A power distribution network cascading failure risk assessment method considering the hydraulic load effect of power distribution cabinets

Abstract

The application discloses a power distribution network cascading failure risk assessment method considering the hydraulic load effect of power distribution cabinets, relates to the technical field of power distribution network flood disaster risk assessment, and comprises the following steps: obtaining power distribution network basic data parameters and flood inflow data, obtaining the point water depth and point flow velocity of the power distribution cabinet based on the water conservation principle and the outflow boundary condition; further calculating the hydraulic load effect of the power distribution cabinet, establishing a comprehensive strength model, and calculating the time-varying failure probability of the power distribution cabinet by using the load-strength interference relationship; generating the power distribution network node failure state according to the failure probability of the power distribution cabinet, combining the power flow calculation and branch over-limit tripping mechanism to simulate the cascading failure evolution process caused by the flood disaster; and finally outputting a series of risk indexes. The application solves the problems that the power distribution cabinet equipment failure mechanism in the prior art under the flood scene only depends on a single failure rate function and the power distribution network topology fault and power flow interlocking influence are difficult to be uniformly evaluated.

Description

Technical Field

[0001] This invention relates to the field of risk assessment technology for flood disasters in power distribution networks, and more specifically, to a method for risk assessment of cascading failures in power distribution networks that takes into account the hydraulic load effect of distribution cabinets. Background Technology

[0002] In recent years, extreme rainstorms and urban flooding have occurred frequently. Many switchgear and other equipment in power distribution networks are located in areas prone to water accumulation. The depth, flow velocity, and duration of flooding directly affect their sealing performance, structural stability, and power supply reliability. After a local switchgear failure, the impact will propagate throughout the power distribution network through processes such as node failure, branch line outages, load shifting, and power flow redistribution, leading to a cascading failure. Therefore, accurately characterizing the impact of flood evolution on the status of switchgear equipment and assessing the propagation process of equipment failure to power distribution network operational risks has become an urgent technical problem to be solved.

[0003] To address the aforementioned issues, existing risk assessment methods for cascading failures in distribution networks under flood disasters are developed in three stages: at the level of distribution cabinet failure criteria, rainfall intensity, water depth thresholds, or empirical failure rate functions are typically used as the basis for judgment; at the level of failure mechanism description, distribution cabinet failure is simplified into a deterministic threshold event; and at the level of power grid risk assessment, the probability of distribution cabinet failure is calculated independently.

[0004] However, a deeper analysis of the existing technological system reveals certain shortcomings: Existing methods simplify distribution cabinet failures into isolated topology outage events, which makes it difficult to reflect the differences in the damage to distribution cabinets under different equipment parameter conditions; failure criteria mostly use fixed thresholds or empirical failure rates, and the failure probability lacks physical interpretation; and there is insufficient risk assessment of distribution network cascading failures caused by distribution cabinet failures. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a risk assessment method for cascading failures in power distribution networks that considers the hydraulic load effect of distribution cabinets. By calculating flood water level and flow velocity through the conservation of water volume in flood storage areas, the water depth and flow velocity at the distribution cabinet location are further obtained. The structural force load of the distribution cabinet is calculated, and a load-strength interference failure probability model is established in conjunction with the comprehensive strength of the distribution cabinet. Finally, power flow simulation is performed to obtain the risk results, thereby solving the problems in the prior art where the failure mechanism of distribution cabinet equipment in flood scenarios relies only on a single failure rate function and the cascading effects of power distribution network topology faults and power flow are difficult to assess in a unified manner.

[0006] To achieve the above objectives, the present invention provides the following technical solution, comprising the following steps: A method for assessing the risk of cascading failures in a power distribution network considering the hydraulic load effect of distribution cabinets includes the following steps: Based on the water exchange process and the principle of water conservation, combined with the installation elevation of the distribution cabinet and the Manning roughness coefficient, the water depth and flow velocity at the distribution cabinet location are obtained. The hydraulic load acting on the distribution cabinet is calculated based on the water depth and flow velocity at the specified points, thus forming a hydraulic load effect. A comprehensive strength model for the distribution cabinet is established, and the failure probability of the distribution cabinet is calculated based on the load-strength interference relationship between the hydraulic load effect and the comprehensive strength model. Based on the failure probability, the fault status of the distribution network node is generated, and the simulation results are obtained by combining power flow calculation and branch over-limit tripping mechanism to simulate cascading failures. The risk indicators of the distribution network are obtained from the simulation results.

