Transmission tower collapse risk forecasting method considering accumulated damage in typhoon process

Abstract

The invention discloses a power transmission tower collapse risk forecasting method considering accumulated damage in a typhoon process, and the method comprises the steps: modeling a time-varying risk assessment process of a power transmission tower into a Markov process, building a power transmission tower transition probability matrix, and determining a corresponding relation between a vulnerability curve and each element in the transition probability matrix; based on the multi-state vulnerability, according to the established risk forecasting model, performing limit state definition, wind-induced response analysis and statistical analysis fitting by utilizing finite element numerical analysis to obtain a multi-state transmission tower vulnerability curve library; and in combination with time-varying wind field forecast information, obtaining transition probabilities among various states of the power transmission tower from the vulnerability curve library, and filling the transition probabilities among various states of the power transmission tower into a transition probability matrix to realize time-varying prediction of the collapse probability of the power transmission tower and the failure probability of the power transmission line. According to the method, the possible initial damage of the transmission tower in the forecasting process is considered, and more reliable collapse risk prediction of the transmission tower is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of disaster risk early warning for power systems, and in particular to a method for predicting the risk of power transmission tower collapse taking into account cumulative damage during a typhoon. Background Art

[0002] Electricity is one of the most important energy sources in modern society. The functions of key infrastructure such as transportation, water conservancy, health care and information technology are highly dependent on the supply of electricity. At present, high-voltage transmission lines supported by transmission towers are usually used to transmit large amounts of electricity. The strong winds caused by typhoons can cause the collapse of transmission towers and paralyze large-scale power grids. The power outages caused by wind damage to transmission towers during typhoons will also affect the operation of various infrastructures, disaster relief and subsequent construction, resulting in serious secondary disasters and socio-economic losses. Unlike other sudden disasters, typhoons are predictable, and the duration of a typhoon is usually about 3 to 10 days. Its predictability and persistence allow people to have a relatively long time to conduct emergency responses before and during disasters, especially providing a valuable time window for risk assessment of transmission towers. This prediction of transmission tower risks can not only provide decision-making support for power management in disaster scenarios, but also reduce the temporal and spatial scope of power failures, shorten the post-disaster repair time, and reduce the socio-economic losses caused by power outages.

[0003] The transmission tower vulnerability model is an important tool for studying the performance and risks of regional power infrastructure under strong wind disasters. It puts a lot of mechanical analysis and complicated finite element processes in front, and can easily and quickly give the probability of damage or failure of transmission towers under a given wind speed, and predict the damage point before the transmission tower collapses. Compared with some statistical prediction methods based on disaster loss data, the transmission tower vulnerability model has the advantage of considering physical mechanisms and is suitable for situations where there is no large amount of disaster loss data. Based on the transmission tower vulnerability model and combined with time-varying wind speed and direction information, the collapse probability of the transmission tower at the corresponding moment in the future can be obtained, realizing real-time risk assessment of the transmission system during typhoons.

[0004] According to full-scale tests, numerical simulations and engineering practices, it is found that during a continuous typhoon, transmission towers may exist in three states: intact state, collapsed state and intermediate damage state. Among them, the damage state of the tower refers to the state in which the components of the transmission tower fail, but this failure will not lead to overall collapse or affect the power transportation of the transmission line. Instead, it may be that some poles break or leave residual deformation, making the transmission tower in a damaged but not collapsed state. During the typhoon, in addition to the collapse and damage of transmission towers, attention should also be paid to the damage and destruction of transmission towers. For transmission towers that are slightly or moderately damaged but not collapsed, although they can still temporarily undertake the task of power transmission, the typhoon is a continuous process. The damage caused by the deformation of the poles in the structure and the failure of the nodes will reduce the reliability of the transmission tower under the continuous strong wind. However, when applying vulnerability to predict the risk of transmission towers during typhoons, only the probability of the transmission tower changing from an intact state to a damaged or collapsed state is considered at each time point, while the probability of the transmission tower changing from a damaged state to a collapsed state during the typhoon is not considered. Ignoring this cumulative damage to transmission towers during typhoons may lead to a systematic underestimation of the risk of transmission tower collapse, which will non-conservatively affect the analysis of subsequent power grid status and the assessment of the resources and time required for power transmission restoration. Summary of the invention

[0005] In order to overcome the deficiencies of the above technologies, the present invention provides a method for predicting the risk of transmission tower collapse taking into account the cumulative damage during a typhoon.

[0006] Terminology explanation:

[0007] 1. TPM: Transition Probability Matrix.

[0008] 2. FEM: Finite Element Model, finite element modeling.

[0009] 3. Pushover analysis: nonlinear static analysis.

[0010] 4. CDF: Cumulative Distribution Function.

[0011] The technical solution adopted by the present invention to overcome the technical problems is:

[0012] A method for predicting the risk of transmission tower collapse considering the cumulative damage during a typhoon, comprising the following steps:

[0013] S1. Establish a risk prediction model based on Markov property: Assume that there are three states of transmission towers during a typhoon, namely intact state, damaged state, and collapsed state. Model the time-varying risk assessment process of transmission towers as a Markov process, then establish the transmission tower transfer probability matrix and determine the corresponding relationship between the vulnerability curve and each element in the transfer probability matrix;

[0014] S2. Establishing a multi-state transmission tower vulnerability curve library: Based on multi-state vulnerability, according to the Markov-based risk prediction model established in step S1, using finite element numerical analysis, limit state definition, wind-induced response analysis and statistical analysis fitting are performed to obtain a multi-state transmission tower vulnerability curve library;

[0015] S3. In combination with the time-varying wind field forecast information, the transition probabilities between various states of the transmission tower are obtained from the multi-state transmission tower vulnerability curve library obtained in step S2, and then the transition probabilities between various states of the transmission tower are filled into the transition probability matrix established in step S1, thereby realizing the time-varying prediction of the collapse probability of the transmission tower and the failure probability of the transmission line.

[0016] Further, step S1 includes:

[0017] S11. Assume that during a typhoon, there are three states of transmission towers, namely intact state, damaged state, and collapsed state;

[0018] S12. Assume that the probability that the transmission tower is in a certain state at the current moment depends only on the state of the transmission tower at the previous moment, and model the time-varying risk assessment process of the transmission tower as a Markov process;

[0019] S13. According to the total probability formula, a transfer probability matrix of the transmission tower at any time is established;

[0020] S14. Determine the corresponding relationship between the vulnerability curve and each element in the transfer probability matrix.

