Planning-oriented resilience evaluation method for power transmission system under gale disaster

Abstract

The application discloses a planning-oriented power transmission system resilience evaluation method under a large wind disaster, and is used to solve the problem that the prior art cannot reflect the different power transmission corridor space-time failure probability when a typhoon crosses through with attenuation, and belongs to the technical field of power transmission system elasticity evaluation framework. After preparing typhoon data, the following steps are performed: S1, a typhoon probability model is constructed, and after inputting the typhoon data, the uncertainty characteristics of the typhoon are described; S2, a time-space vulnerability model of the power transmission corridor is constructed, the uncertainty characteristics of the typhoon are inputted, and the relationship between the failure probability of each power transmission corridor and the wind speed is quantified from the time and space scales; and S3, a resilience evaluation model is constructed, and the time-space vulnerability model of the power transmission corridor is quantified from the system and component angles to quantify the elasticity of the power transmission system.

Description

Technical Field

[0001] This invention belongs to the technical field of power transmission system resilience assessment framework, and specifically relates to a method for assessing the resilience of power transmission systems under strong wind disasters for planning purposes. Background Technology

[0002] Against the backdrop of global climate change, extreme weather events such as typhoons occur frequently, leading to unprecedented large-scale power outages worldwide. Despite this, the impact of extreme weather conditions is often overlooked in the planning of power transmission systems.

[0003] To overcome this problem, research on the resilience of power systems to natural disasters has received widespread attention in recent years. It is necessary to seek an effective method to capture the adverse effects of severe weather events that may occur during the planning period on the transmission system. Obtaining such information during the planning phase is crucial for enhancing system resilience.

[0004] Many extreme weather events can impact power transmission systems. During typhoons, extremely high wind speeds often lead to a surge in the failure rate and probability of transmission components. Such failures typically result in severe load shedding. Therefore, it is necessary to establish typhoon wind field models to predict wind speeds at different geographical locations. To this end, many scholars have studied typhoon wind field models for various application scenarios. Batts et al. established a wind field model considering post-landfall attenuation using empirical formulas; Shapirob established a wind field model by solving the momentum equation of atmospheric motion; Meng et al. established an analytical wind field model based on perturbation equilibrium equations. These models can be used to calculate the wind speed at any point in the typhoon wind field. However, typhoons typically occur randomly in the planning prospect, making it difficult for existing models to capture their probabilistic characteristics. Therefore, considering various potential typhoon disasters during the planning period, long-term forecasting is difficult to initiate preventative measures. The challenge of incorporating the impact of severe weather into the resilience assessment of the planning system lies in revealing the relationship between the failure probability of each transmission component and wind speed. To address this issue, Liu and Singh, as well as Li et al., used fuzzy clustering methods to obtain a nonlinear relationship between wind speed and transmission line failure rate.

[0005] Liu et al. and Han et al. used statistical regression to predict wind speeds associated with power outages during severe weather. While these models are generally reasonable, they only provide a certain probability of failure for transmission components under typhoon conditions. They do not accurately capture the effects of wind speed variations over time. To overcome these shortcomings, Panteli et al. determined the failure probability of transmission components using a wind speed function, constructing a fragility curve. Wang et al. and Li et al. showed that the component failure rate is related to the square of the wind speed. Due to the lack of spatiotemporal typhoon models, the aforementioned literature typically assumes that transmission systems are exposed to the same wind speed; however, actual transmission systems usually cover large geographical areas. Therefore, when a typhoon passes, different transmission corridors, and even different sections of the same corridor, may experience different wind speeds. Thus, it is necessary to establish a model that reflects the spatiotemporal failure probability of different transmission corridors as the typhoon weakens.

[0006] As is well known in the industry, Pantelie et al. extended the elastic triangle to a multiphase elastic trapezoid. The elastic trapezoid can further quantify the rate at which system performance degrades when severe weather events strike the system, as well as the rate at which the system recovers to its previous normal state.

[0007] As is well known in the industry, a smaller load reduction indicates a higher system resilience. Johnson et al. used the Monte Carlo simulation (MCS) method to evaluate the resilience of power systems. Monte Carlo simulation is a stochastic simulation method widely used in power system reliability assessment.

[0008] The existing technology has the following drawbacks: I. Typhoons typically occur randomly in planning scenarios, making it impossible for existing models to capture their probabilistic characteristics.

[0009] II. Existing models cannot effectively capture the effects of continuous changes in wind speed over time.

[0010] III. Regarding elasticity indicators, most existing elasticity indicators are based on the elasticity triangle, which is defined as the integral of system performance degradation and degradation time.

[0011] IV. Espinoza et al. and Amirioun et al. quantified system resilience as a function of load demand that cannot be provided due to the catastrophic effects of extreme weather conditions. Summary of the Invention

[0012] The purpose of this invention is to provide a method for assessing the resilience of power transmission systems under high wind disasters for planning purposes, in order to solve the aforementioned technical problems.

