Typhoon disaster-based power transmission network performance evaluation method and device, terminal equipment and storage medium

Abstract

The application discloses a kind of based on typhoon disaster transmission network performance evaluation method, device, terminal equipment and storage medium, by according to the electrical information of each structural member in transmission network, topological information, geographic information, equipment information, construct including geographic factor transmission network model, fully consider the geographic factor of structural member, and then according to typhoon parameter and geographic information, construct for predicting the change trend of wind speed and wind direction under the influence of geographic factor typhoon time-varying model, fully consider the influence of wind direction on transmission network, then calculate the failure probability of each structural member independent failure in transmission network and the related failure probability of influencing other structural member failure, fully consider the correlation between each structure in transmission network, finally according to a number of failure simulation scenarios, distribution probability, typhoon time-varying model and transmission network model, calculate the performance evaluation index that can accurately measure the performance of transmission network in response to typhoon disaster.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, and in particular to a power transmission network performance evaluation method and device based on typhoon disasters, a terminal device and a storage medium. BACKGROUND

[0002] The power transmission line has the characteristics of many points, wide range, long-term exposure in the wild, and its safe and stable operation is closely related to the climate environment. In recent years, typhoon disasters not only occur more and more frequently, but also their grades are getting higher and higher, which has brought serious threats to the power transmission line. China is located in the western Pacific and is seriously affected by typhoons. The southeast coastal areas are more vulnerable to typhoon disasters.

[0003] In recent years, in the performance research of the power transmission line in the typhoon disaster, a simplified power grid topology model is mainly used, only the topological structure and electrical parameters of the power transmission network are considered, and the geographical factors are ignored. In the same typhoon, the wind force borne by the structural members in different geographical positions is different. Secondly, in the process of typhoon scene simulation, the influence of wind direction is ignored. Finally, only the independent failure probability of each structural member is considered, and the correlation between structures is ignored. Therefore, the performance of the power transmission network in the typhoon disaster cannot be accurately evaluated at present, which further leads to the fact that sufficient preventive work cannot be done before the typhoon arrives, and great economic losses are caused many times. SUMMARY

[0004] The present application provides a power transmission network performance evaluation method and device based on typhoon disasters, terminal equipment and storage medium, which can.

[0005] An embodiment of the present application provides a power transmission network performance evaluation method based on typhoon disasters, comprising:

[0006] Obtaining electrical information, topological information, geographical information, device information and typhoon parameters of a plurality of structural members in the power transmission network to be evaluated;

[0007] According to the device information, the electrical information, the topological information and the geographical information, a power transmission network model is constructed;

[0008] According to the typhoon parameters and the geographical information, a typhoon time-varying model is constructed; wherein the typhoon time-varying model is used to predict the change trend of the typhoon wind speed and the typhoon wind direction under the influence of geographical factors;

[0009] According to the typhoon time-varying model and the power transmission network model, failure probabilities of each structure component at several time points and related failure probabilities are predicted; the failure probability is a probability of failure of each structure component affected by a typhoon; and the related failure probability is a probability of failure of each structure component affected by a typhoon, which further causes failure of other connected structure components.

[0010] According to the typhoon time-varying model, the power transmission network model, the failure probability, and the related failure probability, failure simulation scenarios of several power transmission networks and distribution probabilities corresponding to each failure simulation scenario are generated.

[0011] According to the typhoon time-varying model, the power transmission network model, the failure simulation scenario, and the distribution probability, a performance evaluation score of the power transmission network against typhoon disasters is calculated.

[0012] Further, the structure component includes a power transmission tower and a power transmission conductor; and the geographic information includes longitude and latitude coordinates of several power transmission towers.

[0013] The power transmission network model is constructed according to the device information, the electrical information, the topological information, and the geographic information, and includes:

[0014] According to the topological information and the longitude and latitude coordinates of each power transmission tower, conductor lengths and conductor direction angles of power transmission conductors between adjacent power transmission towers are calculated.

[0015] According to the conductor direction angle, power transmission tower directions of power transmission towers at two ends of each power transmission conductor are calculated.

[0016] According to the topological information, the conductor lengths, the conductor direction angles, and the power transmission tower directions, an initial power transmission network model is constructed.

[0017] According to the device information and the electrical information, device parameters and electrical parameters of each structure component in the initial power transmission network model are perfected to generate the power transmission network model.

[0018] Further, the typhoon parameters include typhoon static parameters and typhoon time-varying coefficients; and the geographic information further includes terrain heights, ground states, and terrain directions of each structure component.

[0019] The typhoon time-varying model is constructed according to the typhoon parameters and the geographic information, and includes:

[0020] According to the typhoon static parameters, a static initial typhoon model is constructed.

[0021] constructing an initial typhoon time-varying model for predicting the change trend of typhoon wind speed and typhoon wind direction according to the typhoon time-varying coefficient and the initial typhoon model;

[0022] generating corresponding terrain correction coefficients according to the terrain height, the ground state and the terrain direction respectively;

[0023] adding the terrain correction coefficients to the initial typhoon time-varying model to construct the typhoon time-varying model.