[0021] Furthermore, step S1 specifically includes the following:

[0022] S11. Assume that there are three states of transmission towers during a typhoon: intact state, damaged state, and collapsed state, denoted by , , ;

[0023] S12, set the transmission tower current The probability of being in a certain state at any time Depends only on the last moment Transmission tower status , the time-varying risk assessment process of transmission towers is modeled as a Markov process:

[0024] (1)

[0025] In formula (1), For transmission towers The probability of being in various states at all times; The transmission tower from the previous moment Status Transfer to current Status of the moment The probability of , ;

[0026] Transmission towers Damage occurs at any time, The probability of collapse at a certain moment is expressed as: , the probability is The wind load intensity at the moment is related to the wind load intensity. The factors affecting the wind load include wind speed and wind direction. Let the probability be the wind speed and wind direction The function is expressed as:

[0027] (2)

[0028] In formula (2), for The forecast wind speed at the time; for The forecast wind direction at the moment; Abbreviated as , its corner mark Indicates that the state at the previous moment is a damaged state, and the state at the current moment is a collapsed state;

[0029] S13. The transmission tower is in an intact state, a damaged state, or a collapsed state. The damage state includes multiple types of damage states, which are expressed as , Indicates the type of damage state, Indicated in Always in state The probability of ,in, , then the transmission tower is The state probability vector at the moment is:

[0030] (3)

[0031] Among them, for any Moment, satisfaction , and the initial state probability vector of the transmission tower π 0 = [ π O 0 , π D 1 0 , π D 2 0 ,…, π D n 0 , π C 0 ] T for [1, 0,0,…,0, 0] T , assuming that the initial state of the transmission tower is intact;

[0032] According to the total probability formula, the transfer probability matrix of the transmission tower at any time is established to realize the multi-state risk prediction of the transmission tower at continuous time. Assuming that the various damage states of the transmission tower will not transfer to each other, then:

[0033] (4)

[0034] (5)

[0035] In formula (4), It means that the transmission tower is in good condition. The probability of remaining intact at all times; It means that the transmission tower is in good condition. The moment occurs The probability of class damage, ; It means that the transmission tower is in good condition. The probability of collapse occurring at any moment; Indicates that the transmission tower is In the case of damage Always keep the Probability of class damage; Indicates that the transmission tower is In the case of damage The probability of collapse occurring at any moment; Indicates that the transmission tower is In the case of damage The probability of returning to an intact state at any time; Indicates that the transmission tower is in a collapsed state. The probability of returning to an intact state at any time; Indicates that the transmission tower is in a collapsed state. Always keep the probability of collapse in mind;

[0036] In formula (5), Indicates that the transmission tower is The state probability vector at the moment;

[0037] S14. The probability of damage or failure of the transmission tower structure under different load intensities is described by the fragility curve, which is recorded as the exceedance probability. The exceedance probability of damage or failure of the transmission tower is expressed as follows:

[0038] (6)

[0039] In formula (6), represents the response of the structure under this load intensity; is the limit state index of the structural response, including the limit state index of the damage state and collapse state limit state indicators ; The elements in the transfer probability matrix and the exceedance probability of the transmission tower at the previous moment under different load intensities have a corresponding relationship as shown in formula (7)-formula (11). The transfer probability matrix is ​​filled by vulnerability:

[0040] (7)

[0041] (8)

[0042] (9)

[0043] (10)

[0044] (11)

[0045] The factors affecting the different damage states of transmission towers include at least the change of wind direction angle. Assuming that only a certain type of damage occurs under a specific wind direction angle, Indicates damage status The wind attack angle set when it occurs is:

[0046] (12).

[0047] Further, step S2 includes:

[0048] S21. Establish a finite element numerical simulation model of the transmission tower;

[0049] S22, using a certain threshold of the top displacement of the transmission tower as a limit state indicator to determine the damage state and collapse state of the transmission tower described in step S1;

[0050] S23, using a linear filtering method to generate random wind speed time history samples of different wind speeds as input for the dynamic time history analysis of the transmission tower line system, and taking the maximum top displacement of the transmission tower in the wind-induced response time history as the wind-induced response of the transmission tower;

[0051] S24, the maximum top displacement of the transmission tower under different wind speed time series samples is counted, and compared with the limit state index set in step S22, the probability of damage or collapse of the transmission tower under different wind speeds is counted, and the cumulative distribution function of the log-normal distribution is used to fit to obtain a vulnerability curve library.

[0052] Furthermore, step S21 specifically includes: establishing a numerical simulation model of the transmission tower and a numerical simulation model of the transmission tower line system in finite element software respectively.

[0053] Furthermore, step S22 specifically includes:

[0054] (1) For the damage state, the damage state of the transmission tower is defined as: the critical state where the pole buckling first occurs in the transmission tower; the pole that buckles for the first time is the damaged pole of the transmission tower. Different damaged poles correspond to different damage states, and the top displacement of the transmission tower in this state is taken as the limit indicator of this type of damage state. ; Use formula (13) to determine whether each member of the transmission tower is buckled:

[0055] (13)

[0056] In formula (13), is the axial pressure on the poles of the transmission tower; is the cross-sectional area of ​​the pole of the transmission tower; It is the design value of tensile, compressive and flexural strength; is the stability factor of the axially compressed members of the transmission tower;

[0057] During a typhoon, changes in wind direction will cause changes in the size and spatial distribution of wind loads, resulting in different damage paths and affecting the damaged components of the transmission tower. Therefore, the damage state of the transmission tower is checked in combination with the Pushover analysis results, where Pushover analysis represents nonlinear static analysis. With preset angles as intervals, there are several types of damage states under wind attack angles from 0° to 360°. The numerical simulation model of the transmission tower with a certain initial damage is obtained by the model correction method of reducing the elastic modulus of the damaged rod, which is used to study the vulnerability of the transmission tower from the damaged state to the collapsed state.

[0058] (2) For the collapse state, determine the limit state index of the transmission tower collapse state based on the Pushover curve The numerical relationship with tower height is applicable to transmission towers in intact state and transmission towers in damaged state.

