Power distribution network equipment failure probability calculation method based on extreme rainfall

Abstract

The invention discloses a power distribution network equipment failure probability calculation method based on extreme rainfall, and relates to the field of power grid planning and scene generation, and the method comprises the steps: carrying out the modeling of an abnormal rainfall process, obtaining a rainfall intensity sequence, calculating the time sequence change of the ponding depth in a unit area, and constructing a power distribution network equipment failure rate piecewise function model; based on the power distribution network equipment failure rate piecewise function model, evaluating an electrical fault risk caused by rainwater immersion, and obtaining the direct failure rate of the power distribution network equipment; constructing an indirect failure model by using a saturated water vapor pressure analysis and judgment technology, analyzing the insulation performance of the power distribution network equipment in the condensation environment, and determining the indirect failure rate of the power distribution network equipment; and constructing a comprehensive operation risk index based on the direct failure rate and the indirect failure rate of the power distribution network equipment, and judging the state risk level of the power distribution network equipment. According to the method, the comprehensive operation risk index is constructed by fusing the dual failure rates, and dynamic evaluation and graded response to the state of the power distribution equipment are realized.

Description

Technical Field

[0001] This invention relates to the field of power grid planning and scenario generation, and more specifically, to a method for calculating the failure probability of distribution network equipment under extreme rainfall. Background Technology

[0002] Currently, frequent extreme rainfall poses a serious threat to the safe operation of urban power distribution equipment, especially in low-lying areas or underground substations, where equipment is prone to short circuits and insulation breakdowns due to water accumulation or condensation. Existing methods mostly rely on static classification or empirical models, lacking the ability to dynamically model the evolution of water accumulation driven by rainfall and the impact of high humidity on equipment failure rates. This makes it difficult to simultaneously quantify both direct failures caused by flooding and indirect insulation degradation caused by humidity. Therefore, there is an urgent need to develop a failure probability calculation method that integrates rainfall hydrological characteristics and equipment environmental response mechanisms to achieve more accurate risk assessment and condition determination.

[0003] There are currently no effective solutions to the problems in the relevant technologies. Summary of the Invention

[0004] To address the problems in related technologies, this invention proposes a method for calculating the failure probability of power distribution network equipment under extreme rainfall, thereby overcoming the aforementioned technical problems in existing related technologies.

[0005] Therefore, the specific technical solution adopted by the present invention is as follows:

[0006] A method for calculating the failure probability of power distribution network equipment under extreme rainfall, the method comprising:

[0007] Using historical rainfall data and the Chicago model, we modeled the abnormal precipitation process, obtained the rainfall intensity sequence, and calculated the temporal variation of water depth per unit area based on the rainfall intensity sequence.

[0008] Based on the temporal variation of water depth per unit area, a piecewise function model of the failure rate of distribution network equipment is constructed. Based on the piecewise function model of the failure rate of distribution network equipment, the risk of electrical faults caused by rainwater soaking is assessed, and the direct failure rate of distribution network equipment is obtained.

[0009] Using saturated water vapor pressure analysis and dew point temperature determination techniques, an indirect failure model was constructed. Based on the indirect failure model, the insulation performance of power distribution network equipment under condensation conditions was analyzed, and the indirect failure rate of the power distribution network equipment was determined.

[0010] A comprehensive operational risk index is constructed based on the direct and indirect failure rates of distribution network equipment to determine the state risk level of distribution network equipment and match optimized treatment schemes for distribution network equipment.

[0011] Preferably, using historical rainfall data and the Chicago model, modeling abnormal precipitation processes, obtaining rainfall intensity sequences, and calculating the temporal variation of water depth per unit area based on the rainfall intensity sequences includes:

[0012] Historical rainfall data is obtained, and Chicago peak front and Chicago peak rear models are constructed by combining the rainfall peak coefficient to model abnormal precipitation processes and calculate the instantaneous rainfall intensity at any time point.

[0013] The water depth at any point in time is obtained based on the instantaneous rainfall intensity, and a water depth calculation function is generated by combining it with the drainage rate per unit area to obtain the temporal variation of water depth within a unit area.

[0014] Preferably, a piecewise function model of the failure rate of distribution network equipment is constructed based on the temporal variation of water depth per unit area; based on the piecewise function model of the failure rate of distribution network equipment, the risk of electrical faults caused by rainwater immersion is assessed, and the direct failure rate of distribution network equipment is obtained, including:

[0015] The water depth for the target time period is obtained by measuring the temporal variation of water depth per unit area, and the water depth is combined with the flood control height to construct a piecewise function model of the failure rate of the power distribution network equipment.

[0016] The target detection time period is discretized into several time intervals according to the requirements. Combined with the piecewise function model of water depth and failure rate per unit area, the direct failure probability of distribution network equipment within the time interval is calculated.

[0017] The total failure probability of the power distribution network equipment is obtained by multiplying the direct failure probabilities over several time intervals, and the electrical faults caused by rainwater immersion are assessed based on the total failure probability.