[0024] Further, according to the typhoon time-varying model and the power transmission network model, the failure probability of each structure component at several time points is predicted, including:

[0025] According to the typhoon time-varying model and the power transmission network model, the target wind speed and the target wind direction of the typhoon at each time point are predicted, and the target power transmission tower and the target power transmission conductor affected by the typhoon are counted;

[0026] According to the target wind speed, the target wind direction, and the target power transmission tower direction of the target power transmission tower at each time point, the first wind attack angle of each target power transmission tower is calculated;

[0027] According to the target wind speed, the target wind direction, the target conductor direction angle and the target conductor length of the target power transmission conductor at each time point, the second wind attack angle of several stress points of each target power transmission conductor is calculated;

[0028] According to the first wind attack angle, a power transmission tower vulnerability model for characterizing the influence of wind load on each power transmission tower is constructed;

[0029] According to the second wind attack angle, a power transmission conductor vulnerability model for characterizing the influence of wind load on each power transmission conductor is constructed;

[0030] According to the Bayesian update framework and the device parameters in the power transmission network model, the aging probability of each power transmission tower and each power transmission conductor over time is generated;

[0031] According to the aging probability, the power transmission tower vulnerability model, and the power transmission conductor vulnerability model, the failure probability of each power transmission tower and each power transmission conductor at several time points is calculated.

[0032] Further, according to the typhoon time-varying model and the power transmission network model, the related failure probability of each structure component at several time points is predicted, including:

[0033] According to the geographical information of each structure component in the power transmission network model, a spatial correlation matrix of the power transmission network is constructed;

[0034] According to the equipment information and the electrical information of each structural component in the power transmission network model, a time correlation matrix of the power transmission network is constructed;

[0035] According to the topological information of each structural component in the power transmission network model, the spatial correlation matrix, and the time correlation matrix, an importance degree of each structural component in the power transmission network is calculated, and a network topological correlation matrix is constructed according to the importance degree;

[0036] According to the network topological correlation matrix and the failure probability of each structural component at several time points, a related failure probability of each structural component at several time points is predicted.

[0037] Further, the performance evaluation index of the power transmission network in response to the typhoon disaster is calculated according to the typhoon time-varying model, the power transmission network model, the failure simulation scenario, and the distribution probability, including:

[0038] According to the typhoon time-varying model and the power transmission network model, a target fault removal scheme with which the power transmission network needs to pay the minimum cost to restore normal operation in each failure simulation scenario is generated;

[0039] According to the target fault removal scheme, a fault duration, a fault influence degree, an average fault duration, and a load cut-off amount of the power transmission network are calculated;

[0040] According to the distribution probability, the fault duration, the fault duration, the fault influence degree, the average fault duration, and the load cut-off amount, a performance evaluation score of the power transmission network in response to the typhoon disaster is calculated.

[0041] Further, the target fault removal scheme with which the power transmission network restores normal operation fastest in each failure simulation scenario is generated according to the typhoon time-varying model and the power transmission network model, including:

[0042] Taking the generation cost, the node voltage amplitude, the node voltage phase angle, and the load cut-off amount as decision variables, and constructing a target function for minimizing the generation cost, the generation power, the load cut-off cost, and the load cut-off amount;

[0043] A plurality of initial fault removal schemes are randomly generated;

[0044] According to the typhoon time-varying model, the power transmission network model, and the failure simulation scenario, an initial generation cost, an initial generation power, an initial load cut-off cost, and an initial load cut-off amount required for the power transmission network to implement each initial fault removal scheme are simulated, and a target function value of each initial fault removal scheme is calculated according to the target function;

[0045] The initial fault removal scheme corresponding to the minimum target function value is selected for iterative optimization until the target function value converges, thereby generating a target fault removal scheme corresponding to each failure simulation scenario.

[0046] Another embodiment of the present application provides a typhoon disaster-based power transmission network performance evaluation device, comprising:

[0047] A data acquisition module is configured to acquire electrical information, topological information, geographical information, device information, and typhoon parameters of a plurality of structural components in a power transmission network to be evaluated.

[0048] A first model construction module is configured to construct a power transmission network model based on the device information, the electrical information, the topological information, and the geographical information.

[0049] A second model construction module is configured to construct a typhoon time-varying model based on the typhoon parameters and the geographical information, wherein the typhoon time-varying model is configured to predict the variation trend of typhoon wind speed and typhoon wind direction under the influence of geographical factors.

[0050] A probability calculation module is configured to predict the failure probability and the related failure probability of each structural component at a plurality of time points based on the typhoon time-varying model and the power transmission network model, wherein the failure probability is the probability of failure of each structural component under the influence of a typhoon, and the related failure probability is the probability of failure of each structural component under the influence of a typhoon, which further leads to the failure of other connected structural components.

[0051] A scenario generation module is configured to generate a plurality of failure simulation scenarios of the power transmission network and the distribution probability corresponding to each failure simulation scenario based on the typhoon time-varying model, the power transmission network model, the failure probability, and the related failure probability.

[0052] A performance evaluation module is configured to calculate the performance evaluation score of the power transmission network in response to typhoon disasters based on the typhoon time-varying model, the power transmission network model, the failure simulation scenario, and the distribution probability.

[0053] Another embodiment of the present application provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements a typhoon disaster-based power transmission network performance evaluation method according to any one of the embodiments when executing the computer program.

[0054] Another embodiment of the present application provides a storage medium comprising a stored computer program, wherein the storage medium controls a device in which the storage medium is located to perform a typhoon disaster-based power transmission network performance evaluation method according to any one of the above embodiments when the computer program is running.