[0059] Furthermore, step S23 specifically includes:

[0060] Firstly, the linear filtering method is used to generate random wind speed time history samples of different wind speeds, and the corresponding dynamic wind loads are calculated as the input of the dynamic time history analysis of the transmission tower line system; the maximum top displacement of the transmission tower in the response time history is taken as the wind-induced response of the transmission tower;

[0061] Then, several wind speed time histories are generated for each wind speed, corresponding to several wind-induced response samples, and the sample size is determined through sensitivity analysis.

[0062] Furthermore, in step S24, when fitting the fragility curve of the transmission tower, it is ensured that the fragility curve of the damaged transmission tower is steeper than the fragility curve of the intact transmission tower, that is, the standard deviation of the fragility curve is fitted by the cumulative distribution function of the lognormal distribution. for: ,in, Vulnerability curve The standard deviation of Vulnerability curve In order to avoid the collapse probability of intact transmission towers in the probability interval [0,0.2] exceeding the collapse probability of damaged transmission towers, formula (14) is used instead of formula (8):

[0063] (14).

[0064] Further, step S3 includes:

[0065] S31. Based on the time-varying wind field forecast information, at a certain prediction time step , substituting the wind angle of attack and wind speed into the corresponding vulnerability curve to obtain the transition probability matrix Each element in The value of

[0066] S32, repeat step S31 to obtain the ;

[0067] S33, from the initial probability π 0 = [1, 0,0,…,0, 0] T The transmission tower is intact and rolling Multiply , , , ,get , updated It is used to provide the time-varying collapse probability of transmission towers considering cumulative damage, and the collapse probability of each transmission tower is used to predict the failure probability of transmission lines in the target area.

[0068] Furthermore, in step S33, the obtained collapse probability of each transmission tower is used to predict the failure probability of the transmission line in the target area, which is expressed as:

[0069] (15)

[0070] In formula (15), Indicated in Transmission lines at all times The failure probability; Indicates transmission line The number of upper transmission towers; Indicated in Transmission lines at all times Transmission tower probability of collapse.

[0071] The beneficial effects of the present invention are:

[0072] 1. Traditional risk prediction methods only consider the probability of transmission towers collapsing from an intact state at each moment, while ignoring the intermediate damage state of transmission towers during typhoons, resulting in a systematic underestimation of the time-varying collapse risk of transmission towers. The risk prediction method considering the cumulative damage of transmission towers proposed in the present invention combines mechanical analysis with consideration of the possible initial damage of transmission towers during the forecasting process, thereby achieving a more reliable prediction of the risk of transmission tower collapse.

[0073] 2. Unlike a large number of forecasting methods that only focus on the collapse of transmission towers, the present invention can also predict the damage risk of transmission towers and determine vulnerable lines, which helps relevant decision-making departments to make decisions such as optimizing material allocation and scheduling in response to typhoon disasters, which is of great significance to improving the emergency response capabilities of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a schematic diagram of a method for predicting the risk of transmission tower collapse taking into account the cumulative damage during a typhoon according to an embodiment of the present invention.

[0075] Figure 2 This is a numerical simulation model of a transmission tower according to an embodiment of the present invention.

[0076] Figure 3 It is a numerical simulation model of the transmission tower line system described in the embodiment of the present invention.

[0077] Figure 4 These are the front view, side view and cross-sectional view of a damaged transmission tower at a wind attack angle of 0° according to an embodiment of the present invention.

[0078] Figure 5 The diagrams are the front view, side view and cross-sectional view of a damaged transmission tower at wind attack angles of 30° and 60° according to an embodiment of the present invention.

[0079] Figure 6 These are the front view, side view and cross-sectional view of a damaged transmission tower under a 90° wind attack angle according to an embodiment of the present invention.

[0080] Figure 7 These are the front view, side view and cross-sectional view of a damaged transmission tower at a wind attack angle of 120° according to an embodiment of the present invention.

[0081] Figure 8 These are the front view, side view and cross-sectional view of a damaged transmission tower at a wind attack angle of 150° according to an embodiment of the present invention.

[0082] Fig. 9 These are the front view, side view and cross-sectional view of a damaged transmission tower at a wind attack angle of 180° according to an embodiment of the present invention.

[0083] Fig.10 The pushover curve and the collapsed top displacement of the transmission tower under the wind attack angle of 0°-90° described in the embodiment of the present invention.

[0084] Fig.11 It is the Pushover curve and the collapsed top displacement of the transmission tower under the wind attack angle of 90°-180° described in the embodiment of the present invention.

[0085] Fig.12 It is the collapse vulnerability curve of intact transmission tower under 0° wind attack angle under different sample sizes described in the embodiment of the present invention.

[0086] Fig.13 The most unfavorable wind attack angle described in the embodiment of the present invention , , , , There are 5 types of state transition vulnerability curves.

[0087] Fig.14 This is a wind speed and wind direction forecast information diagram for the next 12 hours at the location of the example single tower described in an embodiment of the present invention.

[0088] Fig.15 This is a comparison chart of the prediction results of a single transmission tower using the method considering cumulative damage and the method not considering cumulative damage described in an embodiment of the present invention.

[0089] Fig.16 This is a probability diagram of a single transmission tower being in different states at different times according to an embodiment of the present invention. DETAILED DESCRIPTION

[0090] In order to facilitate those skilled in the art to better understand the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The following is only exemplary and does not limit the protection scope of the present invention.

[0091] The present invention discloses a method for predicting the risk of power transmission tower collapse taking into account the cumulative damage during a typhoon. Figure 1 As shown, the steps include:

[0092] S1. Establish a risk prediction model based on Markov property: Assume that there are three states of transmission towers during a typhoon, namely intact state, damaged state, and collapsed state. Model the time-varying risk assessment process of transmission towers as a Markov process, then establish the transmission tower transfer probability matrix and determine the corresponding relationship between the vulnerability curve and each element in the transfer probability matrix;

[0093] S2. Establishing a multi-state transmission tower vulnerability curve library: Based on multi-state vulnerability, according to the Markov-based risk prediction model established in step S1, using finite element numerical analysis, limit state definition, wind-induced response analysis and statistical analysis fitting are performed to obtain a multi-state transmission tower vulnerability curve library;

[0094] S3. In combination with the time-varying wind field forecast information, the transition probabilities between various states of the transmission tower are obtained from the multi-state transmission tower vulnerability curve library obtained in step S2, and then the transition probabilities between various states of the transmission tower are filled into the transition probability matrix established in step S1, thereby realizing the time-varying prediction of the collapse probability of the transmission tower and the failure probability of the transmission line.