[0018] Preferably, the construction process of the piecewise function model of failure rate is based on the water depth per unit area, the flood control height, and the height of the cable joint relative to the ground.

[0019] Furthermore, the piecewise function model of the failure rate is divided into a stable failure rate stage, a linear growth failure rate stage, and an exponential growth failure rate stage. When the water level is lower than the flood control height, it is the stable failure rate stage. When the water level is higher than the flood control height but lower than the cable joint height relative to the ground, it is the linear growth failure rate stage. When the water level is higher than the cable joint height relative to the ground, it is the exponential growth failure rate stage.

[0020] The preferred expression for the piecewise function model of failure rate is:

[0021]

[0022] In the formula, λ w (t) represents the failure rate of distribution network equipment w at time t, dw (t) represents the water accumulation height of the grid where the distribution network equipment w is located at time t, D w D represents the flood protection height of the power distribution network equipment w. Bw λ represents the height of the cable joint of the power distribution equipment w in the station relative to the ground level, λ0 represents the foundation failure rate under normal weather or when the water depth is lower than the flood control height, f represents the rate of change of the control failure rate with the increase of water depth, and g, h, and i represent the parameters of the exponential stage.

[0023] Preferably, an indirect failure model is constructed using saturated water vapor pressure analysis and dew point temperature determination techniques. Based on this model, the insulation performance of distribution network equipment under condensation conditions is analyzed, and the indirect failure rate of the distribution network equipment is determined, including:

[0024] Based on the saturated water vapor pressure at zero temperature, calculate the saturated water vapor pressure of air on the water or ice surface, and combine the saturated water vapor pressure of air with relative humidity to calculate the saturated water vapor pressure of air.

[0025] The air condensation point temperature is calculated using the air saturated water vapor pressure and the saturated water vapor pressure at zero temperature, and then compared with the air condensation point temperature and the surface temperature of the power distribution network equipment.

[0026] Based on the comparison results, the condensation state of the distribution network equipment is obtained, an indirect failure rate model is constructed, and the insulation performance of the distribution network equipment under condensation state is evaluated to determine the indirect failure rate of the distribution network equipment.

[0027] Preferably, based on the comparison results, the condensation state of the distribution network equipment is obtained, an indirect failure rate model is constructed, and the insulation performance of the distribution network equipment under condensation state is evaluated. The indirect failure rate of the distribution network equipment is determined, including:

[0028] If the surface temperature of the power distribution equipment is less than or equal to the air dew point temperature, it indicates that the water vapor in the air has reached saturation and condensed on the surface of the power distribution equipment. It is determined that there is condensation on the surface of the power distribution equipment, and the insulation performance of the power distribution equipment is reduced.

[0029] Based on the thermal conductivity coefficient and condensation sensitivity parameters, an indirect failure rate model is constructed when condensation exists on the surface of distribution network equipment to determine the indirect failure rate of distribution network equipment under condensation conditions.

[0030] If the surface temperature of the power distribution equipment is greater than the air condensation point temperature, and the ratio between the air saturated water vapor pressure and the saturated water vapor pressure at zero temperature is greater than the threshold, it indicates that there is a risk of condensation on the surface of the power distribution equipment, and the insulation performance of the power distribution equipment is at risk of being reduced.

[0031] Based on the humidity critical threshold and humidity sensitivity parameters, an indirect failure rate model is constructed when there is a risk of condensation on the surface of distribution network equipment, and the indirect failure rate of distribution network equipment under the condensation risk state is determined.

[0032] Preferably, the expression for the indirect failure rate model when there is a risk of condensation on the surface of distribution network equipment is as follows:

[0033]

[0034] In the formula, λ i λ represents the indirect failure rate of distribution network equipment. ibase λ represents the baseline failure rate under non-condensation conditions. imax E represents the maximum failure rate under condensation conditions, and E represents the air saturated water vapor pressure. s θ represents the air saturated water vapor pressure, θ represents the humidity critical threshold, and p represents the humidity sensitivity parameter.

[0035] Preferably, a comprehensive operational risk index is constructed based on the direct and indirect failure rates of distribution network equipment to determine the state risk level of the distribution network equipment and match optimized treatment schemes for the distribution network equipment, including:

[0036] A comprehensive operational risk index is constructed based on the direct and indirect failure rates of distribution network equipment. Based on the comprehensive operational risk index and threshold ranges, the operational status risk levels of distribution network equipment are classified. The operational status risk levels include normal status, status of concern, expected status, and risk status.

[0037] Based on the risk level of the operating status, an adaptive matching optimization solution is selected, and the increment of the comprehensive operating risk index within a unit of time is determined to obtain the rate of change of the operating status and analyze the upward trend of the rate of change of the operating status.

[0038] The operating status of distribution network equipment in the future is predicted based on the upward trend, and the operation process of distribution network equipment is optimized by combining the state lag recovery mechanism.