[0055] By implementing the present application, the following beneficial effects are achieved:

[0056] The present application discloses a typhoon disaster-based power transmission network performance evaluation method and device, a terminal device, and a storage medium. The method constructs a power transmission network model containing geographical factors according to electrical information, topological information, geographical information, and device information of each structural component in the power transmission network, fully considers the geographical differences of each structural component, and then constructs a typhoon time-varying model for predicting the change trend of typhoon wind speed and typhoon wind direction under the influence of geographical factors according to typhoon parameters and geographical information, fully considers the influence of typhoon wind direction on the power transmission network, and then calculates the failure probability of each structural component in the power transmission network and the related failure probability of influencing other structural components according to the power transmission network model and the power transmission network model, fully considers the correlation between each structure in the power transmission network, and finally calculates a performance evaluation index that can accurately measure the performance of the power transmission network in response to typhoon disasters according to a number of failure simulation scenarios, corresponding distribution probabilities, a typhoon time-varying model, and a power transmission network model. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 is a flowchart of a typhoon disaster-based power transmission network performance evaluation method according to an embodiment of the present application.

[0058] Figure 2 is a structural diagram of a typhoon disaster-based power transmission network performance evaluation device according to an embodiment of the present application. DETAILED DESCRIPTION

[0059] To make the purposes, technical solutions, and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of this application; the use of the terms "including," "comprising," or "having" and variations thereof herein is intended to be broad and encompass the terms "consisting of" and "consisting essentially of" and variations thereof. Unless otherwise required by context, singular terms shall include pluralities and vice versa.

[0061] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise explicitly and specifically limited.

[0062] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearance of the phrase in various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily independent or alternative embodiments to each other. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0063] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents that the front and rear associated objects are a "or" relationship.

[0064] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two), and similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0065] In the description of the embodiments of the present application, unless otherwise explicitly specified and limited, the technical terms "mounting", "connecting", "connecting", "fixing" and the like should be understood in a broad sense, for example, it can be fixedly connected, or it can be detachably connected, or it can be integrated; it can be mechanical connection, or it can be electrical connection; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0066] Reference Figure 1 is a flowchart of a power transmission network performance evaluation method based on typhoon disaster provided by an embodiment of the present application, which comprises:

[0067] S1, obtaining electrical information, topological information, geographical information, equipment information and typhoon parameters of a plurality of structural components in a power transmission network to be evaluated;

[0068] In a preferred embodiment of the present application, the structural components include power plants, substations, power transmission towers and power transmission lines; the power plant data includes name, location, installed capacity, fuel type and capacity factor; the substation data includes location, voltage level and type (power transmission or power distribution); the power transmission line data includes voltage level and connection relationship; the power transmission tower data includes location; wherein the location of each structural component is represented by longitude and latitude coordinates.

[0069] S2, constructing a power transmission network model according to the equipment information, the electrical information, the topological information and the geographical information;

[0070] Preferably, the structural components include power transmission towers and power transmission lines; and the geographical information includes longitude and latitude coordinates of a plurality of the power transmission towers.

[0071] The constructing of the power transmission network model according to the equipment information, the electrical information, the topological information and the geographical information includes:

[0072] S21, calculating the conductor length and the conductor direction angle of the power transmission lines between adjacent power transmission towers according to the topological information and the longitude and latitude coordinates of each power transmission tower;

[0073] S22, calculating the power transmission tower direction of the power transmission towers at both ends of each power transmission line according to the conductor direction angle;

[0074] S23, constructing an initial power transmission network model according to the topological information, the conductor length, the conductor direction angle and the power transmission tower direction;

[0075] S24, perfecting the equipment parameters and the electrical parameters of each structural component in the initial power transmission network model according to the equipment information and the electrical information, to generate the power transmission network model.

[0076] In a preferred embodiment of the present application, the conductor segment span length is calculated as:

[0077] ;

[0078] wherein, is the span length of the i-1th segment conductor; is the central angle (radian); is the radius of the earth; and specifically, the calculation formula of the central angle is as follows:

[0079] ;

[0080] wherein, is the latitude difference between two adjacent towers; is the longitude difference between two adjacent towers; is the latitude (radian) of the i-1th and ith tower.

[0081] Calculate the direction of the conductor:

[0082]

[0083] wherein, is the direction angle (radian) of the i-1th conductor.

[0084] Calculate the direction of the transmission tower:

[0085]

[0086] wherein, is the direction angle (radian) of the i-1th tower; is the direction angle (radian) of the ith conductor.

[0087] Finally, according to the equipment information and the electrical information, the equipment parameters and the electrical parameters of each structural member in the initial power transmission network model are perfected, including series impedance and parallel admittance, so as to generate the power transmission network model.