[0095] In order to better understand the above technical solution, the exemplary embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. These are only exemplary embodiments of the present invention. However, it should be understood that the present invention can also be implemented in various forms and is not limited to the embodiments described herein. These embodiments are intended to enable those skilled in the art to understand the present invention more clearly and thoroughly.

[0096] A method for predicting the risk of transmission tower collapse considering the cumulative damage during a typhoon, comprising the following steps:

[0097] S1. Establish a risk prediction model based on Markov property: Assume that there are three states of transmission towers during a typhoon, namely intact state, damaged state, and collapsed state. Model the time-varying risk assessment process of transmission towers as a Markov process, and then establish the transmission tower transfer probability matrix and determine the corresponding relationship between the vulnerability curve and the elements in the transfer probability matrix.

[0098] In this embodiment, step S1 specifically includes the following:

[0099] S11. Assume that there are three states of transmission towers during a typhoon: intact state (Intact), damaged state (Damage), and collapsed state (Collapse), denoted as , , In the intact state, the poles in the transmission tower undergo elastic deformation due to slight wind and deadweight, and the tower structure as a whole also undergoes linear deformation. The power supply is normal and no maintenance is required. In the damaged state, individual poles of the transmission tower undergo inelastic deformation. Although the tower structure as a whole still undergoes linear deformation, which has little impact on power supply, it reduces the reliability of the tower structure in future events. When the tower structure of the transmission tower as a whole enters nonlinear deformation and produces a large top displacement, it reaches a collapse state. At this time, the power supply is interrupted and it cannot be used any more. This is the collapsed state.

[0100] S12, set the transmission tower current The probability of being in a certain state at any time Depends only on the last moment Transmission tower status , regardless of the path to reach the current state, the time-varying risk assessment process of transmission towers is modeled as a Markov process:

[0101] (1)

[0102] In formula (1), For transmission towers The probability of being in various states at all times; The transmission tower from the previous moment Status Transfer to current Status of the moment The probability of , ;

[0103] Transmission towers Damage occurs at any time, The probability of collapse at a certain moment is expressed as: , the probability is The wind load intensity at the moment is related to the wind load intensity. The factors affecting the wind load include wind speed and wind direction. Let the probability be the wind speed and wind direction The function is expressed as:

[0104] (2)

[0105] In formula (2), for The forecast wind speed at the time; for The forecast wind direction at the moment; Abbreviated as , its corner mark It means that the state at the previous moment is the damaged state, and the state at the current moment is the collapsed state.

[0106] S13. The transmission tower is in an intact state, a damaged state, or a collapsed state. The damage state includes multiple types of damage states, which are expressed as , Indicates the type of damage state, Indicated in Always in state The probability of ,in, , then the transmission tower is The state probability vector at the moment is:

[0107] (3)

[0108] Among them, for any Moment, satisfaction , and the initial state probability vector of the transmission tower π 0 = [ π O 0 , π D 1 0 , π D 2 0 ,…, π D n 0 , π C 0 ] T for [1, 0,0,…,0, 0] T , assuming that the initial state of the transmission tower is intact;

[0109] According to the total probability formula, a transfer probability matrix of the transmission tower at any time is established to realize the multi-state risk prediction of the transmission tower at continuous time. This embodiment adopts the transfer probability matrix TPM to realize the multi-state risk prediction of the transmission tower at continuous time. Since the deformation and buckling of the poles of the transmission tower poles during the typhoon are usually irreversible, and the transfer between damage states involves complex mechanical analysis and damage evolution analysis at the pole level, which is beyond the research scope of this embodiment, it is assumed that the various damage states of the transmission tower will not transfer to each other, then:

[0110] (4)

[0111] (5)

[0112] In formula (4), It means that the transmission tower is in good condition. The probability of remaining intact at all times; It means that the transmission tower is in good condition. The moment occurs The probability of class damage, ; It means that the transmission tower is in good condition. The probability of collapse occurring at any moment; Indicates that the transmission tower is In the case of damage Always keep the Probability of class damage; Indicates that the transmission tower is In the case of damage The probability of collapse occurring at any moment; Indicates that the transmission tower is In the case of damage The probability of returning to an intact state at any time; Indicates that the transmission tower is in a collapsed state. The probability of returning to an intact state at any time; Indicates that the transmission tower is in a collapsed state. Always keep the probability of collapse in mind;

[0113] In formula (5), Indicates that the transmission tower is The state probability vector at time.

[0114] S14. The probability of damage or failure of the transmission tower structure under different load intensities is described by the fragility curve, which is recorded as the exceedance probability. The exceedance probability of damage or failure of the transmission tower is expressed as follows:

[0115] (6)

[0116] In formula (6), represents the response of the structure under this load intensity; is the limit state index of the structural response, including the limit state index of the damage state and collapse state limit state indicators The elements in the transfer probability matrix have a corresponding relationship with the exceedance probability of the transmission tower in the previous state under different load intensities as shown in formula (7)-formula (11). The transfer probability matrix is ​​filled by vulnerability:

[0117] (7)

[0118] (8)

[0119] (9)

[0120] (10)

[0121] (11)

[0122] The factors affecting the different damage states of transmission towers include at least the change of wind direction angle. Assuming that only a certain type of damage occurs under a specific wind direction angle, Indicates damage status The wind attack angle set when it occurs is:

[0123] (12).

[0124] S2. Establish a multi-state transmission tower vulnerability curve library: Based on multi-state vulnerability, according to the Markov-based risk prediction model established in step S1, finite element numerical analysis is used to perform limit state definition, wind-induced response analysis and statistical analysis fitting to obtain a multi-state transmission tower vulnerability curve library.

