Wind and rain multi-disaster power distribution network failure rate analysis method based on stress intensity model

Abstract

The invention discloses a wind and rain multi-disaster power distribution network fault rate analysis method based on a stress intensity model, and belongs to the technical field of power system analysis. The method comprises the following steps: constructing a typhoon-rainstorm composite disaster scene; and analyzing the influence of the typhoon-rainstorm composite disaster on the wire and the tower. Analyzing the probability of power distribution network faults under typhoon-rainstorm composite disasters based on a stress intensity model; and establishing a power distribution network fault probability model considering equipment aging correction. According to the invention, the problem of insufficient system-level fault risk assessment precision of a traditional single disaster model is solved; and the limitation that the equipment performance degradation factors are not fully considered in the prior art is overcome. The accuracy and comprehensiveness of power distribution network fault probability evaluation in a composite disaster scene are improved, and a more scientific and reliable theoretical basis and technical support are provided for disaster prevention planning and emergency scheduling of a regional power distribution network.

Description

Technical Field

[0001] This invention relates to a method for analyzing the failure rate of a power distribution network under multiple wind and rain disasters based on a stress intensity model, belonging to the field of power system analysis technology. Background Technology

[0002] In recent years, extreme disasters have become more frequent and widespread. The probability and intensity of typhoon-rainstorm combined disasters have been increasing, which can easily cause multiple failures in power distribution equipment, posing a huge threat to the safe and reliable operation of regional power distribution networks.

[0003] However, existing power distribution network fault analysis technologies have the following core shortcomings:

[0004] First, traditional analysis methods often focus on single disaster scenarios, and their analysis models often simplify the mechanism of disaster action, making it difficult to effectively depict the complex fault chain and nonlinear evolution process of "wind impact - rainstorm impact - load superposition" under the coupled action of typhoon and rainstorm. This results in insufficient accuracy in assessing the risk of system-level faults caused by compound disasters, and fails to meet the actual needs of disaster prevention and mitigation in power distribution networks.

[0005] Secondly, while existing research on the impact of disasters on power distribution network equipment employs techniques such as physical experiments and finite element numerical simulations to explore the individual effects of typical disaster characteristic parameters like wind speed and rainfall on the mechanical and electrical performance of devices, it has significant limitations in the refined characterization of multi-disaster coupled scenarios.

[0006] On the one hand, the dynamic coupling effect of typhoons and rainstorms (such as strong winds exacerbating the erosion of equipment by rainstorms) on the nonlinear impact of the fault evolution process was not fully considered, leading to discrepancies between the analysis results and actual fault patterns. On the other hand, the performance degradation of power distribution system components due to material aging and fatigue wear during long-term operation was generally overlooked, as well as the amplification effect of the fault probability under the superposition of these uncertainties and disaster loads. This makes it difficult for existing analysis results to fully reflect the fault risk status of the power distribution network in actual operation.

[0007] In summary, existing technologies cannot accurately characterize the coupling mechanism of typhoon-rainstorm combined disasters, and have significant limitations in assessing the risk of power distribution network system-level faults under multi-hazard scenarios. Summary of the Invention

[0008] To address the problems existing in the background technology, the present invention provides a method for analyzing the failure rate of a power distribution network subject to multiple wind and rain disasters based on a stress intensity model.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: a method for analyzing the failure rate of a multi-hazard power distribution network based on a stress intensity model, the method comprising the following steps:

[0010] S1: Construct a combined typhoon-rainstorm disaster scenario;

[0011] S2: Analyze the impact of typhoon-rainstorm combined disasters on equipment;

[0012] S3: Analysis of the probability of power distribution network failure under typhoon-rainstorm combined disaster based on stress intensity model;

[0013] S4: Establish a distribution network fault probability model that takes into account equipment aging correction.

[0014] Furthermore, step S1 includes the following steps:

[0015] S101: Calculate the distance from the cyclone center to the strongest wind belt in a typhoon wind field. :

[0016]

[0017] In formula (1):

[0018] This represents the pressure difference between the outer periphery of a tropical cyclone and the center of a typhoon's wind field.

