Power transmission network performance evaluation method and device based on typhoon disaster, terminal equipment and storage medium

Abstract

The invention discloses a power transmission network performance evaluation method and device based on typhoon disasters, terminal equipment and a storage medium, a power transmission network model containing geographic factors is constructed according to electrical information, topological information, geographic information and equipment information of each structural member in a power transmission network, the geographic factors of the structural members are fully considered, and the power transmission network performance evaluation method and device based on typhoon disasters are obtained. According to the typhoon parameters and the geographic information, a typhoon time-varying model used for predicting the wind speed and the wind direction change trend of the typhoon under the influence of the geographic factors is constructed, and the influence of the wind direction on the power transmission network is fully considered; then calculating the failure probability of the independent fault of each structural member in the power transmission network and the related failure probability influencing the faults of other structural members, fully considering the correlation among the structures in the power transmission network, and finally calculating the failure probability of the power transmission network according to a plurality of failure simulation scenes, the distribution probability, a typhoon time-varying model and a power transmission network model. And calculating a performance evaluation index capable of accurately measuring the typhoon disaster coping performance of the power transmission network.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, device, terminal equipment and storage medium for evaluating the performance of a power transmission network based on typhoon disasters. Background Art

[0002] Transmission lines are characterized by being numerous, wide, and exposed outdoors for long periods of time. Their safe and stable operation is closely related to the climate environment in which they are located. In recent years, typhoon disasters have not only occurred more and more frequently, but also become increasingly severe, posing a serious threat to transmission lines. China is located in the western Pacific Ocean and is severely affected by typhoons, and the southeastern coastal areas are more vulnerable to typhoon disasters.

[0003] In recent years, the performance research of transmission lines in typhoon disasters mainly adopts simplified grid topology models, which only consider the topological structure and electrical parameters of the transmission network, ignoring geographical factors. In the same typhoon, the wind forces borne by structural components in different geographical locations are different. Secondly, in the process of typhoon scene simulation, the influence of wind speed is mainly considered, and the influence of wind direction is ignored. Finally, only the independent failure probability of each structural component is considered, and the correlation between structures is ignored. Therefore, it is currently impossible to make an accurate assessment of the performance of the transmission network in typhoon disasters, which leads to the current inability to do adequate prevention work before the typhoon arrives, causing huge economic losses many times. Summary of the invention

[0004] The embodiments of the present invention provide a method, apparatus, terminal equipment and storage medium for evaluating the performance of a power transmission network based on typhoon disasters, which can.

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

[0006] Obtain electrical information, topological information, geographic information, equipment information, and typhoon parameters of several structural components in the power transmission network to be evaluated;

[0007] constructing a power transmission network model according to the device information, the electrical information, the topology information, and the geographic information;

[0008] A typhoon time-varying model is constructed according to the typhoon parameters and the geographic information; wherein the typhoon time-varying model is used to predict the changing trend of typhoon wind speed and typhoon wind direction under the influence of geographical factors;

[0009] According to the typhoon time-varying model and the power transmission network model, the failure probability and the related failure probability of each of the structural components at several moments are predicted; wherein the failure probability is the probability of each of the structural components failing due to the typhoon; the related failure probability is the probability of each of the structural components failing due to the typhoon, thereby causing failure of other connected structural components;

[0010] Generate a number of failure simulation scenarios of the power transmission network and distribution probabilities corresponding to the failure simulation scenarios according to the typhoon time-varying model, the power transmission network model, the failure probability, and the related failure probability;

[0011] A performance evaluation score of the power transmission network in responding to typhoon disasters is calculated based on the typhoon time-varying model, the power transmission network model, the failure simulation scenario, and the distribution probability.

[0012] Furthermore, the structural components include: transmission towers and transmission wires; the geographic information includes: longitude and latitude coordinates of several transmission towers;

[0013] The step of constructing a power transmission network model according to the device information, the electrical information, the topology information, and the geographic information includes:

[0014] Calculating the length and direction angle of the transmission lines between adjacent transmission towers according to the topological information and the longitude and latitude coordinates of each transmission tower;

[0015] Calculating the transmission tower directions of the transmission towers at both ends of each transmission wire according to the conductor direction angle;

[0016] Constructing an initial transmission network model according to the topology information, the conductor length, the conductor direction angle, and the transmission tower direction;

[0017] According to the equipment information and the electrical information, the equipment parameters and electrical parameters of each structural component in the initial power transmission network model are improved to generate the power transmission network model.

