CN117422438B - Method and device for determining reinforcement scheme of power transmission line - Google Patents

Method and device for determining reinforcement scheme of power transmission line Download PDF

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CN117422438B
CN117422438B CN202311288410.6A CN202311288410A CN117422438B CN 117422438 B CN117422438 B CN 117422438B CN 202311288410 A CN202311288410 A CN 202311288410A CN 117422438 B CN117422438 B CN 117422438B
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牟善科
陆建忠
杨楠
庄侃沁
王峥
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East China Branch Of State Grid Corp ltd
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Abstract

The invention discloses a method and a device for determining a power transmission line reinforcement scheme, which relate to the technical field of power system safety and mainly solve the problem of how to improve the economy of the power transmission line reinforcement scheme on the premise of meeting the elastic requirement of a power transmission network under disasters, and comprise the steps of obtaining historical typhoons related parameters and power transmission network related parameters to simulate the condition of each power transmission line in the power transmission network affected by typhoons so as to obtain a plurality of historical fault scenes; determining a set of frangible lines in the power transmission grid based on the historical fault scenario; performing cluster analysis on a plurality of historical fault scenes to obtain a plurality of typical fault scenes; training the constructed power grid elasticity assessment model based on the vulnerable line set and the plurality of typical fault scenes to obtain a trained power grid elasticity assessment model; and determining a reinforcement scheme of the power transmission network from the fragile line based on the typhoon related parameters to be analyzed and the power grid elasticity evaluation model.

Description

Method and device for determining reinforcement scheme of power transmission line
Technical Field
The invention relates to the technical field of power system safety, in particular to a method and a device for determining a power transmission line reinforcement scheme.
Background
With the increasing global natural disasters, there is increasing interest in building "elastic grids" that have resilience to extreme disturbance events. As a key link for transmitting power, the power distribution network has a relatively complex structure and a larger scale, and the hidden trouble of fault cascading is particularly remarkable under the background that high fault rate of electrical elements is caused by extreme weather.
At present, in the implementation process of researching elastic measures, although the risk of cascading failures is considered, the protection of the fragile line is rarely considered, and the break of the fragile line under typhoon disasters can cause multiple risks. How to better account for the fragile lines in the process of formulating the power transmission network reinforcement scheme, and meanwhile, the improvement of the economy of the reinforcement scheme on the premise of meeting the elastic requirement of the power transmission network under disasters becomes a problem to be solved urgently.
Disclosure of Invention
In view of this, the invention provides a method and a device for determining a power transmission line reinforcement scheme, which mainly aims to solve the problem of how to improve the economy of the power transmission line reinforcement scheme on the premise of meeting the elastic requirement of a power transmission network under a disaster.
According to one aspect of the present invention, there is provided a method for determining a power transmission line reinforcement scheme, including:
Acquiring historical typhoon related parameters and power transmission network related parameters, and simulating the conditions of each power transmission line in the power transmission network affected by typhoons to obtain a plurality of historical fault scenes;
determining a set of frangible lines in the power transmission grid based on the historical fault scenario; performing cluster analysis on a plurality of historical fault scenes to obtain a plurality of typical fault scenes;
training the constructed power grid elasticity assessment model based on the vulnerable line set and the plurality of typical fault scenes to obtain a trained power grid elasticity assessment model;
and determining a reinforcement scheme of the power transmission network from the fragile line based on the typhoon related parameters to be analyzed and the power grid elasticity evaluation model.
Further, the obtaining the historical typhoon related parameter and the power transmission network related parameter simulates a condition that each power transmission line in the power transmission network is affected by typhoon, and the obtaining a plurality of historical fault scenes includes:
determining element position wind speeds corresponding to the respective element positions in the power transmission network based on the historical typhoon related parameters and the power transmission network related parameters; the element positions are used for representing the line segment positions and the tower positions in each power transmission line;
Determining a current cumulative fault probability of each power transmission line based on the element position wind speed;
sampling based on the current accumulated fault rate, and determining a target power transmission line in a broken circuit state;
and integrating the target power transmission line to obtain the historical fault scene.
Further, the determining the wind speed corresponding to each element position in the power transmission network based on the historical typhoon related parameter and the power transmission network related parameter includes:
simulating the time sequence change process of typhoons by adopting a batts wind field model based on the historical typhoons related parameters to obtain the maximum wind speed corresponding to each moment;
acquiring the element positions from the relevant parameters of the power transmission network, and determining the relative positions between the element positions and typhoon center positions at all moments;
determining the element position wind speed corresponding to each of the element positions over each period of time based on the maximum wind speed and the relative position.
Further, the determining the current cumulative fault probability of each power transmission line based on the element position wind speed includes:
determining the current accumulated fault probability of the towers and the current accumulated fault probability of the line sections of each power transmission line based on the element position wind speed;
And determining the current accumulated fault probability of the power transmission line based on the total number of towers, the total number of line segments, the current accumulated fault probability of the towers and the current accumulated fault probability of the line segments in the power transmission line.
Further, the determining the current accumulated fault probability of the tower and the current accumulated fault probability of the line section of each power transmission line based on the element position wind speed comprises:
acquiring the tower position wind speed and the line segment position wind speed of each period corresponding to each power transmission line from the element position wind speed;
determining the tower fault probability corresponding to each period based on the tower position wind speed and the tower design wind speed; determining the fault probability of the line section corresponding to each period based on the wind speed of the line section and the design wind speed of the line section;
and accumulating the tower fault probability and the line segment fault probability based on the current time period to obtain the current accumulated fault probability of the tower and the current accumulated fault probability of the line segment corresponding to each power transmission line.
Further, the sampling processing based on the current accumulated fault rate, and determining the target transmission line in the open circuit state includes:
Comparing the current accumulated fault probability with random numbers in the range of [0,1 ];
if the current accumulated fault probability is larger than the random number, extracting the power transmission lines corresponding to the current accumulated fault probability from all the power transmission lines to obtain sampled power transmission lines;
and acquiring a fault state checking result of the sampling power transmission line, and determining the sampling power transmission line as the target power transmission line in a broken state if the fault state checking result contains a fault element.