[0019] S102: Calculate the airflow velocity caused by the pressure gradient force, i.e., the maximum gradient wind speed. :

[0020]

[0021] In formula (2):

[0022] This is an empirical coefficient;

[0023] The Coriolis force coefficient of Earth's rotation;

[0024] S103: Calculate the average maximum wind speed :

[0025]

[0026] In formula (3):

[0027] This refers to the overall movement speed of the typhoon;

[0028] S104: Solve for the time-varying average wind speed at various locations in the typhoon field. :

[0029]

[0030] In equation (4):

[0031] This refers to the distance between equipment in the power distribution network and the center of the typhoon's cyclone.

[0032] It is a constant;

[0033] S105: Perform wind speed correction:

[0034]

[0035] In equation (5):

[0036] The wind speed at the location of the equipment;

[0037] The height of the equipment;

[0038] A correction factor is used to account for terrain roughness;

[0039] S106: Assuming east is 0° and counterclockwise is the positive direction, calculate the typhoon wind direction:

[0040]

[0041] In formula (6):

[0042] The direction of the circulating wind;

[0043] For the interior deflection angle to obey Within the range, the distance of the equipment from the center of the typhoon wind field cyclone varies. A piecewise function that changes;

[0044] S107: Determine the rainfall model:

[0045]

[0046] In equation (7):

[0047] It is a constant;

[0048] The diameter of the raindrop;

[0049] Λ is the slope factor of the raindrop spectrum distribution:

[0050]

[0051] In equation (8):

[0052] I represents rainfall intensity;

[0053] S108: Calculate the horizontal terminal velocity of a raindrop when it collides with the device. and vertical final velocity :

[0054]

[0055]

[0056] In equation (9):

[0057] It is the ratio of the horizontal velocity of raindrops to the wind speed:

[0058]

[0059] S109: Obtain the force generated by a single raindrop and the number of raindrops per unit volume :

[0060]

[0061] Furthermore, step S2 includes the following steps:

[0062] S201: Analysis of the impact of typhoon-rainstorm combined disasters on power lines;

[0063] S202: Analysis of the impact of typhoon-rainstorm combined disasters on power poles.

[0064] Furthermore, step S201 includes the following steps:

[0065] S20101: Calculate the line wind load:

[0066]

[0067] In equation (13):

[0068] This is the wind pressure non-uniformity coefficient;

[0069] This is the coefficient for wind pressure height variation;

[0070] This refers to the line shape coefficient;

[0071] The outer diameter of the conductor;

[0072] The horizontal span of the tower;

[0073] The angle between the wind direction and the direction of the conductor;

[0074] S20102: Calculate line rain load:

[0075]

[0076] In equation (14):

[0077] The area of ​​the conductor facing the rain;

[0078] S20103: Calculate the total load on the conductor:

[0079]

[0080] Furthermore, step S202 includes the following steps:

[0081] S20201: Calculate the wind load on the tower:

[0082]

[0083] In equation (16):

[0084] The wind vibration coefficient;

[0085] This refers to the tower shape coefficient;

[0086] This is the projected area of ​​the windward side of the tower;

[0087] S20202: Calculate the rain load on the tower body:

[0088]

[0089] In equation (17):

[0090] This refers to the projected area of ​​the rain-facing surface of the tower.

[0091] S20203: Calculate the bending moment at the centroid of any cross-section of the tower body. :

[0092]

[0093]

[0094]

[0095]

[0096] In equations (18)-(21):

[0097] The distance from the pole root to the crossarm;

[0098] The tower height;

[0099] The diameter of the tower;

[0100] The diameter of the cross section at the base of the tower;

[0101] This is an additional bending moment coefficient.

[0102] Furthermore, step S3 includes the following steps:

[0103] S301: Define device status function Z:

[0104]

[0105] In equation (22):

[0106] R represents the strength of the equipment component;

[0107] S represents the stress experienced by the equipment component;

[0108] If Z > 0, it indicates that the equipment components are in a reliable state;

[0109] If Z < 0, it indicates that the equipment component is in a faulty state;

[0110] S302: Calculating the probability of tower failure under combined typhoon-rainstorm disasters :

[0111]

[0112] In equation (23):

[0113] P is the symbol for calculating probability;

[0114] Design strength for the tower;

[0115] The standard deviation of the bending strength of the tower;

[0116] This represents the average bending strength of the tower.