[0125] Further, step S2 includes:

[0126] S21. Establish a finite element numerical simulation model: Establish a finite element numerical simulation model of the transmission tower to provide a basis for the subsequent finite element numerical analysis. Specifically, a numerical simulation model of the transmission tower and a numerical simulation model of the transmission tower line system are established in the finite element software. This embodiment takes the 2300ZM1 cathead transmission tower as an example. According to the "DL / T5154-2012 Technical Regulations for the Structural Design of Overhead Transmission Line Towers" (hereinafter referred to as the "Technical Regulations") and other relevant materials, a transmission tower numerical model and a transmission tower line system numerical model are established in the commercial finite element software ABAQUS. The transmission tower line system described in this embodiment takes the "three towers and two lines" structure as an example, that is, it includes three transmission towers and a ground wire connecting two adjacent transmission towers. The three transmission towers are set as A tower, B tower, and C tower respectively. The ground wire includes a center conductor, two side conductors and two ground wires. The numerical model of the transmission tower and the numerical model of the transmission tower line system are respectively as follows: Figure 2 and Figure 3 As shown. Among them, since the angle steel bars of the transmission tower are slender bars, many studies have shown that beam elements can better simulate the real performance of the angle steel bars of the transmission tower. Therefore, B31 beam elements are used to model the angle steel bars. Each bar is rigidly connected. According to the consistent modal defect method, an overall initial geometric defect of 0.1% of the tower height is applied according to the lowest-order overall buckling mode. The transmission line can only withstand tension but not bending moment and pressure. Therefore, a numerical simulation model of the transmission line is established by using multiple T3D2 three-dimensional truss elements that are hinged everywhere and set with no compression characteristics, and the cooling method is used to apply prestress to the transmission line.

[0127] S22. Definition of limit state: A certain threshold value of the displacement of the top of the transmission tower is used as a limit state indicator to determine the damage state and collapse state of the transmission tower described in step S1.

[0128] Specifically, defining the limit state is a key step in the fragility analysis. The static wind load is calculated according to the Technical Regulations and applied to a single tower numerical simulation model (i.e., a single transmission tower numerical simulation model). Nonlinear static analysis (i.e., Pushover analysis) is performed at various wind attack angles in the commercial finite element software ABAQUS. A certain threshold of the displacement at the top of the transmission tower is used as the limit state indicator to determine the damage state and collapse state of the transmission tower described in step S1.

[0129] (1) For the damage state, the damage state of the transmission tower is defined as: the critical state where the first buckling of the pole occurs in the transmission tower; the axial pressure of each pole of the transmission tower is tracked during the pushover process, and the stability calculation formula of the solid-web axially compressed pole in the "Steel Structure Design Standard" is used to determine whether each pole is buckled (i.e. damaged). The pole that buckles for the first time is the damaged pole of the transmission tower. Different damaged poles correspond to different damage states, and the top displacement of the transmission tower under this state is used as the limit indicator of this type of damage state. ; Use formula (13) to determine whether each member of the transmission tower is buckled:

[0130] (13)

[0131] In formula (13), is the axial pressure on the poles of the transmission tower; is the cross-sectional area of ​​the pole of the transmission tower; It is the design value of tensile, compressive and flexural strength; is the stability coefficient of the axially compressed member of the transmission tower, which is modified according to the slenderness ratio of the member and the steel grade. and section classification according to the "Steel Structure Design Standard", among which, Indicates the yield strength of steel.

[0132] During a typhoon, the wind direction may change significantly. When the wind direction changes, the size and spatial distribution of the wind load will change, resulting in different damage paths and affecting the damaged components of the transmission tower. Therefore, the damage status of the transmission tower is checked in combination with the Pushover results.

[0133] In this embodiment, 30° is used as an interval to obtain 10 types of damage states under wind attack angles of 0° to 360°, and the corresponding wind attack angles are 0°, 30° or 60° (i.e., the damage states corresponding to a wind attack angle of 30° and a wind attack angle of 60° are the same), 90°, 120°, 150°, 180°, 210°, 240°, 270°, 300° or 330° (i.e., the damage states corresponding to a wind attack angle of 300° and a wind attack angle of 330° are the same). Figure 4-Figure 9As shown in the figure, the damage situation of 0°~180° is shown. Since the damage situation of 210°~330° is symmetrical with that of 30°~150°, it is omitted. It includes 10 types of states, denoted as . Figure 4-Figure 9 There are three sub-pictures in each figure. From left to right, they are the front view, side view and cross-section view of the damaged transmission tower under different wind attack angles. Figure 4-Figure 9 In the figure, the red part represents the damaged pole. In damage mechanics, the reduction of the elastic modulus of the pole is generally used to represent damage. The numerical simulation model of the transmission tower with a certain initial damage is obtained by the model correction method of reducing the elastic modulus of the damaged pole, which is used to study the vulnerability of the transmission tower from the damaged state to the collapse state. (2) For the collapse state, the limit state index of the collapse state of the transmission tower is determined according to the Pushover curve. The numerical relationship between the tower height and the pushover curve in this embodiment is as follows: Fig.10 and Fig.11 As shown, Fig.10 Pushover curve and top displacement of transmission tower collapse under wind attack angle of 0°-90°. Fig.11 Pushover curves and top displacement of transmission tower collapse under wind attack angles of 90°-180°, and limit state indicators of transmission tower collapse It is 2.5% of the tower height and is applicable to transmission towers in intact state and transmission towers in damaged state.

[0134] S23. Analysis of wind-induced response of multi-state transmission towers: The linear filtering method is used to generate random wind speed time-history samples of different wind speeds as the input of the dynamic time-history analysis of the transmission tower line system. The maximum top displacement of the transmission tower in the wind-induced response time-history is taken as the wind-induced response of the transmission tower.

[0135] In order to obtain the wind-induced response of transmission towers considering the tower-line coupling effect, nonlinear dynamic time-history analysis is required. First, the linear filtering method is used to generate random wind speed time-history samples of different wind speeds, which are converted into dynamic wind loads according to the "Technical Regulations" as the input of the dynamic time-history analysis of the transmission tower-line system; the maximum top displacement of the transmission tower in the response time history is taken as the wind-induced response of the transmission tower.