Further, the determining a set of frangible lines in the power transmission grid based on the historical failure scenario includes:
acquiring a target power transmission line in a broken state from the historical fault scene; predicting whether disconnection phenomenon occurs at each stage in the target power transmission line or not based on the maximum prediction depth, the tide deviation and the overload degree, and obtaining an intermediate prediction fault chain;
calculating corresponding fault chain probability based on the intermediate prediction fault chain, and determining a fault chain risk value based on the fault chain probability and fault chain load loss;
determining a target fault chain with system risk from the intermediate predicted fault chains based on the fault chain risk value;
Converting all the target fault chains into a fault network diagram; the nodes in the fault network diagram are formed by lines in the target fault chain, and edges are formed by weighted line flow evaluation indexes;
and calculating the degree index value of each node in the fault network diagram, and selecting the first n nodes with larger degree index values to construct the vulnerable line set.
Further, the training operation on the constructed power grid elasticity assessment model based on the vulnerable line set and the plurality of typical fault scenes, and obtaining the trained power grid elasticity assessment model includes:
determining an objective function of the grid elasticity assessment model based on the fragile line reinforcement cost and the typhoon load loss cost;
setting constraint conditions, and solving the objective function based on the constraint conditions; so that the objective function meets a set elasticity index threshold value, and a trained power grid elasticity evaluation model is obtained; the constraint conditions include: node power balance constraint, generator output constraint, load shedding constraint, wind curtailment constraint, balance node phase angle constraint, line direct current power flow constraint, line capacity constraint and vulnerable line maximum allowable break constraint.
Further, the determining the reinforcement scheme of the power transmission network from the fragile line based on the typhoon related parameters to be analyzed and the power grid elasticity evaluation model includes:
adopting the power grid elasticity evaluation model to perform elasticity index evaluation processing on the typhoon related parameters to be analyzed;
and if the elastic index solving result meeting the elastic index threshold can be obtained, determining a reinforcement scheme of the power transmission network from the fragile line based on the elastic index solving result.
According to another aspect of the present invention, there is provided a device for determining a reinforcement scheme of a power transmission line, including:
the scene simulation module is used for acquiring historical typhoon related parameters and power transmission network related parameters to simulate the conditions of each power transmission line in the power transmission network affected by typhoons so as to obtain a plurality of historical fault scenes;
a determining module for determining a set of frangible lines in the power transmission network based on the historical fault scenario; performing cluster analysis on a plurality of historical fault scenes to obtain a plurality of typical fault scenes;
the model training module is used for training the constructed power grid elasticity assessment model based on the vulnerable line set and the plurality of typical fault scenes to obtain a trained power grid elasticity assessment model;
And the scheme determining module is used for determining the reinforcement scheme of the power transmission network from the fragile line based on the typhoon related parameters to be analyzed and the power grid elasticity evaluation model.
Further, the scene simulation module includes:
a wind speed determining unit for determining element position wind speeds corresponding to respective element positions in a power transmission network based on the historical typhoon related parameter and the power transmission network related parameter; the element positions are used for representing the line segment positions and the tower positions in each power transmission line;
a probability determination unit for determining a current cumulative failure probability of each of the power transmission lines based on the element position wind speed;
the sampling determining unit is used for carrying out sampling processing based on the current accumulated fault rate and determining a target power transmission line in a broken state;
and the integration unit is used for integrating the target transmission line to obtain the historical fault scene.
Further, the wind speed determining unit is further configured to:
simulating the time sequence change process of typhoons by adopting a batts wind field model based on the historical typhoons related parameters to obtain the maximum wind speed corresponding to each moment;
acquiring the element positions from the relevant parameters of the power transmission network, and determining the relative positions between the element positions and typhoon center positions at all moments;
Determining the element position wind speed corresponding to each of the element positions over each period of time based on the maximum wind speed and the relative position.
Further, the probability determining unit is further configured to:
determining the current accumulated fault probability of the towers and the current accumulated fault probability of the line sections of each power transmission line based on the element position wind speed;
and determining the current accumulated fault probability of the power transmission line based on the total number of towers, the total number of line segments, the current accumulated fault probability of the towers and the current accumulated fault probability of the line segments in the power transmission line.
Further, the probability determining unit is further configured to:
acquiring the tower position wind speed and the line segment position wind speed of each period corresponding to each power transmission line from the element position wind speed;
determining the tower fault probability corresponding to each period based on the tower position wind speed and the tower design wind speed; determining the fault probability of the line section corresponding to each period based on the wind speed of the line section and the design wind speed of the line section;
and accumulating the tower fault probability and the line segment fault probability based on the current time period to obtain the current accumulated fault probability of the tower and the current accumulated fault probability of the line segment corresponding to each power transmission line.
Further, the sample determining unit is further configured to:
comparing the current accumulated fault probability with random numbers in the range of [0,1 ];
if the current accumulated fault probability is larger than the random number, extracting the power transmission lines corresponding to the current accumulated fault probability from all the power transmission lines to obtain sampled power transmission lines;
and acquiring a fault state checking result of the sampling power transmission line, and determining the sampling power transmission line as the target power transmission line in a broken state if the fault state checking result contains a fault element.
Further, the determining module includes:
the disconnection prediction unit is used for acquiring a target power transmission line in a disconnection state from the historical fault scene; predicting whether disconnection phenomenon occurs at each stage in the target power transmission line or not based on the maximum prediction depth, the tide deviation and the overload degree, and obtaining an intermediate prediction fault chain;
the risk determining unit is used for calculating corresponding fault chain probability based on the intermediate prediction fault chain and determining a fault chain risk value based on the fault chain probability and fault chain load loss;
a target determining unit, configured to determine a target fault chain with a system risk from the intermediate predicted fault chains based on the fault chain risk value;
The conversion unit is used for converting all the target fault chains into a fault network diagram; the nodes in the fault network diagram are formed by lines in the target fault chain, and edges are formed by weighted line flow evaluation indexes;
and the set construction unit is used for calculating the degree index value of each node in the fault network graph and selecting the first n nodes with larger degree index values to construct the vulnerable line set.
Further, the model training module is further configured to:
determining an objective function of the grid elasticity assessment model based on the fragile line reinforcement cost and the typhoon load loss cost;
setting constraint conditions, and solving the objective function based on the constraint conditions; so that the objective function meets a set elasticity index threshold value, and a trained power grid elasticity evaluation model is obtained; the constraint conditions include: node power balance constraint, generator output constraint, load shedding constraint, wind curtailment constraint, balance node phase angle constraint, line direct current power flow constraint, line capacity constraint and vulnerable line maximum allowable break constraint.