[0007] In a preferred embodiment, the step of obtaining the point water depth and point flow velocity of the distribution cabinet based on the water exchange process and the principle of water conservation, combined with the installation elevation of the distribution cabinet and the Manning roughness coefficient, includes: establishing a water level-water surface area relationship based on the topographic features of the flood storage area, and constructing a water storage-water level function; calculating the total discharge flow rate by combining the flood inflow, the orifice discharge process, and the weir flow process; updating the water storage rate based on the difference between the inflow and the total discharge flow rate and the principle of water conservation, and back-calculating the flood water level based on the updated water storage rate, iteratively generating the water level process, and using the difference between the water level and the installation elevation of the distribution cabinet as the point water depth of the distribution cabinet; and obtaining the point flow velocity of the distribution cabinet based on the water level process, combined with the installation elevation of the distribution cabinet and the Manning roughness coefficient.

[0008] In a preferred embodiment, the step of calculating the hydraulic load acting on the distribution cabinet based on the water depth and flow velocity at the location, and forming a hydraulic load effect, includes: calculating the structural force load acting on the distribution cabinet based on the water depth and flow velocity at the location, combined with the width of the distribution cabinet facing the flow, and normalizing the result to obtain the structural load effect; determining the sealing load effect corresponding to the sealing action based on the water depth at the location, and combining the structural load effect with the sealing load effect to obtain the hydraulic load effect of the distribution cabinet.

[0009] In a preferred embodiment, establishing the comprehensive strength model of the distribution cabinet includes: determining the strength of the sealing sub-sub ...

[0010] In a preferred embodiment, the calculation of the failure probability of the distribution cabinet using the load-strength interference relationship includes: constructing a load-strength function based on the comprehensive strength model of the distribution cabinet and the hydraulic load effect; using the comprehensive strength as a random variable, calculating the time-varying failure probability of the distribution cabinet according to the probability distribution interference relationship between the load effect and the comprehensive strength, wherein when the value of the function is less than zero, the distribution cabinet is determined to be in a failure state.

[0011] In a preferred embodiment, the simulation of cascading faults by combining power flow calculation and branch over-limit tripping mechanism includes: performing power flow calculation of the distribution network based on the branch operating status, node fault status and load transfer status; generating branch over-limit ratio based on the calculation results; determining the branch tripping probability based on the over-limit ratio when the over-limit ratio is greater than 1; updating the network topology status based on the branch tripping probability, and repeating the power flow calculation until the preset conditions are met.

[0012] The technical effects and advantages of the distribution network cascading failure risk assessment method considering the hydraulic load effect of distribution cabinets in this invention are as follows: This invention incorporates water level-area relationships, inflow processes, and orifice and weir discharge processes into the flood risk assessment of power distribution networks, enabling continuous calculation from regional flood evolution to water depth and flow velocity at distribution cabinet locations. It unifies sealing capacity, structural impact resistance, and service aging into a comprehensive strength model, interfering with the load effects of hydrodynamics, hydrostatic pressure, and buoyancy. This allows the failure probability to dynamically change with water depth, flow velocity, and individual distribution cabinet differences, providing a clear physical meaning. Furthermore, it links the distribution cabinet failure probability with the distribution network topology, power flow calculations, and branch over-limit tripping probabilities, simulating power flow redistribution and cascading failure evolution caused by local faults. It outputs risk indicators, generating comparable assessment results under different flood intensities, installation elevations, and distribution cabinet strength parameters. This improves the physical interpretability and engineering applicability of the risk assessment, providing quantitative decision-making basis for power distribution network flood control upgrades, emergency repairs, and pre-disaster reinforcement. It effectively solves the problems in existing technologies where the failure mechanism of distribution cabinet equipment in flood scenarios relies solely on a single failure rate function, and the difficulty in uniformly assessing the cascading effects of distribution network topology faults and power flow. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the flood disaster power distribution network risk assessment method of the present invention; Figure 2 This is a schematic diagram illustrating the water level and representative flow velocity changes in an extreme flood scenario according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the time-varying failure probability process of each power distribution cabinet in an embodiment of the present invention; Figure 4 This is a schematic diagram showing the summary results of key risk indicators in an embodiment of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0015] Example 1, Figure 1 This invention presents a method for assessing the risk of cascading failures in power distribution networks, considering the hydraulic load effect of distribution cabinets, comprising the following steps: Step S1: Acquisition and organization of basic data.

