A method and system for quantitatively assessing the probability of faults and recovery time in power distribution networks under extreme weather conditions.

Abstract

This invention discloses a method and system for quantitatively assessing the probability of failure and recovery time of power distribution networks under extreme weather conditions. The method includes constructing a limit state function for the power distribution network and using a stratified sampling method to assess the failure probability of components under the influence of aging and wind speed; quantifying the inter-pole dependency using a three-pole system failure model and calculating the pole failure probability using a binomial distribution; segmenting overhead lines and calculating the conductor failure probability using a line-pole series model; calculating waiting time, arrival time, and response time, and combining these with the repair time to calculate the total repair time; calculating the vulnerability rate and recovery rate of power system components to assess the resilience of the power distribution network under different aging conditions; and including modules for assessing component wind resistance, calculating system failure probability, calculating recovery time, and quantifying resilience indicators. This significantly improves the accuracy of failure probability and recovery time estimation, better reflects the impact of extreme weather on recovery efficiency, and supports more scientific emergency response and resource allocation.

Description

Technical Field

[0001] This invention relates to the field of power distribution network fault prediction and resilience assessment technology, specifically to a method and system for quantitatively assessing the probability of faults and recovery time of power distribution networks under extreme weather conditions. Background Technology

[0002] With the increasing frequency of extreme weather events due to global climate change, overhead power distribution lines, as a crucial component of the power system, are highly susceptible to strong winds and other factors due to their exposure to the elements, leading to widespread power outages and disruptions. This severely impacts the stability of the power system and the normal operation of society. Therefore, accurately assessing the probability of power distribution network failures and recovery times under extreme weather conditions, and quantifying the system's resilience and recovery speed, is of great significance for enhancing the resilience of the power system, optimizing emergency response, and ensuring power supply security.

[0003] Traditional modeling methods for distribution network faults and recovery rely heavily on empirical statistics and simplistic assumptions. They typically assume independent component failures, neglecting the mechanical dependencies and aging effects between poles and conductors in the distribution network, thus failing to accurately capture complex fault mechanisms under extreme weather conditions. Furthermore, existing recovery time assessments largely employ empirical estimations, lacking detailed modeling of the recovery process's various stages. This makes it difficult to accurately reflect the impact of extreme weather on system recovery efficiency, leading to inaccurate recovery time predictions and hindering the rational allocation of post-disaster emergency resources and the improvement of system recovery capabilities. Therefore, there is an urgent need for a method combining vulnerability analysis and time decomposition modeling to systematically assess the fault and recovery characteristics of distribution networks under extreme weather conditions, quantify system vulnerability and recovery capabilities, and improve the risk prevention and post-disaster recovery capabilities of power systems. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for quantitatively assessing the probability of failure and recovery time of power distribution networks under extreme weather conditions, in order to solve the technical problems of existing technologies that ignore the mechanical dependence and aging effect between poles and conductors in power distribution networks and lack detailed modeling of the time of each stage of the recovery process.

[0005] A method for quantitatively assessing the probability of faults and recovery time in power distribution networks under extreme weather conditions includes the following steps:

[0006] Construct the limit state function of the distribution network, combine the hierarchical sampling method to evaluate the dynamic strength decay of power system components under the influence of aging factors, and calculate the failure probability of the corresponding wind speed scenario.

[0007] The three-pole system failure model is used to quantify the dependency between the central pole and adjacent poles, and the binomial distribution is used to calculate the failure probability of the distribution network caused by the poles.

[0008] The overhead line is divided into several sections. The failure probability of each section is calculated using the line pole series model. Then the failure probability of the entire distribution network line due to the conductor is calculated.

[0009] Based on the failure probability of wind speed scenarios, the failure probability of the distribution network caused by power poles, and the failure probability of the distribution network caused by conductors, the failure probability of the distribution network under extreme weather conditions is calculated. Based on the failure probability of the distribution network under extreme weather conditions, the status of overhead lines is predicted, the required repairs are identified, and the repair time is calculated. , This indicates the repair time required for the maintenance team to fix all damaged components;

[0010] Calculate the waiting time separately Arrival time and response time and in combination with repair time The total time required to repair overhead power lines after an extreme weather event is calculated by adding the results; among which, This indicates the waiting time before the extreme weather event can be repaired. This indicates the arrival time of the maintenance team from the starting point to the location of the fault; Indicates the response time delayed due to human factors;

[0011] Based on the total time required to repair overhead power lines after extreme weather events, the vulnerability rate and recovery rate of power system components are calculated, and the resilience of the power distribution network under different aging conditions is assessed based on the vulnerability rate and recovery rate of power system components.