[0018] Furthermore, the typhoon parameters include: typhoon static parameters and typhoon time-varying coefficients; the geographic information also includes: the terrain height, surface state and terrain direction where each of the structural components is located;

[0019] The step of constructing a typhoon time-varying model according to the typhoon parameters and the geographic information includes:

[0020] According to the typhoon static parameters, construct a static initial typhoon model;

[0021] According to the typhoon time-varying coefficient and the initial typhoon model, construct an initial typhoon time-varying model for predicting the changing trend of typhoon wind speed and typhoon wind direction;

[0022] Generate corresponding terrain correction coefficients according to the terrain height, the surface state and the terrain direction;

[0023] The terrain correction coefficient is added to the initial typhoon time-varying model to construct the typhoon time-varying model.

[0024] Furthermore, based on the typhoon time-varying model and the power transmission network model, the failure probability of each of the structural components at several moments is predicted, including:

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

[0026] Calculate the first wind attack angle of each target transmission tower according to the target wind speed, target wind direction, and target transmission tower direction of the target transmission tower at each moment;

[0027] Calculate the second wind attack angle of several force-bearing points of each target transmission line according to the target wind speed, target wind direction, target line direction angle of the target transmission line and target line length at each moment;

[0028] According to the first wind attack angle, construct a transmission tower vulnerability model for characterizing the impact of wind load on each transmission tower;

[0029] According to the second wind attack angle, constructing a transmission line vulnerability model for characterizing the influence of wind load on each transmission line;

[0030] Generate an aging probability of each of the transmission towers and each of the transmission wires aging over time according to a Bayesian update framework and device parameters in the transmission network model;

[0031] The failure probability of each of the transmission towers and each of the transmission lines at a number of moments is calculated according to the aging probability, the transmission tower vulnerability model, and the transmission line vulnerability model.

[0032] Furthermore, the relevant failure probabilities of the structural components at several moments are predicted based on the typhoon time-varying model and the power transmission network model, including:

[0033] Constructing a spatial correlation matrix of the power transmission network according to the geographic information of each of the structural components in the power transmission network model;

[0034] Constructing a time correlation matrix of the power transmission network according to the equipment information and electrical information of each of the structural components in the power transmission network model;

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

[0036] According to the network topology correlation matrix and the failure probability of each of the structural components at a number of moments, the relevant failure probability of each of the structural components at a number of moments is predicted.

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

[0038] Generating a target fault removal scheme with the minimum cost required to restore normal operation of the power transmission network in each failure simulation scenario according to the typhoon time-varying model and the power transmission network model;

[0039] Calculating the fault duration, fault impact, average fault duration, and load shedding amount of the power transmission network according to the target fault removal plan;

[0040] A performance evaluation score of the power transmission network in responding to typhoon disasters is calculated based on the distribution probability, the fault duration, the fault duration, the fault impact degree, the average fault duration, and the load cutoff amount.

[0041] Furthermore, the generating of a target fault removal scheme for the fastest restoration of normal operation of the power transmission network in each of the failure simulation scenarios according to the typhoon time-varying model and the power transmission network model includes:

[0042] The power generation cost, node voltage amplitude, node voltage phase angle, and load cutoff amount are used as decision variables, and an objective function is constructed to minimize the power generation cost, power generation, load cutoff cost, and load cutoff amount;

[0043] Randomly generate several initial fault removal schemes;

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

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

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

[0047] A data acquisition module, used to acquire electrical information, topological information, geographic information, equipment information, and typhoon parameters of several structural components in the power transmission network to be evaluated;

[0048] A first model building module, configured to build a power transmission network model according to the device information, the electrical information, the topology information, and the geographic information;

[0049] A second model building module is used to build a typhoon time-varying model according to the typhoon parameters and the geographic information; wherein the typhoon time-varying model is used to predict the changing trend of typhoon wind speed and typhoon wind direction under the influence of geographical factors;

[0050] A probability calculation module, used to predict the failure probability and related failure probability of each of the structural components at several moments according to the typhoon time-varying model and the power transmission network model; wherein the failure probability is the probability of each of the structural components failing due to the typhoon; the related failure probability is the probability of each of the structural components failing due to the typhoon, thereby causing failure of other connected structural components;

[0051] A scenario generation module, configured to generate 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;

[0052] A performance evaluation module is used to calculate the performance evaluation score of the power transmission network in responding 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 invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a method for evaluating the performance of a power transmission network based on typhoon disasters as described in any one of the embodiments.

[0054] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a method for evaluating power transmission network performance based on typhoon disasters as described in any of the above embodiments.