[0016] In this embodiment, the acquisition and organization of basic data specifically includes the following steps: S101, Obtain distribution network topology data: Obtain distribution network topology data from the distribution network geographic information system (GIS) and dispatch automation system (SCADA), including node number, node type, node connectivity, and branch start and end node numbers.

[0017] S102, Obtain node load data: Active and reactive power data for each load node are obtained from the electricity consumption information collection system and used as the basic input for power flow calculation. In addition, the proportion of load that can be transferred to each load node needs to be marked. This proportion reflects the load transfer capability of the distribution network under local fault conditions, and the value ranges from 0 to 1.

[0018] S103, Obtain branch capacity data: The rated capacity of each branch is obtained from the power distribution network equipment ledger and design documents, and is used for over-limit judgment in subsequent power flow calculations.

[0019] S104, Obtain the installation elevation data of the power distribution cabinet: The installation elevation of each distribution cabinet relative to the flood storage area's reference plane was obtained from the distribution cabinet installation drawings and on-site survey data. .

[0020] S105, Obtain physical parameter data of the power distribution cabinet: Obtain the geometric dimensions of the distribution cabinet, including cabinet height, from the equipment manufacturer's technical manual. (m) Frontal width (m) Cabinet depth (m). In addition, sealing performance parameters also need to be obtained. and structural performance parameters . S106, Obtain hydrological parameters for the flood storage area: Obtain the topographic and hydraulic parameters of the flood storage area from hydrological data and water conservancy engineering designs, including: the highest water level. Maximum water surface area Topographic geometric effect index p, foundation flow cross-section width Manning roughness coefficient Reference roughness coefficient Roughness correction index γ, minimum water depth threshold Maximum flow rate limit The above parameters reflect the topographical features and hydraulic characteristics of the flood storage area.

[0021] S107, Obtain inflow and outflow parameters: Obtained from hydrological forecasting and flood discharge facility design: Inflow process Orifice outflow coefficient Cross-sectional area of ​​the orifice Ao, height of the orifice center Weir flow coefficient Weir width , weir crest elevation Disaster evolution and progress .

[0022] Step S2: Based on the water exchange process and the principle of water conservation, establish a flood level evolution model.

[0023] In this embodiment, establishing the flood level evolution model specifically includes the following steps: S201, establish the relationship between water level and water surface area based on the topographic features of the flood storage area, and construct a water storage capacity-water level function based on the relationship between water level and water surface area; Specifically, based on the relationship between the water level and the water surface area of ​​the flood storage or detention area, the water surface area and corresponding water storage capacity at water level h are determined:

[0024]

[0025] in, The water surface area is (m²). The water storage capacity is expressed in m³. This is the area index. To simulate the geometric exponent of topography and shoreline expansion with water level, when When >0, The water level increases monotonically, which is consistent with the topographical water storage characteristics.

[0026] Area coefficient Estimation: Using the known highest water level and maximum water surface area Perform the reverse calculation: = .

[0027] S202, calculate the total discharge rate by combining the orifice discharge process and the weir discharge process; Specifically, the discharge volume is calculated separately based on the orifice discharge and the weir discharge. When the water level is lower than the center height of the orifice or the crest elevation of the weir, the corresponding outflow term is set to zero.

[0028] Orifice discharge flow rate:

[0029] in, Let be the orifice discharge flow rate (m³ / s) for the k-th time period. The orifice discharge coefficient is... Let g be the cross-sectional area of ​​the orifice (m²), and g be the acceleration due to gravity. Let the water level be the water level in the k-th time period. The height of the orifice center (m). When ≤ hour, =0.