[0088] S3, according to the typhoon parameters and the geographical information, a typhoon time-varying model is constructed; wherein, the typhoon time-varying model is used to predict the change trend of the typhoon wind speed and the typhoon wind direction under the influence of geographical factors;

[0089] Preferably, the typhoon parameters include typhoon static parameters and typhoon time-varying coefficients; and the geographical information further includes the terrain height, the ground state and the terrain direction of each structural member;

[0090] The typhoon time-varying model is constructed according to the typhoon parameters and the geographical information, including:

[0091] S31, an initial typhoon model is constructed according to the typhoon static parameters;

[0092] S32, an initial typhoon time-varying model for predicting the change trend of the typhoon wind speed and the typhoon wind direction is constructed according to the typhoon time-varying coefficients and the initial typhoon model;

[0093] S33, corresponding terrain correction coefficients are respectively generated according to the terrain height, the ground state and the terrain direction;

[0094] ​​​S34, adding the terrain correction coefficient in the initial typhoon time-varying model to construct the typhoon time-varying model.

[0095] In a preferred embodiment of the present application, the static parameters of the typhoon include: maximum wind speed, maximum wind speed radius, Holland B parameter, asymmetry coefficient, azimuth angle, maximum wind speed azimuth angle.

[0096] Static initial typhoon model:

[0097] ;

[0098] wherein, is the wind speed at the distance r from the typhoon center and the azimuth angle θ; is the maximum wind speed; is the maximum wind speed radius; B is the Holland B parameter for controlling the wind speed profile shape; A is the asymmetry coefficient; θ is the azimuth angle; is the maximum wind speed azimuth angle.

[0099] Further, the typhoon time-varying coefficient includes: intensity decay coefficient, radius expansion coefficient, parameter fluctuation coefficient and angular frequency;

[0100] Initial typhoon time-varying model:

[0101] ;

[0102] ;

[0103] ;

[0104] wherein, , , are the time-varying maximum wind speed, maximum wind speed radius and Holland B parameter respectively; , , are initial values respectively; t is time; α is the intensity decay coefficient; β is the radius expansion coefficient; γ, ω are the parameter fluctuation coefficient and angular frequency respectively.

[0105] Finally, the terrain correction coefficient includes: terrain height correction coefficient, terrain height correction coefficient, and an index parameter related to the ground roughness;

[0106] Typhoon time-varying model:

[0107] ;

[0108] ;

[0109] wherein, where z is the height of the wind speed; is the terrain height correction coefficient; is the terrain direction correction coefficient; is the wind speed at height z; is the reference height; and α is an exponential parameter related to the surface roughness.

[0110] S4, according to the typhoon time-varying model and the power transmission network model, predicting the failure probability of each structural member at several time points and the related failure probability; wherein the failure probability is the probability of failure of each structural member affected by the typhoon; and the related failure probability is the probability of failure of each structural member affected by the typhoon, which in turn causes the failure of other structural members connected thereto;

[0111] In a preferred embodiment of the present application, for each power transmission tower, the wind speed and wind direction data at the location are extracted from the wind field model according to its latitude and longitude, and the actual wind attack angle is calculated in combination with the tower direction angle and the wind direction. For each section of the conductor, considering that the conductor spans a certain distance, multiple points of wind speed and wind direction data along the length of the conductor are extracted to calculate the average wind speed and equivalent wind attack angle of the conductor. Based on the mapped wind field data, the wind load acting on the structure is calculated. The response of the structure under the wind load, i.e. the failure probability and the related failure probability, is calculated using the finite element method or a simplified model.

[0112] Preferably, according to the typhoon time-varying model and the power transmission network model, predicting the failure probability of each structural member at several time points comprises:

[0113] S411, according to the typhoon time-varying model and the power transmission network model, predicting the target wind speed and target wind direction of the typhoon at each time point, and counting the target power transmission towers and target power transmission conductors affected by the typhoon;

[0114] S412, according to the target wind speed, target wind direction, and target power transmission tower direction of the target power transmission tower at each time point, calculating the first wind attack angle of each target power transmission tower;

[0115] S413, according to the target wind speed, target wind direction, target conductor direction angle of the target power transmission conductor, and target conductor length at each time point, calculating the second wind attack angle of several stress points of each target power transmission conductor;

[0116] S414, constructing a power transmission tower vulnerability model for characterizing the influence of wind load on each power transmission tower according to the first wind attack angle;

[0117] S415, constructing a power transmission conductor vulnerability model for characterizing the influence of wind load on each power transmission conductor according to the second wind attack angle;

[0118] S416, generating aging probabilities of each of the power transmission towers and each of the power transmission conductors according to the Bayesian updating framework and the equipment parameters in the power transmission network model;

[0119] S417, calculating failure probabilities of each of the power transmission towers and each of the power transmission conductors at several time points according to the aging probabilities, the power transmission tower vulnerability model, and the power transmission conductor vulnerability model.

[0120] In a preferred embodiment of the present application, the power transmission tower vulnerability model is:

[0121] ;

[0122] wherein, is the power transmission tower failure probability; is a standard normal distribution cumulative function; IM is an intensity measure (such as wind speed); is a structural resistance mean value; is a structural resistance standard deviation; is an intensity measure uncertainty;

[0123] Specifically,

[0124] ;

[0125] ;

[0126] ;

[0127] wherein, is an age influence coefficient is a corrosion influence coefficient; is a wind direction influence coefficient; t is a structure age; θ is a wind attack angle.

[0128] The conductor vulnerability model is:

[0129] ;

[0130] wherein, is the conductor failure probability; is a failure rate; is an exposure time;

[0131] Specifically,

[0132] ;

[0133] wherein, is a span influence coefficient; is a temperature influence coefficient; L is a span length; ΔT is a temperature change.

