Distribution line risk assessment method and system based on lightning and associated disaster coupling probability model

Abstract

The invention is suitable for the technical field of distribution network risk assessment, and provides a distribution line risk assessment method and system based on a lightning and associated disaster coupling probability model, and the method comprises the steps: obtaining meteorological data, geographic data and distribution line parameters of a target region; calculating a first fault probability of the lightning disaster lower line section or the tower and a second fault probability of the associated disaster lower line section or the tower; combining the disaster type conversion probability and the disaster accompanying time coefficient, determining the third fault probability of the lower segment or the tower of the comprehensive disaster, calculating the failure probability of the distribution line, and screening a typical fault scene set by using the system information entropy; calculating a line fault consequence value, and generating a line risk index in combination with the failure probability; and finally, generating a target risk line list according to the sorting of the line risk indexes. Based on the influence on the line fault probability under the condition of expected associated disasters of lightning disasters, the accuracy of line fault prediction in extreme weather is improved.

Description

Technical Field

[0001] This invention relates to the field of distribution network risk assessment technology, and in particular to a method and system for risk assessment of distribution lines based on a coupled probability model of lightning and associated disasters. Background Technology

[0002] Currently, in the southern region, high temperatures and heavy rainfall, complex terrain, and frequent thunderstorms not only directly cause lightning tripping and damage to power distribution lines, but also, in conjunction with other extreme disasters, further exacerbate the threat to power grid safety. To avoid large-scale power outages under low-probability but high-risk extreme weather conditions, many studies currently utilize historical lightning disaster data provided by meteorological departments, combined with meteorological and topographical data from the affected areas, to construct disaster-prone environment sensitivity models for power distribution network risk assessment; or they combine methods such as the analytic hierarchy process (AHP), fuzzy theory, and machine learning with production scheduling information resources, using professional power system analysis techniques to detect the power grid's operational status and conduct risk assessments of power distribution lines.

[0003] However, existing models are based solely on lightning as a single disaster, failing to consider the coupling effects of associated disasters such as rainstorms and landslides. Furthermore, they do not account for the temporal sequence of disasters, statically assessing risk at a single moment and failing to reflect the dynamic evolution of the disaster chain. Additionally, failure scenario analysis often employs exhaustive methods without considering failure rates to filter high-probability scenarios, resulting in high computational costs and difficulty in focusing on key risk links. This makes it impossible to accurately characterize risk evolution paths when facing complex disasters in southern regions, thus failing to meet the risk assessment needs of complex scenarios.

[0004] In view of this, a method and system for risk assessment of distribution lines based on a coupled probability model of lightning and associated disasters is proposed. Summary of the Invention

[0005] This invention provides a risk assessment method for power distribution lines based on a coupled probability model of lightning and associated disasters, which is used to solve the problem of difficulty in meeting the risk assessment needs of complex scenarios.

[0006] The first aspect of this invention provides a method for risk assessment of distribution lines based on a coupled probability model of lightning and associated disasters, comprising: Real-time acquisition of meteorological data, geographic data, and power line parameters for the target area; The first probability of failure of the line segment or tower affected by lightning is calculated based on the meteorological data and power line parameters; at the same time, the second probability of failure of the line segment or tower affected by associated disasters is calculated based on the meteorological data and geographical data. Based on the first and second failure probabilities, the third failure probability of the line segment or tower under the comprehensive disaster is calculated by combining the disaster type conversion probability and the disaster accompanying time coefficient. The failure probability of the distribution line is calculated based on the third failure probability. The system information entropy is used to filter multiple failure scenarios that meet the entropy range constraint and each line experiences at most one failure at each time, and a typical failure scenario set is generated. For each line in the typical fault scenario set, the value of the line fault consequences is calculated based on the node load loss, manual repair time, and economic value of load loss, and a line risk index is generated by combining the failure probability. A list of target risk routes is generated based on the ranking of the route risk indicators.