[0030] Weir discharge flow rate:

[0031] in, Let be the weir discharge rate (m³ / s) for the k-th time period. The weir flow coefficient ( / s), The width of the weir is (m). The elevation of the weir crest (m) is given, and the exponent 3 / 2 represents the standard hydraulic relationship for broad-crested weirs. ≤ hour, = 0.

[0032] Total discharge flow:

[0033] S203, based on the difference between the inflow and the total outflow and the principle of water conservation, updates the water storage and back-calculates the flood level based on the updated water storage, and iteratively generates the water level process; Specifically, the water storage update and water level calculation are based on the difference between the inflow and the total outflow to update the water storage, and the water level for the next period is calculated from the updated water storage:

[0034]

[0035] in, Let be the inflow rate (m³ / s) for the k-th time period. Let be the water storage volume (m³) in the k-th time period. The evolution of disasters (s) The function ensures that the water storage capacity is not negative.

[0036] By iteratively calculating each time period, a complete curve of the flood level change over time can be obtained, i.e., the water level process, as shown in the attached figure. Figure 2 As shown.

[0037] Step S3: Calculate the water depth and flow velocity at the distribution cabinet location.

[0038] In this embodiment, the calculation of water depth and flow velocity at the distribution cabinet location specifically includes the following steps: S301, Calculate the water depth at the distribution cabinet location based on the water level process and the installation elevation of the distribution cabinet:

[0039] in, Let be the water depth (m) at the location of distribution cabinet i in the k-th time period. Let be the water level (m) for the k-th time period. Let represent the installation elevation (m) of distribution cabinet i. Physical meaning: When the water level is higher than the installation elevation of the distribution cabinet, the cabinet is submerged, and the water depth at that point is the difference between the two. When the water level is lower than the installation elevation, the cabinet is not submerged, and the water depth at that point is zero. Distribution cabinets with lower installation elevations will be submerged in earlier stages of flooding and will have a greater water depth at the same water level.

[0040] S302, using Manning's roughness coefficient to correct the effective hydraulic width of the flood storage area:

[0041] in, The effective hydraulic width (m). The width of the basic flow cross section (m). Manning's roughness coefficient γ is the roughness coefficient for reference, and γ is the roughness correction index. and The function is used to limit the correction coefficient within the range of [0.25, 1.50] to avoid distortion of the effective width under extreme roughness conditions.

[0042] S303, based on total flow rate, effective hydraulic width, and water level process, estimates the representative flow velocity in the flood storage area:

[0043] in, Let be the representative flow velocity (m / s) for the k-th time period. The minimum water depth threshold (m) is given. This represents the maximum flow velocity limit (m / s). Where ( / 2 The average of the inflow and outflow is taken as an approximation of the flow rate. The representative velocity calculated above reflects the overall flow intensity of the flood storage area and is suitable for narrow, gently changing flood storage areas where the difference in flow rate between the inlet and outlet sections is small. In actual flooding, the representative velocity changes with variations in water level and inflow. It will also change over time, as shown in the attached document. Figure 2 As shown.

[0044] S304, Calculate the flow velocity at the distribution cabinet location based on the representative flow velocity of the flood storage area and the location correction coefficient:

[0045] in, Let be the point flow velocity (m / s) of distribution cabinet i in the k-th time period. This is the location correction factor (reflecting the influence of the local topography at the location of the distribution cabinet on the flow velocity, generally taken as 0.5 to 1.5, which can be determined according to the local topographic undulations and water flow path; a larger value is taken for distribution cabinets located near the main channel, and a smaller value is taken for distribution cabinets located on the side or in dead zones). To flood the indicator variable, when When the value is >0, the value is 1, indicating that the distribution cabinet is in a flooded state; when = 0 is taken as 0, indicating that the distribution cabinet is not submerged and the flow velocity is zero.

[0046] Step S4: Calculation and normalization of hydraulic load on the distribution cabinet.

[0047] In this embodiment, the calculation and normalization of the hydraulic load of the distribution cabinet specifically includes the following steps: S401, Determine the submerged height of the distribution cabinet based on the water depth at the point obtained in S3:

[0048] in, Let be the flooding height (m) of distribution cabinet i in the k-th time period. The water depth at the point is in meters (m). The height of the distribution cabinet is in meters (m). When the water depth is less than the cabinet height, the submerged height is equal to the actual water depth, resulting in partial submersion. When the water depth exceeds the cabinet height, the submerged height is equal to the cabinet height, resulting in complete submersion. The water head above the top of the cabinet will no longer increase the submerged height, but its contribution will be retained in the hydrostatic pressure calculation.