Further, the scheme determination module is further configured to:
adopting the power grid elasticity evaluation model to perform elasticity index evaluation processing on the typhoon related parameters to be analyzed;
And if the elastic index solving result meeting the elastic index threshold can be obtained, determining a reinforcement scheme of the power transmission network from the fragile line based on the elastic index solving result.
By means of the technical scheme, the technical scheme provided by the embodiment of the invention has at least the following advantages:
compared with the prior art, the method simulates the condition that each power transmission line in the power transmission network is influenced by typhoons by acquiring the historical typhoons related parameters and the power transmission network related parameters to obtain a plurality of historical fault scenes; determining a set of frangible lines in the power transmission grid based on the historical fault scenario; performing cluster analysis on a plurality of historical fault scenes to obtain a plurality of typical fault scenes; training the constructed power grid elasticity assessment model based on the vulnerable line set and the plurality of typical fault scenes to obtain a trained power grid elasticity assessment model; and determining a reinforcement scheme of the power transmission network from the fragile line based on the relevant parameters of typhoons to be analyzed and the power grid elasticity evaluation model, so that not only is the power transmission network elasticity requirement met under typhoons disasters, but also the influence of the fragile line on a power transmission system is considered, and a more economic power transmission line reinforcement scheme is designed.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
fig. 1 is a flow chart illustrating a method for determining a power transmission line reinforcement scheme according to an embodiment of the present invention;
fig. 2 is a flow chart illustrating a method for determining another power transmission line reinforcement scheme according to an embodiment of the present invention;
fig. 3 is a flow chart illustrating a method for determining a further power transmission line reinforcement scheme according to an embodiment of the present invention;
fig. 4 is a flow chart illustrating a method for determining a further power transmission line reinforcement scheme according to an embodiment of the present invention;
Fig. 5 shows a schematic structural diagram of a determining device for another power transmission line reinforcement scheme according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The embodiment of the invention provides a method for determining a power transmission line reinforcement scheme, as shown in fig. 1, the method comprises the following steps:
101. acquiring historical typhoon related parameters and power transmission network related parameters, and simulating the conditions of each power transmission line in the power transmission network affected by typhoons to obtain a plurality of historical fault scenes;
in the embodiment of the invention, the current execution end acquires the historical typhoon related parameters and the power transmission network related parameters to simulate the condition that each power transmission line in the power transmission network is influenced by typhoons, so as to obtain a plurality of historical fault scenes. The historical typhoon related parameters include initial center pressure difference, typhoon login points, travelling speed, paths and the like, and the embodiment of the invention is not particularly limited. The power transmission network related parameters include the output cost and the installed capacity of the generator, the existing line capacity and the reinforcement cost, the load of the bus, the geographic coordinates and the like, and the embodiment of the invention is not particularly limited. The historical fault scene is a scene of fault situations such as disconnection of power transmission lines in a power transmission network, for example, the power transmission network comprising 50 power transmission lines is simulated once, 5 power transmission lines are disconnected, and 45 power transmission lines are not disconnected, namely, the historical fault scene is obtained; and performing simulation again, wherein 10 power transmission lines are disconnected and 40 power transmission lines are not disconnected, namely another historical fault scene and the like.
102. Determining a set of frangible lines in the power transmission grid based on the historical fault scenario; performing cluster analysis on a plurality of historical fault scenes to obtain a plurality of typical fault scenes;
in the embodiment of the invention, the current execution end determines a vulnerable line set in the power transmission network based on a historical fault scene. The vulnerable line is a set of lines that are susceptible to typhoons and cause failure. The current execution end performs cluster analysis on a plurality of historical fault scenes to obtain a plurality of typical fault scenes. The method for cluster analysis comprises K-means clustering, FCM clustering and the like, and the embodiment of the invention is not particularly limited. It should be noted that, the data size of the typical fault scenario is far smaller than the data size of the historical fault scenario, and the calculation amount of model training can be greatly reduced when the power grid elasticity evaluation model is trained in the later stage.
103. Training the constructed power grid elasticity assessment model based on the vulnerable line set and the plurality of typical fault scenes to obtain a trained power grid elasticity assessment model;
in the embodiment of the invention, the current execution terminal performs training operation on the constructed power grid elasticity assessment model based on the fragile line set and a plurality of typical fault scenes, namely, the fragile line set is used as a line set to be reinforced in the power grid elasticity assessment model, and the typical fault scenes are used as input of the power grid elasticity assessment model to train, so that a trained power grid elasticity assessment model is obtained.
104. And determining a reinforcement scheme of the power transmission network from the fragile line based on the typhoon related parameters to be analyzed and the power grid elasticity evaluation model.
In the embodiment of the invention, the current execution end adopts a trained power grid elasticity evaluation model to evaluate and process the elasticity index of the typhoon related parameters to be analyzed; before elastic index evaluation processing, an elastic index threshold value is set, an elastic index solving result obtained by model solving is compared with the elastic index threshold value, and if the elastic index solving result meeting the elastic index threshold value can be obtained by solving, a reinforcing scheme of the power transmission network is determined from the fragile line based on the elastic index solving result.
Further, as a refinement and expansion of the specific implementation manner of the foregoing embodiment, in order to more accurately convert the influence of typhoons on the power transmission network into a fault scenario of the power transmission network, another method for determining a power transmission line reinforcement scheme is provided, as shown in fig. 2, the steps of obtaining historical typhoons related parameters and power transmission network related parameters to simulate the condition of each power transmission line in the power transmission network affected by typhoons, and obtaining a plurality of historical fault scenarios includes:
201. determining element position wind speeds corresponding to the respective element positions in the power transmission network based on the historical typhoon related parameters and the power transmission network related parameters;
In the embodiment of the invention, the current execution end adopts a batts wind field model to simulate the time sequence change process of typhoons based on the historical typhoons related parameters, and the maximum wind speed corresponding to each moment is obtained. The batts wind field model simulation formula is as follows:
wherein Δh (t) represents the typhoon center pressure difference at time t; Δh (t) land ) Typhoon center differential pressure representing login time tland;and θ represents the direction of typhoon travel and the angle between the coastline and the north direction, respectively; />Representing the maximum ground wind speed at time t; v H Representing typhoon travel speed; />Representing the maximum wind speed at time t; k represents a position coefficient and takes a value of 6.93.