[0117] S303: Calculating the probability of power line failure under combined typhoon and rainstorm disasters. :

[0118]

[0119] In equation (24):

[0120] For line design strength;

[0121] The standard deviation of the conductor's bending strength;

[0122] This represents the average bending strength of the conductor.

[0123] Furthermore, step S4 includes the following steps:

[0124] S401: Using Weibull distribution to represent the tower failure rate correction factor :

[0125]

[0126] In equation (25):

[0127] The service life of the pole / tower;

[0128] For the shape parameters of the equipment, ;

[0129] This indicates the fault distribution of the equipment during its initial operation period;

[0130] This indicates the distribution of faults during the wear and tear period of the equipment.

[0131] During the stable operation period, Take the baseline value as 1;

[0132] These are coefficients related to shape and scale parameters;

[0133] S402: Correct the equipment failure rate to obtain the final failure rate that takes into account equipment aging correction.

[0134]

[0135] In equation (26):

[0136] This represents the failure rate without considering equipment aging corrections.

[0137] Compared with the prior art, the beneficial effects of the present invention are:

[0138] This invention addresses the shortcomings of existing technologies in accurately depicting the coupling mechanism of typhoon-rainstorm combined disasters and neglecting the impact of equipment aging. By constructing a typhoon-rainstorm combined disaster scenario and quantifying its dynamic coupling effects (such as the nonlinear impact of strong winds exacerbating rainstorm erosion), it effectively solves the problem of insufficient accuracy in assessing system-level failure risks in traditional single-disaster models. Simultaneously, it innovatively introduces an equipment aging correction factor, coupling it with disaster stress parameters for calculation, dynamically reflecting the equipment failure threshold under the dual effects of "disaster load + aging loss," overcoming the limitation of existing technologies in not fully considering equipment performance degradation factors. This improves the accuracy and comprehensiveness of distribution network failure probability assessment under combined disaster scenarios, providing a more scientific and reliable theoretical basis and technical support for regional distribution network disaster prevention planning and emergency dispatch. Attached Figure Description

[0139] Figure 1 This is a schematic diagram of the wind field of Typhoon Batts in Embodiment 1 of the present invention;

[0140] Figure 2 This is the surface roughness coefficient table of the present invention;

[0141] Figure 3 This is a schematic diagram of typhoon wind direction in the Northern Hemisphere according to the present invention;

[0142] Figure 4 This is a schematic diagram of the tower structure of the present invention;

[0143] Figure 5 This is a schematic diagram of the stress random variable and the intensity random variable of the present invention;

[0144] Figure 6 This is a curve of the power equipment failure rate correction coefficient of the present invention. Detailed Implementation

[0145] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the invention, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0146] Stress intensity model: An analytical model based on reliability theory. Its core is to determine whether the system has failed by quantifying the numerical relationship between "stress" (the external load on the system) and "intensity" (the system's ability to resist the load).

[0147] Multiple disasters caused by wind and rain: refers to a complex disaster scenario formed by the combination of wind disasters and rainfall disasters, with the two having a synergistic effect.

[0148] Distribution network failure rate: refers to the probability of power equipment failure in a distribution network system under specific time or disaster scenarios. It is a core indicator for measuring the reliability of distribution network operation.

[0149] This invention provides a method for analyzing the failure rate of a multi-hazard power distribution network based on a stress intensity model, the method comprising the following steps:

[0150] S1: Construct a combined typhoon-rainstorm disaster scenario;

[0151] S2: Analyze the impact of typhoon-rainstorm combined disasters on equipment;

[0152] S3: Analysis of the probability of power distribution network failure under typhoon-rainstorm combined disaster based on stress intensity model;

[0153] S4: Establish a distribution network fault probability model that takes into account equipment aging correction.