[0013] This invention is achieved using the following technical solution: A method for assessing the resilience of power transmission systems under high wind disasters for planning purposes includes the following steps: S1. Construct a typhoon probability model, and describe the uncertainty characteristics of typhoons after inputting typhoon data; S2. Construct a spatiotemporal vulnerability model for power transmission corridors, inputting typhoon uncertainty characteristics and outputting a quantitative relationship between the failure probability of each power transmission corridor and wind speed on both temporal and spatial scales. S3. Construct a resilience assessment model to quantify the resilience of the power transmission system from the perspective of system and composition by developing a spatiotemporal vulnerability model for the power transmission corridor.

[0014] More preferably, the typhoon probability model randomly generates a large number of typhoon scenarios.

[0015] Furthermore, the typhoon probability model incorporates a combined enumeration method to facilitate the generation of various potential typhoon disasters.

[0016] In a further optimized approach, S1 combines historical typhoon data to obtain the probability density function of key typhoon parameters, and then uses the probability density function to form a typhoon probability model.

[0017] In a further optimized manner, in S2, the failure probability of the power transmission corridor is calculated for each typhoon scenario.

[0018] Furthermore, preferably, the resilience assessment model incorporates the IISE method, which facilitates the calculation of the system and its component indicators.

[0019] Further preferred, the system and its component indicators include average load loss, maximum load loss, and fault frequency of each transmission corridor.

[0020] In a further preferred embodiment, in S3, random sampling is used to determine whether each transmission corridor is faulty, and the load loss of the system is calculated.

[0021] First, this invention proposes a typhoon probability model that uses the probability distribution of key parameters to describe the uncertainty characteristics of typhoons, enabling us to generate a series of typhoon disasters and their probabilities of occurrence. Second, considering the movement and attenuation of typhoons, a spatiotemporal vulnerability model for power transmission corridors is established. These models quantify the relationship between the failure probability of each power transmission corridor and wind speed at both temporal and spatial scales. Finally, a power transmission system resilience assessment model is established to quantify the resilience of the power transmission system from the perspective of the system and its components.

[0022] A combined enumeration method is introduced into the probabilistic typhoon model to generate various potential typhoon hazards, and the failure probability of transmission corridors under each typhoon is further obtained through a spatiotemporal vulnerability model. Furthermore, embedding the IISE method into the resilience assessment model can effectively calculate the system and its component indices.

[0023] Numerical results also validated the accuracy and effectiveness of the framework. The findings can also provide insights into potential resilience enhancement measures for similar or unforeseen typhoon disasters in the future. This method can effectively and economically enable the resilient expansion and upgrading of planned power transmission systems. Attached Figure Description

[0024] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A diagram illustrating the movement of a typhoon.

[0027] Figure 2 This is a flowchart illustrating the resilience assessment process for power transmission systems under planned high-wind disasters.

[0028] Figure 3 This shows the wiring diagram and location diagram of Embodiment 1.

[0029] Figure 4 This is a schematic diagram of wind speed in Example 1.

[0030] Figure 5 A diagram illustrating the probability of failure.

[0031] Figure 6 The graph shows the calculation results of the system's resilience index. Detailed Implementation

[0032] To better understand the above-mentioned objectives, features, and advantages of the present invention, the solutions of the present invention will be further described below. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other.

[0033] In this description, it should be noted that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. It should also be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joint" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0034] Many specific details are set forth in the following description in order to provide a full understanding of the invention, but the invention may also be practiced in other ways different from those described herein; obviously, the embodiments in the specification are only some embodiments of the invention, and not all embodiments.

[0035] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0036] Example 1: A method for assessing the resilience of power transmission systems under planned high-wind disasters. After preparing typhoon data, the following steps are performed: S1. Construct a typhoon probability model, and describe the uncertainty characteristics of typhoons after inputting typhoon data.

[0037] By combining historical typhoon data, the probability density functions of key typhoon parameters are obtained, and a typhoon probability model is constructed using these probability density functions.

[0038] The typhoon probability model introduces a combined enumeration method to facilitate the generation of various potential typhoon disasters and generates a large number of typhoon scenarios through random sampling. S2. Construct a spatiotemporal vulnerability model for power transmission corridors, inputting typhoon uncertainty characteristics and outputting a quantitative relationship between the failure probability of each power transmission corridor and wind speed on both temporal and spatial scales.

[0039] For each typhoon scenario obtained above, calculate the failure probability of the power transmission corridor.

[0040] S3. Construct a resilience assessment model to quantify the resilience of the power transmission system from the perspective of system and composition by developing a spatiotemporal vulnerability model for the power transmission corridor.

[0041] The resilience assessment model incorporates the IISE method, which facilitates the calculation of system and component indices.

[0042] The system and its component indicators include average load loss, maximum load loss, and fault frequency of each transmission corridor.

[0043] By randomly sampling to determine whether each transmission corridor is faulty and calculating the system's load loss, the resilience of the system can be assessed. Furthermore, the above process is repeated multiple times to statistically analyze indicators such as the system's average load loss, maximum load loss, and fault frequency of each transmission corridor, thereby more accurately assessing the system's resilience. Example 2

[0044] The coastal areas of Guangdong Province are frequently affected by typhoons. We have attached the IEEE RTS-79 test system to a geographical map of Guangdong Province. We assume that one transmission tower is placed every 500 meters along the transmission corridor. Since the transmission line is divided into several segments, each 500 meters long, the locations of all segments and towers can be estimated.