[0039] Preferably, the expression for the rate of change of operating state is:

[0040] ΔΛ(t)=(w1·λw+w2·λi)-Λ(t-Δt);

[0041] In the formula, ΔΛ(t) represents the rate of change of operating state, w1 and w2 represent the weighting coefficients of the direct failure rate and indirect failure rate of distribution network equipment, respectively, and λ w λ represents the direct failure rate of distribution network equipment. i Δt represents the indirect failure rate of distribution network equipment, Δt represents the time step, and Λ(t) represents the comprehensive risk index.

[0042] The beneficial effects of this invention are as follows:

[0043] 1. This invention integrates the influence characteristics of water accumulation evolution process and environmental humidity changes on equipment failure, and constructs a dual modeling mechanism of direct failure rate and indirect failure rate. In order to characterize the dynamic characteristics of the rainfall process, the Chicago rainfall pattern modeling method is introduced to extract rainfall time sequence and water accumulation evolution data. In view of the nonlinear law of the relationship between water accumulation height and equipment failure, a direct failure rate modeling method based on piecewise functions is proposed. At the same time, in order to characterize the insulation degradation risk caused by condensation, an indirect failure rate model based on the joint judgment of dew point temperature and saturated vapor pressure is constructed. Finally, the dual failure rate is integrated to construct a comprehensive operational risk index, so as to realize dynamic assessment and graded response of the status of power distribution equipment.

[0044] 2. This invention can accurately reflect the comprehensive failure risk of power distribution equipment under the influence of external flooding and internal humidity during rainstorms. Through risk index time series analysis and trend judgment, it can realize continuous monitoring and classification of equipment operating status, assisting in intelligent early warning and control response. It is applicable to typical power distribution facilities such as high-voltage switchgear, ring main units, and substations, and is especially suitable for deployment in urban low-lying areas, underground substations and other scenarios that are susceptible to water environment. It can significantly improve the resilience of urban power grids in the face of extreme weather disasters, reduce failure rate and ensure the safe and stable operation of the power grid.

[0045] 3. Compared with existing equipment risk assessment methods that mainly rely on static indicators or empirical rules, this invention introduces a piecewise function mechanism in direct failure modeling to more accurately characterize the nonlinear response features of water accumulation height and equipment failure rate; in indirect failure modeling, it integrates dew point temperature and saturated water vapor pressure judgment logic to improve the dynamic perception capability of insulation degradation under humid conditions. At the same time, by constructing a unified risk assessment indicator system and state level classification mechanism, it realizes closed-loop management from risk identification to early warning response. It not only has strong engineering applicability, but can also be extended to various power distribution scenarios and various equipment types, providing key support for power grid operation and control, disaster prevention and control, and emergency operation and maintenance. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart of a method for calculating the failure probability of power distribution network equipment under extreme rainfall according to an embodiment of the present invention;

[0048] Figure 2This is an extreme rainfall time series diagram in a method for calculating the failure probability of distribution network equipment under extreme rainfall according to an embodiment of the present invention;

[0049] Figure 3 This is a diagram showing the water accumulation height of a high-voltage switchgear in a method for calculating the failure probability of power distribution equipment under extreme rainfall, according to an embodiment of the present invention.

[0050] Figure 4 This is a diagram showing the direct failure rate and failure probability of high-voltage switchgear in a method for calculating the failure probability of power distribution network equipment under extreme rainfall, according to an embodiment of the present invention.

[0051] Figure 5 This is a schematic diagram of the internal structure of a high-voltage switchgear and its flooding failure in a method for calculating the failure probability of power distribution equipment under extreme rainfall according to an embodiment of the present invention.

[0052] Figure 6 This is a graph showing the relationship between the failure rate of high-voltage switchgear and saturated vapor pressure (including condensation state) in a method for calculating the failure probability of power distribution network equipment under extreme rainfall, according to an embodiment of the present invention. Detailed Implementation

[0053] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention.

[0054] According to an embodiment of the present invention, a method for calculating the failure probability of power distribution network equipment under extreme rainfall is provided.

[0055] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 1 As shown, according to an embodiment of the present invention, a method for calculating the failure probability of distribution network equipment under extreme rainfall includes:

[0056] Step S1: Using historical rainfall data and the Chicago model, model the abnormal precipitation process, obtain the rainfall intensity sequence, and calculate the temporal change of water depth per unit area based on the rainfall intensity sequence;

[0057] Step S2: Based on the temporal variation of water depth per unit area, construct a piecewise function model of the failure rate of distribution network equipment; based on the piecewise function model of the failure rate of distribution network equipment, assess the risk of electrical faults caused by rainwater soaking and obtain the direct failure rate of distribution network equipment;

[0058] Step S3: Using saturated water vapor pressure analysis and dew point temperature judgment technology, construct an indirect failure model, and analyze the insulation performance of distribution network equipment under condensation environment based on the indirect failure model to determine the indirect failure rate of distribution network equipment.