[0136] Then, several wind speed time series are generated for each wind speed, corresponding to several wind-induced response samples, and the sample size is determined through sensitivity analysis. The specific determination process is as follows:

[0137] Due to the complexity of the finite element model of the dynamic wind load and the transmission tower line system, nonlinear dynamic analysis is very time-consuming. In addition, considering the characteristics of probabilistic analysis, a large number of Monte Carlo simulations are required at different wind attack angles and transmission tower states, which requires a lot of computing resources. Therefore, optimizing the computing efficiency is crucial. The results of the sample size sensitivity analysis are as follows Fig.12 As shown in Table 1, Fig.12 The collapse vulnerability curves of intact transmission towers under 0° wind attack angle are fitted under different sample sizes. 20 samples, 50 samples, 80 samples, and 100 samples are fitted respectively. Fig.12 It can be observed that although there is an obvious deviation between the results of 20 samples and 100 samples in the low collapse probability range (less than 0.2, i.e. less than 20%), this deviation tends to be conservative; in the high collapse probability range (greater than 0.8, i.e. greater than 80%), the difference between the results of 20 samples and 100 samples is small. Table 1 shows the collapse probability of different sample sizes near the median wind speed (collapse probability of 0.5, i.e. 50%) (26.3 m / s -27m / s). The results under 20 sample sizes have a slight error compared with other sample sizes. This difference is mainly due to the resolution change caused by different sample sizes. For 20 samples, the resolution is 0.05 (i.e. 1 / 20=0.05), which may result in a deviation of 5% for each wind speed sample. In addition, considering the inherent variability of multiple Monte Carlo simulation results, this difference is acceptable in engineering practice. Therefore, this embodiment determines 20 wind speed time history samples to improve calculation efficiency.

[0138] Table 1 Collapse probability at different wind speeds with different sample sizes

[0139]

[0140] S24, fitting vulnerability curve library: statistically analyzing the maximum top displacement of the transmission tower under different wind speed time series samples, comparing it with the limit state index set in step S22, statistically analyzing the probability of damage or collapse of the transmission tower under different wind speeds, and fitting the cumulative distribution function of the log-normal distribution to obtain the vulnerability curve library, such as Fig.13 When fitting the fragility curve of the transmission tower, it is ensured that the fragility curve of the damaged transmission tower is steeper than the fragility curve of the intact transmission tower, that is, the standard deviation of the fragility curve is fitted with the cumulative distribution function of the log-normal distribution. for: ,in, Vulnerability curve The standard deviation of Vulnerability curve In order to avoid the collapse probability of intact transmission towers in the probability interval [0,0.2] exceeding the collapse probability of damaged transmission towers, formula (14) is used instead of formula (8):

[0141] (14).

[0142] S3. In combination with the time-varying wind field forecast information, the transition probabilities between various states of the transmission tower are obtained from the multi-state transmission tower vulnerability curve library obtained in step S2, and then the transition probabilities between various states of the transmission tower are filled into the transition probability matrix established in step S1, thereby realizing the time-varying prediction of the collapse probability of the transmission tower and the failure probability of the transmission line.

[0143] Specifically, step S3 includes:

[0144] S31. Based on the time-varying wind field forecast information, at a certain prediction time step , substituting the wind angle of attack and wind speed into the corresponding vulnerability curve to obtain the transition probability matrix Each element in The value of .

[0145] S32, repeat step S31 to obtain the .

[0146] S33, from the initial probability π 0 = [1, 0,0,…,0, 0] T The transmission tower is intact and rolling Multiply , , , ,get , updated It is used to provide the time-varying collapse probability of transmission towers considering cumulative damage. The collapse probability of each transmission tower is used to predict the failure probability of the transmission line in the target area, which is expressed as:

[0147] (15)

[0148] In formula (15), Indicated in Transmission lines at all times The failure probability; Indicates transmission line The number of upper transmission towers; Indicated in Transmission lines at all times Transmission tower probability of collapse.

[0149] This embodiment combines meteorological forecast information to predict the time-varying collapse risk of transmission towers in a typhoon (set as Typhoon F) scenario occurring in a coastal province (set as Province Z) in XXXX. Typhoon F landed in the coastal area of ​​Province Z at 1:45 on August 10, XXXX (UTC time 17:45 on August 9), where UTC (Coordinated Universal Time) refers to Coordinated Universal Time. To illustrate the effectiveness of considering cumulative damage in the present invention, the case uses the 2300ZM1 Maotou straight line transmission tower as an example to predict the failure probability (regional scale) of the transmission network in Province Z and the collapse probability of a single tower (i.e., a single transmission tower scale), and compares it with the traditional model that does not consider cumulative damage.

[0150] In this embodiment, Province Z belongs to the coastal area, and the total length of the power transmission system used exceeds 9,000 kilometers, including more than 700 power transmission lines and more than 30,000 power transmission towers. The time range considered is from 12:00 on August 9 to 24:00 on August 9, XXXX UTC time (including the predicted landing time of Typhoon F). The example single-tower numerical meteorological forecast model predicts once every hour within this range. The wind speed and wind direction information of the example single tower location in the next 12 hours is shown in the figure below. Fig.14 shown.

[0151] The results under Typhoon F are analyzed as follows.

[0152] (1) Comparison of the scale of a single transmission tower

[0153] The prediction results of a single transmission tower by the method considering cumulative damage in this embodiment and the traditional method not considering cumulative damage are as follows: Fig.15 As shown. Both the method considering cumulative damage described in this embodiment and the traditional method not considering cumulative damage show that the risk of transmission tower collapse increased significantly at 16:00 UTC time on August 9. Table 2 counts the number of transmission towers (in units) in different collapse probability intervals obtained by the method considering cumulative damage described in this embodiment and the traditional method not considering cumulative damage since 15:00 UTC time on August 9. The results of Table 2 show that when cumulative damage is not considered, the number of transmission towers at high risk (collapse probability greater than 0.9) is significantly underestimated. At the level of a single transmission tower, ignoring cumulative damage leads to an underestimation of the collapse risk by about 25%. Fig.16The probability of a single transmission tower being in different states at different times is further demonstrated, indicating that the underestimation of the traditional method without considering the cumulative damage is mainly due to the failure to consider the cumulative damage during the typhoon process, which significantly increases the risk of collapse in subsequent time steps. For example, at 15:00, although the probability of collapse of the transmission tower is relatively low and similar to the prediction results of the traditional method without considering the cumulative damage, the probability of being in a damaged state is high, and the transmission tower in a damaged state is more likely to collapse. Therefore, the collapse risk is significantly increased at 16:00, which affects the risk assessment of the regional power grid. Therefore, transmission towers with a high probability of damage should be monitored and maintained in a timely manner, otherwise the wind resistance of the transmission tower in future strong wind events may be reduced. This result also shows the potential of applying multi-state risk prediction results to collapse trend warning, maintenance resource deployment and other links.