In the embodiment of the invention, a current execution end acquires element positions from relevant parameters of a power transmission network, and determines relative positions between the element positions and typhoon center positions at all moments, wherein a calculation formula of the relative positions and a maximum wind speed radius is as follows:
x H (t)＝x H (land)+v H (t)tcosδ (5)
y H (t)＝y H (land)+v H (t)tcosδ (6)
wherein, (x) comp ,y comp ) Coordinates representing the location of the grid element; (x) H (t),y H (t)) represents coordinates of a typhoon center position at time t; (x) H (land),y H (land)) represents typhoon center position coordinates at login time tland; delta represents the included angle between the typhoon path and the coastline; Δh (t) represents the typhoon center pressure difference at time t; Representing the typhoon maximum wind speed radius at time t. In the embodiment of the invention, the element positions are used for representing the line section positions and the tower positions in each power transmission line, namely, the relative positions between the line section positions and the typhoon center positions and the relative positions between the tower positions and the typhoon center positions are obtained through calculation.
In the embodiment of the present invention, the current execution end determines the element position wind speed corresponding to each element position in each period based on the maximum wind speed and the relative position, and the specific formula is as follows:
wherein v is comp (t) the element position wind speed at time t; d (t) represents the relative position of the typhoon center from the element at time t;representing the typhoon maximum wind speed radius at time t.
202. Determining a current cumulative fault probability of each power transmission line based on the element position wind speed;
in the embodiment of the invention, the current execution terminal determines the current accumulated fault probability of the towers and the current accumulated fault probability of the line sections of each power transmission line based on the element position wind speed, and the method comprises the following steps:
202-1, acquiring tower position wind speed and line segment position wind speed of each period corresponding to each power transmission line from element position wind speed;
202-2, determining tower fault probabilities corresponding to all time periods based on the tower position wind speed and the tower design wind speed;
in the embodiment of the invention, the current execution end calculates the tower fault probability under the typhoon disaster by adopting the fragile function, and the tower fault probability can be calculated by the following formula in the duration process of typhoons:
wherein mu l,m (t i ) The mth tower representing the transmission line l at time t i V of failure probability of (v) l,m (t i ) The mth tower representing the transmission line l at time t i Is set at the wind speed of (2);is a constant parameter; />The wind speed of the tower is designed, and the value is 35m/s. Δt is the unit time interval, 1h is taken herein, and NT is the total time interval.
In addition, the current execution end determines the fault probability of the line segment corresponding to each period based on the wind speed of the line segment and the design wind speed of the line segment; in the embodiment of the invention, the fault probability of the power transmission line section in the duration process of typhoons can be calculated by the following formula:
wherein mu l,n (t i ) An nth line segment representing a transmission line l at a time t i V of failure probability of (v) l,n (t i ) An nth line segment representing a transmission line l at a time t i Is set in the air velocity of (1),and ρ represents a shape parameterThe number Δl represents the length of the line section between adjacent towers, +.>Representing the wind speed of the line design, 30m/s is taken.
202-3, carrying out accumulation processing on the tower fault probability and the line segment fault probability based on the current time period to obtain the current accumulated fault probability of the tower and the current accumulated fault probability of the line segment corresponding to each power transmission line.
In the embodiment of the present invention, the current execution end performs the accumulation processing on the tower fault probability and the line segment fault probability calculated in the step 202-2, and the accumulation formula is as follows:
where Δt is the unit time interval, 1h is taken herein, and nt is the total time interval. P is p l,m The current accumulated fault probability of the tower of the mth tower of the power transmission line is the current accumulated fault probability of the tower of the mth tower of the power transmission line; p is p l，n The fault probability is currently accumulated for the line segment of the nth line segment of the transmission line l.
202-4, determining the current accumulated fault probability of the power transmission line based on the total number of towers in the power transmission line, the total number of line segments, the current accumulated fault probability of the towers and the current accumulated fault probability of the line segments.
In the embodiment of the invention, the current execution end determines the current accumulated fault probability P of the power transmission line l in the typhoon duration process l The formula of (2) is as follows:
wherein K and L represent the total number of towers and the total number of line sections of the first transmission line.
203. Sampling based on the current accumulated fault rate, and determining a target power transmission line in a broken circuit state;
in the embodiment of the invention, the current execution end compares the current accumulated fault probability with random numbers in the range of [0,1 ]; if the current accumulated fault probability is smaller than the random number, the power transmission line is considered to have no breaking risk, and the power transmission line is not sampled; if the current accumulated fault probability is larger than the random number, the power transmission lines are considered to have the disconnection risk, and the power transmission lines corresponding to the current accumulated fault probability are extracted from all the power transmission lines to obtain sampled power transmission lines.
After the current execution end obtains the sampled power transmission line, notifying relevant staff of the power transmission network to go to the site to conduct fault state investigation on the sampled power transmission line, after manual investigation, obtaining a fault state investigation result of the relevant staff on the sampled power transmission line, and if the fault state investigation result contains a fault element, determining the sampled power transmission line as a target power transmission line in a broken state.
204. And integrating the target power transmission line to obtain the historical fault scene.
In the embodiment of the invention, the current execution end integrates the target power transmission line determined by the fault state investigation result of the whole power transmission network to obtain the historical fault scene of the power transmission network. If the power transmission network comprising 50 power transmission lines is subjected to fault state investigation based on the historical typhoon related parameters A, and the power transmission lines 1, 3 and 5 comprise fault elements, the power transmission lines 1, 3 and 5 are determined as target power transmission lines and integrated, so that a historical fault scene is obtained; the power transmission network including 50 power transmission lines is subjected to fault state investigation based on the historical typhoon related parameters B, if it is determined that the power transmission lines 12, 14 and 17 include fault elements, the power transmission lines 12, 14 and 17 are determined as target power transmission lines and integrated, and another historical fault scene is obtained.