[0007] Furthermore, the expression for the first failure probability includes: Direct lightning tripping rate : Induced lightning trip rate : Total lightning trip rate : Transient failure rate : Permanent failure rate : in: for Ground flash density data within a given time period; For the arc-building rate, , This represents the average operating voltage gradient of the insulator string; The width of the projection of the topmost conductor onto the ground; This refers to the lightning strike range on one side of the conductor. , For conductor striking distance, , The average height of the conductor. This represents the amplitude of the lightning current. Let be the probability density function of the lightning current amplitude. The height of the intersection of the exposed arc on the ground and the exposed arc on the conductor is equal to The corresponding lightning current at that time The x-coordinate of the intersection of the conductor strike distance and the ground strike distance; The line's lightning withstand level; The distance from the lightning strike point to the power line; This is the permanent fault conversion factor, with a value of 0.195.

[0008] Furthermore, the expression for the second failure probability includes: Accumulated rainfall disaster probability : Hourly rainfall disaster probability : Probability of associated disasters : Risk assessment index : Comprehensive failure probability of associated disasters : in: This refers to the effective cumulative rainfall over 10 days. , For the first Rainfall; This represents the maximum hourly rainfall of the day. , This represents the actual rainfall measured within 24 hours. Forecast rainfall for the next 3 hours; For the terrain risk parameters of the tower location, For the risk parameters of hidden dangers in the tower foundation, Geological risk parameters for the location of the tower; , , These are the weighting coefficients.

[0009] Furthermore, the disaster type conversion probability represents the strength of the induced relationship between lightning and different types of disasters; the disaster accompaniment time coefficient represents the period during which the associated disaster accompanies lightning, including the later stage of the disaster, the middle stage of the disaster, and the entire disaster process.

[0010] Furthermore, the expression for the third failure probability includes: in: For line segments or towers Failure probability under combined disasters; This represents the total number of associated disaster types. As an associated disaster The probability of disaster type transformation. As an associated disaster The disaster-accompanying time coefficient.

[0011] Furthermore, the calculation of the failure probability of the power distribution line based on the third fault probability includes: in: For the line The probability of failure, For the line Center line segment or tower The total number.

[0012] Furthermore, the method of using system information entropy to filter multiple fault scenarios that satisfy the entropy range constraint and where each line experiences at most one fault at each time moment generates a typical fault scenario set, including: Filter to meet The failure scenarios, among which This represents the system information entropy value in the fault scenario. and Determined based on the 90% confidence interval of the entropy value distribution of historical failure scenarios. for Timetable The fault status, This refers to a collection of distribution network lines.

[0013] Furthermore, for each line in the set of typical fault scenarios, the value of the line fault consequences is calculated based on the node load loss, manual repair time, and economic value of the load loss. This is combined with the failure probability to generate line risk indicators, including: The power outage area is determined based on the location of the faulty component and the switch operation logic, where the switch types include automatic switches, manual switches and tie switches; Calculate the load loss at each load node, combine the manual repair time and the economic loss coefficient per unit time to calculate the value of the fault consequences, and generate line risk indicators.

[0014] Furthermore, the step of generating a target risk route list based on the ranking of the route risk indicators includes: Sort the routes in descending order of risk indicators and select routes with risk indicators higher than a preset threshold, or select the top K high-risk routes according to a fixed ratio. Output a list of target risk routes and their corresponding risk indicator values ​​and the value of failure consequences.

[0015] A second aspect of the present invention provides a distribution line risk assessment system based on a coupled probability model of lightning and associated disasters, comprising: The data acquisition unit is used to acquire meteorological data, geographic data, and power line parameters of the target area in real time. The first and second failure probability calculation units are used to calculate the first failure probability of the line segment or tower affected by lightning disaster based on the meteorological data and power distribution line parameters; and at the same time, to calculate the second failure probability of the line segment or tower affected by associated disaster based on the meteorological data and geographical data. The third fault probability calculation unit is used to calculate the third fault probability of the line segment or tower under the comprehensive disaster based on the first fault probability and the second fault probability, combined with the disaster type conversion probability and the disaster accompanying time coefficient. The typical fault scenario set generation unit is used to calculate the failure probability of the distribution line based on the third fault probability, and to use system information entropy to filter multiple fault scenarios that meet the entropy range constraint and that each line experiences at most one fault at each time, thereby generating a typical fault scenario set. The line risk index generation unit is used to calculate the line fault consequence value for each line in the typical fault scenario set based on the node load loss, manual repair time, and economic value of load loss, and generate line risk indexes in combination with failure probability. The target risk route list generation unit is used to generate a target risk route list based on the sorting of the route risk indicators.