[0012] To optimize the above technical solution, the specific measures also include:

[0013] The process of constructing the distribution network limit state function, combining a stratified sampling method to evaluate the dynamic strength of power system components under the influence of aging factors, and calculating the failure probability of corresponding wind speed scenarios includes:

[0014] Establish the limit state function of the distribution network and determine whether a single component has failed by comparing the structural strength with the load;

[0015] The power distribution limit state function is as follows:

[0016] ;

[0017] in, Indicates the performance of power system components, Indicates the strength of power system components. Represents the random intensity parameter. Represents the deterministic strength parameter. This indicates the load on the power distribution network under extreme weather conditions. This represents the random load demand parameter. Indicates parameters for deterministic load requirements;

[0018] Furthermore, based on the limit state function of the distribution network, stratified sampling is performed on the distribution network. The random variable space is divided into several layers, and samples are generated in each layer and the failure rate is statistically analyzed. The total failure probability is then calculated using a weighted average. The specific formula for stratified sampling is as follows:

[0019] ;

[0020] in, Indicates the probability of failure. Indicates the first j Layer weights Indicates the first j Layer failure count, Indicates the first j Total number of floors;

[0021] The failure risk of the distribution network under extreme conditions is assessed and quantified using a function of wind speed. A vulnerability curve is constructed between the failure probability of a single component in the distribution network and wind speed, as follows:

[0022] ;

[0023] in, This indicates the structural fragility of individual components in the power distribution network. This represents the cumulative function of the standard normal distribution. Indicates wind speed. This indicates the median wind speed. Represents the logarithmic standard deviation;

[0024] Furthermore, using a function of the pole's remaining strength, vulnerability curves under different lifetimes are derived, and a pole strength decay model over time is constructed as follows:

[0025] ;

[0026] in, Indicates the first t The remaining strength of the pole after 1 year Indicates the initial strength of the pole. This represents the external strength attenuation coefficient of the first pole. This represents the external strength attenuation coefficient of the second pole. This represents the internal strength attenuation coefficient of the first pole. This represents the internal strength attenuation coefficient of the second pole.

[0027] Furthermore, the process of quantifying the dependency between the central pole and adjacent poles using a three-pole system failure model and calculating the probability of power distribution network failures caused by poles using a binomial distribution includes:

[0028] The failure model for the three-bar system is constructed as follows:

[0029] ;

[0030] in, This represents the failure probability of a three-bar system. This indicates the probability of failure of the central utility pole. Indicates the probability of failure of adjacent poles;

[0031] In the failure model of a three-pole system, the upper bound is defined as the failure of all poles completely independently, and the lower bound is defined as the failure of all poles completely dependent on each other. The specific formulas for the upper and lower bounds are as follows:

[0032] ;

[0033] in, This represents the probability of a fault in a power distribution network caused by pole failure. This indicates the total number of poles in the power distribution network.

[0034] Furthermore, the process of dividing the overhead line into several sections, calculating the failure probability of each section using a line pole series model, and then calculating the failure probability of the entire distribution network line due to conductors includes:

[0035] The conductors of the distribution network are divided into multiple sections, each containing several spans, to construct a series model of the line poles. The failure probability of a single conductor section is calculated using a binomial distribution, as shown in the following formula:

[0036] ;

[0037] in, Indicates the probability of single-segment failure. Indicates the span within the section. This represents the failure probability of a single-span conductor.

[0038] In the series failure model of power line poles, the probability of series failure caused by the failure of a single section of conductor leading to the overall failure of the line is as follows:

[0039] ;

[0040] in, This represents the probability of a fault caused by a line failure in a power distribution network. Indicates the total number of sections into which the conductor is divided;

[0041] Combining the three-pole system model and the line pole series model, the overall failure probability of the entire distribution network due to pole faults or conductor failures in the entire section is calculated as follows:

[0042] ;

[0043] in, P This represents the overall failure probability of the distribution network.

[0044] Furthermore, the waiting time is calculated separately. Arrival time and response time and in combination with repair time The total time required to repair overhead power lines after an extreme weather event is calculated by adding the results; where:

[0045] The calculation formula is:

[0046] ;

[0047] in, Indicates the end time of the extreme event. Indicates the time when the interference occurred;

[0048] The calculation formula is:

[0049] ;

[0050] in, This indicates the distance between the fault location and the starting point of the repair team. This indicates the average speed of the vehicle under maintenance;

[0051] The calculation formula is:

[0052] ;

[0053] in, This indicates the number of damaged poles in the faulty line. Indicates the repair time for a single utility pole. The binary variable indicating the cause of the damage to the utility pole. Indicates the number of damaged sections in the faulty line. Indicates the repair time for a single segment. A binary variable indicating that the cause of the damage is the wire;

[0054] Determined based on delays caused by human factors;

[0055] The formula for the total time required to repair overhead power lines after an extreme weather event is as follows:

[0056] .

[0057] Furthermore, the calculation of the vulnerability rate and recovery rate of power system components based on the total time required to repair overhead power lines after an extreme weather event specifically includes:

[0058] Calculate the vulnerability rate based on the start and end times of the extreme event. The specific formula is as follows:

[0059] ;

[0060] in, This indicates the system performance level at the end of an extreme event. Indicates the end time of the extreme event. This indicates the system performance level at the start of an extreme event. Indicates the start time of the extreme event;

[0061] The end time of the extreme event is obtained by calculating the recovery rate based on the start time of the extreme event and the total time required to repair the overhead power lines after the extreme weather event. The specific formula is as follows:

[0062] ;

[0063] in, This indicates the system's performance level when it returns to normal. This indicates the time it takes for the system to return to normal.