[0055] The following beneficial effects are achieved by implementing the present invention:

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

[0057] Figure 1 It is a flow chart of a method for evaluating power transmission network performance based on typhoon disasters provided by one embodiment of the present invention.

[0058] Figure 2 It is a structural schematic diagram of a typhoon disaster-based power transmission network performance evaluation device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[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 clearly and specifically defined.

[0062] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may 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, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0064] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). 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 clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0066] See also Figure 1 , is a flow chart of a method for evaluating power transmission network performance based on typhoon disasters provided by an embodiment of the present invention, comprising:

[0067] S1. Obtain electrical information, topological information, geographic information, equipment information, and typhoon parameters of several structural components in the power transmission network to be evaluated;

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

[0069] S2. constructing a power transmission network model according to the device information, the electrical information, the topology information, and the geographic information;

[0070] Preferably, the structural components include: transmission towers and transmission wires; the geographic information includes: longitude and latitude coordinates of several transmission towers;

[0071] The step of constructing a power transmission network model according to the device information, the electrical information, the topology information, and the geographic information includes:

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

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

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

[0075] S24. According to the device information and the electrical information, improve the device parameters and electrical parameters of each structural component in the initial power transmission network model to generate the power transmission network model.

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

[0077] L i-1 =dRR;

[0078] Among them, L i-1 is the span length of the i-1th wire segment; dR is the central angle (radians); R is the radius of the earth; specifically, the calculation formula for the central angle is as follows:

[0079]

[0080] Among them, Δlat is the latitude difference between two adjacent towers; Δlon is the longitude difference between two adjacent towers; Tla i-1 ,Tla i is the latitude (radians) of the i-1th and i-th towers.

[0081] Calculate the wire direction:

[0082]

[0083] Among them, Ψc i-1 is the direction angle (radians) of the i-1th wire segment.

[0084] Calculate the transmission tower orientation:

[0085]

[0086] Among them, Ψt i-1 is the direction angle of the i-1th tower (radians); Ψc i is the direction angle (radians) of the i-th wire segment.

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

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

[0089] Preferably, the typhoon parameters include: typhoon static parameters and typhoon time-varying coefficients; the geographic information also includes: the terrain height, surface state and terrain direction where each of the structural components is located;

[0090] The step of constructing a typhoon time-varying model according to the typhoon parameters and the geographic information includes:

[0091] S31. Constructing a static initial typhoon model according to the typhoon static parameters;

[0092] S32, constructing an initial typhoon time-varying model for predicting the changing trend of typhoon wind speed and typhoon wind direction according to the typhoon time-varying coefficient and the initial typhoon model;

[0093] S33, generating corresponding terrain correction coefficients according to the terrain height, the surface state and the terrain direction;

[0094] S34. Adding the terrain correction coefficient to the initial typhoon time-varying model to construct the typhoon time-varying model.

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

[0096] Static initial typhoon model:

[0097]

[0098] Where V(r,θ) is the wind speed at a distance r from the typhoon center and an azimuth angle θ; V m is the maximum wind speed; R m is the maximum wind speed radius; B is the Holland B parameter, which is used to control the shape of the wind speed profile; A is the asymmetry coefficient; θ is the azimuth angle; θ max is the azimuth of maximum wind speed.

[0099] Furthermore, the typhoon time-varying coefficients include: intensity attenuation coefficient, radius expansion coefficient, parameter fluctuation coefficient and angular frequency;

[0100] Initial typhoon time-varying model:

[0101]

[0102] R m =R m (0)+βt;

[0103]

[0104] Among them, V m (t),R m , B(t) are the time-varying maximum wind speed, maximum wind speed radius and Holland B parameter; V m (0),R m (0), B(0) are the initial values ​​respectively; t is the time; α is the intensity attenuation coefficient; β is the radius expansion coefficient; γ, ω are the parameter fluctuation coefficient and angular frequency respectively.

[0105] Finally, the terrain correction factor includes: terrain height correction factor, terrain height correction factor, and exponential parameters related to surface roughness;

[0106] Typhoon time-varying model:

[0107]

[0108] Among them, V terrain (r,θ,z) is the wind speed after considering the terrain; z is the height; K t (z) is the terrain height correction factor; K d(θ) is the terrain direction correction coefficient; U(z) is the wind speed at height z; z ref is the reference height; α is an exponential parameter related to the surface roughness.