[0016] As can be seen from the above technical solutions, the present invention has the following advantages: This invention first acquires meteorological data, geographical data, and power line parameters for the target area. Then, it calculates the failure probability of line segments or towers under lightning disasters and the failure probability of line segments or towers under associated disasters. Based on the failure probabilities under lightning disasters and associated disasters, and combining the disaster type conversion probability and the disaster accompaniment time coefficient, it calculates the coupled failure probability of line segments or towers under comprehensive disasters. Based on the coupled failure probability, it calculates the failure probability, uses information entropy to filter multiple failure scenarios that meet the requirements, and generates a set of typical failure scenarios. For each line in the set of typical failure scenarios, it generates a line risk index, and finally, a list of high-risk lines is generated. This invention's method accurately assesses the occurrence and evolution of lightning and associated disasters when facing complex disasters in southern regions, solving the problem of difficulty in meeting the risk assessment needs of complex scenarios. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart of an embodiment of a power distribution line risk assessment method based on a coupled probability model of lightning and associated disasters in this invention. Figure 2 This is a schematic diagram of the strike distance model for overhead power distribution lines in this invention. Figure 3 This is a system function curve diagram of the power distribution network under extreme weather conditions in this invention; Figure 4 This is a simulation experiment topology diagram of the Fangchenggang 800 Line in Guangxi Province used in this invention. Figure 5 This is a fault probability curve of the Guangxi Fangchenggang 800 line under lightning and associated disasters in the simulation experiment of this invention; Figure 6 This is a probability distribution diagram of entropy values ​​corresponding to different scenarios in this invention; Figure 7 This is a fault multiplicity distribution diagram in this invention; Figure 8 This is a diagram showing the load loss distribution of each node after a fault in this invention. Detailed Implementation

[0018] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “corresponding to,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0019] Example 1 The implementation method in this embodiment can be implemented in a system, on a server, or on a terminal; no specific limitation is made. The following section will describe the power distribution line risk assessment method based on a coupled probability model of lightning and associated disasters in this application from the perspective of system implementation. Please refer to... Figure 1 The method provided in this application includes the following steps: S11. Real-time acquisition of meteorological data, geographic data, and power line parameters of the target area; In this embodiment, obtaining meteorological data for a certain region may include lightning activity data, associated disaster activity data, rainfall data, etc.; geographical data may include topographic data, geological data, etc.; power distribution line parameters may include various parameters of the power distribution network architecture, and specific parameters are referred to the data mentioned in the following steps.

[0020] S12. Calculate the first failure probability of the line segment or tower affected by lightning disaster based on meteorological data and power distribution line parameters; at the same time, calculate the second failure probability of the line segment or tower affected by associated disaster based on meteorological data and geographical data; 1. The calculation process for the first failure probability is as follows: The accompanying disasters of lightning strikes on power distribution lines here mainly include heavy rain, landslides, floods, and mudslides. Unlike extreme weather events such as typhoons and heavy rain, which affect the failure rate of power distribution lines through mechanical loads, thunderstorms can cause overhead power distribution lines to trip. Some of these trips are transient and can be recovered by automatic reclosing, while others are permanent faults, possibly caused by insulator detachment or conductor breakage. The probability of tripping is related to the ground flash density, lightning withstand level, insulation strength, and lightning current amplitude.

[0021] The lightning tripping rate is calculated using the strike distance method. The point of impact when lightning strikes the ground is uncertain, depending on which object the discharge path first enters within its strike distance, and the strike distance of the conductor. Internal lightning strikes cause power outages, with the strike distance to the ground being [missing information]. Internally induced lightning trips the circuit breaker; the striking distance model for medium-voltage overhead distribution lines is as follows: Figure 2 As shown.

[0022] Wire distance The formula for calculation is: , The average height of the conductor. Lightning current amplitude; ground strike distance The formula for calculation is: The height of the intersection of the exposed arc on the ground and the exposed arc on the conductor is equal to... The corresponding lightning current : Lightning attraction range on one side of overhead power distribution lines The calculation formula is: , Let be the probability density function of the lightning current amplitude. Therefore, in general areas The line tripping rate, direct lightning tripping rate, and induced lightning tripping rate within the time period are expressed as follows: Direct lightning tripping rate : Induced lightning trip rate : Total lightning trip rate : in: for Ground flash density data within a given time period; For the arc-building rate, , This represents the average operating voltage gradient of the insulator string; The width of the projection of the topmost conductor onto the ground; The x-coordinate of the intersection of the conductor strike distance and the ground strike distance; The line's lightning withstand level; This represents the distance from the point of lightning strike to the power line.