It should be noted that, the probability of line failure increases under typhoon disasters, and the line failure may change the structure of the system to cause power flow transfer, and as the number of broken lines increases, the system becomes more fragile, and at this time, if the fragile line breaks, a subsequent cascading failure may be triggered. The main characteristic of the cascading failure is that the front and rear failures have causality, and the causality among the failures enables the development mode of the cascading failure to be consistent with the failure chain model.
Further, as a refinement and extension of the foregoing embodiment, in order to consider a cascading failure risk of the power transmission network and determine a vulnerable line in the power transmission network based on the cascading failure risk, another determination method of a power transmission line reinforcement scheme is provided, as shown in fig. 3, and the determining, based on the historical failure scenario, a vulnerable line set in the power transmission network includes:
301. acquiring a target power transmission line in a broken state from the historical fault scene; predicting whether disconnection phenomenon occurs at each stage in the target power transmission line or not based on the maximum prediction depth, the tide deviation and the overload degree, and obtaining an intermediate prediction fault chain;
in the embodiment of the invention, the current execution end considers that the transformer and the generator generally cannot fail under typhoon disasters, so that the fault chain considered in the embodiment of the invention is only composed of lines, and thus, the target power transmission line in the open-circuit state is obtained from a historical fault scene; and predicting whether disconnection phenomenon occurs at each stage in the target power transmission line based on the maximum prediction depth, the power flow deviation and the overload degree, so as to obtain an intermediate prediction fault chain. In the embodiment of the invention, the change of the line flow direction is further considered on the basis of the line flow evaluation indexes (Branch Loading Assessment Index, BLAI). The following indexes comprehensively consider the load flow deviation and overload degree on the residual lines after fault triggering.
Wherein,represents the flow of line w in phase k+1, < >>Representing the flow of line w in k phase, +.>The current limit of line w at stage k+1 is indicated.
The above indexThe method consists of an absolute value function and an exponential function. The absolute value function represents the current deviation of the line w after the previous disconnection, and the larger the absolute value function value is, the larger the current value fluctuation of the line w is; the exponential function represents the overload degree of the power flow on the line w after the previous disconnection, and the higher the exponential function value is, the higher the possibility that the power flow on the line w approaches or exceeds the limit value is.
302. Calculating corresponding fault chain probability based on the intermediate prediction fault chain, and determining a fault chain risk value based on the fault chain probability and fault chain load loss;
in the embodiment of the invention, the current execution end calculates the corresponding fault chain probability P (ζ) based on the intermediate prediction fault chain, and the specific calculation formula is as follows:
wherein b represents the b-th order prediction link of the failure chain ζ, assuming that an index is usedRepresenting the fault probability of the link, if the result of the b-th order prediction link is that the line w is disconnected, the step of predicting the link is that ∈>And can be calculated from equation (14). d, d max To set the maximum predicted depth of the fault chain based on engineering experience,when the predicted depth is greater than d max Stopping searching:
dim{ζ}＝d max (16)
in the embodiment of the invention, the current execution terminal is based on the probability of the fault chain and the load loss of the fault chain, and the risk value of the fault chain is obtained by multiplying the probability of the fault chain and the load loss of the fault chain after damage, and the specific formula is as follows:
Risk(ζ)＝P(ζ)×C(ζ) (17)
wherein P (ζ) represents the probability of a failed link ζ, and C (ζ) represents the load loss of the failed link ζ. Wherein the calculation of C (ζ) calculates the load loss under different fault chains based on the optimal power flow model.
303. Determining a target fault chain with system risk from the intermediate predicted fault chains based on the fault chain risk value;
in the embodiment of the invention, the current execution end screens and eliminates risk-free fault chains through the size of the risk value in order to construct a final fault chain set, and reserves the fault chain with system risk as a target fault chain in the power transmission network.
304. Converting all the target fault chains into a fault network diagram; the nodes in the fault network diagram are formed by lines in the target fault chain, and edges are formed by weighted line flow evaluation indexes;
in the embodiment of the invention, the current execution end converts all target fault chains into a fault network diagram, namely a directed weight diagram G h = { V, E }; node v= { L in the failure network diagram 1 ,L 2 ,...,L i The edge e= { W is composed of lines in the target fault chain 1 ,W 2 ,...,W i The weighted line flow assessment index. The calculation formula of the fault network diagram is as follows:
wherein F represents the total number of failed chains.
305. And calculating the degree index value of each node in the fault network diagram, and selecting the first n nodes with larger degree index values to construct the vulnerable line set.
In the embodiment of the invention, after the current execution end obtains the fault network diagram, the numerical value of each node in the fault network diagram is calculated through the degree index, and n preamble nodes with higher degree values are selected to construct the vulnerable line set.
Further, as a refinement and expansion of the foregoing embodiment, in order to improve economy of the reinforcement scheme on the premise of meeting the requirement of the grid elasticity under a disaster, another method for determining the reinforcement scheme of the power transmission line is provided, as shown in fig. 4, and the step of training the constructed grid elasticity assessment model based on the vulnerable line set and the plurality of typical fault scenarios, where the obtaining the trained grid elasticity assessment model includes:
401. determining an objective function of the grid elasticity assessment model based on the fragile line reinforcement cost and the typhoon load loss cost;
In the embodiment of the invention, the current execution end is based on the fragile line reinforcement cost F H And typhoon load loss cost F ls Determining an objective function of the power grid elasticity evaluation model, wherein the specific formula is as follows:
min F＝F H +F ls (19)
wherein l represents a line number; Γ represents the set of weak lines used for reinforcement decisions; c (C) l Representing the reinforcement cost of line l. Z is Z l Is a binary variable representing the reinforcement status of the line l; when Z is l When=1, it means that line l is reinforced; when Z is l When=0, line l is not reinforced. N (N) S Representing the total number of typhoon fault scenes; omega s Weighting representing typhoon fault scenariosCoefficients; n (N) B Representing the total number of buses; ρ ls Representing the load loss punishment cost under typhoon disasters; p is p ls,b,s Representing the load loss amount of the bus b under the typhoon fault scene s.
402. Setting constraint conditions, and solving the objective function based on the constraint conditions; and enabling the objective function to meet a set elasticity index threshold value, and obtaining the trained power grid elasticity evaluation model.