[0059] Step S4: Construct a comprehensive operational risk index based on the direct and indirect failure rates of distribution network equipment, determine the state risk level of distribution network equipment, and match the optimized processing scheme for distribution network equipment.

[0060] In one embodiment, modeling an abnormal precipitation process using historical rainfall data and the Chicago model, obtaining a rainfall intensity sequence, and calculating the temporal variation of water depth per unit area based on the rainfall intensity sequence includes: obtaining historical rainfall data, constructing a Chicago peak front model and a Chicago peak rear model in combination with the rainfall peak coefficient to model the abnormal precipitation process, calculating the instantaneous rainfall intensity at any time point; obtaining the water depth at any time point based on the instantaneous rainfall intensity, and generating a water depth calculation function in combination with the drainage rate per unit area to obtain the temporal variation of water depth per unit area.

[0061] In one embodiment, a piecewise function model of the failure rate of distribution network equipment is constructed based on the temporal variation of water depth per unit area. Based on this model, the risk of electrical faults caused by rainwater inundation is assessed, and the direct failure rate of the distribution network equipment is obtained. This includes: obtaining the water depth for a target time period based on the temporal variation of water depth per unit area, and combining the water depth with flood control height to construct a piecewise function model of the failure rate of the distribution network equipment; discretizing the target detection time period into several time intervals as required, and calculating the direct failure probability of the distribution network equipment within each time interval by combining the water depth per unit area with the piecewise function model of the failure rate; obtaining the product of the direct failure probabilities within several time intervals to obtain the total failure probability of the distribution network equipment, and assessing electrical faults caused by rainwater inundation based on the total failure probability.

[0062] In one embodiment, an indirect failure model is constructed using saturated water vapor pressure analysis and dew point temperature determination techniques. Based on this model, the insulation performance of distribution network equipment under condensation conditions is analyzed to determine the indirect failure rate of the equipment. This includes: calculating the saturated water vapor pressure on a water or ice surface based on the saturated water vapor pressure at zero temperature; combining the saturated water vapor pressure with relative humidity to calculate the air saturated water vapor pressure; calculating the air dew point temperature using the air saturated water vapor pressure and the saturated water vapor pressure at zero temperature; comparing the air dew point temperature with the surface temperature of the distribution network equipment; obtaining the condensation state of the equipment based on the comparison results; constructing an indirect failure rate model; evaluating the insulation performance of the equipment under condensation conditions; and determining the indirect failure rate of the equipment.

[0063] In one embodiment, the condensation state of the distribution network equipment is obtained based on the comparison results, an indirect failure rate model is constructed, and the insulation performance of the distribution network equipment under condensation state is evaluated. Determining the indirect failure rate of the distribution network equipment includes: if the surface temperature of the distribution network equipment is less than or equal to the air dew point temperature, it indicates that water vapor in the air has reached saturation and condensed on the surface of the distribution network equipment, indicating that condensation exists on the surface of the distribution network equipment, and the insulation performance of the distribution network equipment is reduced; based on the thermal conductivity coefficient and condensation sensitivity parameters, an indirect failure rate model is constructed when condensation exists on the surface of the distribution network equipment to determine the indirect failure rate of the distribution network equipment under condensation state; if the surface temperature of the distribution network equipment is greater than the air dew point temperature, and the ratio between the saturated water vapor pressure of the air and the saturated water vapor pressure at zero temperature is greater than a threshold, it indicates that there is a risk of condensation on the surface of the distribution network equipment, and the insulation performance of the distribution network equipment is at risk of reduction; based on the humidity critical threshold and humidity sensitivity parameters, an indirect failure rate model is constructed when there is a risk of condensation on the surface of the distribution network equipment to determine the indirect failure rate under the condensation risk state.

[0064] In one embodiment, constructing a comprehensive operational risk index based on the direct and indirect failure rates of distribution network equipment, determining the state risk level of distribution network equipment, and matching optimization solutions for distribution network equipment includes: constructing a comprehensive operational risk index based on the direct and indirect failure rates of distribution network equipment, and classifying the operational state risk level of distribution network equipment based on the comprehensive operational risk index and threshold ranges; adaptively matching optimization solutions according to the operational state risk level, determining the increment of the comprehensive operational risk index per unit time, obtaining the rate of change of operational state, and analyzing the upward trend of the rate of change of operational state; predicting the operational state of distribution network equipment in future periods based on the upward trend, and optimizing the operation process of distribution network equipment in conjunction with a state lag recovery mechanism.

[0065] To facilitate understanding of the above technical solutions of the present invention, the following provides a detailed description of the operation method for calculating the failure probability of power distribution network equipment in actual practice.