[0023] This study uses historical data fitting to obtain the functional relationship between permanent line faults and cumulative tripping rate. Data on lightning-induced tripping and permanent fault occurrences over many years are collected, and the least squares method is used to fit the functional relationship. A proportional coefficient showing a linear relationship between the two is obtained from historical data of a specific region. 0.195, from which the transient failure rate of overhead power distribution lines can be obtained. and permanent failure rate They are respectively: ; .

[0024] The overall failure probability of power distribution lines under lightning-related disasters is mainly determined by the probability of occurrence of associated disasters and the risk assessment index. Based on relevant data collected by the power meteorological department, micro-topographic information of the disaster-stricken area, and data from the power distribution line inspection system, the following failure probability calculation model can be obtained.

[0025] 2. The calculation process for the second failure probability is as follows: The overall failure probability of power distribution lines under lightning-related disasters is mainly determined by the probability of induced disasters and the hazard assessment index. Based on relevant data collected by the power meteorological department, micro-topographic information of the affected area, and data from the power distribution line inspection system, the failure probability under related disasters can be obtained. Based on the disaster-causing mechanism analysis, the main factors affecting lightning-related disasters include: the effective cumulative rainfall within 10 days. : , For the first Rainfall for the day; maximum hourly rainfall for the day : , This represents the actual rainfall measured within 24 hours. This forecasts rainfall for the next 3 hours.

[0026] Based on the above factors, the probability of associated disasters can be obtained by fitting the effective cumulative rainfall. : The probability of associated disasters fitted with the maximum hourly rainfall of the day. : Therefore, the probability of lightning-related disasters is high. : The disaster risk assessment index is determined based on the terrain, geology, and tower parameters of the tower's location. The calculation formula is as follows: ,in For the terrain risk parameters of the tower location, For the risk parameters of hidden dangers in the tower foundation, Geological risk parameters for the location of the tower; , , These are the weighting coefficients. , , In summary, the overall failure probability of power distribution lines under associated disasters... for .

[0027] S13. Based on the first and second failure probabilities, combined with the disaster type conversion probability and the disaster accompanying time coefficient, calculate the third failure probability of the line segment or tower under the comprehensive disaster. In this embodiment, the disaster type conversion probability represents the strength of the induced relationship between lightning and different types of disasters; the disaster accompaniment time coefficient represents the time period during which an associated disaster accompanies lightning, including the later stage, the middle stage, and the entire process of the disaster. As time and space dimensions continue to shift, due to the uncertainty of the micro-topography and micro-meteorological conditions of power distribution lines, there are induced and coupled relationships between disaster-causing factors. Under lightning disasters, there are often accompanying disasters such as rainstorms, landslides, and floods, which can also impact the stable operation of the power system. To accurately describe the probability of power line breaks or tower collapses under extreme natural disasters, the disaster type conversion probability and the disaster accompaniment time coefficient are introduced.

[0028] Therefore, the formula for calculating the probability of coupled faults in power distribution lines (the third fault probability) is as follows: in: For line segments or towers Failure probability under combined disasters; This represents the total number of associated disaster types. As an associated disaster The probability of disaster type transformation. As an associated disaster The disaster-accompanying time coefficient.

[0029] S14. Calculate the failure probability of the distribution line based on the third failure probability, and use the system information entropy to filter out multiple failure scenarios that meet the entropy range constraints and where each line experiences at most one failure at each time, and generate a set of typical failure scenarios. First, calculate the system information entropy value for the fault scenario: ,in For a set of distribution network lines, indicating time. line The fault status (1 for fault, 0 for normal). For the line exist The probability of failure at any given time. Filtering to meet the criteria. Fault scenarios, and Determined based on the 90% confidence interval of the entropy distribution of historical failure scenarios.