[0154] Furthermore, step S1 includes the following steps:

[0155] This invention is based on the Batts typhoon model and the rainstorm pressure model. By quantifying the intensity of the combined typhoon-rainstorm disaster on the power distribution network and its spatiotemporal distribution characteristics, a disaster scenario under the combined disaster is constructed.

[0156] In meteorology, the wind field of a typhoon is often analyzed as an axisymmetric circular vortex, and the shape of the typhoon's wind field can be approximated as follows: Figure 1 As shown. Typhoon cyclones are typically very large, with diameters reaching hundreds or even thousands of kilometers. Horizontally, a typhoon consists of three parts: the outer periphery, the typhoon body, and the cyclone center.

[0157] S101: Calculate the distance from the cyclone center to the strongest wind belt in a typhoon wind field. :

[0158] (1)

[0159] In formula (1):

[0160] The pressure difference (hPa) between the outer periphery of a tropical cyclone and the center of a typhoon wind field is related to the time after the typhoon makes landfall. The outer periphery pressure of the cyclone is taken as 1010 hPa.

[0161] S102: Calculate the airflow velocity caused by the pressure gradient force, i.e., the maximum gradient wind speed. :

[0162] (2)

[0163] In formula (2):

[0164] This is an empirical coefficient;

[0165] The Coriolis force coefficient of Earth's rotation;

[0166] S103: Calculate the average maximum wind speed :

[0167] (3)

[0168] In formula (3):

[0169] This refers to the overall movement speed of the typhoon;

[0170] S104: Solve for the time-varying average wind speed at various locations in the typhoon field. :

[0171] (4)

[0172] In equation (4):

[0173] This refers to the distance between equipment in the power distribution network and the center of the typhoon's cyclone.

[0174] It is a constant, depending on the different typhoons. Interval changes;

[0175] S105: Perform wind speed correction:

[0176] (5)

[0177] In equation (5):

[0178] The wind speed (m / s) at the location of the equipment (conductors, towers, etc.);

[0179] The height of the equipment (m);

[0180] To account for the correction factor for terrain roughness, its value is as follows: Figure 2 As shown;

[0181] S106: Typhoon wind direction is determined by both the circulation wind direction and the typhoon's movement direction. In the Northern Hemisphere, typhoon wind direction is as follows: Figure 3 As shown. Let east be 0° and counter-clockwise be the positive direction. Calculate the typhoon wind direction:

[0182] (6)

[0183] In formula (6):

[0184] The direction of the circulating wind;

[0185] For the interior deflection angle to obey Within the range, the distance of the equipment from the center of the typhoon wind field cyclone varies. A piecewise function that changes;

[0186] S107: Determine the rainfall model:

[0187] This invention changes the traditional method of depicting rainstorm disasters by using 24-hour rainfall intensity. Instead, it uses the kinetic energy theorem to calculate the force generated by a single raindrop and the number of raindrops per unit volume to depict the intensity of the rainstorm.

[0188] In nature, rainfall models follow certain statistical laws, and their distribution conforms to a negative exponential distribution. The Marshall-Palmer distribution is currently the most widely used distribution in academia.

[0189] (7)

[0190] In equation (7):

[0191] The constant is taken as 8000, and the unit is units·m. -3 ·mm -1 ;

[0192] The diameter of the raindrop is in mm;

[0193] Λ is the slope factor of the raindrop spectrum distribution:

[0194] (8)

[0195] In equation (8):

[0196] I represents rainfall intensity, measured in mm / h;

[0197] S108: When raindrops leave the cloud layer and begin to fall, they accelerate due to gravity, and experience increasing air resistance during their descent. When these two forces are equal, the raindrop's falling speed remains constant; this speed is called the raindrop's vertical terminal velocity. The raindrop's horizontal terminal velocity is not the same as the downwind speed, but is proportional to its vertical terminal velocity. (Diameter is...) As raindrops are about to fall to the ground, calculate the horizontal terminal velocity of the raindrops when they collide with the equipment. and vertical final velocity :

[0198] (9)

[0199] (10)

[0200] In equation (9):

[0201] It is the ratio of the horizontal velocity of raindrops to the wind speed:

[0202] (11)

[0203] S109: Obtain the force generated by a single raindrop and the number of raindrops per unit volume :

[0204] (12)

[0205] Furthermore, step S2 includes the following steps:

[0206] S201: Analysis of the impact of typhoon-rainstorm combined disasters on power lines;

[0207] S202: Analysis of the impact of typhoon-rainstorm combined disasters on power poles.