[0064] Furthermore, the assessment of distribution network resilience under different aging states based on the vulnerability rate and recovery rate of power system components specifically includes:

[0065] When vulnerability rate A vulnerability rate >0 indicates a continuous decline in system performance, high vulnerability, and weak risk resistance; when the vulnerability rate is... =0 indicates that the system performance is unaffected and the system has full resilience; when the vulnerability rate is... <0 indicates that the system has strong resilience to extreme weather conditions and its performance degrades slowly;

[0066] When recovery rate A recovery rate >0 indicates that the system has strong recovery capabilities; when the recovery rate is >0, the system has strong recovery capabilities. =0 indicates that system performance has not recovered; when the recovery rate is 0, it means that the system performance has not recovered. <0 indicates that the system recovery was completely interrupted and the recovery capability was severely insufficient.

[0067] As another important technical solution, the present invention also provides a quantitative assessment system for fault probability and recovery time of power distribution networks under extreme weather conditions, comprising:

[0068] The component wind resistance assessment module is used to construct the distribution network limit state function and combine the stratified sampling method to assess the dynamic strength of power system components under the influence of aging factors and calculate the failure probability of their corresponding wind speed scenarios.

[0069] The system failure probability calculation module is used to quantify the dependency between the central pole and adjacent poles using a three-pole system failure model, and to calculate the probability of distribution network failure due to pole failure using a binomial distribution. The overhead line is divided into several sections, and the failure probability of each section is calculated using a line-pole series model. This is then extended to the entire line to calculate the probability of distribution network failure due to conductor failure.

[0070] The recovery time calculation module is used to calculate the failure probability of the distribution network under extreme weather conditions based on the failure probability of wind speed scenarios, the failure probability of the distribution network caused by power poles, and the failure probability of the distribution network caused by conductors. Based on the failure probability of the distribution network under extreme weather conditions, the module predicts the status of overhead lines, confirms the required repairs, and calculates the repair time. , This indicates the repair time required for the maintenance team to fix all damaged components;

[0071] Calculate the waiting time separately Arrival time and response time and in combination with repair time The total time required to repair overhead power lines after an extreme weather event is calculated by adding the results; among which, This indicates the waiting time before the extreme weather event can be repaired. This indicates the arrival time of the maintenance team from the starting point to the location of the fault; Indicates the response time delayed due to human factors;

[0072] The resilience index quantification module is used to calculate the vulnerability rate and recovery rate of power system components, and to assess the resilience of the distribution network under different aging conditions based on the vulnerability rate and recovery rate of power system components.

[0073] The present invention also proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described method for quantitatively assessing the probability of faults and recovery time of a power distribution network under extreme weather conditions.

[0074] The present invention also proposes a computer-readable storage medium storing a computer program that enables a computer to execute a quantitative assessment method for fault probability and recovery time under extreme weather conditions in a power distribution network, as described above.

[0075] Compared with the prior art, the beneficial effects of the present invention are:

[0076] 1. This invention combines limit state functions with stratified sampling to dynamically assess the failure probability of poles and conductors under aging and wind speed, overcoming the limitations of traditional empirical statistical methods.

[0077] 2. This invention introduces a three-pole system model, which considers the mechanical dependence between the central pole and adjacent poles, and can accurately reflect the cascading failure mechanism. For example, the failure of one pole will increase the load on adjacent poles, avoiding the errors caused by assuming that each pole fails independently in the traditional model.

[0078] 3. This invention performs segmented modeling of the conductor and adopts a series system model, which can quantify the impact of local failures on the entire line, thereby improving the accuracy of fault risk prediction.

[0079] 4. This invention proposes two indicators, vulnerability rate and recovery rate, and for the first time realizes a numerical comparison of risk resistance capability and recovery speed. The combination of the two can evaluate the comprehensive resilience of the system under different aging conditions, and provide a basis for long-term maintenance and upgrade planning.

[0080] 5. This invention divides the recovery process into four stages: waiting time, arrival time, repair time, and response time, and models each stage independently, which significantly improves the accuracy of repair time prediction after extreme weather and helps to efficiently allocate emergency resources. Attached Figure Description

[0081] Figure 1 The flowchart illustrates a method for quantitatively assessing the probability of faults and recovery time in a power distribution network under extreme weather conditions, as provided in this embodiment of the invention.

[0082] Figure 2 This is a schematic diagram illustrating the fault probability of different parts of an overhead power distribution line, provided as an embodiment of the present invention.

[0083] Figure 3 This is a schematic diagram of the overall timeline and the status of overhead lines after an extreme event, provided as an embodiment of the present invention.

[0084] Figure 4 This is a comparison chart of quantitative indicators for system evaluation provided in an embodiment of the present invention.

[0085] Figure 5 This is a vulnerability curve showing the relationship between the failure probability of a single power grid component and wind speed in an embodiment of the present invention. Detailed Implementation

[0086] The present invention will be further described in detail below through specific embodiments, but it should not be construed as limiting the scope of the subject matter of the present invention to the following embodiments. All technologies implemented based on the above content of the present invention fall within the scope of the present invention.