[0109] S4. Predicting the failure probability and the related failure probability of each of the structural components at several moments according to the typhoon time-varying model and the power transmission network model; wherein the failure probability is the probability of each of the structural components failing due to the typhoon; and the related failure probability is the probability of each of the structural components failing due to the typhoon, thereby causing failure of other connected structural components;

[0110] In a preferred embodiment of the present invention, for each transmission tower, the wind speed and wind direction data of the location are extracted from the wind field model according to its longitude and latitude, and the actual wind attack angle is calculated by combining the tower's azimuth angle and wind direction. For each section of the conductor, considering that the conductor spans a certain distance, it is necessary to extract the wind speed and wind direction data of multiple points along the length of the conductor 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 action of the wind load, that is, the failure probability and the related failure probability, is calculated using the finite element method or a simplified model.

[0111] Preferably, predicting the failure probability of each of the structural components at several moments according to the typhoon time-varying model and the power transmission network model includes:

[0112] S411, predicting the target wind speed and target wind direction of the typhoon at each moment according to the typhoon time-varying model and the power transmission network model, and counting the target transmission towers and target transmission lines affected by the typhoon;

[0113] S412, calculating a first wind attack angle of each target transmission tower according to the target wind speed, target wind direction, and target transmission tower direction of the target transmission tower at each moment;

[0114] S413, calculating the second wind attack angle of a plurality of force bearing points of each target transmission line according to the target wind speed, target wind direction, target line direction angle of the target transmission line and target line length at each moment;

[0115] S414. Constructing a transmission tower vulnerability model for characterizing the impact of wind load on each transmission tower according to the first wind attack angle;

[0116] S415. Constructing a transmission line vulnerability model for characterizing the influence of wind load on each transmission line according to the second wind attack angle;

[0117] S416, generating an aging probability of each of the transmission towers and each of the transmission wires aging over time according to the Bayesian update framework and the equipment parameters in the transmission network model;

[0118] S417: Calculate the failure probability of each of the transmission towers and each of the transmission lines at a number of moments according to the aging probability, the transmission tower vulnerability model, and the transmission line vulnerability model.

[0119] In a preferred embodiment of the present invention, the transmission tower vulnerability model:

[0120]

[0121] Among them, P f-tower is the failure probability of the transmission tower; Φ() is the standard normal distribution cumulative function; IM is the intensity measure (such as wind speed); μ R is the mean value of structural resistance; R is the standard deviation of structural resistance; IM is the intensity measure uncertainty;

[0122] Specifically,

[0123] Among them, k age is the age influence coefficient k corr is the corrosion influence coefficient; k dir is the wind direction influence coefficient; t is the structural age; θ is the wind attack angle.

[0124] Wire vulnerability model:

[0125] P f-conductor =1-e -νT ;

[0126] Among them, P f-conductor is the failure probability of the wire; ν is the failure rate; T is the exposure time;

[0127] Specifically,

[0128]

[0129] Among them, k span is the span influence coefficient; k temp is the temperature influence coefficient; L is the span length; ΔT is the temperature change.

[0130] Consider the time-varying characteristics of structural capacity to improve the accuracy of failure probability prediction. Expand the static fragility analysis to a dynamic time-varying analysis. First, it is necessary 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 extension of microcracks), maintenance and repair activities, etc.

[0131] Model the time-varying structure capacity:

[0132]

[0133] Among them, R(t) is the time-varying structural capacity; R(0) is the initial capacity; λ(t) is the time-varying degradation rate.

[0134] Furthermore, Bayesian updating allows us to continuously adjust model parameters based on new observation data, and this updating process can be achieved using the following particle filter algorithm.

[0135]

[0136] Among them, W t i is the weight of the ith particle at time t; o t is the observed value at time t; s t i is the state of the ith particle at time t; p(o t |s t i ) is the likelihood function.

[0137] With an adaptive model, we can make more accurate structural capacity predictions. In this step, we use the updated model parameters to predict future structural capacity and failure probability:

[0138] a) Using the updated parameter distribution, we can obtain the probability distribution of the time-varying structural capacity R(t);

[0139] 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;

[0140] c) Define the limit state function: G(R(t),S(t))=R(t) is S(t):

[0141] P f (t) = P[G(R(t), S(t))≤0];

[0142] Where: P f (t) is the failure probability at time t; G(R(t), S(t)) is the limit state function; R(t) is the structural capacity at time t; S(t) is the load effect at time t.