[0045] System wiring and location diagram as shown below Figure 3 As shown. The yellow crosses indicate the location of each bus, and the blue curves represent the coastline of Guangdong Province. The simulation time interval is set to 1 hour.

[0046] Before considering the probabilistic characteristics of various typhoons, the calculation process is illustrated using the actual typhoon "Mangkhut" as an example. The original central pressure difference was 58 hPa, the translational velocity was 30 km / h, the direction of motion was west-northwest, and the landing point was 21.9◦N / 112.5◦E. The typhoon's path is as follows... Figure 3 The green dashed arrow indicates the location. The green circle represents the radius of the maximum hourly wind speed. The proposed method was validated using corridors at five randomly selected locations: corridor 2 (buses 1-3), corridor 8 (buses 4-9), corridor 12 (buses 8-9), corridor 20 (buses 12-13), and corridor 27 (buses 15-24). These corridors contain a series of line segments and towers.

[0047] The wind speed in the middle of these corridors is like Figure 4 As shown, the wind speed in different corridors changes over time as the typhoon moves through the system.

[0048] Taking Corridor 2 as an example, in the initial stage of typhoon landfall, as the distance between Corridor 2 and the typhoon center decreases, the wind speed continuously increases. However, when the distance is less than the maximum wind radius, the wind speed begins to decrease. As the typhoon continues to move, the distance begins to increase again, but it is still less than the maximum wind radius, and the wind speed decreases. When the distance continues to increase to the maximum wind radius, the wind speed begins to decrease again, and further decreases as the typhoon moves away.

[0049] The cumulative failure probability of these corridors is as follows: Figure 5 As shown, the probability increases over time. This is because wind speed increases dramatically in the early stages of a typhoon's landfall. Similarly, the cumulative failure probability of each transmission corridor exhibits similar behavior.

[0050] As the typhoon moves and weakens, its wind force decreases and its speed drops, yet the increase in the cumulative failure probability remains very slow. The cumulative failure probability of the remaining corridors can be calculated using the same method.

[0051] Using historical typhoon data, probability density functions for key parameters within a given region were obtained. These functions were then used in a probabilistic typhoon model, and the probability of various potential typhoon disasters was calculated through combination enumeration. Each set of key parameters generates only one specific typhoon. The main typhoon parameters for this region were based on Northwest Pacific tropical cyclone tracking data from 1949 to 2011 provided by the China Meteorological Administration, and Guangzhou Province coastline geographic coordinate data from 2000. The probability density function of each key parameter was divided into ten equal parts, with a combination enumeration number of 10,000. Under each generated typhoon, the recovery index was obtained through repeated calculations. The calculation results of the system resilience index are as follows: Figure 6 As shown.

[0052] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the present invention. Although detailed descriptions have been provided with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments, and all should be covered by the protection scope of the claims.

Claims

1. A method for assessing the resilience of power transmission systems under severe wind disasters, characterized by: Includes the following steps: S1. Construct a typhoon probability model, and describe the uncertainty characteristics of typhoons after inputting typhoon data; S2. Construct a spatiotemporal vulnerability model for power transmission corridors, inputting typhoon uncertainty characteristics and outputting a quantitative relationship between the failure probability of each power transmission corridor and wind speed on both temporal and spatial scales. S3. Construct a resilience assessment model to quantify the resilience of the power transmission system from the perspective of system and composition by developing a spatiotemporal vulnerability model for the power transmission corridor.

2. The method for assessing the resilience of power transmission systems under severe wind disasters oriented towards planning, as described in claim 1, is characterized in that: The typhoon probability model randomly generates a large number of typhoon scenarios.

3. The method for assessing the resilience of power transmission systems under severe wind disasters oriented towards planning, as described in claim 2, is characterized in that: The typhoon probability model incorporates a combined enumeration method to facilitate the generation of various potential typhoon disasters.

4. The method for assessing the resilience of power transmission systems under severe wind disasters oriented towards planning, as described in claim 2, is characterized in that: S1 combines historical typhoon data to obtain the probability density function of key typhoon parameters, and then uses the probability density function to form a typhoon probability model.

5. The method for assessing the resilience of power transmission systems under severe wind disasters oriented towards planning, as described in claim 2, is characterized in that: In S2, the failure probability of the power transmission corridor is calculated for each typhoon scenario.

6. The method for assessing the resilience of power transmission systems under severe wind disasters oriented towards planning, as described in claim 1, is characterized in that: The resilience assessment model incorporates the IISE method, which facilitates the calculation of the system and its component indicators.

7. The method for assessing the resilience of power transmission systems under severe wind disasters oriented towards planning, as described in claim 6, is characterized in that: The system and its component indicators include average load loss, maximum load loss, and fault frequency of each transmission corridor.

8. The method for assessing the resilience of power transmission systems under severe wind disasters oriented towards planning, as described in any one of claims 1-7, is characterized in that: In S3, random sampling is used to determine whether each transmission corridor is faulty and to calculate the load loss of the system.

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