[0134] To improve the accuracy of failure probability prediction, we need to consider the time-varying characteristics of structural capacity. We extend the static fragility analysis to dynamic time-varying analysis. First, we need to identify the factors that affect the change of structural capacity over time: material aging (such as fatigue and corrosion of steel), environmental impact (such as sea salt erosion, ultraviolet radiation), cumulative damage (such as the expansion of micro-cracks), maintenance and repair activities, etc.

[0135] Establish a time-varying model of structural capacity:

[0136] ;

[0137] where, is the time-varying structural capacity; is the initial capacity; λ(t) is the time-varying degradation rate.

[0138] Further, Bayesian updating can allow us to continuously adjust model parameters based on new observation data. We use the following particle filter algorithm to implement this update process.

[0139] ;

[0140] where, is the weight of the i-th particle at time t; is the observation value at time t; is the state of the i-th particle at time t; is the likelihood function.

[0141] With an adaptive model, we can make more accurate predictions of structural capacity. In this step, we use the updated model parameters to predict future structural capacity and failure probability:

[0142] a) Using the updated parameter distribution, we can get the probability distribution of the time-varying structural capacity R(t);

[0143] b) At the same time, we need to consider the time-varying load effect S(t), which can be obtained from the wind field model;

[0144] c) Define the limit state function: G(R(t), S(t)) = R(t) for S(t):

[0145] ;

[0146] where: is the failure probability at time t; is the limit state function; R(t) is the structural capacity at time t; S(t) is the load effect at time t.

[0147] Preferably, according to the typhoon time-varying model and the power transmission network model, the related failure probability of each structural member at several time points is predicted, including:

[0148] S421, according to the geographical information of each structural member in the power transmission network model, a spatial correlation matrix of the power transmission network is constructed;

[0149] S422, according to the equipment information and electrical information of each structural member in the power transmission network model, a time correlation matrix of the power transmission network is constructed;

[0150] S423, according to the topological information of each structural member in the power transmission network model, the spatial correlation matrix, and the time correlation matrix, the importance of each structural member in the power transmission network is calculated, and a network topology correlation matrix is constructed according to the importance;

[0151] S424, according to the network topology correlation matrix and the failure probability of each structural member at several time points, the related failure probability of each structural member at several time points is predicted.

[0152] In a preferred embodiment of the present application, the basic spatial correlation function is as follows:

[0153] ;

[0154] Wherein, ρ (d) is the correlation coefficient; d is the distance between structures; d corr is the correlation distance.

[0155] After considering the correction of terrain and environmental factors, wherein:

[0156] ;

[0157] Wherein, is the structure type influence coefficient; is the environmental influence coefficient; is the structure type index; is the environmental condition index;

[0158] The spatial correlation matrix is constructed , wherein

[0159] ;

[0160] The basic time correlation function is as follows:

[0161] ;

[0162] Wherein, is the time correlation coefficient; t is the time interval; tcorr is the time correlation scale;

[0163] After correction considering load and maintenance factors, where:

[0164] ;

[0165] where, is the load impact coefficient; is the maintenance impact coefficient; is the load level indicator; is the maintenance level indicator; Constructing the time correlation matrix where represents the correlation of components i and j in the time dimension.

[0166] a) Incorporating spatial and temporal correlations:

[0167] ;

[0168] b) Considering the impact of network topology:

[0169] ;

[0170] where is the adjustment function based on network topology, for example:

[0171] Components directly connected may have higher correlation, lines sharing the same substation may have higher correlation.

[0172] a) Convert the marginal failure probability and correlation matrix to joint failure probability using the Gaussian Copula method:

[0173] ;

[0174] where, is the two-dimensional standard normal distribution function; is the inverse function of the standard normal distribution; and are the marginal failure probabilities of components i and j at time t; is the correlation coefficient of components i and j at time t.

[0175] Through this process, the transition from time-varying analysis of individual components to system-level analysis considering the correlation of the entire network is realized. This method not only considers the independent characteristics of individual components, but also captures the mutual influence between them, thus providing a more comprehensive and accurate system reliability evaluation.

[0176] S5, generating a plurality of failure simulation scenarios of the power transmission network and a distribution probability corresponding to each of the failure simulation scenarios according to the typhoon time-varying model, the power transmission network model, the failure probability, and the related failure probability;

[0177] In a preferred embodiment of the present application, a system reliability model of the power transmission network is constructed.

[0178] ;

[0179] wherein, is the system reliability; is the failure probability of the ith component;

[0180] Specifically,

[0181] ; ;

[0182] wherein, is the redundancy influence coefficient; is the importance influence coefficient; is the redundancy; is the importance index;

[0183] A cascading failure model is constructed.

[0184] ;

[0185] wherein, is the cascading failure probability; λ is the cascading failure rate; N is the system size;

[0186] Specifically,

[0187] ;

[0188] wherein, is the topology influence coefficient; is the load level influence coefficient; is the topology complexity index; is the load level ratio.

[0189] S6, calculating a performance evaluation score of the power transmission network against typhoon disasters according to the typhoon time-varying model, the power transmission network model, the failure simulation scenarios, and the distribution probability.

