimal solution with the active defense strategy in each decision cycle as a variable and the power system performance constraint as a constraint condition, and executing the corresponding target active defense strategy before the next decision cycle based on the obtained target active defense strategy, thereby greatly reducing the load loss as a whole and effectively improving the resilience of the power system under typhoon weather events.

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, the cyclical nature of global climate change has increased the risks of climate change worldwide, and the frequency of typhoons has also increased. The increasing incidence and severity of typhoons pose new challenges to the safe, stable, and economical operation of power systems, making the development of power system resilience particularly important.

[0003] Power system resilience refers to its ability to withstand external disturbances and recover quickly after failures. Typhoons are low-probability, high-risk events. Responding to them requires dispatching departments to take preventative measures before they occur, implement control measures during the event, and quickly restore power afterward to minimize the impact and losses. This ability to withstand typhoons and mitigate their impact is a fundamental capability required of a resilient power system. To better prepare for typhoons and mitigate their impact, power research institutions around the world have intensified their research on the safe and stable operation of power systems during typhoons.

[0004] Traditional power system reliability analysis typically uses a table of expected failures, directly specifying expected power system failures and then performing offline calculations on these expected failures. However, this table fails to consider the impact of the actual power system environment on the failure rate of power system components. Furthermore, to more fully understand the consequences of various power system component failures and the necessary measures, the number of expected failures in the table must be increased, which in turn creates the dimensionality curse for large power systems. Furthermore, due to the expected dimensionality curse, the table of expected accidents often fails to account for clustered failures, which are common during typhoon events. Furthermore, traditional emergency control measures involve identifying the faulty component after a system failure and then taking appropriate actions to minimize load loss. This passive defense strategy lacks preventive and holistic approach, and the load loss minimization sought 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 in advance for the power system components 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 during 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 position corresponding to multiple consecutive decision cycles of the typhoon to be predicted is obtained based on the path similarity method, and the predicted speed of the typhoon to be predicted in each decision cycle is calculated based on the Holland model and the predicted position;

[0009] Obtain 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 based on 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 based on 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 based on the failure probability;

[0011] An objective function is constructed based on the load loss function, the load penalty function and the Markov state probability function. 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. An 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 load loss function is determined based on 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 based on the power supply and power demand of the power system in each Markov state. The Markov state probability function is determined based on 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 ramp rate constraints.

[0012] According to the target active defense strategy, a corresponding target active defense strategy is executed before the typhoon to be predicted 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 trajectory parameters and current movement parameters of the typhoon to be predicted and the path similarity method includes:

[0014] Screening geographically similar historical typhoons corresponding to the typhoon to be predicted based on the trajectory parameters of the typhoon to be predicted and the trajectory parameters of historical typhoons;

[0015] Calculating the similarity of the direction and speed of the geographically similar historical typhoons and the typhoon to be predicted based on the trajectory parameters of the typhoon to be predicted, the trajectory parameters of the geographically similar historical typhoons, and the decision period, selecting geographically similar historical typhoons with a direction and speed similarity greater than a preset threshold as target similar historical typhoons, and calculating the similarity weights of the target similar historical typhoons;

[0016] Based on the current movement parameters, the similarity weight, and the trajectory parameters of the target similar historical typhoon, the predicted movement direction and speed of the next trajectory point of the typhoon to be predicted are calculated; and based on the trajectory parameters of the typhoon to be predicted and the predicted movement direction and speed, the predicted position of the next trajectory point of the typhoon to be predicted is calculated.

[0017] According to the technical solution provided in this application, the calculation of the predicted speed of the typhoon to be predicted in each decision cycle based on the Holland model and the predicted 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 track point with a distance r from the current track 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 method of obtaining risk fault components based on the predicted positions and the distribution positions of the power components, and constructing a Markov state topology network of the power system based on the risk fault components based on the Markov theory to determine the Markov state of the power system includes:

[0022] selecting, according to the predicted location and the distribution locations of the power components, 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, use all expected operating states of each risk fault component as topological nodes, connect them sequentially according to decision cycles, 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 in this application, calculating the fault transfer probability of the power system according to the fault probability includes:

[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 indicates that the risk fault component k is operating normally, and 0 indicates that the risk fault component k is faulty. 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 this application, before constructing the objective function based on 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; wherein, 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; 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 i-th Markov state under the active defense strategy at time t; k represents 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 this application, the objective function is constructed based on 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 is configured to obtain, based on the trajectory parameters and current movement parameters of the typhoon to be predicted, the predicted positions corresponding to multiple consecutive decision cycles of the typhoon to be predicted based on the path similarity method, and calculate the predicted speed of the typhoon to be predicted in each decision cycle based on the Holland model and the predicted positions;

[0048] a construction module, configured to obtain 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;

[0049] a calculation module, 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;

[0050] A processing module is used to construct an objective function based on a load loss function, a load penalty function, and a Markov state probability function, and to construct an optimization model of the Markov state topology network and calculate the optimal solution based on the active defense strategy in each decision cycle and the power system performance constraint as a constraint condition, so as to obtain the target active defense strategy in each decision cycle; wherein the load loss function is determined based on the fault transfer probability and defense strategy load loss of the power system in each Markov state, the load penalty function is determined based on the power supply and power demand of the power system in each Markov state, and the Markov state probability function is determined based on 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 ramp rate constraints;

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

[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 present application has the following advantages: the present application provides an active defense method, device, equipment, and medium for the impact of typhoon weather on the power system. The method, device, and medium obtain the predicted position and predicted speed based on the trajectory parameters and movement parameters of the predicted typhoon based on the path similarity method and the Holland model; construct a Markov state topology network based on the risk fault components determined by the predicted position based on Markov theory and calculate the fault transfer probability; construct an objective function based on the load loss function, load penalty function, and Markov state probability function, and use the active defense strategy in each decision cycle as a variable and the power system performance constraint as a constraint condition to construct an optimization model of the Markov state topology network and calculate the optimal solution. The corresponding target active defense strategy is executed before the next decision cycle based on the obtained target active defense strategy. The active defense strategy provided by the present application considers both the typhoon motion state and the impact of the typhoon on the component failure probability, effectively resolving the contradiction between "dimensionality curse" and "sufficiency". In addition, the active defense strategy provided by the present application takes into account the clustered failures caused by typhoon events, does not miss potential failures with high damage but low probability of occurrence, and can pre-generate the optimal active output of the power plant under all expected conditions of the system before the typhoon arrives. At the same time, the typhoon's wind speed distribution is taken into account, and different observation points have different impacts on power system components, which can more accurately predict the impact of typhoons on power system components. Taking into account all expected failure scenarios of the system, the optimal active power output of the power plant corresponding to each system state comprehensively considers the current state load loss and load satisfaction, 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 following is a brief introduction to the drawings required for the description of the embodiment. 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 any 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 in an embodiment of the present application;

[0058] Figure 3 A schematic diagram of an active defense device for protecting power systems from the effects of typhoon weather, 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 solutions of the present application, the technical solutions 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 scope of protection of the present application. Specifically, the embodiments described are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this 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. Therefore, once an item is defined in one figure, it does not need to be further defined or 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 apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or inherent to these processes, methods, products or apparatuses.

[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, this figure is a flow chart of an active defense method for the impact of typhoon weather on the power system provided by this embodiment, the method comprising the following steps:

[0064] S101. According to the trajectory parameters and current movement parameters of the typhoon to be predicted, the predicted positions corresponding to multiple 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 decision cycle is calculated based on the Holland model and the predicted positions;

[0065] Specifically, the typhoon to be predicted is any typhoon currently in progress, 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 movement parameter includes the moving speed and movement 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 movement parameter can also include other parameters, which can be set and adjusted according to actual conditions, and are not specifically limited 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: screening geographically similar historical typhoons corresponding to the typhoon to be predicted based on the trajectory parameters of the typhoon to be predicted and the trajectory parameters of historical typhoons, then calculating the similarity of movement direction and speed between the geographically similar historical typhoons and the typhoon to be predicted based on the trajectory parameters of the typhoon to be predicted, the trajectory parameters of the geographically similar historical typhoons and the decision cycle, screening 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 based on 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 based on 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 based on 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, obtaining 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 based on the risk fault components based on Markov theory to determine the Markov state of the power system;

[0067] Specifically, based on the predicted position of the next track point of the to-be-predicted typhoon and the distribution positions of the power components, the power components that the track of the to-be-predicted typhoon passes through are screened as risk failure components. Then, based on the Markov theory, all expected states of the risk failure components are determined. All expected operating states of each of the risk failure components are used as topological nodes, which are connected in sequence according to the decision cycle to form a Markov state topological network of the power system. 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 the risk failure components arranged in sequence according to the decision cycle.