In the embodiment of the invention, the current execution end sets constraint conditions for the objective function before solving the objective function. The constraint conditions in the embodiment of the invention comprise: node power balance constraint, generator output constraint, load shedding constraint, wind curtailment constraint, balance node phase angle constraint, line direct current power flow constraint, line capacity constraint and vulnerable line maximum allowable break constraint.
a. The node power balance constraint has the following specific formula:
wherein Ω b Representing a generator set in the bus b; gamma ray b Representing a wind power field set of a bus b; p (P) G,g,s Representing the power output of generator g at a scene s. P is p wc,r,s Representing the power output of wind farm r at scene s.Is the maximum output of the wind farm r in the scene s; f (f) mn(l),s Representing the active power flow of a line l (the buses at two ends of the line are m and n respectively); p is p b,s Representing the total load of bus b under scene s.
b. The generator output constraint comprises the following specific formula:
wherein,and->The limits of maximum and minimum output power of generator g in scenario s, respectively.
c. The specific formula of the load shedding constraint is as follows:
0≤p ls,b,s ≤p b,s (24)
d. the specific formula of the wind abandoning constraint is as follows:
wherein,is a limitation of the minimum output power of the wind farm r in the scene s.
e. The specific formula of the balance node phase angle constraint is as follows:
wherein θ ref Representing the balanced node phase angle.
f. The specific formula of the line direct current power flow constraint is as follows:
/>
wherein θ m,s And theta n,s The voltage phase angles of the buses m and n under the scene s are respectively, B mn(l) Indicating the admittance of line l. Wherein v is l,s And z l Are binary variables, v l,s Representing the effect of typhoons in scene s on line l, the final state of line l in typhoon scene s can be determined in combination with two variables. v l,es The value is determined by typhoon simulation, when v l,s =0, representing the typhoon simulationIn the process, the line l is influenced by typhoons and is disconnected, but whether the line l can normally operate or not at the moment depends on a reinforcement decision variable z l . If z l Line l may operate normally, =1. Otherwise, line l will be shut down. When v l,s In the case where =1 represents scene s, line l is not affected by typhoons, no matter z l The number is what, and the line l can work normally.
g. The line capacity constraint is as follows:
wherein,representing the maximum power flow limit of line l.
h. The maximum allowable break constraint of the vulnerable line is as follows:
where τ represents the proportion of lines in the frangible line set that are not allowed to break. τ takes the value 0 to 1: when τ=1, it means that any of the frangible lines is not allowed to break at this time, the frangible line protection rate is at most 100% at this time, and when τ=0, it means that all of the frangible lines are allowed to break at this time, the frangible line protection rate may be at the lowest value of 0%.
It should be noted that, in the embodiment of the present invention, the line capacity constraint is nonlinear due to the constraint condition, which may make the model solving difficult. Therefore, the linear processing is performed on the line capacity constraint by adopting a large M method, and the expression after conversion is as follows:
|f l,s -B l (θ m(l),s -θ n(l),s )|≤M(1-(z l +v l,s -z l v l,s )) (30)
In the embodiment of the invention, the current execution end solves the objective function based on the constraint condition so that the objective function meets the set elastic index threshold value to obtain the trained power grid elasticity evaluation model.
Compared with the prior art, the method simulates the condition that each power transmission line in the power transmission network is affected by typhoons by acquiring the historical typhoons related parameters and the power transmission network related parameters to obtain a plurality of historical fault scenes; determining a set of frangible lines in the power transmission grid based on the historical fault scenario; performing cluster analysis on a plurality of historical fault scenes to obtain a plurality of typical fault scenes; training the constructed power grid elasticity assessment model based on the vulnerable line set and the plurality of typical fault scenes to obtain a trained power grid elasticity assessment model; and determining a reinforcement scheme of the power transmission network from the fragile line based on the relevant parameters of typhoons to be analyzed and the power grid elasticity evaluation model, so that not only is the power transmission network elasticity requirement met under typhoons disasters, but also the influence of the fragile line on a power transmission system is considered, and a more economic power transmission line reinforcement scheme is designed.
As an implementation of the method shown in fig. 1, an embodiment of the present invention provides a device for determining a power transmission line reinforcement scheme, as shown in fig. 5, where the device includes:
the scene simulation module 51 is configured to obtain historical typhoon related parameters and power transmission network related parameters, and simulate conditions of each power transmission line in the power transmission network affected by typhoons, so as to obtain a plurality of historical fault scenes;
a determination module 52 for determining a set of frangible lines in the power transmission grid based on the historical fault scenario; performing cluster analysis on a plurality of historical fault scenes to obtain a plurality of typical fault scenes;
the model training module 53 is configured to perform a training operation on the constructed power grid elasticity assessment model based on the vulnerable line set and the plurality of typical fault scenarios, so as to obtain a trained power grid elasticity assessment model;
a scenario determination module 54 for determining a reinforcement scenario for the power transmission grid from the weak line based on the typhoon related parameters to be analyzed and the grid elasticity assessment model.
Further, the scene simulation module 51 includes:
a wind speed determining unit for determining element position wind speeds corresponding to respective element positions in a power transmission network based on the historical typhoon related parameter and the power transmission network related parameter; the element positions are used for representing the line segment positions and the tower positions in each power transmission line;
A probability determination unit for determining a current cumulative failure probability of each of the power transmission lines based on the element position wind speed;
the sampling determining unit is used for carrying out sampling processing based on the current accumulated fault rate and determining a target power transmission line in a broken state;
and the integration unit is used for integrating the target transmission line to obtain the historical fault scene.
Further, the wind speed determining unit is further configured to:
simulating the time sequence change process of typhoons by adopting a batts wind field model based on the historical typhoons related parameters to obtain the maximum wind speed corresponding to each moment;
acquiring the element positions from the relevant parameters of the power transmission network, and determining the relative positions between the element positions and typhoon center positions at all moments;
determining the element position wind speed corresponding to each of the element positions over each period of time based on the maximum wind speed and the relative position.
Further, the probability determining unit is further configured to:
determining the current accumulated fault probability of the towers and the current accumulated fault probability of the line sections of each power transmission line based on the element position wind speed;
and determining the current accumulated fault probability of the power transmission line based on the total number of towers, the total number of line segments, the current accumulated fault probability of the towers and the current accumulated fault probability of the line segments in the power transmission line.