[0143] Preferably, predicting the relevant failure probability of each of the structural components at several moments according to the typhoon time-varying model and the power transmission network model includes:

[0144] S421, constructing a spatial correlation matrix of the power transmission network according to the geographic information of each of the structural components in the power transmission network model;

[0145] S422, constructing a time correlation matrix of the power transmission network according to the equipment information and electrical information of each of the structural components in the power transmission network model;

[0146] S423, calculating the importance of each structural component in the power transmission network according to the topological information of each structural component in the power transmission network model, the spatial correlation matrix, and the temporal correlation matrix, and constructing a network topology correlation matrix according to the importance;

[0147] S424. Predicting the related failure probabilities of the structural components at a number of moments according to the network topology correlation matrix and the failure probabilities of the structural components at a number of moments.

[0148] In a preferred embodiment of the present invention, the basic spatial correlation function is as follows:

[0149]

[0150] Where ρ(d) is the correlation coefficient; d is the distance between structures; d corr is the relevant distance.

[0151] After corrections for terrain and environmental factors, among which:

[0152]

[0153] Among them, k type is the influence coefficient of the structural type; k env is the environmental impact coefficient; I type is the structural type indicator; I env It is an indicator of environmental conditions;

[0154] Constructing the spatial correlation matrix R ij ,in

[0155] R s,ij =ρ(D ij );

[0156] The basic time correlation function is as follows:

[0157]

[0158] Where, ρ(t) is the time correlation coefficient; t is the time interval; tcorr is the time correlation scale;

[0159] After correction for load and maintenance factors, where:

[0160]

[0161] Among them, k load is the load influence coefficient; k maint is the maintenance influence coefficient; I load is the load level indicator; I maint is the maintenance level indicator; construct the time correlation matrix R t , where R t,ij Represents the correlation between components i and j in the time dimension.

[0162] a) Combining spatial and temporal correlation:

[0163]

[0164] b) Consider the impact of network topology:

[0165]

[0166] Where f(Topo ij ) is an adjustment function based on the network topology, for example:

[0167] Directly connected components may have higher correlations, and lines sharing the same substation may have higher correlations.

[0168] a) Use the Gaussian Copula method to convert the marginal failure probability and correlation matrix into a joint failure probability:

[0169] P(F i ∩F j ,t)=Φ 2 (Φ -1 (P f,i (t)),Φ -1 (P f,j (t); R ij (t));

[0170] Among them, Φ 2 is the two-dimensional standard normal distribution function; Φ -1 is the inverse function of the standard normal distribution; P f,i (t) and P f,j (t) is the marginal failure probability of components i and j at time t; R ij (t) is the correlation coefficient between components i and j at time t.

[0171] Through this process, a shift from time-varying analysis of individual components to system-level analysis that considers the correlation of the entire network is achieved. This approach not only considers the independent characteristics of each component, but also captures the mutual influence between them, thus providing a more comprehensive and accurate assessment of system reliability.

[0172] S5. Generate several failure simulation scenarios of the power transmission network and distribution probabilities corresponding to the failure simulation scenarios according to the typhoon time-varying model, the power transmission network model, the failure probability, and the related failure probability;

[0173] In a preferred embodiment of the present invention, a system reliability model of a power transmission network is constructed:

[0174] Rsys=Π(1-p f,i );

[0175] Among them, Rsys is the system reliability; p f,i is the failure probability of the i-th component;

[0176] Specifically,

[0177]

[0178] Among them, k red is the redundancy influence coefficient; k imp is the importance influence coefficient; N red is the redundancy; I imp is an indicator of importance;

[0179] Constructing a cascading failure model:

[0180]

[0181] Among them, P cascade is the probability of cascading failure; λ is the cascading failure rate; N is the system size;

[0182] Specifically,

[0183]

[0184] Among them, k topo is the topological influence coefficient; k load is the load level influence coefficient; I topo is the topological complexity index; L ratio is the load level ratio.

[0185] S6. Calculate the performance evaluation score of the power transmission network in responding to typhoon disasters according to the typhoon time-varying model, the power transmission network model, the failure simulation scenario, and the distribution probability.

[0186] Preferably, the calculating of the performance evaluation index of the power transmission network in coping with typhoon disasters according to the typhoon time-varying model, the power transmission network model, the failure simulation scenario, and the distribution probability includes:

[0187] S61, generating a target fault removal scheme with the minimum cost required to restore normal operation of the power transmission network in each failure simulation scenario according to the typhoon time-varying model and the power transmission network model;

[0188] Preferably, generating, based on the typhoon time-varying model and the power transmission network model, a target fault removal scheme for the power transmission network to resume normal operation as quickly as possible in each of the failure simulation scenarios comprises:

[0189] S611, using power generation cost, node voltage amplitude, node voltage phase angle, and load cutoff amount as decision variables, and constructing an objective function that minimizes power generation cost, power generation power, load cutoff cost, and load cutoff amount;

[0190] S612, randomly generating several initial fault removal schemes;

[0191] S613, according to the typhoon time-varying model, the transmission network model, and the failure simulation scenario, simulate the initial power generation cost, initial power generation power, initial load shedding cost, and initial load shedding amount required for the transmission network to implement each initial fault removal scheme, and calculate the objective function value of each initial fault removal scheme according to the objective function;

[0192] S614: Select an initial fault removal scheme corresponding to the minimum objective function value for iterative optimization until the objective function value converges, and generate a target fault removal scheme corresponding to each of the failure simulation scenarios.