[0190] Preferably, the calculating of the performance evaluation index of the power transmission network against typhoon disasters according to the typhoon time-varying model, the power transmission network model, the failure simulation scenarios, and the distribution probability comprises:

[0191] S61, generating, according to the typhoon time-varying model and the power transmission network model, a target fault removal scheme with which the power transmission network in each of the failure simulation scenarios needs to pay the least cost to recover normal operation;

[0192] Preferably, the target fault removal scheme with which the power transmission network in each of the failure simulation scenarios recovers normal operation the fastest comprises:

[0193] S611, taking the power generation cost, the node voltage amplitude, the node voltage phase angle and the load cut-off amount as decision variables, and constructing a target function for minimizing the power generation cost, the power generation power, the load cut-off cost and the load cut-off amount;

[0194] S612, randomly generating a plurality of initial fault removal schemes;

[0195] S613, according to the typhoon time-varying model, the power transmission network model and the failure simulation scenario, simulating initial power generation cost, initial power generation power, initial load cut-off cost and initial load cut-off amount required for the power transmission network to implement each initial fault removal scheme, and calculating a target function value of each initial fault removal scheme according to the target function;

[0196] S614, selecting an initial fault removal scheme corresponding to the smallest target function value for iterative optimization until the target function value converges, and generating a target fault removal scheme corresponding to each failure simulation scenario.

[0197] In a preferred embodiment of the present application, the target function is set as:

[0198] ;

[0199] Wherein, is the power generation cost; is the power generation power; is the load cut-off cost; is the load cut-off amount;

[0200] Constraint conditions:

[0201] Power balance constraint: ; Generator capacity constraint: ; Line capacity constraint: ; Node voltage constraint: ;

[0202] Decision variables:

[0203] ;

[0204] Where V is the node voltage amplitude; θ is the node voltage phase angle.

[0205] For each failure scenario obtained from step S5:

[0206] a) Update network topology: remove failed lines and substations; check network connectivity, identify islands.

[0207] b) Adjust constraints: update line capacity limits; consider equipment overload capabilities and temporary ratings.

[0208] c) Solve AC optimal power flow problem: use interior point method or Newton method to solve nonlinear optimization problem; if unable to converge, gradually increase load shedding amount until convergence.

[0209] d) Record key outputs: load shedding amount; generation dispatch; line power flow; node voltage.

[0210] S62, according to the target fault removal scheme, calculate the fault duration, fault impact degree, average fault duration and load shedding amount of the power transmission network;

[0211] S63, according to the distribution probability, the fault duration, the fault duration, the fault impact degree, the average fault duration and the load shedding amount, calculate the performance evaluation score of the power transmission network in response to typhoon disaster.

[0212] In a preferred embodiment of the present application, the following performance evaluations are preset: expected unserved power (EENS), system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), energy deficiency index (EIU). Finally, according to the above several performance evaluation indexes, the performance evaluation score is calculated.

[0213] Specifically,

[0214] a) Expected unserved power (EENS):

[0215] ;

[0216] Where, is the probability of the i-th failure scenario; is the load shedding amount of the i-th failure scenario; is the duration of the i-th failure scenario;

[0217] b) System average interruption frequency index (SAIFI):

[0218] ;

[0219] Where, is the failure rate of the ith component; is the number of users affected by the failure of the ith component; is the total number of users;

[0220] c) System Average Interruption Duration Index (SAIDI):

[0221]

[0222] where, is the annual average interruption time caused by the failure of the ith component;

[0223] d) Energy Insufficiency Index (EIU):

[0224]

[0225] where, is the total demand energy.

[0226] The embodiment provides a typhoon disaster-based power transmission network performance evaluation method, which comprises the following steps: constructing a power transmission network model containing geographical factors according to electrical information, topological information, geographical information and equipment information of each structural component in the power transmission network, fully considering the geographical factors of the structural components, then constructing a typhoon time-varying model for predicting the wind speed and wind direction change trend under the influence of geographical factors according to typhoon parameters and geographical information, fully considering the influence of wind direction on the power transmission network, then calculating the failure probability of independent failure of each structural component in the power transmission network and the related failure probability of affecting other structural component failures, fully considering the correlation between each structure in the power transmission network, and finally calculating performance evaluation indexes capable of accurately measuring the performance of the power transmission network in response to typhoon disasters according to a plurality of failure simulation scenarios, distribution probabilities, the typhoon time-varying model and the power transmission network model.

[0227] Referring to Figure 2 is a structural schematic diagram of a typhoon disaster-based power transmission network performance evaluation device provided by an embodiment of the present application, comprising:

[0228] a data acquisition module configured to acquire electrical information, topological information, geographical information, equipment information of a plurality of structural components in a power transmission network to be evaluated, and typhoon parameters;

[0229] a first model construction module configured to construct a power transmission network model according to the equipment information, the electrical information, the topological information and the geographical information;

[0230] ​​a second model construction module, configured to construct a typhoon time-varying model according to the typhoon parameters and the geographic information, wherein the typhoon time-varying model is configured to predict the variation trend of the typhoon wind speed and the typhoon wind direction under the influence of the geographic factors;

[0231] a probability calculation module, configured to predict the failure probability and the related failure probability of each structural member at a plurality of time instants according to the typhoon time-varying model and the power transmission network model, wherein the failure probability is the probability of failure of each structural member under the influence of the typhoon, and the related failure probability is the probability of failure of each structural member under the influence of the typhoon, which further leads to the failure of other structural members connected thereto;

[0232] a scenario generation module, configured to generate a plurality of failure simulation scenarios of the power transmission network and the distribution probability corresponding to each failure simulation scenario according to the typhoon time-varying model, the power transmission network model, the failure probability, and the related failure probability;

[0233] a performance evaluation module, configured to calculate the performance evaluation score of the power transmission network in response to the typhoon disaster according to the typhoon time-varying model, the power transmission network model, the failure simulation scenario, and the distribution probability.