[0154] Table 2 Number of transmission towers with different collapse probability ranges

[0155] (2) Regional scale comparison

[0156] The regional time-varying risk forecast of all transmission towers in the Z province was conducted using the method considering cumulative damage in this embodiment and the traditional method not considering cumulative damage. Table 3 shows the number of transmission lines (in "lines") in different failure probability intervals in the region obtained by the method considering cumulative damage in this embodiment and the traditional method not considering cumulative damage since 15:00 UTC on August 9. It can be seen from Table 3 that compared with the traditional method not considering cumulative damage, the number of transmission lines in the high-risk interval (failure probability greater than 0.8) predicted by the method considering cumulative damage in this embodiment is greater than the number of transmission lines in the traditional method not considering cumulative damage. Although the increase is small, the impact range of a transmission line is usually large. Therefore, the method considering cumulative damage in this embodiment is of great significance for actual power grid risk management. At the regional level, at the time of typhoon landing, the failure probability of transmission lines near the typhoon landing point is relatively high, and ignoring cumulative damage leads to an underestimated risk of transmission line failure of about 15%, which is lower than the error observed at the single tower level. The reason for this difference is that under typhoon conditions, most transmission towers are located in low wind speed areas, and their probability of collapse is almost zero; while transmission towers exposed to extremely high wind speeds tend to collapse earlier, making the cumulative damage during the typhoon less impactful. The difference in predictions is small in both low wind speeds (≤22m / s) and extremely high wind speeds (≥28m / s), so at the regional level, the prediction gap is smaller than at the single tower level. This actual typhoon case fully demonstrates that for the risk forecast of transmission towers during typhoons, cumulative damage cannot be ignored, which is of great significance for disaster loss assessment, emergency response and recovery strategies, and enhancing grid resilience.

[0157] Table 3 Number of transmission lines with failure probability in different intervals

[0158] The above only describes the basic principles and preferred embodiments of the present invention. Those skilled in the art may make many changes and improvements based on the above description, and these changes and improvements should fall within the protection scope of the present invention.

Claims

1. A method for predicting the risk of transmission tower collapse considering the cumulative damage during a typhoon, characterized in that: The steps include: S1. Establish a risk prediction model based on Markov property: Assume that there are three states of transmission towers during a typhoon, namely intact state, damaged state, and collapsed state. Model the time-varying risk assessment process of transmission towers as a Markov process, then establish the transmission tower transfer probability matrix and determine the corresponding relationship between the vulnerability curve and each element in the transfer probability matrix; S2. Establishing a multi-state transmission tower vulnerability curve library: Based on multi-state vulnerability, according to the Markov-based risk prediction model established in step S1, using finite element numerical analysis, limit state definition, wind-induced response analysis and statistical analysis fitting are performed to obtain a multi-state transmission tower vulnerability curve library; S3. In combination with the time-varying wind field forecast information, the transition probabilities between various states of the transmission tower are obtained from the multi-state transmission tower vulnerability curve library obtained in step S2, and then the transition probabilities between various states of the transmission tower are filled into the transition probability matrix established in step S1, thereby realizing the time-varying prediction of the collapse probability of the transmission tower and the failure probability of the transmission line.

2. The method for predicting the risk of transmission tower collapse considering the cumulative damage during a typhoon according to claim 1 is characterized in that: Step S1 includes: S11. Assume that during a typhoon, there are three states of transmission towers, namely intact state, damaged state, and collapsed state; S12. Assume that the probability that the transmission tower is in a certain state at the current moment depends only on the state of the transmission tower at the previous moment, and model the time-varying risk assessment process of the transmission tower as a Markov process; S13. According to the total probability formula, a transfer probability matrix of the transmission tower at any time is established; S14. Determine the corresponding relationship between the vulnerability curve and each element in the transfer probability matrix.