[0193] In a preferred embodiment of the present invention, the objective function is set as:

[0194]

[0195] Among them, C g is the power generation cost; P g is the power generation; C l is the load shedding cost; P l is the load shedding amount;

[0196] Constraints:

[0197] Power balance constraint: ∑P g -∑P d -∑P loss =0; Generator capacity constraint: P g,min ≤Pg ≤P g,max ; Line capacity constraint: |P ij |≤P ij,max ;Node voltage constraint: V min ≤V≤V max ;

[0198] Decision variables:

[0199] x=[P g ,V,θ,P l ];

[0200] Where V is the node voltage amplitude; θ is the node voltage phase angle.

[0201] For each failure scenario obtained from step S5:

[0202] a) Update network topology: remove failed lines and substations; check network connectivity and identify isolated islands.

[0203] b) Adjust constraints: Update line capacity limits; consider equipment overload capabilities and temporary ratings.

[0204] c) Solve the AC optimal power flow problem: Use the interior point method or Newton method to solve the nonlinear optimization problem; if convergence is not possible, gradually increase the load cutoff amount until convergence.

[0205] d) Record key outputs: load shedding; generation scheduling; line power flow; node voltage.

[0206] S62. Calculate the fault duration, fault impact, average fault duration, and load shedding amount of the power transmission network according to the target fault removal plan;

[0207] S63. Calculate the performance evaluation score of the power transmission network in responding to typhoon disasters according to the distribution probability, the fault duration, the fault duration, the fault impact degree, the average fault duration, and the load cutoff amount.

[0208] In a preferred embodiment of the present invention, the following performance evaluations are preset: expected unpowered energy (EENS), system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and energy deficiency index (EIU). Finally, the performance evaluation score is calculated based on the above performance evaluation indicators.

[0209] Specifically,

[0210] a) Expected Unsupplied Energy (EENS):

[0211]

[0212] Among them, P i is the probability of the i-th failure scenario; D i is the load shedding amount of the i-th fault scenario; T i is the duration of the i-th fault scenario;

[0213] b) System Average Interruption Frequency Index (SAIFI):

[0214]

[0215] Among them, λ i is the failure rate of the i-th component; N i is the number of users affected by the failure of the i-th component; N T is the total number of users;

[0216] c) System Average Interruption Duration Index (SAIDI):

[0217]

[0218] Among them, U i is the annual average downtime caused by the failure of the i-th component;

[0219] d) Energy Deficit Index (EIU):

[0220] EIU=EENS / E total ;

[0221] Among them, E total is the total energy demand.

[0222] This embodiment provides a method for evaluating the performance of a power transmission network based on typhoon disasters. A power transmission network model including geographical factors is constructed according to the electrical information, topological information, geographical information, and equipment information of each structural component in the power transmission network, and the geographical factors of the structural components are fully considered. Then, according to the typhoon parameters and the geographical information, a typhoon time-varying model is constructed for predicting the wind speed and wind direction change trends under the influence of geographical factors. The influence of wind direction on the power transmission network is fully considered. Then, the failure probability of independent failure of each structural component in the power transmission network and the related failure probability affecting the failure of other structural components are calculated, and the correlation between the structures in the power transmission network is fully considered. Finally, according to several failure simulation scenarios, distribution probabilities, typhoon time-varying models, and power transmission network models, performance evaluation indicators that can accurately measure the performance of the power transmission network in responding to typhoon disasters are calculated.