[0208] Furthermore, step S201 includes the following steps:

[0209] S20101: Under combined typhoon and rainstorm disasters, in addition to bearing its own weight, the conductor also bears the loads of typhoons and rainstorms. Calculate the wind load on the line:

[0210] (13)

[0211] In equation (13):

[0212] The wind pressure non-uniformity coefficient is taken as 0.61;

[0213] The wind pressure height variation coefficient is set to 1;

[0214] This refers to the line shape coefficient;

[0215] The outer diameter of the conductor;

[0216] The horizontal span of the tower;

[0217] The angle between the wind direction and the direction of the conductor;

[0218] S20102: Calculate line rain load:

[0219] (14)

[0220] In equation (14):

[0221] The area exposed to rain by the conductor is measured in square meters (m²). 2 ;

[0222] S20103: Calculate the total load on the conductor:

[0223] (15)

[0224] Furthermore, step S202 includes the following steps:

[0225] S20201: Under the combined effects of typhoon and rainstorm disasters, in addition to bearing its own weight load, the tower mainly bears the wind and rain loads on the tower body and the line loads. Calculation of tower wind load:

[0226] (16)

[0227] In equation (16):

[0228] The wind vibration coefficient is taken as 1.5;

[0229] The tower shape coefficient is set to 1.

[0230] The projected area of ​​the windward side of the tower, in meters. 2 .

[0231] S20202: Calculate the rain load on the tower body:

[0232] (17)

[0233] In equation (17):

[0234] The projected area of ​​the rain-facing surface of the tower, in meters. 2 .

[0235] S20203: After comprehensively considering the three types of loads, calculate the bending moment at the centroid of any cross-section of the tower body. :

[0236] (18)

[0237] (19)

[0238] (20)

[0239] (twenty one)

[0240] Since pole collapse and breakage caused by disasters usually occur at the base of the tower, the values ​​of the variables in equations (18) to (21) are as follows: Figure 4 As shown;

[0241] In equations (18)-(21):

[0242] The distance from the base of the pole to the crossarm (m);

[0243] The tower height is in meters (m).

[0244] The diameter of the tower tip (m);

[0245] The diameter of the tower root section (m);

[0246] This is an additional bending moment coefficient.

[0247] Furthermore, step S3 includes the following steps:

[0248] S301: This invention uses a generalized stress-strength interference model to analyze the probability of power distribution network failure under typhoon-rainstorm combined disasters. For a specific mechanical system, whether its structure fails depends on the strength R and the stress S it experiences. The equipment state function Z is defined as follows:

[0249] (twenty two)

[0250] In equation (22):

[0251] R represents the strength of the equipment component;

[0252] S represents the stress experienced by the equipment component;

[0253] If Z > 0, it indicates that the equipment components are in a reliable state;

[0254] If Z < 0, it indicates that the equipment component is in a faulty state;

[0255] like Figure 5 As shown, and Let be the probability density functions of stress and strength, respectively. Their overlapping portion indicates that the structural strength is less than the stress. In this case, the structure cannot withstand the impact, and the equipment fails due to impact.

[0256] S302: Calculating the probability of tower failure under combined typhoon-rainstorm disasters :

[0257] (twenty three)

[0258] In equation (23):

[0259] P is the symbol for calculating probability;

[0260] Design strength for the tower;

[0261] The standard deviation of the bending strength of the tower;

[0262] This represents the average bending strength of the tower.

[0263] S303: Calculating the probability of power line failure under combined typhoon and rainstorm disasters. :

[0264] (twenty four)

[0265] In equation (24):

[0266] For line design strength;

[0267] The standard deviation of the conductor's bending strength;

[0268] This represents the average bending strength of the conductor.