[0087] A method for quantitatively assessing the probability of faults and recovery time in power distribution networks under extreme weather conditions includes the following steps:

[0088] Construct the limit state function of the distribution network, combine the hierarchical sampling method to evaluate the dynamic strength decay of power system components under the influence of aging factors, and calculate the failure probability of the corresponding wind speed scenario.

[0089] The three-pole system failure model is used to quantify the dependency between the central pole and adjacent poles, and the binomial distribution is used to calculate the failure probability of the distribution network caused by the poles.

[0090] The overhead line is divided into several sections. The failure probability of each section is calculated using the line pole series model. Then the failure probability of the entire distribution network line due to the conductor is calculated.

[0091] Based on the failure probability of wind speed scenarios, the failure probability of the distribution network caused by power poles, and the failure probability of the distribution network caused by conductors, the failure probability of the distribution network under extreme weather conditions is calculated. Based on the failure probability of the distribution network under extreme weather conditions, the status of overhead lines is predicted, the required repairs are identified, and the repair time is calculated. , This indicates the repair time required for the maintenance team to fix all damaged components;

[0092] Calculate the waiting time separately Arrival time and response time and in combination with repair time The total time required to repair overhead power lines after an extreme weather event is calculated by adding the results; among which, This indicates the waiting time before the extreme weather event can be repaired. This indicates the arrival time of the maintenance team from the starting point to the location of the fault; Indicates the response time delayed due to human factors;

[0093] Based on the total time required to repair overhead power lines after extreme weather events, the vulnerability rate and recovery rate of power system components are calculated, and the resilience of the power distribution network under different aging conditions is assessed based on the vulnerability rate and recovery rate of power system components.

[0094] In some implementations, a limit state function for the distribution network is constructed, and a stratified sampling method is used to assess the dynamic strength of power system components under the influence of aging factors, calculating the failure probability of the corresponding wind speed scenario. The process includes:

[0095] A limit state function for the distribution network under extreme weather conditions is established. By comparing structural strength with load, it is determined whether a single component (such as a pole or conductor) has failed. The limit state function for the distribution network is as follows:

[0096] ;

[0097] in, Indicates the performance of power system components, Indicates the strength of power system components. Represents random strength parameters (such as material strength). Indicates deterministic strength parameters (such as design strength). This indicates the load on the power distribution network under extreme weather conditions. This represents parameters related to random load demand (such as wind speed distribution). Represents deterministic load requirement parameters (such as geometric dimensions).

[0098] Preferably, when G(X)≥0, it indicates that the distribution network is structurally intact; when G(X)<0, it indicates that there is structural failure in the distribution network.

[0099] Based on the limit state function of the distribution network, stratified sampling is performed on the distribution network. The random variable space is divided into several layers (such as partitioning by disaster intensity). Samples are generated in each layer, and the failure rate is statistically analyzed. The total failure probability is then calculated using a weighted average. The specific formula for stratified sampling is as follows:

[0100] ;

[0101] in, Indicates the probability of failure. Indicates the first j Layer weights Indicates the first j Layer failure count, Indicates the first j Total number of floors.

[0102] A vulnerability curve is constructed to represent the probability of failure of a single component in a power distribution network versus wind speed. This curve describes the probability of failure of vulnerable components (such as poles and conductors) in overhead power distribution lines at different wind speeds. It serves as a core tool for assessing and quantifying the failure risk of the power distribution network under extreme conditions. The specific relationship is shown below:

[0103] ;

[0104] in, This indicates the structural fragility of individual components in the power distribution network. This represents the cumulative function of the standard normal distribution. Indicates wind speed. This indicates the median wind speed. It represents the logarithmic standard deviation.

[0105] As a preferred option, The higher the value, the greater the probability that the component will fail at that wind speed, and the system is in a high-risk state; conversely, the lower the value, the greater the probability that the component will fail at that wind speed. The smaller the value, the more stable the component is at that wind speed, and the stronger the system's ability to withstand risks.

[0106] Considering the strength reduction of wooden utility poles due to decay and corrosion, a residual strength model for the poles is constructed, and the vulnerability curves under different lifespans are derived. The pole strength reduction model over time is shown below:

[0107] ;

[0108] in, Indicates the first t The remaining strength of the pole after 1 year Indicates the initial strength of the pole. This represents the external strength attenuation coefficient of the first pole, which is 0.014418 in this embodiment. This represents the external strength attenuation coefficient of the second pole, which is 0.10683 in this embodiment. This represents the internal strength attenuation coefficient of the first pole, which is 1.3E-4 in this embodiment. This represents the internal strength attenuation coefficient of the second pole, which is 1.864 in this embodiment.

[0109] As a preferred option, A higher value indicates less strength degradation and better durability of the pole from its original distance; conversely, a lower value indicates less strength degradation and better durability. The smaller the value, the greater the attenuation of the pole's original strength, making it more prone to collapse or damage under extreme conditions such as windstorms.