[0223] See also Figure 2 , is a schematic diagram of a structure of a power transmission network performance evaluation device based on typhoon disasters provided by an embodiment of the present invention, comprising:

[0224] A data acquisition module, used to acquire electrical information, topological information, geographic information, equipment information, and typhoon parameters of several structural components in the power transmission network to be evaluated;

[0225] A first model building module, configured to build a power transmission network model according to the device information, the electrical information, the topology information, and the geographic information;

[0226] A second model building module is used to build a typhoon time-varying model according to the typhoon parameters and the geographic information; wherein the typhoon time-varying model is used to predict the changing trend of typhoon wind speed and typhoon wind direction under the influence of geographical factors;

[0227] A probability calculation module, used to predict the failure probability and related failure probability of each of the structural components at several moments according to the typhoon time-varying model and the power transmission network model; wherein the failure probability is the probability of each of the structural components failing due to the typhoon; the related failure probability is the probability of each of the structural components failing due to the typhoon, thereby causing failure of other connected structural components;

[0228] A scenario generation module, configured to generate 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;

[0229] A performance evaluation module is used to calculate the performance evaluation score of the power transmission network in responding to typhoon disasters based on the typhoon time-varying model, the power transmission network model, the failure simulation scenario, and the distribution probability.

[0230] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0231] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0232] Another preferred embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a method for evaluating the performance of a power transmission network based on typhoon disasters as described in any one of the above embodiments.

[0233] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0234] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0235] The memory can be used to store the computer program, and the processor realizes 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, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0236] Another preferred embodiment of the present invention provides a storage medium, the storage medium is a computer-readable storage medium, the computer program is stored in the computer-readable storage medium, and the computer program, when executed by the processor, can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0237] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for evaluating the performance of a power transmission network based on typhoon disasters, characterized in that: include: Obtain electrical information, topological information, geographic information, equipment information, and typhoon parameters of several structural components in the power transmission network to be evaluated; constructing a power transmission network model according to the device information, the electrical information, the topology information, and the geographic information; A typhoon time-varying model is constructed according to the typhoon parameters and the geographic information; wherein the typhoon time-varying model is used to predict the changing trend of typhoon wind speed and typhoon wind direction under the influence of geographical factors; According to the typhoon time-varying model and the power transmission network model, the failure probability and the related failure probability of each of the structural components at several moments are predicted; wherein the failure probability is the probability of each of the structural components failing due to the typhoon; the related failure probability is the probability of each of the structural components failing due to the typhoon, thereby causing failure of other connected structural components; Generate a number of failure simulation scenarios of the power transmission network and distribution probabilities corresponding to the failure simulation scenarios according to the typhoon time-varying model, the power transmission network model, the failure probability, and the related failure probability; A performance evaluation score of the power transmission network in responding to typhoon disasters is calculated based on the typhoon time-varying model, the power transmission network model, the failure simulation scenario, and the distribution probability.

2. A method for evaluating power transmission network performance based on typhoon disasters as claimed in claim 1, characterized in that: The structural components include: transmission towers and transmission wires; the geographic information includes: longitude and latitude coordinates of several transmission towers; The step of constructing a power transmission network model according to the device information, the electrical information, the topology information, and the geographic information includes: Calculating the length and direction angle of the transmission lines between adjacent transmission towers according to the topological information and the longitude and latitude coordinates of each transmission tower; Calculating the transmission tower directions of the transmission towers at both ends of each transmission wire according to the conductor direction angle; Constructing an initial transmission network model according to the topology information, the conductor length, the conductor direction angle, and the transmission tower direction; According to the equipment information and the electrical information, the equipment parameters and electrical parameters of each structural component in the initial power transmission network model are improved to generate the power transmission network model.

3. A method for evaluating power transmission network performance based on typhoon disasters as claimed in claim 2, characterized in that: The typhoon parameters include: typhoon static parameters and typhoon time-varying coefficients; the geographic information also includes: the terrain height, surface state and terrain direction of each structural component; The step of constructing a typhoon time-varying model according to the typhoon parameters and the geographic information includes: According to the typhoon static parameters, construct a static initial typhoon model; According to the typhoon time-varying coefficient and the initial typhoon model, construct an initial typhoon time-varying model for predicting the changing trend of typhoon wind speed and typhoon wind direction; Generate corresponding terrain correction coefficients according to the terrain height, the surface state and the terrain direction; The terrain correction coefficient is added to the initial typhoon time-varying model to construct the typhoon time-varying model.