[0269] Furthermore, step S4 includes the following steps:

[0270] S401: This invention discovers that the failure rate of equipment in disaster scenarios is related to both the inherent bending strength developed during the manufacturing process and the actual years of operation. For the same model of equipment, the relationship between the number of failures and the years of operation conforms to a bathtub curve. During the initial operation phase, the equipment is in a break-in period, at which time the initial failure frequency is relatively high, but it gradually decreases and approaches a constant value during the stable operation period. However, when the equipment enters the wear and tear period, due to the long years of operation, phenomena such as cracking of the protective layer and corrosion will occur, and correspondingly, its failure rate will continue to increase with the increase of the operation time.

[0271] Based on equipment failure data from power sector statistics at different service lifespans, the least squares method was used to fit the data. Figure 6 The equipment failure rate correction factor curve parameters are shown. The tower failure rate correction factor is represented using a Weibull distribution. :

[0272] (25)

[0273] In equation (25):

[0274] The service life of the pole / tower;

[0275] For the shape parameters of the equipment, ;

[0276] This indicates the fault distribution of the equipment during its initial operating period (bathtub curve). part);

[0277] The fault distribution of equipment during its wear-out period (bathtub curve) part);

[0278] During the stable operation period, Take the baseline value as 1;

[0279] These are coefficients related to shape and scale parameters;

[0280] S402: Correct the equipment failure rate to obtain the final failure rate that takes into account equipment aging correction.

[0281] (26)

[0282] In equation (26):

[0283] The failure rate does not take into account equipment aging corrections, i.e., the probability of tower failure under disaster conditions. or the probability of conductor failure under disaster .

[0284] Example 1:

[0285] This study focuses on the 10kV regional distribution network in Xiamen, Fujian Province. This area is frequently affected by combined typhoon and rainstorm disasters. The simplified model of the distribution network uses the IEEE 33-node network, and the geographical coordinates of the nodes are fitted using GIS. The poles are made of C30 grade concrete, with a pole spacing of 50 meters, and the conductors are of type JKLGYJ-185 / 10. The disaster scenario is Typhoon Meranti, which made landfall in Xiamen at 2:00 AM on September 15, 2016. At the time of landfall, the typhoon's central pressure was 940 hPa, the wind force reached level 15, the angle with the due east direction was about 135°, and it moved forward in a straight line at a speed of 20 m / s, with a local rainfall of 101 mm / h.

[0286] This invention analyzes the failure probability of each node as the typhoon approaches and moves away. The failure probability of each node exhibits significant dynamic changes, and these changes are highly consistent with the dynamic evolution of typhoon intensity, distance, and the impact of heavy rain. This fully demonstrates the accuracy and effectiveness of this invention in dynamically tracking failure probabilities during disasters.

[0287] During the approaching phase of a typhoon (6-2 hours before landfall), as the typhoon center gradually approaches, wind speeds at coastal nodes gradually increase from 10 m / s to 30 m / s, and rainfall increases from 10 mm / h to 50 mm / h. At this time, the probability of equipment failure varies significantly depending on its degree of aging within the same area. Under the same increased disaster stress, equipment with a higher degree of aging approaches its critical strength value earlier, resulting in a more pronounced increase in failure risk.

[0288] During the peak of a typhoon's impact (2 hours before landfall to 4 hours after), wind speeds along the coast surge to 40-50 m / s, localized rainfall reaches 101 mm / h, and water depth increases rapidly. The probability of equipment failure rises sharply, with the impact of aging equipment becoming more pronounced. During this stage, the disaster stress increases significantly, creating a cumulative effect with the reduced strength of aging equipment. This invention accurately captures this differentiation in failure probability caused by differences in equipment aging, highly consistent with the actual situation where older equipment is more prone to failure during this stage.

[0289] During the typhoon's move away phase (4-10 hours after landfall), wind speeds gradually decrease from 40 m / s to 15 m / s, rainfall decreases from 101 mm / h to 20 mm / h, and water depth slowly recedes. The probability of failure at each node gradually decreases accordingly, but the degree of equipment aging still affects the rate of decline. Severely aged equipment, due to its weaker strength recovery ability, experiences a relatively slower decrease in failure probability. This invention clearly reflects the impact of equipment aging on the trend of decreasing failure probability during the weakening of disaster impact. Highly aged equipment, due to its already compromised strength, still has a higher failure probability than less aged equipment, even with reduced stress.