3. The method for predicting the risk of transmission tower collapse considering the cumulative damage during a typhoon according to claim 2 is characterized in that: Step S1 specifically includes the following: S11. Assume that there are three states of transmission towers during a typhoon: intact state, damaged state, and collapsed state, denoted by , , ; S12, set the transmission tower current The probability of being in a certain state at any time Depends only on the last moment Transmission tower status , the time-varying risk assessment process of transmission towers is modeled as a Markov process: （1） In formula (1), For transmission towers The probability of being in various states at all times; The transmission tower from the previous moment Status Transfer to current Status of the moment The probability of , ; Transmission towers Damage occurs at any time, The probability of collapse at a certain moment is expressed as: , the probability is The wind load intensity at the moment is related to the wind load intensity. The factors affecting the wind load include wind speed and wind direction. Let the probability be the wind speed and wind direction The function is expressed as: （2） In formula (2), for The forecast wind speed at the time; for The forecast wind direction at the moment; Abbreviated as , its corner mark Indicates that the state at the previous moment is a damaged state, and the state at the current moment is a collapsed state; S13. The transmission tower is in an intact state, a damaged state, or a collapsed state. The damage state includes multiple types of damage states, which are expressed as , Indicates the type of damage state, Indicated in Always in state The probability of ,in, , then the transmission tower is The state probability vector at time is: （3） Among them, for any Moment, satisfaction , and the initial state probability vector of the transmission tower is for , assuming that the initial state of the transmission tower is intact; According to the total probability formula, the transfer probability matrix of the transmission tower at any time is established to realize the multi-state risk prediction of the transmission tower at continuous time. Assuming that the various damage states of the transmission tower will not transfer to each other, then: （4） （5） In formula (4), It means that the transmission tower is in good condition. The probability of remaining intact at all times; It means that the transmission tower is in good condition. The moment occurs The probability of class damage, ; It means that the transmission tower is in good condition. The probability of collapse occurring at any moment; Indicates that the transmission tower is In the case of damage Always keep the Probability of class damage; Indicates that the transmission tower is In the case of damage The probability of collapse occurring at any moment; Indicates that the transmission tower is In the case of damage The probability of returning to an intact state at any moment; Indicates that the transmission tower is in a collapsed state. The probability of returning to an intact state at any moment; Indicates that the transmission tower is in a collapsed state. Always keep the probability of collapse in mind; In formula (5), Indicates that the transmission tower is The state probability vector at the moment; S14. The probability of damage or failure of the transmission tower structure under different load intensities is described by the fragility curve, which is recorded as the exceedance probability. The exceedance probability of damage or failure of the transmission tower is expressed as follows: （6） In formula (6), represents the response of the structure under this load intensity; is the limit state index of the structural response, including the limit state index of the damage state and collapse state limit state indicators ; The elements in the transfer probability matrix and the exceedance probability of the transmission tower at the previous moment under different load intensities have a corresponding relationship as shown in formula (7)-formula (11). The transfer probability matrix is ​​filled by vulnerability: （7） （8） （9） （10） （11） The factors affecting the different damage states of transmission towers include at least the change of wind direction angle. Assuming that only a certain type of damage occurs under a specific wind direction angle, Indicates damage status The wind attack angle set when it occurs is: （12）。 4. The method for predicting the risk of transmission tower collapse considering the cumulative damage during a typhoon according to claim 3 is characterized in that: Step S2 includes: S21. Establish a finite element numerical simulation model of the transmission tower; S22, using a certain threshold of the top displacement of the transmission tower as a limit state indicator to determine the damage state and collapse state of the transmission tower described in step S1; S23, using a linear filtering method to generate random wind speed time history samples of different wind speeds as input for the dynamic time history analysis of the transmission tower line system, and taking the maximum top displacement of the transmission tower in the wind-induced response time history as the wind-induced response of the transmission tower; S24, the maximum top displacement of the transmission tower under different wind speed time series samples is counted, and compared with the limit state index set in step S22, the probability of damage or collapse of the transmission tower under different wind speeds is counted, and the cumulative distribution function of the log-normal distribution is used to fit to obtain a vulnerability curve library.

5. The method for predicting the risk of transmission tower collapse considering the cumulative damage during a typhoon according to claim 4 is characterized in that: Step S21 specifically includes: establishing a numerical simulation model of a transmission tower and a numerical simulation model of a transmission tower line system in finite element software respectively.

6. The method for predicting the risk of transmission tower collapse considering the cumulative damage during a typhoon according to claim 4 is characterized in that: Step S22 specifically includes: (1) For the damage state, the damage state of the transmission tower is defined as: the critical state where the pole buckling first occurs in the transmission tower; the pole that buckles for the first time is the damaged pole of the transmission tower. Different damaged poles correspond to different damage states, and the top displacement of the transmission tower in this state is taken as the limit indicator of this type of damage state. ; Use formula (13) to determine whether each member of the transmission tower is buckled: （13） In formula (13), is the axial pressure on the poles of the transmission tower; is the cross-sectional area of ​​the pole of the transmission tower; It is the design value of tensile, compressive and flexural strength; is the stability factor of the axially compressed members of the transmission tower; During a typhoon, changes in wind direction will cause changes in the size and spatial distribution of wind loads, resulting in different damage paths and affecting the damaged components of the transmission tower. Therefore, the damage state of the transmission tower is checked in combination with the Pushover analysis results, where Pushover analysis represents nonlinear static analysis. With preset angles as intervals, there are several types of damage states under wind attack angles from 0° to 360°. The numerical simulation model of the transmission tower with a certain initial damage is obtained by the model correction method of reducing the elastic modulus of the damaged rod, which is used to study the vulnerability of the transmission tower from the damaged state to the collapsed state. (2) For the collapse state, determine the limit state index of the transmission tower collapse state based on the Pushover curve The numerical relationship with tower height is applicable to transmission towers in intact state and transmission towers in damaged state.

7. The method for predicting the risk of transmission tower collapse considering the cumulative damage during a typhoon according to claim 4 is characterized in that: Step S23 specifically includes: Firstly, the linear filtering method is used to generate random wind speed time history samples of different wind speeds, and the corresponding dynamic wind loads are calculated as the input of the dynamic time history analysis of the transmission tower line system; the maximum top displacement of the transmission tower in the response time history is taken as the wind-induced response of the transmission tower; Then, several wind speed time histories are generated for each wind speed, corresponding to several wind-induced response samples, and the sample size is determined through sensitivity analysis.

8. The method for predicting the risk of transmission tower collapse considering the cumulative damage during a typhoon according to claim 4 is characterized in that: In step S24, when fitting the fragility curve of the transmission tower, it is ensured that the fragility curve of the damaged transmission tower is steeper than the fragility curve of the intact transmission tower, that is, the standard deviation of the fragility curve is fitted by the cumulative distribution function of the lognormal distribution for: ,in, Vulnerability curve The standard deviation of Vulnerability curve In order to avoid the collapse probability of the intact transmission tower in the probability interval [0,0.2] exceeding the collapse probability of the damaged transmission tower, formula (14) is used instead of formula (8): （14）。 9. The method for predicting the risk of transmission tower collapse considering the cumulative damage during a typhoon according to any one of claims 3 to 8, characterized in that: Step S3 includes: S31. Based on the time-varying wind field forecast information, at a certain prediction time step , substituting the wind angle of attack and wind speed into the corresponding vulnerability curve to obtain the transition probability matrix Each element in The value of S32, repeat step S31 to obtain the ; S33, from the initial probability The transmission tower is intact and rolling Multiply , , , ,get , updated It is used to provide the time-varying collapse probability of transmission towers considering cumulative damage, and the collapse probability of each transmission tower is used to predict the failure probability of transmission lines in the target area.

10. The method for predicting the risk of transmission tower collapse considering the cumulative damage during a typhoon according to claim 9, characterized in that: In step S33, the obtained collapse probability of each transmission tower is used to predict the failure probability of the transmission line in the target area, which is expressed as: （15） In formula (15), Indicated in Transmission lines at all times The failure probability of Indicates transmission line The number of upper transmission towers; Indicated in Transmission lines at all times Transmission tower probability of collapse.

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