4. A method for evaluating power transmission network performance based on typhoon disasters as claimed in claim 3, characterized in that: According to the typhoon time-varying model and the power transmission network model, the failure probability of each of the structural components at several moments is predicted, including: According to the typhoon time-varying model and the power transmission network model, the target wind speed and target wind direction of the typhoon are predicted at each moment, and the target transmission towers and target transmission lines affected by the typhoon are counted; Calculate the first wind attack angle of each target transmission tower according to the target wind speed, target wind direction, and target transmission tower direction of the target transmission tower at each moment; Calculate the second wind attack angle of several force-bearing points of each target transmission line according to the target wind speed, target wind direction, target line direction angle of the target transmission line and target line length at each moment; According to the first wind attack angle, construct a transmission tower vulnerability model for characterizing the impact of wind load on each transmission tower; According to the second wind attack angle, constructing a transmission line vulnerability model for characterizing the influence of wind load on each transmission line; Generate an aging probability of each of the transmission towers and each of the transmission wires aging over time according to a Bayesian update framework and device parameters in the transmission network model; The failure probability of each of the transmission towers and each of the transmission lines at a number of moments is calculated according to the aging probability, the transmission tower vulnerability model, and the transmission line vulnerability model.

5. A method for evaluating power transmission network performance based on typhoon disasters as claimed in claim 4, characterized in that: According to the typhoon time-varying model and the power transmission network model, the relevant failure probability of each of the structural components at several moments is predicted, including: Constructing a spatial correlation matrix of the power transmission network according to the geographic information of each of the structural components in the power transmission network model; Constructing a time correlation matrix of the power transmission network according to the equipment information and electrical information of each of the structural components in the power transmission network model; According to the topological information of each of the structural components in the power transmission network model, the spatial correlation matrix, and the temporal correlation matrix, the importance of each structural component in the power transmission network is calculated, and according to the importance, a network topological correlation matrix is ​​constructed; According to the network topology correlation matrix and the failure probability of each of the structural components at a number of moments, the relevant failure probability of each of the structural components at a number of moments is predicted.

6. A method for evaluating power transmission network performance based on typhoon disasters as claimed in claim 5, characterized in that: The calculating, according to the typhoon time-varying model, the power transmission network model, the failure simulation scenario, and the distribution probability, a performance evaluation index of the power transmission network in coping with typhoon disasters includes: Generating a target fault removal scheme with the minimum cost required to restore normal operation of the power transmission network in each failure simulation scenario according to the typhoon time-varying model and the power transmission network model; Calculating the fault duration, fault impact, average fault duration, and load shedding amount of the power transmission network according to the target fault removal plan; A performance evaluation score of the power transmission network in responding to typhoon disasters is calculated based on the distribution probability, the fault duration, the fault duration, the fault impact degree, the average fault duration, and the load cutoff amount.

7. A method for evaluating power transmission network performance based on typhoon disasters as claimed in claim 6, characterized in that: The generating, according to the typhoon time-varying model and the power transmission network model, a target fault removal scheme for the power transmission network to resume normal operation as quickly as possible in each of the failure simulation scenarios comprises: The power generation cost, node voltage amplitude, node voltage phase angle, and load cutoff amount are used as decision variables, and an objective function is constructed to minimize the power generation cost, power generation, load cutoff cost, and load cutoff amount; Randomly generate several initial fault removal schemes; According to the typhoon time-varying model, the transmission network model, and the failure simulation scenario, the initial power generation cost, initial power generation power, initial load shedding cost, and initial load shedding amount required for the transmission network to implement each initial fault removal scheme are simulated, and according to the objective function, the objective function value of each initial fault removal scheme is calculated; An initial fault removal scheme corresponding to the minimum objective function value is selected for iterative optimization until the objective function value converges, thereby generating a target fault removal scheme corresponding to each failure simulation scenario.

8. A typhoon-based power transmission network performance evaluation device, characterized in that: include: A data acquisition module, used to acquire electrical information, topological information, geographic information, equipment information, and typhoon parameters of several structural components in the power transmission network to be evaluated; A first model building module, configured to build a power transmission network model according to the device information, the electrical information, the topology information, and the geographic information; A second model building module is used to build a typhoon time-varying model according to the typhoon parameters and the geographic information; wherein the typhoon time-varying model is used to predict the changing trend of typhoon wind speed and typhoon wind direction under the influence of geographical factors; A probability calculation module, used to predict the failure probability and related failure probability of each of the structural components at several moments according to the typhoon time-varying model and the power transmission network model; wherein the failure probability is the probability of each of the structural components failing due to the typhoon; the related failure probability is the probability of each of the structural components failing due to the typhoon, thereby causing failure of other connected structural components; A scenario generation module, used to generate a number 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; A performance evaluation module is used to calculate the performance evaluation score of the power transmission network in responding to typhoon disasters based on the typhoon time-varying model, the power transmission network model, the failure simulation scenario, and the distribution probability.

9. A terminal device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a method for evaluating the performance of a power transmission network based on typhoon disasters as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a method for evaluating power transmission network performance based on typhoon disasters as described in any one of claims 1 to 7.

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