[0066] Step 1: Simulate extreme rainfall events using the Chicago rainfall pattern modeling method to extract hydrological time-series characteristics affecting equipment operation, as detailed below:

[0067] (1) Extreme rainfall events are modeled based on the Chicago rainfall pattern model. The Chicago rainfall pattern time series diagram is shown below. Figure 2 As shown, its instantaneous rainfall intensity function includes two stages: before and after the peak. The calculation formula for the stage before the peak is as follows:

[0068]

[0069] The formula for calculating the latter part of the peak is:

[0070]

[0071] In the formula, t represents the duration of rainfall, i(t) represents the instantaneous rainfall intensity at time t, r represents the peak rainfall coefficient, and a, b, and c all represent local long-term historical rainfall data. These are empirical parameters obtained through statistical analysis, and they are usually related to the return period of rainfall.

[0072] (2) Calculate the time series of water depth per unit area to obtain dynamic water input. This mainly involves combining the rainfall intensity series output by the Chicago model with the drainage system efficiency to calculate the time series change of water depth per unit area. The water height diagram of the high-voltage switchgear is shown below. Figure 3 As shown, the specific expression is:

[0073] h(t+Δt)=h(t)+[i(t)-q(t)]·φΔt;

[0074] In the formula, h(t) represents the water depth at time t (in mm); q(t) represents the drainage rate per unit area (in mm / min), which can be approximated by an exponential decay form to describe the decreasing trend of drainage efficiency; Δt is the time step; φ represents the corrected area factor, which takes into account the drainage area and the effect of water accumulation; and i(t) represents the instantaneous rainfall intensity at time t.

[0075] Step 2: Quantify the impact of water accumulation height on direct equipment failure using a piecewise failure rate function construction method, and assess the risk of electrical faults caused by rainwater immersion, as detailed below:

[0076] (1) The direct failure rate model of the equipment takes the high-voltage switchgear as the research object. The internal structure of the high-voltage switchgear and its flood failure diagram are shown in the figure below. Figure 5 As shown, a piecewise function is used to construct the function based on different water depths. The specific steps are as follows:

[0077] Phase 1: When the water level is lower than the equipment’s flood protection design height, the failure rate is low and relatively stable, and is dominated only by equipment aging or non-flooding factors. The failure rate is set to a constant.

[0078] Second stage: When the water level exceeds but does not reach the cable joint installation height, the equipment begins to be affected by water, and the failure rate increases linearly with the water level.

[0079] The third stage: When the water level reaches or exceeds the height of the cable joint, critical components are submerged, and the failure rate increases exponentially. These three stages are characterized using constant, linear, and exponential functions respectively, accurately capturing the physical characteristics of each stage and flexibly reflecting the dynamic changes in the failure rate. The piecewise function model of the high-voltage switchgear failure rate is as follows:

[0080]

[0081] In the formula, λ w (t) represents the failure rate of power distribution equipment w at time t, usually representing the probability or rate of equipment failure per unit time, d w (t) represents time t, and D represents the water level in the grid where the power distribution equipment w is located. w D represents the design flood protection height of a substation (room) or prefabricated substation, i.e., the threshold at which the equipment can withstand the maximum water accumulation height without significantly affecting its performance. Bw λ0 represents the base failure rate (constant) under normal weather conditions or when the water level is below the flood control height. f represents the slope parameter of the linear phase, which controls the rate of change of the failure rate with the increase of the water level. g, h, and i represent the parameters of the exponential phase, which control the magnitude, rate of increase, and offset of the exponential growth, respectively.

[0082] (2) The probability of direct failure is determined by the failure rate λ. w (t) The failure probability within [t, t+Δt] is calculated as follows:

[0083]

[0084] In the formula, λ represents the probability of equipment failure within a time period Δt, τ represents the water accumulation height, and λ represents the water level. w (τ) represents the failure rate when the water accumulation height is τ. When Δt is sufficiently small, the piecewise function model of the failure rate of the high-voltage switchgear can be approximated as:

[0085]

[0086] For a long time interval [0, T], time can be discretized into multiple short time intervals Δt. The overall failure probability is the product of the failure probabilities of each time interval. From this, the total failure probability of the high-voltage switchgear can be obtained. Finally, the direct failure rate and failure probability of the high-voltage switchgear are shown in the figure below. Figure 4 As shown:

[0087]

[0088] Step 3: Establish an indirect failure model based on dew point determination and saturated vapor pressure analysis to characterize the degradation mechanism of equipment insulation performance under condensation conditions; specifically:

[0089] (1) The dew point temperature judgment method based on the improved Manus formula is used to determine condensation. Specifically, the saturated water vapor pressure E (unit hPa) of air at α℃ on the water surface (α>0℃) or ice surface (α≤0℃) is calculated as follows:

[0090]

[0091] In the formula, E s E0 represents the saturated water vapor pressure (in hPa), and E0 is the saturated water vapor pressure (in hPa) at 0℃. j and k represent coefficients. When α > 0℃, the water surface values ​​are a = 7.5 and b = 237.3; when α ≤ 0℃, the ice surface values ​​are a = 9.5 and b = 265.5.