[0110] In some implementations, such as Figure 5 As shown, the vulnerability curves of a single component in a distribution network under extreme wind speeds intuitively reflect the trend of component failure probability as wind speed increases: at low wind speeds, the failure probability increases slowly; once the wind speed exceeds the critical value of 40–50 m / s, the failure probability exhibits a sharp nonlinear increase, approaching 100% at 70 m / s. This curve provides important quantitative basis for wind-resistant design of distribution networks, risk assessment, and responses to the increasing frequency of extreme weather events in the context of climate change.

[0111] In some implementations, such as Figure 2 As shown, a three-pole system failure model is used to quantify the dependency between the central pole and adjacent poles. A binomial distribution is employed to calculate the probability of failure in the distribution network caused by poles. The process includes:

[0112] Considering that the failure of the central pole will increase the mechanical load on adjacent poles, the impact of a single pole on the overall distribution network is determined by analyzing the layout of the three-phase conductors, the stress distribution, and the influence of extreme weather on the structure. The model formula for the three-pole system is shown below:

[0113] ;

[0114] in, This represents the failure probability of a three-bar system. This indicates the probability of failure of the central utility pole. This indicates the probability of failure of adjacent poles.

[0115] In the three-pole system model, two extreme assumptions are made: the upper bound is set for the complete independent failure of all poles, and the lower bound is set for the complete dependent failure of all poles. These serve as conservative boundaries for probabilistic estimation of cascading pole failures in real-world complex scenarios. The specific formulas for the upper and lower bounds are as follows:

[0116] ;

[0117] in, This represents the probability of a fault in a power distribution network caused by pole failure. This indicates the total number of poles in the power distribution network.

[0118] The overhead line is divided into several sections. A series model of the line poles is used to calculate the failure probability of each section. Then, the failure probability of the entire distribution network line due to conductor failure is calculated. The process includes:

[0119] The conductors of the distribution network are divided into multiple sections, each containing several spans, and a series model of the line poles is constructed. The failure of a single span reduces the failure probability of other spans within the same section, but different sections are independent of each other. The failure probability of each section is calculated using a binomial distribution to obtain the failure probability of the entire line. The specific formula for the failure probability of a single conductor section is shown below:

[0120] ;

[0121] in, Indicates the probability of single-segment failure. Indicates the span within the section. This represents the failure probability of a single-span conductor.

[0122] In the series failure model of power line poles, the probability of series failure caused by the failure of a single section of conductor leading to the overall failure of the line is as follows:

[0123] ;

[0124] in, This represents the probability of a fault caused by a line failure in a power distribution network. This indicates the total number of sections into which the conductor is divided.

[0125] Combining the three-pole system model and the line pole series model, the overall failure probability of the distribution network due to pole faults or conductor failures in certain sections is as follows:

[0126] ;

[0127] in, P This represents the overall failure probability of the distribution network.

[0128] like Figure 3 As shown, calculate the waiting time respectively. Arrival time and response time and in combination with repair time The total time required to repair overhead power lines after an extreme weather event is calculated by adding the results; where:

[0129] In some implementations... The formula for calculating the waiting time required to dispatch repair personnel after the weather improves, when repairs cannot be carried out during extreme weather events, is as follows:

[0130] ;

[0131] in, Indicates the end time of the extreme event. Indicates the time when the interference occurred.

[0132] Based on the distance between the fault location and the starting point of the repair team, as well as the average vehicle speed, a Poisson distribution is used for modeling. The calculation formula is as follows:

[0133] ;

[0134] in, This indicates the distance between the fault location and the starting point of the repair team. This indicates the average speed of the vehicle under maintenance.

[0135] The calculation formula is determined by the number of damaged components and the repair time for each component:

[0136] ;

[0137] In some implementations... This indicates the number of damaged poles in the faulty line. Indicates the repair time for a single utility pole. The binary variable representing the cause of the damage to the utility pole, when... A value of 1 indicates that the pole is damaged. Indicates the number of damaged sections in the faulty line. Indicates the repair time for a single segment. A binary variable indicating the cause of damage as a wire; when A value of 1 indicates that the wire is damaged.

[0138] Delays are determined based on human factors, such as the operator's perception of the emergency and the coordination ability of maintenance personnel.

[0139] The formula for the total time required to repair overhead power lines after an extreme weather event is as follows:

[0140] ;

[0141] Based on the total time required to repair overhead power lines after an extreme weather event, the vulnerability rate and recovery rate of power system components are calculated, specifically including:

[0142] Calculate the vulnerability rate based on the start and end times of the extreme event. The specific formula is as follows:

[0143] ;

[0144] in, This indicates the system performance level at the end of an extreme event. Indicates the end time of the extreme event. This indicates the system performance level at the start of an extreme event. Indicates the start time of the extreme event.

[0145] The end time of the extreme event is obtained by calculating the recovery rate based on the start time of the extreme event and the total time required to repair the overhead power lines after the extreme weather event. The specific formula is as follows:

[0146] ;

[0147] in, This indicates the system's performance level when it returns to normal. This indicates the time it takes for the system to return to normal.