[0068] S103. Calculating the failure probability of each risk failure component based on 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 based on 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 tolerance when facing extreme natural disasters. When the dangerous force caused by extreme natural disasters is greater than this critical tolerance, the probability of component failure 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; where 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, and a is a preset parameter, parameter a=0.6931.

[0072] S104. Construct an objective function based on 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 and the power system performance constraint as a constraint condition, construct an optimization model of the Markov state topology network and calculate the optimal solution to obtain the target active defense strategy in each decision cycle; wherein the load loss function is determined based on the fault transfer probability and defense strategy load loss of the power system in each Markov state, the load penalty function is determined based on the power supply and power demand of the power system in each Markov state, and the Markov state probability function is determined based on 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 ramp rate constraints;

[0073] Specifically, a load loss function is constructed based on the fault transfer probability of the power system under each Markov state and the load loss of the defense strategy. A load penalty function is then constructed based on the power supply and power demand of the power system under each Markov state. A Markov state probability function is then constructed based on the Markov state of the power system. Finally, an objective function is constructed based on the load loss function, load penalty function, and Markov state probability function. Using the active defense strategy within each decision cycle as a variable and the power system performance constraints as constraints, an optimization model of the Markov state topology network is constructed and the optimal solution is calculated to obtain the target active defense strategy within each decision cycle. The active defense strategy includes adjusting the active power output of the generator and may also include other defense strategies. These strategies can be set and adjusted based on actual conditions and are not specifically limited here. The power system performance constraints of the active defense strategy include power flow balance constraints, branch active and reactive power flow constraints, generator active and reactive power output constraints, node voltage constraints, load shedding constraints, and generator ramp rate constraints.

[0074] 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 column vector of active power 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] Branch active and reactive power flow constraints (if a DC power flow model is used, only branch active power flow constraints are included):

[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] Typically, a power plant has multiple generators capable of adjusting active and reactive power. Each generator has a minimum and maximum active and reactive output. Therefore, the generator active and reactive power constraints (if a DC power flow model is used, only the upper and lower limits of the generator active power are constrained) 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 lower limit of the voltage at 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 topology network, which is the target active defense strategy, and the generator output active power is adjusted.

[0107] Further on the basis of the above embodiment, 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 and current movement parameters of the typhoon to be predicted includes:

[0108] Screening geographically similar historical typhoons corresponding to the typhoon to be predicted based on the trajectory parameters of the typhoon to be predicted and the trajectory parameters of historical typhoons;

[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] The historical typhoon is taken as the geographically similar historical typhoon; wherein, la(i, j) is the latitude of the jth track point of the ith historical typhoon; lap(j) is the latitude of the jth track point that the typhoon to be predicted has passed; lo(i, j) is the longitude of the jth track point of the ith historical typhoon; lop(j) is the longitude of the jth track 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] Calculating the similarity of the direction and speed of the geographically similar historical typhoons and the typhoon to be predicted based on the trajectory parameters of the typhoon to be predicted, the trajectory parameters of the geographically similar historical typhoons, and the decision period, selecting geographically similar historical typhoons with a direction and speed similarity greater than a preset threshold as target similar historical typhoons, and calculating the similarity weights of the target similar historical typhoons;

[0112] Specifically, based on the trajectory parameters of the typhoon to be predicted and the decision period, the first movement speed and the second movement speed of the typhoon to be predicted are calculated; then, based on the trajectory parameters of the geographically similar historical typhoon and the decision period, the third movement speed and the fourth movement speed of the geographically similar historical typhoon are calculated; then, based on the first movement speed and the third movement speed, the first average speed difference between the typhoon to be predicted and the geographically similar historical typhoon is calculated; then, based on the second movement speed and the fourth movement speed, the second average speed difference between the typhoon to be predicted and the geographically similar historical typhoon 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] Based on the current movement parameters, the similarity weight, and the trajectory parameters of the target similar historical typhoon, the predicted movement direction and speed of the next trajectory point of the typhoon to be predicted are calculated; and based on the trajectory parameters of the typhoon to be predicted and the predicted movement direction and 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 direction angle of the current track point of the typhoon to be predicted, the first moving direction current speed and the second moving direction 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 period, the first moving direction average speed and the second moving direction average speed of each target similar historical typhoon are calculated; according to the formula:

[0118]

[0119] Calculate the predicted moving direction speed of the next track point of the typhoon to be predicted (including the first moving direction predicted speed perpendicular to the latitude direction and the second moving direction predicted speed perpendicular to the longitude direction); wherein, vap1 is the first moving direction predicted speed of 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 direction average speed of the i-th target similar historical typhoon, 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 direction predicted speed of 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 direction 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 direction average speed and the second moving direction average speed are the average speed perpendicular to the latitude direction and the average speed perpendicular to the longitude direction, 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 period; 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 step of calculating the predicted speed of the typhoon to be predicted in each decision cycle according to the Holland model and the predicted 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 track point with a distance r from the current track 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 p outside the typhoon is 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 its 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 pressure, typhoon location, etc.

[0130] Further to the above embodiment, the step of obtaining risky faulty components according to the predicted positions and the distribution positions of the power components, constructing a Markov state topology network of the power system according to the risky faulty components based on Markov theory, and determining the Markov state of the power system includes:

[0131] selecting, according to the predicted location and the distribution locations of the power components, 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 components of the power system is usually high enough, in order to reduce the number of Markov states, it is assumed that the power system components that the typhoon has not passed through are operating reliably during the entire weather event. In this way, based on the predicted position and the distribution position of the power components, the power components that are expected to fail along the trajectory of the predicted typhoon are screened out and are treated as risk failure components.

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

[0134] Specifically, based on the Markov theory, under typhoon weather events, it is believed 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 out of operation and faulty components). With a decision cycle of 6 hours, in each decision cycle, the components of the power system have only one expected state, and it is believed 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, suppose the Markov process is divided into three decision cycles, and the state in the first decision cycle is S 11 , the number of risk fault components in the second decision cycle is 1, then the expected state of the fault components in the second decision cycle is offline or normal (i.e. S 21 and S 22 ), the number of risk fault components in the third decision cycle is 2, and the expected state of the fault 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 as follows 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 and the next expected state of the power system are considered simultaneously, thereby converting the uncertain sequence of power system states 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 indicates that the risk fault component k is operating normally, and 0 indicates that the risk fault component k is faulty. λ is the failure probability of the risk fault component.

[0141] Specifically, for a basic power element of a power system method, 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 express the failure transfer probability of each risk failure 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; wherein, 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; 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 i-th Markov state under the active defense strategy at time t; k represents 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 events, power system dispatchers should proactively adjust generator output based on the current state of the power system in each decision cycle, taking into account the expected state of the next decision cycle, to minimize overall losses caused by the typhoon. Typhoons often cause widespread power outages, and with the arrival of a typhoon, the power of power components must be adjusted, resulting in load loss. However, if power drops below a certain level, it is expected to cause an imbalance between supply and demand. If the power supply remains sufficient even after adjusting the component power, the penalty cost is zero. If the power supply is less than the demand, a penalty cost will be incurred. In short, the goal of the proactive defense strategy is to minimize power system load losses and maximize 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 convert 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] Combined with 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 is a schematic diagram of the active defense device provided by this application against the impact of typhoon weather on the power system, the device includes:

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

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

[0164] a calculation module 203 for calculating the failure probability of each risk failure component based on 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 based on the failure probability;

[0165] Processing module 204 is configured to construct an objective function based on a load loss function, a load penalty function, and a Markov state probability function, and to construct an optimization model of the Markov state topology network using the active defense strategy in each decision cycle as a variable and the power system performance constraint as a constraint condition, and to calculate an optimal solution to obtain a target active defense strategy in each decision cycle; wherein the load loss function is determined based on the fault transfer probability and the defense strategy load loss of the power system in each Markov state, the load penalty function is determined based on the power supply and power demand of the power system in each Markov state, and the Markov state probability function is determined based on the Markov state of the power system; and 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 ramp rate constraints;