[0092] (2) Calculate the water vapor pressure (in %RH) of air at α℃ with a known relative humidity of F, i.e., the saturated water vapor pressure E (in hPa):

[0093]

[0094] (3) According to the thermal engineering and calculation code for building doors, windows and glass curtain walls, the air condensation point temperature T d This can ultimately be expressed as the Manus correction formula, as follows:

[0095]

[0096] In the formula, T d The dew point temperature is represented by , l and m represent correction parameters, and E0 is the saturated water vapor pressure at 0°C, with a value of 6.11 hPa; E s Let l represent the saturated water vapor pressure (in hPa), and E represent the saturated water vapor pressure of air. Therefore, according to the Manus modified calculation formula, there are three main factors affecting condensation: temperature α (which affects the values ​​of l and m), relative humidity F, and saturated water vapor pressure E. s .

[0097] The indirect failure rate model comprehensively considers whether condensation occurs inside the high-voltage switchgear and the risk of moisture infiltration under near-saturated air humidity conditions. The following expression for the indirect failure rate under a dual mechanism is constructed, and the relationship between the high-voltage switchgear failure rate and saturated vapor pressure (including condensation state) is finally obtained as shown in the figure below. Figure 6 (The vertical axis represents the failure rate, the horizontal axis represents the saturated vapor pressure, blue dots represent the non-condensing state, orange dots represent the condensing state, and the black error bars represent the mean of each group.)

[0098]

[0099] In the formula, λ represents the indirect failure rate, λ base λ represents the baseline failure rate under non-condensation conditions. max f(E) represents the maximum failure rate under condensation conditions. s ) represents a correction function related to the saturated vapor pressure level, used to characterize the further moderating effect of humidity conditions on the failure rate, where:

[0100]

[0101] E s >E threshold Otherwise, it is 0, E max E represents the maximum saturated vapor pressure. threshold This represents the key saturated vapor pressure level, where E s Indicates saturated water vapor pressure;

[0102]

[0103] In the formula, E represents the actual water vapor pressure (unit: hPa), RH represents the relative humidity (%RH), and T represents the temperature inside the cabinet (°C);

[0104]

[0105] In the formula, E s (T) represents the saturated water vapor pressure at temperature T (unit: hPa). surface =T*(1-γ) represents the surface temperature of the component (unit: °C), γ represents the thermal conductivity coefficient, and k represents the condensation sensitivity parameter.

[0106] Specifically, the condensation state determination mechanism is based on a real-time comparison of the surface temperature of the equipment's electrical components with the air dew point temperature, and uses this to determine whether the equipment has entered a high humidity-induced fault state. This includes:

[0107] When the surface temperature of the electrical components of the equipment is less than or equal to the air dew point temperature (T), the following conditions are met: s ≤T d When the air is saturated with water vapor and condenses on the equipment surface, it is considered to be in a "condensation state". The indirect failure rate of the equipment is the failure rate under the condensation state.

[0108] When T s >T d At this time, the air is unsaturated and condensation will not form, so the equipment failure rate remains at the baseline level.

[0109] Even if the condensation conditions are not met, but the ratio of the actual vapor pressure to the saturated vapor pressure is greater than the threshold, the equipment may still experience a decrease in insulation performance due to water vapor penetration. The failure rate is adjusted according to the following formula:

[0110]

[0111] In the formula, λ i Indicates the indirect failure rate of the current device; λ ibase λ represents the baseline failure rate under the current non-condensation state. imax E represents the maximum failure rate under the current condensation state. Sθ represents the saturated water vapor pressure, E represents the actual saturated water vapor pressure, θ represents the humidity critical threshold, and p represents the humidity sensitivity parameter.

[0112] Step 4: Construct a power distribution equipment operation status assessment system by integrating multiple failure risk factors to assist in the formulation of risk prevention and control strategies under extreme rainfall. The power distribution equipment operation status assessment system includes:

[0113] Fusion direct failure rate λ w And indirect failure rate λ i The comprehensive operational risk indicator Λ(t) is defined as follows:

[0114] Λ(t)=w1·λ w +w2·λ i ;

[0115] Where w1 and w2 are the weighting coefficients for direct and indirect failure risks, respectively, satisfying w1+w2=1.

[0116] Based on the comprehensive risk index Λ(t) and the set threshold range, the equipment operating status is divided into four risk levels:

[0117] Normal state: Λ(t) < δ1, equipment operates reliably;

[0118] Attention status: δ1≤Λ(t)<δ2, indicating the need to pay attention to environmental changes;

[0119] Warning status: δ2≤Λ(t)<δ3, maintenance intervention is recommended;

[0120] High-risk status: Λ(t)≥δ3, it is recommended to suspend operations or take immediate action.

[0121] δ1, δ2, and δ3 are the state classification thresholds.