Further, the probability determining unit is further configured to:
acquiring the tower position wind speed and the line segment position wind speed of each period corresponding to each power transmission line from the element position wind speed;
determining the tower fault probability corresponding to each period based on the tower position wind speed and the tower design wind speed; determining the fault probability of the line section corresponding to each period based on the wind speed of the line section and the design wind speed of the line section;
and accumulating the tower fault probability and the line segment fault probability based on the current time period to obtain the current accumulated fault probability of the tower and the current accumulated fault probability of the line segment corresponding to each power transmission line.
Further, the sample determining unit is further configured to:
comparing the current accumulated fault probability with random numbers in the range of [0,1 ];
if the current accumulated fault probability is larger than the random number, extracting the power transmission lines corresponding to the current accumulated fault probability from all the power transmission lines to obtain sampled power transmission lines;
and acquiring a fault state checking result of the sampling power transmission line, and determining the sampling power transmission line as the target power transmission line in a broken state if the fault state checking result contains a fault element.
Further, the determining module 52 includes:
the disconnection prediction unit is used for acquiring a target power transmission line in a disconnection state from the historical fault scene; predicting whether disconnection phenomenon occurs at each stage in the target power transmission line or not based on the maximum prediction depth, the tide deviation and the overload degree, and obtaining an intermediate prediction fault chain;
the risk determining unit is used for calculating corresponding fault chain probability based on the intermediate prediction fault chain and determining a fault chain risk value based on the fault chain probability and fault chain load loss;
a target determining unit, configured to determine a target fault chain with a system risk from the intermediate predicted fault chains based on the fault chain risk value;
the conversion unit is used for converting all the target fault chains into a fault network diagram; the nodes in the fault network diagram are formed by lines in the target fault chain, and edges are formed by weighted line flow evaluation indexes;
and the set construction unit is used for calculating the degree index value of each node in the fault network graph and selecting the first n nodes with larger degree index values to construct the vulnerable line set.
Further, the model training module 53 is further configured to:
Determining an objective function of the grid elasticity assessment model based on the fragile line reinforcement cost and the typhoon load loss cost;
setting constraint conditions, and solving the objective function based on the constraint conditions; so that the objective function meets a set elasticity index threshold value, and a trained power grid elasticity evaluation model is obtained; the constraint conditions include: node power balance constraint, generator output constraint, load shedding constraint, wind curtailment constraint, balance node phase angle constraint, line direct current power flow constraint, line capacity constraint and vulnerable line maximum allowable break constraint.
Further, the scheme determination module 54 is further configured to:
adopting the power grid elasticity evaluation model to perform elasticity index evaluation processing on the typhoon related parameters to be analyzed;
and if the elastic index solving result meeting the elastic index threshold can be obtained, determining a reinforcement scheme of the power transmission network from the fragile line based on the elastic index solving result.
Compared with the prior art, the method simulates the condition of each power transmission line in the power transmission network affected by typhoons by acquiring the historical typhoons related parameters and the power transmission network related parameters to obtain a plurality of historical fault scenes; determining a set of frangible lines in the power transmission grid based on the historical fault scenario; performing cluster analysis on a plurality of historical fault scenes to obtain a plurality of typical fault scenes; training the constructed power grid elasticity assessment model based on the vulnerable line set and the plurality of typical fault scenes to obtain a trained power grid elasticity assessment model; and determining a reinforcement scheme of the power transmission network from the fragile line based on the relevant parameters of typhoons to be analyzed and the power grid elasticity evaluation model, so that not only is the power transmission network elasticity requirement met under typhoons disasters, but also the influence of the fragile line on a power transmission system is considered, and a more economic power transmission line reinforcement scheme is designed.
It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may alternatively be implemented in program code executable by computing devices, so that they may be stored in a memory device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than that shown or described, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps within them may be fabricated into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (9)

1. The method for determining the power transmission line reinforcement scheme is characterized by comprising the following steps of:
Acquiring historical typhoon related parameters and power transmission network related parameters, and simulating the conditions of each power transmission line in the power transmission network affected by typhoons to obtain a plurality of historical fault scenes;
determining a set of frangible lines in the power transmission grid based on the historical fault scenario; performing cluster analysis on a plurality of historical fault scenes to obtain a plurality of typical fault scenes;
training the constructed power grid elasticity assessment model based on the vulnerable line set and the plurality of typical fault scenes to obtain a trained power grid elasticity assessment model;
determining a reinforcement scheme of the power transmission network from the fragile line based on typhoon related parameters to be analyzed and the power grid elasticity evaluation model;
the determining a set of frangible lines in the power transmission grid based on the historical failure scenario comprises:
acquiring a target power transmission line in a broken state from the historical fault scene; and predicting whether disconnection phenomenon occurs at each stage in the target power transmission line based on the maximum prediction depth, the power flow deviation and the overload degree to obtain an intermediate prediction fault chain, wherein the formula is as follows:
wherein, the indexThe method consists of an absolute value function and an exponential function; the absolute value function represents the current deviation on the line w after the previous disconnection; the exponential function represents the overload degree of the power flow on the line w after the previous disconnection; / >Represents the flow of line w in phase k+1, < >>Representing the flow of line w in k phase, +.>Representation ofThe tide limit of the line w in the k+1 stage;
calculating corresponding fault chain probability based on the intermediate prediction fault chain, and determining a fault chain risk value based on the fault chain probability and the fault chain load loss, wherein the formula is as follows:
dim{ζ}＝d max
Risk(ζ)＝P(ζ)×C(ζ)；
wherein b represents the b-th order prediction link of the failure chain ζ, d max Setting the maximum prediction depth of the fault chain according to engineering experience; p (ζ) represents the probability of a failed link ζ, and C (ζ) represents the load loss of the failed link ζ; risk (ζ) represents a Risk value of the failure chain ζ;
determining a target fault chain with system risk from the intermediate predicted fault chains based on the fault chain risk value;
converting all the target fault chains into a fault network diagram; the nodes in the fault network diagram are formed by lines in the target fault chain, and edges are formed by weighted line flow evaluation indexes;
and calculating the degree index value of each node in the fault network diagram, and selecting the first n nodes with larger degree index values to construct the vulnerable line set.