[0148] As a preferred option, such as Figure 4 As shown, the resilience of the distribution network under different aging states is assessed based on the vulnerability rate and recovery rate of power system components, specifically including:

[0149] Vulnerability rate This indicates the rate at which the system's performance degrades per hour under extreme weather conditions, when the vulnerability rate... A vulnerability rate >0 indicates a continuous decline in system performance, high vulnerability, and weak risk resistance; when the vulnerability rate is... =0 indicates that the system performance is unaffected and the system has full resilience; when the vulnerability rate is... A value of <0 indicates that the system has a strong ability to withstand extreme weather conditions and that the system performance deteriorates slowly.

[0150] recovery rate This indicates the system's performance recovery rate per hour after the extreme weather event ends; when the recovery rate... A recovery rate >0 indicates that the system has strong recovery capabilities and a fast recovery speed; when the recovery rate is >0, the system has strong recovery capabilities and a fast recovery speed. =0 indicates that the system performance has not recovered, the recovery capability is poor, or the system has been completely interrupted; when the recovery rate is... A value of <0 indicates that the system recovery was completely interrupted, the recovery capability is severely insufficient, and it must be improved.

[0151] This method performs component vulnerability analysis on the distribution network to quantitatively assess the failure probability and recovery time of the distribution network under extreme weather conditions. The method considers the aging effects of poles and conductors in the distribution network and their interdependence to quantify the system's resilience. Finally, the method proposes a vulnerability rate (…). ) and recovery rate ( These two core indicators can support the expansion planning, resource allocation, and resilience enhancement strategies of the distribution network.

[0152] The overall approach of this quantitative assessment method for the probability of faults and recovery time in power distribution networks under extreme weather conditions first quantifies these parameters. Based on vulnerability analysis, a limit state function combined with stratified sampling is used to evaluate the fault probability of the distribution network under different wind speeds. For overhead distribution lines, a three-pole system model is adopted, considering the mechanical dependence between the central pole and adjacent poles. The conductors are segmented and modeled to calculate the failure probability of individual segments, deriving the fault risk of the entire line and quantifying the system's resilience. Furthermore, the recovery process is decomposed into four stages: waiting time, arrival time, repair time, and response time, each independently modeled to accurately estimate the recovery duration. Finally, the vulnerability rate and recovery rate of the distribution network are calculated to measure the system's resilience and recovery speed, quantifying the impact of extreme weather on the system. Through this overall framework, the distribution network can not only achieve accurate fault prediction before extreme weather events but also accurately calculate its resilience and recovery speed, enhancing its ability to respond to disasters and its overall resilience.

[0153] As another important technical solution, the present invention also provides a quantitative assessment system for fault probability and recovery time of power distribution networks under extreme weather conditions, comprising:

[0154] The component wind resistance assessment module is used to construct the limit state function of the distribution network and combine the stratified sampling method to evaluate the dynamic strength of power system components under the influence of aging factors and calculate the failure probability of their corresponding wind speed scenarios.

[0155] The system failure probability calculation module is used to quantify the dependency between the central pole and adjacent poles using a three-pole system failure model, and to calculate the probability of distribution network failure due to pole failure using a binomial distribution. The overhead line is divided into several sections, and the failure probability of each section is calculated using a line-pole series model. This is then extended to the entire line to calculate the probability of distribution network failure due to conductor failure.

[0156] The recovery time calculation module is used to calculate the failure probability of the distribution network under extreme weather conditions based on the failure probability of wind speed scenarios, the failure probability of the distribution network caused by power poles, and the failure probability of the distribution network caused by conductors. Based on the failure probability of the distribution network under extreme weather conditions, the module predicts the status of overhead lines, confirms the required repairs, and calculates the repair time. , This indicates the repair time required for the maintenance team to fix all damaged components.

[0157] Calculate the waiting time separately Arrival time and response time and in combination with repair time The total time required to repair overhead power lines after an extreme weather event is calculated by adding the results; among which, This indicates the waiting time before the extreme weather event can be repaired. This indicates the arrival time of the maintenance team from the starting point to the location of the fault; This indicates the response time delayed due to human factors.

[0158] The resilience index quantification module is used to calculate the vulnerability rate and recovery rate of power system components, and to assess the resilience of the distribution network under different aging conditions based on the vulnerability rate and recovery rate of power system components.

[0159] In another embodiment of the present invention, an electronic device is proposed, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned method for quantitatively assessing the probability of failure and recovery time of a power distribution network under extreme weather conditions.

[0160] In another embodiment of the present invention, a computer-readable storage medium is provided, storing a computer program that causes a computer to execute the above-described method for quantitatively assessing the probability of failure and recovery time of a power distribution network under extreme weather conditions.

[0161] In the embodiments disclosed in this invention, a computer storage medium may be a tangible medium that may contain or store programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. More specific examples of computer storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0162] In some implementations, such as Figure 1 As shown, this paper first analyzes the vulnerability of key components such as poles and conductors in the distribution network at the component level. Considering the strength degradation caused by aging and corrosion during long-term operation, and the fluctuations in the distribution network load state due to dynamic changes in meteorological disasters, the failure probability under corresponding wind speed scenarios is calculated using limit state functions. After obtaining the failure probability of a single component, the evaluation scope is expanded to the system level. Combining the topology of the distribution network and the overview of meteorological disasters, the overall failure probability of the overhead lines of the distribution network is calculated. Specifically, a three-pole system failure model is used to quantify the dependency relationship between the central pole and adjacent poles, and a line pole series model is used to obtain the failure status of conductor segments. The failure probability of each segment is linked to the overall line state to obtain the number of damaged components of the overhead line to estimate the line repair time. Load flow analysis is combined to evaluate the operating performance of the distribution network under extreme weather conditions, quantifying and recording the information requiring repair of the distribution network system. Finally, quantitative indicators are established to evaluate the system's risk resistance and recovery capabilities, providing a scientific basis for risk management and recovery strategies for the distribution network under extreme weather conditions.