[0030] The selected fault scenarios are grouped according to feeder zones, and the power outage status of components within the same feeder zone is determined by the type of inlet switch. Details are as follows: The scenario selection method can narrow down the number of scenarios to be analyzed, while determining the number, location, and timing of faulty components in each scenario. Distribution network resilience analysis requires understanding load loss caused by extreme weather; therefore, the outage range must be determined based on the network structure and the location of faulty components, and the amount of power loss must be calculated. It is necessary to analyze the power loss under each fault scenario, determining the outage range and duration based on the location of faulty components, network structure, and switch positions. Distribution systems are generally designed to meet the N-1 safety criterion and are designed as closed-loop structures, but must operate in an open-loop mode. When a distribution network component fails, the tie switch activates, and some loads can be restored to power through transfer; some loads are isolated from the faulty area and continue to be powered by the upstream power source; other loads cannot be isolated from the faulty area and experience a power outage until the component is repaired and power is restored. The load outage range under a fault scenario is related to the network structure; the outage duration is related to the segmentation formed by the switch components.

[0031] The duration and extent of power outages are affected by the type and location of switches, thus switches are classified accordingly, including: Automatic Switches (SA): These switches include circuit breakers and fuses. They automatically trip after a fault occurs. If a permanent fault is caused by a thunderstorm, these switches can interrupt the fault current and isolate the faulty area with a short operating time. If a wire or pole breakage occurs, these switches can quickly connect the unaffected area to the upstream power supply or a backup power source, rapidly restoring power to that load. Manual Switches (SM): These switches include disconnect switches and cannot automatically and quickly isolate faults. If a permanent fault is caused by a thunderstorm, these switches cannot directly interrupt the fault current; they must wait for the automatic switch to interrupt the current before isolating the fault and reducing the fault area. If a wire or pole breakage occurs, these switches require manual operation to connect the unaffected area to the upstream power supply or a backup power source, and the switch operation takes a certain amount of time. Interchange Switches (ST): Under normal operating conditions, interchange switches must be in the open state. These switches only close when a fault occurs and load transfer is needed, and the capacity limitations of the transfer path must be considered when transferring load.

[0032] When a component on a feeder fails, the fault can be isolated by a switch, and some loads can be restored to power through transfer. Therefore, the fault range analysis in a fault scenario mainly depends on the location of the switching component; loads within the same section experience the same power outage. For multiple faults, when other components have the same fault condition, the impact of all components within the same section after failure is the same. The concept of a feeder zone simplifies the analysis of the impact range of a power distribution system fault scenario. A feeder zone refers to a set of components that share a common entrance switch. The failure of one component will cause other components within the same feeder zone to lose power. Therefore, the power outage situation of components within a feeder section can be analyzed as a whole on a feeder zone basis, thereby improving the efficiency and speed of power shortage calculations in different scenarios.

[0033] S15. For each line in the typical fault scenario set, calculate the line fault consequence value based on node load loss, manual repair time, and economic value of load loss, and generate line risk indicators by combining failure probability. 1. Determine the power outage area based on the location of the faulty component and the switch operation logic, where the switch types include automatic switches, manual switches, and tie switches; 2. Calculate the load loss at each load node, and combine the manual repair time and the economic loss coefficient per unit time to calculate the value of the fault consequences and generate line risk indicators.

[0034] First, the outage area is determined based on the location of the faulty component and the switching logic (automatic switch SA quickly isolates the fault, manual switch SM delays operation, and tie switch ST transfers load). Then, the load loss at each load node is analyzed based on the network topology. Combined with manual repair time (Depending on switch type) and economic loss coefficient per unit time due to load shedding Calculate the value of the consequences of the failure: Secondly, the probability of line failure. Value of failure consequences Multiply to generate route risk indicators This method quantifies the overall risk level of a single power line; a higher index indicates a greater risk. It can be used to assess the risk level of distribution lines, identify weak points in the power system during lightning strikes, and facilitate timely repair and protection measures. Specifically, during thunderstorms, large-scale power outages occur in the distribution network, and after the extreme weather passes, the distribution network gradually returns to its original normal operating state. Figure 3This is a schematic diagram of the system function curve of the distribution network during the entire process of extreme weather impact. Based on the location, failure time, and repair sequence of the faulty components in each scenario, and the consequences of different component failures, the power loss situation of each node load point is analyzed. The system function curve of the entire process from the occurrence of extreme weather to the restoration of normal operation is obtained, and then the missing area of ​​the load curve of the entire system under the fault scenario is calculated.