2. The method of claim 1, wherein the obtaining historical typhoon related parameters and grid related parameters simulates a typhoon affected condition of each transmission line in the grid, and obtaining a plurality of historical fault scenarios comprises:
Determining element position wind speeds corresponding to the respective element positions in the power transmission network based on the historical typhoon related parameters and the power transmission network related parameters; the element positions are used for representing the line segment positions and the tower positions in each power transmission line;
determining a current cumulative fault probability of each power transmission line based on the element position wind speed;
sampling based on the current accumulated fault rate, and determining a target power transmission line in a broken circuit state;
and integrating the target power transmission line to obtain the historical fault scene.
3. The method of claim 2, wherein determining wind speeds for respective element locations in a grid based on the historical typhoon related parameters and the grid related parameters comprises:
simulating the time sequence change process of typhoons by adopting a batts wind field model based on the historical typhoons related parameters to obtain the maximum wind speed corresponding to each moment;
acquiring the element positions from the relevant parameters of the power transmission network, and determining the relative positions between the element positions and typhoon center positions at all moments;
determining the element position wind speed corresponding to each of the element positions over each period of time based on the maximum wind speed and the relative position.
4. The method of claim 2, wherein said determining a current cumulative probability of failure for each of said power transmission lines based on said element position wind speeds comprises:
determining the current accumulated fault probability of the towers and the current accumulated fault probability of the line sections of each power transmission line based on the element position wind speed;
and determining the current accumulated fault probability of the power transmission line based on the total number of towers, the total number of line segments, the current accumulated fault probability of the towers and the current accumulated fault probability of the line segments in the power transmission line.
5. The method of claim 4, wherein determining a tower current cumulative fault probability and a line segment current cumulative fault probability for each of the power transmission lines based on the element position wind speeds comprises:
acquiring the tower position wind speed and the line segment position wind speed of each period corresponding to each power transmission line from the element position wind speed;
determining the tower fault probability corresponding to each period based on the tower position wind speed and the tower design wind speed; determining the fault probability of the line section corresponding to each period based on the wind speed of the line section and the design wind speed of the line section;
And accumulating the tower fault probability and the line segment fault probability based on the current time period to obtain the current accumulated fault probability of the tower and the current accumulated fault probability of the line segment corresponding to each power transmission line.
6. The method of claim 2, wherein the sampling based on the current cumulative fault rate to determine a target transmission line in a disconnected state comprises:
comparing the current accumulated fault probability with random numbers in the range of [0,1 ];
if the current accumulated fault probability is larger than the random number, extracting the power transmission lines corresponding to the current accumulated fault probability from all the power transmission lines to obtain sampled power transmission lines;
and acquiring a fault state checking result of the sampling power transmission line, and determining the sampling power transmission line as the target power transmission line in a broken state if the fault state checking result contains a fault element.
7. The method of claim 1, wherein the training the constructed grid elasticity assessment model based on the set of frangible lines and the plurality of typical fault scenarios to obtain the trained grid elasticity assessment model comprises:
Determining an objective function of the grid elasticity assessment model based on the fragile line reinforcement cost and the typhoon load loss cost;
setting constraint conditions, and solving the objective function based on the constraint conditions; so that the objective function meets a set elasticity index threshold value, and a trained power grid elasticity evaluation model is obtained; the constraint conditions include: node power balance constraint, generator output constraint, load shedding constraint, wind curtailment constraint, balance node phase angle constraint, line direct current power flow constraint, line capacity constraint and vulnerable line maximum allowable break constraint.
8. The method of claim 1, wherein the determining a reinforcement scheme for the power transmission grid from the frangible line based on the typhoon-related parameters to be analyzed and the grid elasticity assessment model comprises:
adopting the power grid elasticity evaluation model to perform elasticity index evaluation processing on the typhoon related parameters to be analyzed;
and if the elastic index solving result meeting the elastic index threshold can be obtained, determining a reinforcement scheme of the power transmission network from the fragile line based on the elastic index solving result.
9. A power transmission line reinforcement scheme determining apparatus, comprising:
The scene simulation module is used for acquiring historical typhoon related parameters and power transmission network related parameters to simulate the conditions of each power transmission line in the power transmission network affected by typhoons so as to obtain a plurality of historical fault scenes;
a determining module for determining a set of frangible lines in the power transmission network based on the historical fault scenario; performing cluster analysis on a plurality of historical fault scenes to obtain a plurality of typical fault scenes;
the model training module is used for training the constructed power grid elasticity assessment model based on the vulnerable line set and the plurality of typical fault scenes to obtain a trained power grid elasticity assessment model;
the scheme determining module is used for determining a reinforcement scheme of the power transmission network from the fragile line based on the typhoon related parameters to be analyzed and the power grid elasticity evaluation model;
the determining a set of frangible lines in the power transmission grid based on the historical failure scenario comprises:
acquiring a target power transmission line in a broken state from the historical fault scene; and predicting whether disconnection phenomenon occurs at each stage in the target power transmission line based on the maximum prediction depth, the power flow deviation and the overload degree to obtain an intermediate prediction fault chain, wherein the formula is as follows:
Wherein, the indexThe method consists of an absolute value function and an exponential function; the absolute value function represents the current deviation on the line w after the previous disconnection; the exponential function represents the overload degree of the power flow on the line w after the previous disconnection; />Represents the flow of line w in phase k+1, < >>Representing the flow of line w in k phase, +.>Representing the current limit of the line w in the k+1 stage;
calculating corresponding fault chain probability based on the intermediate prediction fault chain, and determining a fault chain risk value based on the fault chain probability and the fault chain load loss, wherein the formula is as follows:
dim{ζ}＝d max
Risk(ζ)＝P(ζ)×C(ζ)；
wherein b represents the b-th order prediction link of the failure chain ζ, d max Setting the maximum prediction depth of the fault chain according to engineering experience; p (ζ) represents the probability of a failed link ζ, and C (ζ) represents the load loss of the failed link ζ; risk (ζ) represents a Risk value of the failure chain ζ;
determining a target fault chain with system risk from the intermediate predicted fault chains based on the fault chain risk value;
converting all the target fault chains into a fault network diagram; the nodes in the fault network diagram are formed by lines in the target fault chain, and edges are formed by weighted line flow evaluation indexes;
and calculating the degree index value of each node in the fault network diagram, and selecting the first n nodes with larger degree index values to construct the vulnerable line set.
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