[0290] Throughout the process, the failure probability curves of equipment with different aging levels at each node, obtained through the analysis of the invention, showed a high degree of agreement (over 88%) with the failure occurrence of equipment with different aging levels at each node of the distribution network under the actual impact of Typhoon Meranti. This fully demonstrates that the invention can not only accurately track the dynamic changes in the failure probability of each node as the typhoon approaches and moves away, but also fully consider the impact of equipment aging on the failure probability. It provides precise decision support for the emergency response of the distribution network to equipment with different aging levels at different disaster stages. For example, during the approaching stage of the typhoon, priority can be given to reinforcing coastal nodes with concentrated old equipment; during the strongest stage of the typhoon's impact, the operational status of highly aged equipment can be closely monitored; and during the moving away stage of the typhoon, priority can be given to inspecting and repairing old equipment. This greatly improves the distribution network's ability to cope with typhoon-rainstorm combined disasters.

[0291] This invention accurately characterizes the coupling effect of complex disasters:

[0292] Compared to traditional single-disaster analysis methods, this invention incorporates the dynamic coupling effect of typhoons and rainstorms into a unified framework. By quantifying the synergistic effect of "wind impact - rainstorm impact - load superposition," it solves the problem of insufficient characterization of complex fault chains in existing technologies. This enables root cause analysis of clustered faults in distribution networks, reducing the fault probability prediction error in complex disaster scenarios by more than 30%.

[0293] This invention integrates the uncertainties of equipment aging:

[0294] To address the shortcomings of existing research that neglects device performance degradation, this invention innovatively introduces an equipment aging factor and couples it with disaster stress parameters for calculation. Through the probability integral of the stress-intensity interference model, the failure threshold of equipment under the dual effects of "disaster load + aging loss" can be dynamically reflected, making the failure probability assessment results closer to the actual operating state. This represents a significant improvement in comprehensiveness compared to traditional models and is particularly suitable for the analysis of distribution network equipment with an operating life of more than 10 years.

[0295] This invention supports the accuracy and efficiency of disaster prevention decision-making:

[0296] Prediction accuracy: The accuracy rate of locating power distribution network fault points caused by typhoon-rainstorm combined disasters is over 85%, which is a significant improvement over numerical simulation methods based on single disasters.

[0297] Computational efficiency: The stress-intensity interference model is used for calculation, and the time taken for fault probability analysis of a single regional distribution network is controlled within 10 minutes, which meets the timeliness requirements of post-disaster emergency decision-making.

[0298] Scope of application: It can cover various types of equipment such as distribution transformers, overhead lines, and towers, and is compatible with analysis scenarios of distribution networks of different voltage levels (10kV-35kV), which solves the limitation of existing physical experimental methods that are only applicable to specific devices.

[0299] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0300] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for analyzing the failure rate of a multi-hazard power distribution network based on a stress intensity model, characterized in that: The method includes the following steps: S1: Construct a combined typhoon-rainstorm disaster scenario; S2: Analyze the impact of typhoon-rainstorm combined disasters on equipment; S3: Analysis of the probability of power distribution network failure under typhoon-rainstorm combined disaster based on stress intensity model; S4: Establish a distribution network fault probability model that takes into account equipment aging correction.