[0163] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications, equivalent substitutions, and improvements made by those skilled in the art to the above embodiments without departing from the scope of the technical solution of the present invention, based on the technical essence of the present invention, shall still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for quantitatively assessing the probability of faults and recovery time in power distribution networks under extreme weather conditions, characterized in that, Includes the following steps: A limit state function for the distribution network is constructed, and a stratified sampling method is used to evaluate the dynamic strength decay of power system components under the influence of aging factors, and the failure probability of the corresponding wind speed scenario is calculated. The limit state function for the distribution network is as follows: ； in, Indicates the performance of power system components, Indicates the strength of power system components. Represents the random intensity parameter. Represents the deterministic strength parameter. This indicates the load on the power distribution network under extreme weather conditions. This represents the random load demand parameter. Indicates deterministic load requirement parameters; A three-pole system failure model is used to quantify the dependency between the central pole and adjacent poles, and a binomial distribution is used to calculate the probability of failure in the distribution network caused by poles; the three-pole system failure model is as follows: ； in, This represents the failure probability of a three-bar system. This indicates the probability of failure of the central utility pole. Indicates the probability of failure of adjacent poles; The overhead line is divided into several sections, each containing several spans, forming a series model of the line poles. The failure probability of a single conductor section is calculated using a binomial distribution, with the specific formula as follows: ； in, Indicates the probability of single-segment failure. Indicates the span within the section. This represents the probability of failure for a single-span conductor. In the series failure model of power line poles, the probability of series failure caused by the failure of a single section of conductor leading to the overall failure of the line is as follows: ； in, This represents the probability of a fault caused by a line failure in a power distribution network. Indicates the total number of sections into which the conductor is divided; Combining the three-pole system model and the line pole series model, the overall failure probability of the entire distribution network due to pole faults or conductor failures in the entire section is calculated as follows: ； in, P Indicates the overall failure probability of the distribution network; Based on the failure probability of wind speed scenarios, the failure probability of the distribution network caused by power poles, and the failure probability of the distribution network caused by conductors, the failure probability of the distribution network under extreme weather conditions is calculated. Based on the failure probability of the distribution network under extreme weather conditions, the status of overhead lines is predicted, the required repairs are identified, and the repair time is calculated. , This indicates the repair time required for the maintenance team to fix all damaged components; Calculate the waiting time separately Arrival time and response time and in combination with repair time The total time required to repair overhead power lines after an extreme weather event is calculated by adding the results; among which, This indicates the waiting time before the extreme weather event can be repaired. This indicates the arrival time of the maintenance team from the starting point to the location of the fault; Indicates the response time delayed due to human factors; Based on the total time required to repair overhead power lines after extreme weather events, the vulnerability rate and recovery rate of power system components are calculated, and the resilience of the power distribution network under different aging conditions is assessed based on the vulnerability rate and recovery rate of power system components.

2. The method for quantitatively assessing the probability of faults and recovery time in a power distribution network under extreme weather conditions, as described in claim 1, is characterized in that: The process of constructing the distribution network limit state function, combining a stratified sampling method to evaluate the dynamic strength degradation of power system components under the influence of aging factors, and calculating the failure probability of corresponding wind speed scenarios includes: A limit state function for the power distribution network is established. By comparing structural strength with load, it is determined whether a single component has failed. The power distribution limit state function is as follows: Based on the limit state function of the distribution network, stratified sampling is performed on the distribution network. The random variable space is divided into several layers, and samples are generated in each layer and the failure rate is statistically analyzed. The total failure probability is then calculated using a weighted average. The specific formula for stratified sampling is as follows: ； in, Indicates the probability of failure. Indicates the first j Layer weights Indicates the first j Layer failure count, Indicates the first j Total number of floors; The failure risk of the distribution network under extreme conditions is assessed and quantified using a function of wind speed. A vulnerability curve is constructed between the failure probability of a single component in the distribution network and wind speed, as follows: ； in, This indicates the structural fragility of individual components in the power distribution network. This represents the cumulative function of the standard normal distribution. Indicates wind speed. This indicates the median wind speed. This represents the logarithmic standard deviation; The vulnerability curves under different lifetimes are derived using a function of the pole's remaining strength, and a model of pole strength decay over time is constructed as follows: ； in, Indicates the first t Remaining strength of the pole after 1 year Indicates the initial strength of the pole. This represents the external strength attenuation coefficient of the first pole. This represents the external strength attenuation coefficient of the second pole. This represents the internal strength attenuation coefficient of the first pole. This represents the internal strength attenuation coefficient of the second pole.