[0035] S16. Generate a list of target risk routes based on the ranking of route risk indicators.

[0036] 1. Sort the routes in descending order of risk index and select the routes with risk indexes higher than the preset threshold, or select the top K high-risk routes according to a fixed ratio; 2. Output a list of target risk routes and their corresponding risk indicator values ​​and failure consequences.

[0037] All lines according to risk indicators Sort in descending order, based on a preset threshold ( 50) or a fixed percentage (e.g., the top 10% of high-risk routes) is used to filter target routes, ensuring the list focuses on high-risk components. Finally, a list of target risk routes is output, with each route associated with three data points: risk indicator values. Priority ranking criteria; the value of the consequences of the failure Quantify economic losses; failure probability This reflects the likelihood of a malfunction occurring.

[0038] The implementation of the above method will be verified by simulation using the Fangchenggang 800 Railway Line in Guangxi: Topology diagram as follows Figure 4 As shown, the above formula is used to calculate the probability of line failure under lightning and accompanying rainstorm disasters. Select 6 time periods, such as Figure 5 As shown. Typical fault scenarios are generated based on the line fault probability, and the entropy value corresponding to each scenario is calculated. The numerical value of the system information entropy satisfies the following: Figure 6 The probability distribution shown is related to the system characteristics. The distribution of entropy values ​​is related to the system's properties. Figure 6 In this example, 90% of the scene entropy values ​​are distributed between (140, 240), indicating that for the data in this example, It should be taken. =140, =240. The higher the probability of an entropy value corresponding to a scene, the greater the likelihood that the scene will occur under extreme weather conditions, indicating that the entropy value... The scenarios are relatively reasonable. For example, lines 5, 3, 49, and 40 have higher failure rates at 14:30, 15:00, 14:00, and 15:15 respectively. Therefore, the scenario in which these four lines fail at the corresponding times is more likely to occur, and the corresponding system entropy value is 180, which is a typical failure scenario. Conversely, the scenario in which the failure occurs is less likely to occur, and the corresponding system entropy value is low, which does not meet the entropy value constraint and does not need to be considered in the resilience analysis.

[0039] The number of faulty components in the fault scenario is analyzed and statistically analyzed to meet the following requirements: Figure 7 The distribution shown illustrates the probability distribution of the fault scale (i.e., the number of faulty components) in a distribution network under extreme weather conditions. This distribution is related to the severity of the extreme weather and the resilience of the power grid itself. Strengthening the power grid components will shift the distribution of the number of faulty components towards a decrease in the number of components under the same extreme weather conditions, thus improving the resilience of the distribution network. In situations such as... Figure 7 As shown, under the condition of maintaining the strength of existing components, the probability of 3 to 5 faults in this area is the highest, while the probability of more than 5 faults or no faults is extremely low, showing an overall distribution with a mid-peak. Accordingly, the fault scale considered in the resilience analysis is mainly 3 to 5 faults.

[0040] Furthermore, simulation calculations were performed on the Fangchenggang 800 line in Guangxi. Typical fault scenarios were extracted using information entropy. It was found that lines 49, 5, 3, and 40 had higher fault rates at 14:00, 14:30, 15:00, and 15:15, respectively. At 15:15, four lines were expected to disconnect. The resulting fault set for the distribution lines was identified as lines 49, 5, 3, and 40. Simultaneously, a DC power flow optimization model was used to calculate the load loss at all load nodes within the anticipated fault set. The load loss at these nodes is as follows: Figure 8 As shown. The total load loss at each node is 1.197MW. The total value of the load loss can be calculated as follows: The value is 10,000 yuan, and the risk index is 51.72.

[0041] Secondly, a risk assessment can be performed on a single fault to obtain the total value and risk indicators of the load loss for each line, as shown in Table 1: Table 1. Fault Consequences and Risk Indicators for Each Risky Line As shown in the table, under lightning disasters, the failure probability of line 49 is 0.261. However, considering the repair time at the load nodes and the fault status of other lines, the consequences of its failure are relatively small. Since the risk index needs to comprehensively consider both the probability and consequences of failure, its risk index is low. Conversely, the probability of failure for lines 5 and 40 is relatively low, but the consequences of their failures are more severe. Therefore, their risk index ranks high among the risky lines.