[0166] The control module 205 is configured to execute a corresponding target active defense strategy according to the target active defense strategy before the typhoon to be predicted enters a 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, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. 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 programs stored in the ROM 302 or the programs loaded from the storage unit 308 into the RAM 303. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An I / O interface 305 is also connected to the bus 304. Here, 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, and the like; an output section 307 including devices such as a cathode ray tube, a liquid crystal display, and a speaker; a storage section 308 including devices such as a hard disk; and a communication section 309 including a network interface card such as a LAN card or a modem. 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. Removable media 311, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 310 as needed, so that computer programs read from the media can be installed in 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 the CPU 301, 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 embodiments, or may exist independently and not be incorporated into the electronic device. The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the 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 embodiments.

[0174] It should be noted that the computer-readable storage medium described in this application may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable storage media may include, for example, but are not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component. Furthermore, in this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. This propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof.

[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 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, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of the present invention, they can also make several improvements, modifications or changes, 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 the power system, characterized in that: include: According to the trajectory parameters and current movement parameters of the typhoon to be predicted, the predicted position corresponding to multiple consecutive decision cycles of the typhoon to be predicted is obtained based on the path similarity method, and the predicted speed of the typhoon to be predicted in each decision cycle is calculated based on the Holland model and the predicted position; Obtain 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 based on 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 based on 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 based on the failure probability; An objective function is constructed based on the load loss function, the load penalty function and the Markov state probability function. 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. An 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 load loss function is determined based on 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 based on the power supply and power demand of the power system in each Markov state. The Markov state probability function is determined based on 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 ramp 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, based on 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 based on the path similarity method includes: Screening geographically similar historical typhoons corresponding to the typhoon to be predicted based on the trajectory parameters of the typhoon to be predicted and the trajectory parameters of historical typhoons; Calculating the similarity of the direction and speed of the geographically similar historical typhoons and the typhoon to be predicted based on the trajectory parameters of the typhoon to be predicted, the trajectory parameters of the geographically similar historical typhoons, and the decision period, selecting geographically similar historical typhoons with a direction and speed similarity greater than a preset threshold as target similar historical typhoons, and calculating the similarity weights of the target similar historical typhoons; Based on the current movement parameters, the similarity weight, and the trajectory parameters of the target similar historical typhoon, the predicted movement direction and speed of the next trajectory point of the typhoon to be predicted are calculated; and based on the trajectory parameters of the typhoon to be predicted and the predicted movement direction and 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 predicted speed of the typhoon to be predicted in each decision cycle according to the Holland model and the predicted 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 track point with a distance r from the current track 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, wherein The step of obtaining risk fault components according to the predicted positions and the distribution positions of the power components, constructing a Markov state topology network of the power system according to the risk fault components based on Markov theory, and determining the Markov state of the power system includes: selecting, according to the predicted location and the distribution locations of the power components, 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, use all expected operating states of each risk fault component as topological nodes, connect them sequentially according to decision cycles, 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, wherein 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 indicates that the risk fault component k is operating normally, and 0 indicates that the risk fault component k is faulty. 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, wherein 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; wherein, 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; 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 represents 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 is configured to obtain, based on the trajectory parameters and current movement parameters of the typhoon to be predicted, the predicted positions corresponding to multiple consecutive decision cycles of the typhoon to be predicted based on the path similarity method, and calculate the predicted speed of the typhoon to be predicted in each decision cycle based on the Holland model and the predicted positions; a construction module, configured to obtain 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; a calculation module, 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; A processing module is used to construct an objective function based on a load loss function, a load penalty function, and a Markov state probability function, and to construct an optimization model of the Markov state topology network and calculate the optimal solution based on the active defense strategy in each decision cycle and the power system performance constraint as a constraint condition, so as to obtain the target active defense strategy in each decision cycle; wherein the load loss function is determined based on the fault transfer probability and defense strategy load loss of the power system in each Markov state, the load penalty function is determined based on the power supply and power demand of the power system in each Markov state, and the Markov state probability function is determined based on 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 ramp rate constraints; The control module is used to execute the corresponding target active defense strategy before the typhoon to be predicted enters the next decision cycle according to the target active defense strategy.

9. An electronic device comprising 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 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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