2. The method for analyzing the failure rate of a multi-hazard power distribution network based on a stress intensity model according to claim 1, characterized in that: S1 includes the following steps: S101: Calculate the distance from the cyclone center to the strongest wind belt in a typhoon wind field. : In formula (1): This represents the pressure difference between the outer periphery of a tropical cyclone and the center of a typhoon's wind field. S102: Calculate the airflow velocity caused by the pressure gradient force, i.e., the maximum gradient wind speed. : In formula (2): This is an empirical coefficient; The Coriolis force coefficient of Earth's rotation; S103: Calculate the average maximum wind speed : In formula (3): This refers to the overall movement speed of the typhoon; S104: Solve for the time-varying average wind speed at various locations in the typhoon field. : In equation (4): This refers to the distance between equipment in the power distribution network and the center of the typhoon's cyclone. It is a constant; S105: Perform wind speed correction: In equation (5): The wind speed at the location of the equipment; The height of the equipment; A correction factor is used to account for terrain roughness; S106: Assuming east is 0° and counterclockwise is the positive direction, calculate the typhoon wind direction: In equation (6): The direction of the circulating wind; For the interior deflection angle to obey Within the range, the distance of the equipment from the center of the typhoon wind field cyclone varies. A piecewise function that changes; S107: Determine the rainfall model: In equation (7): It is a constant; The diameter of the raindrop; Λ is the slope factor of the raindrop spectrum distribution: In equation (8): I represents rainfall intensity; S108: Calculate the horizontal terminal velocity of a raindrop when it collides with the device. and vertical final velocity : In equation (9): It is the ratio of the horizontal velocity of raindrops to the wind speed: S109: Obtain the force generated by a single raindrop and the number of raindrops per unit volume : 。 3. The method for analyzing the failure rate of a multi-hazard power distribution network based on a stress intensity model according to claim 2, characterized in that: S2 includes the following steps: S201: Analysis of the impact of typhoon-rainstorm combined disasters on power lines; S202: Analysis of the impact of typhoon-rainstorm combined disasters on power poles.

4. The method for analyzing the failure rate of a multi-hazard power distribution network based on a stress intensity model according to claim 3, characterized in that: S201 includes the following steps: S20101: Calculate line wind load: In equation (13): This is the wind pressure non-uniformity coefficient; This is the wind pressure height variation coefficient; This refers to the line shape coefficient; The outer diameter of the conductor; The horizontal span of the tower; The angle between the wind direction and the direction of the conductor; S20102: Calculate line rain load: In equation (14): The area of ​​the conductor facing the rain; S20103: Calculate the total load on the conductor: 。 5. The method for analyzing the failure rate of a multi-hazard power distribution network based on a stress intensity model according to claim 4, characterized in that: S202 includes the following steps: S20201: Calculate the wind load on the tower: In equation (16): The wind vibration coefficient; This refers to the tower shape coefficient; This is the projected area of ​​the windward side of the tower; S20202: Calculate the rain load on the tower body: In equation (17): The projected area of ​​the rain-facing surface of the tower; S20203: Calculate the bending moment at the centroid of any cross-section of the tower body. : In equations (18)-(21): The distance from the pole root to the crossarm; The tower height; The diameter of the tower; The diameter of the cross section at the base of the tower; This is an additional bending moment coefficient.

6. The method for analyzing the failure rate of a multi-hazard power distribution network based on a stress intensity model according to claim 5, characterized in that: S3 includes the following steps: S301: Define device status function Z: In equation (22): R represents the strength of the equipment component; S represents the stress experienced by the equipment component; If Z > 0, it indicates that the equipment components are in a reliable state; If Z < 0, it indicates that the equipment component is in a faulty state; S302: Calculating the probability of tower failure under combined typhoon-rainstorm disasters : In equation (23): P is the symbol for calculating probability; Design strength for the tower; The standard deviation of the bending strength of the tower; This represents the average bending strength of the tower. S303: Calculating the probability of power line failure under combined typhoon and rainstorm disasters. : In equation (24): For line design strength; The standard deviation of the conductor's bending strength; This represents the average bending strength of the conductor.

7. The method for analyzing the failure rate of a multi-hazard power distribution network based on a stress intensity model according to claim 6, characterized in that: S4 includes the following steps: S401: Using Weibull distribution to represent the tower failure rate correction factor : In equation (25): The service life of the pole / tower; For the shape parameters of the equipment, ; This indicates the fault distribution of the equipment during its initial operation period; This indicates the distribution of faults during the wear and tear period of the equipment. During the stable operation period, Take the baseline value as 1; These are coefficients related to shape and scale parameters; S402: Correct the equipment failure rate to obtain the final failure rate that takes into account equipment aging correction. In equation (26): This represents the failure rate without considering equipment aging corrections.

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