[0234] It should be noted that the apparatus embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. In addition, the connection relationship between the modules in the apparatus embodiment provided by the present application indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0235] Those skilled in the art can clearly understand that, for the convenience and brevity, the specific working process of the apparatus described above can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0236] Another preferred embodiment of the present application provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements a typhoon disaster-based power transmission network performance evaluation method according to any one of the above embodiments when executing the computer program.

[0237] The terminal device can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The terminal device can include, but is not limited to, a processor and a memory.

[0238] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, and the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor and the like. The processor is a control center of the terminal device, and connects all parts of the terminal device through various interfaces and lines.

[0239] The memory can be used to store the computer program, and the processor can realize various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store an operating system, at least one application program required by a function, and the like; and the data storage area can store data created according to the use of the terminal device, and the like. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0240] Another preferred embodiment of the present application provides a storage medium, which is a computer readable storage medium, and a computer program is stored in the computer readable storage medium, and the computer program, when executed by a processor, can implement the steps of each method embodiment described above. Wherein, the computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0241] The above is the preferred embodiment of the present application, it should be pointed out that, for those skilled in the art, without departing from the principles of the present application, can also make a number of improvements and refinements, these improvements and refinements also considered as the protection scope of the present application.

Claims

1. A method for evaluating performance of a power transmission network based on typhoon disaster, characterized in that, The method comprises: obtaining electrical information, topological information, geographical information, equipment information and typhoon parameters of a plurality of structural components in a power transmission network to be evaluated; constructing a power transmission network model according to the equipment information, the electrical information, the topological information and the geographical information; constructing a time-varying typhoon model according to the typhoon parameters and the geographical information, wherein the time-varying typhoon model is used to predict the change trend of typhoon wind speed and typhoon wind direction under the influence of geographical factors; predicting the target wind speed and target wind direction of the typhoon at each time instant and counting the target power transmission towers and target power transmission lines affected by the typhoon according to the time-varying typhoon model and the power transmission network model, calculating the first wind attack angle of each target power transmission tower according to the target wind speed, the target wind direction and the target power transmission tower direction at each time instant, calculating the second wind attack angle of a plurality of stress points of each target power transmission line according to the target wind speed, the target wind direction, the target power transmission line direction angle and the target power transmission line length at each time instant, constructing a power transmission tower vulnerability model for representing the influence of wind load on each power transmission tower according to the first wind attack angle, constructing a power transmission line vulnerability model for representing the influence of wind load on each power transmission line according to the second wind attack angle, generating the aging probability of each power transmission tower and each power transmission line over time according to the Bayesian updating framework and the equipment parameters in the power transmission network model, and calculating the failure probability of each power transmission tower and each power transmission line at a plurality of time instants according to the aging probability, the power transmission tower vulnerability model and the power transmission line vulnerability model; predicting the relevant failure probability of each structural component at a plurality of time instants according to the time-varying typhoon model and the power transmission network model, wherein the relevant failure probability is the probability that each structural component fails under the influence of the typhoon and further causes the failure of other connected structural components; generating a plurality of failure simulation scenarios of the power transmission network and the distribution probability corresponding to each failure simulation scenario according to the time-varying typhoon model, the power transmission network model, the failure probability and the relevant failure probability; calculating the performance evaluation score of the power transmission network in response to typhoon disasters according to the time-varying typhoon model, the power transmission network model, the failure simulation scenario and the distribution probability.

2. The typhoon disaster-based transmission network performance evaluation method of claim 1, wherein, The structural components include power transmission towers and power transmission lines, and the geographical information includes the longitude and latitude coordinates of a plurality of power transmission towers. The method of constructing a power transmission network model according to the equipment information, the electrical information, the topological information and the geographical information comprises: calculating the line length and line direction angle of the power transmission line between adjacent power transmission towers according to the topological information and the longitude and latitude coordinates of each power transmission tower; calculating the power transmission tower direction of the power transmission tower at both ends of each power transmission line according to the line direction angle; constructing an initial power transmission network model according to the topological information, the line length, the line direction angle and the power transmission tower direction; According to the device information and the electrical information, perfecting the device parameters and the electrical parameters of each structural member in the initial power transmission network model, and generating the power transmission network model.

3. The typhoon disaster-based transmission network performance evaluation method of claim 2, wherein, The typhoon parameters include static typhoon parameters and time-varying typhoon coefficients; and the geographic information further includes the terrain height, the ground state and the terrain direction of each structural member. The constructing the time-varying typhoon model according to the typhoon parameters and the geographic information includes: constructing a static initial typhoon model according to the static typhoon parameters; constructing an initial time-varying typhoon model for predicting the change trend of the typhoon speed and the typhoon direction according to the time-varying typhoon coefficients and the initial typhoon model; generating corresponding terrain correction coefficients according to the terrain height, the ground state and the terrain direction; adding the terrain correction coefficients to the initial time-varying typhoon model to construct the time-varying typhoon model.