[0122] In practical applications, the equipment operating status level is dynamically adjusted based on the status evolution trend to improve the timeliness and robustness of risk identification. Specifically, this includes:

[0123] Define the rate of change of operating status Λ(t) as the increment of the comprehensive risk index per unit time:

[0124] ΔΛ(t)=Λ(t)-Λ(t-Δt);

[0125] In the formula, Δt is the time step;

[0126] When Λ(t) shows an upward trend over multiple consecutive time periods, and it is predicted that Λ(t+n) will exceed a certain warning threshold δ within the next n time periods. k In such cases, the equipment status level is adjusted to "early warning" or "high risk" in advance to trigger the prediction mechanism;

[0127] To avoid misjudgments caused by short-term fluctuations, a state lag recovery mechanism is introduced, which only applies when the comprehensive risk index Λ(t) is continuously below the next level state threshold δ. k-1 And the duration exceeds T delay Only when necessary should the equipment status level be downgraded to ensure the stability of the judgment.

[0128] In summary, by utilizing the above-mentioned technical solutions of this invention, this invention can simultaneously quantify the direct failure risk caused by water accumulation and the indirect failure risk caused by condensation environment, solving the problem of insufficient characterization of the impact of water flooding and humidity in existing methods. By constructing a segmented failure rate model and a humidity-driven insulation degradation model, dynamic assessment and risk classification of equipment operating status can be achieved, improving the predictive ability and operation and maintenance response efficiency of power distribution systems under extreme weather conditions.

[0129] 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, improvements, etc., 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 calculating the failure probability of power distribution network equipment under extreme rainfall, characterized in that, The method includes: Using historical rainfall data and the Chicago model, we modeled the abnormal precipitation process, obtained the rainfall intensity sequence, and calculated the temporal variation of water depth per unit area based on the rainfall intensity sequence. Based on the temporal variation of water depth per unit area, a piecewise function model of the failure rate of distribution network equipment is constructed. Based on the piecewise function model of the failure rate of distribution network equipment, the risk of electrical faults caused by rainwater soaking is assessed, and the direct failure rate of distribution network equipment is obtained. Using saturated water vapor pressure analysis and dew point temperature determination techniques, an indirect failure model was constructed. Based on the indirect failure model, the insulation performance of power distribution network equipment under condensation conditions was analyzed, and the indirect failure rate of the power distribution network equipment was determined. A comprehensive operational risk index is constructed based on the direct and indirect failure rates of distribution network equipment to determine the state risk level of distribution network equipment and match optimized treatment schemes for distribution network equipment.

2. The method for calculating the failure probability of power distribution network equipment under extreme rainfall as described in claim 1, characterized in that, The process of modeling abnormal precipitation events using historical rainfall data and the Chicago model, obtaining rainfall intensity sequences, and calculating the temporal variation of water depth per unit area based on these sequences includes: Historical rainfall data is obtained, and Chicago peak front and Chicago peak rear models are constructed by combining the rainfall peak coefficient to model abnormal precipitation processes and calculate the instantaneous rainfall intensity at any time point. The water depth at any point in time is obtained based on the instantaneous rainfall intensity, and a water depth calculation function is generated by combining it with the drainage rate per unit area to obtain the temporal variation of water depth within a unit area.

3. The method for calculating the failure probability of power distribution network equipment under extreme rainfall as described in claim 1, characterized in that, The failure rate piecewise function model of power distribution network equipment is constructed based on the time-series variation of water depth per unit area. Based on a piecewise function model of distribution network equipment failure rate, the risk of electrical faults caused by rainwater immersion is assessed, and the direct failure rate of distribution network equipment is obtained, including: The water depth for the target time period is obtained by measuring the temporal variation of water depth per unit area, and the water depth is combined with the flood control height to construct a piecewise function model of the failure rate of the power distribution network equipment. The target detection time period is discretized into several time intervals according to the requirements. Combined with the piecewise function model of water depth and failure rate per unit area, the direct failure probability of distribution network equipment within the time interval is calculated. The total failure probability of the power distribution network equipment is obtained by multiplying the direct failure probabilities over several time intervals, and the electrical faults caused by rainwater immersion are assessed based on the total failure probability.

4. The method for calculating the failure probability of power distribution network equipment under extreme rainfall as described in claim 3, characterized in that, The construction process of the failure rate piecewise function model is based on the water depth per unit area, flood control height, and cable joint height relative to the ground. Furthermore, the piecewise function model of the failure rate is divided into a stable failure rate stage, a linear growth failure rate stage, and an exponential growth failure rate stage. When the water level is lower than the flood control height, it is the stable failure rate stage. When the water level is higher than the flood control height but lower than the cable joint height relative to the ground, it is the linear growth failure rate stage. When the water level is higher than the cable joint height relative to the ground, it is the exponential growth failure rate stage.