[0042] Therefore, in the event of lightning disasters, power distribution line operation and dispatch personnel need to take preventive measures for lines with high risk indicators, deploy corresponding emergency repair resources and personnel reserves, avoid large-scale power outages caused by lightning disasters and their associated disasters, and provide a basis for disaster prevention and mitigation command and dispatch.

[0043] The above embodiments comprehensively consider the impact of expected associated disasters of lightning on the probability of line faults, thereby improving the accuracy of line fault prediction in the face of extreme weather conditions.

[0044] Example 2 An embodiment of a power distribution line risk assessment system based on a coupled probability model of lightning and associated disasters in this invention includes the following steps: The data acquisition unit is used to acquire meteorological data, geographic data, and power line parameters of the target area in real time. The first and second failure probability calculation units are used to calculate the first failure probability of the line segment or tower affected by lightning disaster based on meteorological data and power distribution line parameters; and at the same time, to calculate the second failure probability of the line segment or tower affected by associated disaster based on meteorological data and geographical data. The third failure probability calculation unit is used to calculate the third failure probability of the line segment or tower under the comprehensive disaster based on the first failure probability and the second failure probability, combined with the disaster type conversion probability and the disaster accompanying time coefficient. The typical fault scenario set generation unit is used to calculate the failure probability of the distribution line based on the third fault probability, and to use the system information entropy to filter multiple fault scenarios that meet the entropy range constraints and that each line experiences at most one fault at each time, thereby generating a typical fault scenario set. The line risk index generation unit is used to calculate the value of line fault consequences based on node load loss, manual repair time, and economic value of load loss for each line in the typical fault scenario set, and generate line risk indexes by combining failure probability. The target risk route list generation unit is used to generate a target risk route list based on the ranking of route risk indicators.

[0045] For specific limitations regarding the system, please refer to the method limitations described above, which will not be repeated here. Each module in the above system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0046] It is understood that those skilled in the art can combine various implementation methods in the above embodiments under the guidance of the above examples to obtain technical solutions with multiple implementation methods.

[0047] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for risk assessment of distribution lines based on a coupled probability model of lightning and associated disasters, characterized in that, include: Real-time acquisition of meteorological data, geographic data, and power line parameters for the target area; The first probability of failure of the line segment or tower affected by lightning is calculated based on the meteorological data and power line parameters; at the same time, the second probability of failure of the line segment or tower affected by associated disasters is calculated based on the meteorological data and geographical data. Based on the first and second failure probabilities, the third failure probability of the line segment or tower under the comprehensive disaster is calculated by combining the disaster type conversion probability and the disaster accompanying time coefficient. The failure probability of the distribution line is calculated based on the third failure probability. The system information entropy is used to filter multiple failure scenarios that meet the entropy range constraint and each line experiences at most one failure at each time, and a typical failure scenario set is generated. For each line in the typical fault scenario set, the value of the line fault consequences is calculated based on the node load loss, manual repair time, and economic value of load loss, and a line risk index is generated by combining the failure probability. A list of target risk routes is generated based on the ranking of the route risk indicators.

2. The method for risk assessment of distribution lines based on a coupled probability model of lightning and associated disasters as described in claim 1, characterized in that, The expression for the first failure probability includes: Direct lightning tripping rate : Induced lightning trip rate : Total lightning trip rate : Transient failure rate : Permanent failure rate : in: for Ground flash density data within a given time period; For the arc-building rate, , This represents the average operating voltage gradient of the insulator string; The width of the projection of the topmost conductor onto the ground; This refers to the lightning strike range on one side of the conductor. , For conductor striking distance, , The average height of the conductor. This represents the amplitude of the lightning current. Let be the probability density function of the lightning current amplitude. The height of the intersection of the exposed arc on the ground and the exposed arc on the conductor is equal to The corresponding lightning current at that time The x-coordinate of the intersection of the conductor strike distance and the ground strike distance; The lightning withstand level of the line; The distance from the lightning strike point to the power line; This is the permanent fault conversion factor, with a value of 0.

195.