4. The typhoon disaster-based transmission network performance evaluation method of claim 3, wherein, According to the time-varying typhoon model and the power transmission network model, predicting the related failure probability of each structural member at several time points includes: constructing a spatial correlation matrix of the power transmission network according to the geographic information of each structural member in the power transmission network model; constructing a time correlation matrix of the power transmission network according to the device information and the electrical information of each structural member in the power transmission network model; calculating the importance of each structural member in the power transmission network according to the topological information of each structural member in the power transmission network model, the spatial correlation matrix and the time correlation matrix, and constructing a network topological correlation matrix according to the importance; predicting the related failure probability of each structural member at several time points according to the network topological correlation matrix and the failure probability of each structural member at several time points.

5. The typhoon disaster-based transmission network performance evaluation method of claim 4, wherein, According to the time-varying typhoon model, the power transmission network model, the failure simulation scenario and the distribution probability, calculating the performance evaluation score of the power transmission network in coping with typhoon disasters includes: generating a target fault removal scheme with the minimum cost required for the power transmission network to recover normal operation in each failure simulation scenario according to the time-varying typhoon model and the power transmission network model; calculating the fault duration, the fault influence degree, the average fault duration and the load cut-off amount of the power transmission network according to the target fault removal scheme; calculating the performance evaluation score of the power transmission network in coping with typhoon disasters according to the distribution probability, the fault duration, the fault influence degree, the average fault duration and the load cut-off amount.

6. The typhoon disaster-based transmission network performance evaluation method of claim 5, wherein, The generating a target fault removal scheme with the minimum cost required for the power transmission network to recover normal operation in each failure simulation scenario according to the time-varying typhoon model and the power transmission network model includes: taking the generation cost, the node voltage amplitude, the node voltage phase angle and the load cut-off amount as decision variables, and constructing an objective function for minimizing the generation cost, the generation power, the load cut-off cost and the load cut-off amount; randomly generating several initial fault removal schemes; According to the typhoon time-varying model, the power transmission network model, and the failure simulation scenario, initial generation cost, initial generation power, initial load shedding cost, and initial load shedding amount required for the power transmission network to implement each initial fault removal scheme are simulated, and a target function value of each initial fault removal scheme is calculated according to the target function; An initial fault removal scheme corresponding to the smallest target function value is selected for iterative optimization until the target function value converges, so as to generate a target fault removal scheme corresponding to each failure simulation scenario.

7. A typhoon disaster-based power transmission network performance evaluation device, characterized by Comprise: A data acquisition module is configured to acquire electrical information, topological information, geographical information, equipment information, and typhoon parameters of a plurality of structural components in a power transmission network to be evaluated; A first model construction module is configured to construct a power transmission network model according to the equipment information, the electrical information, the topological information, and the geographical information; A second model construction module is configured to construct a typhoon time-varying model according to the typhoon parameters and the geographical information; wherein the typhoon time-varying model is configured to predict the variation trend of typhoon wind speed and typhoon wind direction under the influence of geographical factors; A probability calculation module is configured to predict target wind speed and target wind direction of a typhoon at each time according to the typhoon time-varying model and the power transmission network model, and to count target power transmission towers and target power transmission conductors affected by the typhoon; to calculate first wind attack angles of each target power transmission tower according to the target wind speed, the target wind direction, and a target power transmission tower direction of the target power transmission tower at each time; to calculate second wind attack angles of a plurality of stress points of each target power transmission conductor according to the target wind speed, the target wind direction, a target conductor direction angle, and a target conductor length of the target power transmission conductor at each time; to construct a power transmission tower vulnerability model for representing the influence of wind load on each power transmission tower according to the first wind attack angles; to construct a power transmission conductor vulnerability model for representing the influence of wind load on each power transmission conductor according to the second wind attack angles; to generate aging probabilities of each power transmission tower and each power transmission conductor over time according to a Bayesian updating framework and equipment parameters in the power transmission network model; to calculate failure probabilities of each power transmission tower and each power transmission conductor at a plurality of times according to the aging probabilities, the power transmission tower vulnerability model, and the power transmission conductor vulnerability model; to predict related failure probabilities of each structural component at a plurality of times according to the typhoon time-varying model and the power transmission network model; wherein the related failure probability is a probability that each structural component is affected by a typhoon to cause failure and further cause failure of other connected structural components; A scenario generation module is configured to generate a plurality of failure simulation scenarios of the power transmission network and a distribution probability corresponding to each failure simulation scenario according to the typhoon time-varying model, the power transmission network model, the failure probability, and the related failure probability; A performance evaluation module is configured to calculate a performance evaluation score of the power transmission network in response to a typhoon disaster according to the typhoon time-varying model, the power transmission network model, the failure simulation scenario, and the distribution probability.

8. A terminal device, comprising: The storage medium comprises a stored computer program, wherein the computer program, when running, controls a device where the storage medium is located to perform the typhoon disaster-based power transmission network performance evaluation method according to any one of claims 1 to 6.

9. A storage medium, characterized by The storage medium comprises a stored computer program, wherein the computer program, when running, controls a device where the storage medium is located to perform the typhoon disaster-based power transmission network performance evaluation method according to any one of claims 1 to 6.

Images