5. The method for calculating the failure probability of power distribution network equipment under extreme rainfall as described in claim 4, characterized in that, The expression for the failure rate piecewise function model is: In the formula, λ w (t) represents the failure rate of distribution network equipment w at time t, d w (t) represents the water accumulation height of the grid where the distribution network equipment w is located at time t, D w D represents the flood protection height of the power distribution network equipment w. Bw λ represents the height of the cable joint of the power distribution equipment w in the station relative to the ground level, λ0 represents the foundation failure rate under normal weather or when the water depth is lower than the flood control height, f represents the rate of change of the control failure rate with the increase of water depth, and g, h, and i represent the parameters of the exponential stage.

6. The method for calculating the failure probability of power distribution network equipment under extreme rainfall as described in claim 1, characterized in that, The method utilizes saturated water vapor pressure analysis and dew point temperature determination techniques to construct an indirect failure model. Based on this model, the insulation performance of distribution network equipment under condensation conditions is analyzed to determine the indirect failure rate of the distribution network equipment, including: Based on the saturated water vapor pressure at zero temperature, calculate the saturated water vapor pressure of air on the water or ice surface, and combine the saturated water vapor pressure of air with relative humidity to calculate the saturated water vapor pressure of air. The air condensation point temperature is calculated using the air saturated water vapor pressure and the saturated water vapor pressure at zero temperature, and then compared with the air condensation point temperature and the surface temperature of the power distribution network equipment. Based on the comparison results, the condensation state of the distribution network equipment is obtained, an indirect failure rate model is constructed, and the insulation performance of the distribution network equipment under condensation state is evaluated to determine the indirect failure rate of the distribution network equipment.

7. The method for calculating the failure probability of power distribution network equipment under extreme rainfall as described in claim 6, characterized in that, The process of obtaining the condensation state of distribution network equipment based on comparison results, constructing an indirect failure rate model, and evaluating the insulation performance of distribution network equipment under condensation conditions to determine the indirect failure rate of distribution network equipment includes: If the surface temperature of the power distribution equipment is less than or equal to the air dew point temperature, it indicates that the water vapor in the air has reached saturation and condensed on the surface of the power distribution equipment. It is determined that there is condensation on the surface of the power distribution equipment, and the insulation performance of the power distribution equipment is reduced. Based on the thermal conductivity coefficient and condensation sensitivity parameters, an indirect failure rate model is constructed when condensation exists on the surface of distribution network equipment to determine the indirect failure rate of distribution network equipment under condensation conditions. If the surface temperature of the power distribution equipment is greater than the air condensation point temperature, and the ratio between the air saturated water vapor pressure and the saturated water vapor pressure at zero temperature is greater than the threshold, it indicates that there is a risk of condensation on the surface of the power distribution equipment, and the insulation performance of the power distribution equipment is at risk of being reduced. Based on the humidity critical threshold and humidity sensitivity parameters, an indirect failure rate model is constructed when there is a risk of condensation on the surface of distribution network equipment, and the indirect failure rate of distribution network equipment under the condensation risk state is determined.

8. The method for calculating the failure probability of power distribution network equipment under extreme rainfall as described in claim 7, characterized in that, The expression for the indirect failure rate model when there is a risk of condensation on the surface of the power distribution network equipment is as follows: In the formula, λ i λ represents the indirect failure rate of distribution network equipment. ibase λ represents the baseline failure rate under non-condensation conditions. imax E represents the maximum failure rate under condensation conditions, and E represents the air saturated water vapor pressure. s θ represents the air saturated water vapor pressure, θ represents the humidity critical threshold, and p represents the humidity sensitivity parameter.

9. The method for calculating the failure probability of power distribution network equipment under extreme rainfall as described in claim 1, characterized in that, The method of constructing a comprehensive operational risk index based on the direct and indirect failure rates of distribution network equipment, determining the state risk level of distribution network equipment, and matching optimized processing schemes for distribution network equipment includes: A comprehensive operational risk index is constructed based on the direct and indirect failure rates of distribution network equipment. Based on the comprehensive operational risk index and threshold ranges, the operational status risk levels of distribution network equipment are classified. The operational status risk levels include normal status, status of concern, expected status, and risk status. Based on the risk level of the operating status, an adaptive matching optimization solution is selected, and the increment of the comprehensive operating risk index within a unit of time is determined to obtain the rate of change of the operating status and analyze the upward trend of the rate of change of the operating status. The operating status of distribution network equipment in the future is predicted based on the upward trend, and the operation process of distribution network equipment is optimized by combining the state lag recovery mechanism.

10. The method for calculating the failure probability of power distribution network equipment under extreme rainfall as described in claim 9, characterized in that, The expression for the rate of change of the operating state is: ΔΛ(t)=(w1·λw+w2·λi)-Λ(t-Δt); In the formula, ΔΛ(t) represents the rate of change of operating state, w1 and w2 represent the weighting coefficients of the direct failure rate and indirect failure rate of distribution network equipment, respectively, and λ w λ represents the direct failure rate of distribution network equipment. i Δt represents the indirect failure rate of distribution network equipment, Δt represents the time step, and Λ(t) represents the comprehensive risk index.

Images