3. The method for risk assessment of distribution lines based on a coupled probability model of lightning and associated disasters as described in claim 1, characterized in that, The expression for the second failure probability includes: Accumulated rainfall disaster probability : Hourly rainfall disaster probability : Probability of associated disasters : Risk assessment index : Comprehensive failure probability of associated disasters : in: This refers to the effective cumulative rainfall over 10 days. , For the first Rainfall; This represents the maximum hourly rainfall of the day. , This represents the measured rainfall over a 24-hour period. Forecast rainfall for the next 3 hours; For the terrain risk parameters of the tower location, For the risk parameters of hidden dangers in the tower foundation, Geological risk parameters for the location of the tower; , , These are the weighting coefficients.

4. The method for risk assessment of distribution lines based on a coupled probability model of lightning and associated disasters as described in claim 1, characterized in that, The disaster type conversion probability represents the strength of the induced relationship between lightning and different types of disasters; the disaster accompaniment time coefficient represents the time period during which an associated disaster occurs alongside lightning, including the later stage of the disaster, the middle stage of the disaster, and the entire disaster process.

5. The method for risk assessment of distribution lines based on a coupled probability model of lightning and associated disasters according to any one of claims 1-4, characterized in that, The expression for the third failure probability includes: in: For line segments or towers Failure probability under combined disasters; This represents the total number of associated disaster types. As an associated disaster The probability of disaster type transformation. As an associated disaster The disaster-accompanying time coefficient.

6. The method for risk assessment of distribution lines based on a coupled probability model of lightning and associated disasters as described in claim 5, characterized in that, The calculation of the failure probability of the power distribution line based on the third fault probability includes: in: For the line The probability of failure, For the line Center line segment or tower The total number.

7. The method for risk assessment of distribution lines based on a coupled probability model of lightning and associated disasters as described in claim 1, characterized in that, The method utilizes system information entropy to filter multiple fault scenarios that satisfy entropy range constraints and where each line experiences at most one fault at any given time, generating a typical fault scenario set, including: Filtering satisfies The failure scenarios, among which This represents the system information entropy value in the fault scenario. and Determined based on the 90% confidence interval of the entropy value distribution of historical failure scenarios. for Timetable The fault status, A collection of distribution network lines.

8. The method for risk assessment of distribution lines based on a coupled probability model of lightning and associated disasters according to claim 1, characterized in that, For each line in the set of typical fault scenarios, the consequences of the line fault are calculated based on the node load loss, manual repair time, and economic value of the load loss. A line risk index is then generated by combining the failure probability, including: The power outage area is determined based on the location of the faulty component and the switch operation logic, where the switch types include automatic switches, manual switches and tie switches; Calculate the load loss at each load node, combine the manual repair time and the economic loss coefficient per unit time to calculate the value of the fault consequences, and generate line risk indicators.

9. The method for risk assessment of distribution lines based on a coupled probability model of lightning and associated disasters according to claim 1, characterized in that, The step of generating a target risk route list based on the ranking of the route risk indicators includes: Sort the routes in descending order of risk indicators and select routes with risk indicators higher than a preset threshold, or select the top K high-risk routes according to a fixed ratio. Output a list of target risk routes and their corresponding risk indicator values ​​and the value of failure consequences.

10. A distribution line risk assessment system based on a coupled probability model of lightning and associated disasters, characterized in that, The method described by any one of claims 1-9 includes The data acquisition unit is used to acquire meteorological data, geographic data, and power line parameters of the target area in real time. The first and second failure probability calculation units are used to calculate the first failure probability of the line segment or tower affected by lightning disaster based on the meteorological data and power distribution line parameters; and at the same time, to calculate the second failure probability of the line segment or tower affected by associated disaster based on the meteorological data and geographical data. The third fault probability calculation unit is used to calculate the third fault probability of the line segment or tower under the comprehensive disaster based on the first fault probability and the second fault probability, combined with the disaster type conversion probability and the disaster accompanying time coefficient. The typical fault scenario set generation unit is used to calculate the failure probability of the distribution line based on the third fault probability, and to use system information entropy to filter multiple fault scenarios that meet the entropy range constraint and that each line experiences at most one fault at each time, thereby generating a typical fault scenario set. The line risk index generation unit is used to calculate the line fault consequence value for each line in the typical fault scenario set based on the node load loss, manual repair time, and economic value of load loss, and generate line risk indexes in combination with failure probability. The target risk route list generation unit is used to generate a target risk route list based on the sorting of the route risk indicators.

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