3. The method for quantitatively assessing the probability of faults and recovery time in a power distribution network under extreme weather conditions, as described in claim 1, is characterized in that: In the failure model of a three-pole system, the upper bound is defined as the failure of all poles completely independently, and the lower bound is defined as the failure of all poles completely dependent on each other. The specific formulas for the upper and lower bounds are as follows: ； in, This represents the probability of a fault in a power distribution network caused by pole failure. This indicates the total number of poles in the power distribution network.

4. The method for quantitatively assessing the probability of faults and recovery time in a power distribution network under extreme weather conditions, as described in claim 1, is characterized in that: Calculate the waiting time separately Arrival time and response time and in combination with repair time The total time required to repair overhead power lines after an extreme weather event is calculated by adding the results; where: The calculation formula is: ； in, Indicates the end time of the extreme event. Indicates the time when the interference occurred; The calculation formula is: ； in, This indicates the distance between the fault location and the starting point of the repair team. This indicates the average speed of the vehicle under maintenance; The calculation formula is: ； in, This indicates the number of damaged poles in the faulty line. Indicates the repair time for a single utility pole. The binary variable indicating the cause of the damage to the utility pole. Indicates the number of damaged sections in the faulty line. Indicates the repair time for a single segment. A binary variable indicating that the cause of the damage is the wire; Determined based on the operator's perception of the emergency and the coordination ability of the maintenance personnel; The formula for the total time required to repair overhead power lines after an extreme weather event is as follows: 。 5. The method for quantitatively assessing the probability of faults and recovery time in a power distribution network under extreme weather conditions, as described in claim 1, is characterized in that: The calculation of the vulnerability rate and recovery rate of power system components based on the total time required to repair overhead power lines after extreme weather events specifically includes: Calculate the vulnerability rate based on the start and end times of the extreme event. The specific formula is as follows: ； in, This indicates the system performance level at the end of an extreme event. Indicates the end time of the extreme event. This indicates the system performance level at the start of an extreme event. Indicates the start time of the extreme event; The end time of the extreme event is obtained by calculating the recovery rate based on the start time of the extreme event and the total time required to repair the overhead power lines after the extreme weather event. The specific formula is as follows: ； in, This indicates the system's performance level when it returns to normal. This indicates the time it takes for the system to return to normal.

6. The method for quantitatively assessing the probability of faults and recovery time in a power distribution network under extreme weather conditions, as described in claim 5, is characterized in that: The assessment of distribution network resilience under different aging conditions based on the vulnerability rate and recovery rate of power system components specifically includes: When vulnerability rate A value greater than 0 indicates a continuously declining system performance, high vulnerability, and weak risk resistance; when the vulnerability rate is... =0 indicates that the system performance is unaffected and the system has full resilience; when the vulnerability rate is... <0 indicates that the system has strong resilience to extreme weather conditions and its performance degrades slowly; When recovery rate A recovery rate >0 indicates that the system has strong recovery capabilities; when the recovery rate is >0, the system has strong recovery capabilities. =0 indicates that system performance has not recovered; when the recovery rate is 0, it means that the system performance has not recovered. <0 indicates that the system recovery was completely interrupted and the recovery capability was severely insufficient.

7. The system of claim 1, which provides a quantitative assessment method for fault probability and recovery time in power distribution networks under extreme weather conditions, is characterized in that... include: The component wind resistance assessment module is used to construct the distribution network limit state function and combine the stratified sampling method to assess the dynamic strength of power system components under the influence of aging factors and calculate the failure probability of their corresponding wind speed scenarios. The system failure probability calculation module is used to quantify the dependency between the central pole and adjacent poles using a three-pole system failure model, and to calculate the probability of distribution network failure due to pole failure using a binomial distribution. The overhead line is divided into several sections, and the failure probability of each section is calculated using a line-pole series model. This is then extended to the entire line to calculate the probability of distribution network failure due to conductor failure. The recovery time calculation module is used to calculate the failure probability of the distribution network under extreme weather conditions based on the failure probability of wind speed scenarios, the failure probability of the distribution network caused by power poles, and the failure probability of the distribution network caused by conductors. Based on the failure probability of the distribution network under extreme weather conditions, the module predicts the status of overhead lines, confirms the required repairs, and calculates the repair time. , This indicates the repair time required for the maintenance team to fix all damaged components; Calculate the waiting time separately Arrival time and response time and in combination with repair time The total time required to repair overhead power lines after an extreme weather event is calculated by adding the results; among which, This indicates the waiting time before the extreme weather event can be repaired. This indicates the arrival time of the maintenance team from the starting point to the location of the fault; Indicates the response time delayed due to human factors; The resilience index quantification module is used to calculate the vulnerability rate and recovery rate of power system components, and to assess the resilience of the distribution network under different aging conditions based on the vulnerability rate and recovery rate of power system components.

8. An electronic device, characterized in that, include: The present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements a method for quantitatively assessing the probability of faults and recovery time of a power distribution network under extreme weather conditions as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program causes the computer to execute a quantitative assessment method for fault probability and recovery time of power distribution network under extreme weather conditions as described in any one of claims 1 to 6.

Images