[0049] S402, calculate the projected area of ​​the distribution cabinet facing the current based on the flood height and the width of the incoming flow:

[0050] in, Let be the projected area (m²) of the distribution cabinet i in the k-th time period. The current-facing width of the distribution cabinet (m).

[0051] S403, calculate the hydrodynamic force generated by the velocity impact based on the projected area facing the flow and the velocity at the point:

[0052] in, For kinetic energy (N), ρ is the density of water. The drag coefficient, The velocity at the point is (m / s).

[0053] S404, calculate the resultant hydrostatic pressure based on water depth, submerged height, and width facing the current, and calculate the buoyancy based on the submerged volume of the cabinet:

[0054]

[0055] in, The resultant force of hydrostatic pressure (N). Where g is the buoyancy force (N) and g is the acceleration due to gravity. The cabinet depth is in meters (m).

[0056] S405 normalizes the resultant forces of hydrodynamic and hydrostatic pressures and buoyancy to obtain the structural force load effect, and converts the water depth at the point of view into a sealing load effect:

[0057]

[0058] in, For structural force load effects, For the sealing load effect, The structural force reference value (N, used to normalize the force to a reasonable level, generally taken as a typical value of the cabinet structure's load-bearing capacity). This is a reference value for water depth (m, used to normalize water depth, generally taken as the cabinet height or the submerged height). This is the buoyancy reduction factor (reflecting the contribution of buoyancy to structural failure, generally taken as 0.3 to 0.5).

[0059] The structural force load effect unifies the three forces into an equivalent load index for the cabinet structure's bearing capacity, where buoyancy is converted using a reduction factor. The weight of the submersion depth is reduced; the sealing load effect converts the submersion depth into an equivalent load index for the sealing capacity of the cabinet. These two correspond to the cabinet structure failure mode and the sealing failure mode, respectively.

[0060] S406, by weighting and combining the structural force load effect and the sealing load effect, the normalized load effect of the distribution cabinet, i.e., the hydraulic load effect, is obtained:

[0061] in, Let i be the hydraulic load effect of the distribution cabinet i in the k-th time period. For the sealing load weight, For structural load weights, satisfying + = 1.

[0062] Weight selection instructions: and This reflects the relative importance of sealing failure and structural failure in the overall failure of the distribution cabinet. For distribution cabinets with good sealing performance (sealing rating of IP68 or above), structural failure is dominant and can be considered... = 0.3、 = 0.7; For distribution cabinets with general sealing performance (sealing rating below IP44), sealing failure is the dominant factor, and can be taken as 0.7. =0.7、 = 0.3; generally, this can be taken as... = = 0.5. The weight selection should be consistent with the combined weight of the comprehensive strength model in step S5 to ensure that the load and strength are comparable under the same frame.

[0063] Step S5: Integrate the strength model and time-varying failure probability.

[0064] In this embodiment, the comprehensive strength model and time-varying failure probability specifically include the following steps: S501 converts the allowable differential pressure of the distribution cabinet seal into the critical water depth, and further converts it into the seal strength:

[0065]

[0066] in, The critical water depth for sealing (m). The allowable differential pressure (Pa) for sealing. For the strength of the sealant.

[0067] S502 converts the structural bearing capacity into structural sub-strength:

[0068] in, For the strength of the structural component, The structural bearing capacity (N).

[0069] S503, calculates the aging correction factor for the distribution cabinet based on its service life:

[0070] in, The aging factor of distribution cabinet i. The annual aging rate, This refers to the service life of the distribution cabinet. This is the lower limit of the aging factor; as the service life increases, Linearly decreasing, but not lower than This ensures that even distribution cabinets with long service lives retain a certain minimum load-bearing capacity.

[0071] S504, by weighting the strength of the seal and the structural strength, and incorporating an aging correction factor, yields the mean and standard deviation of the overall strength of the distribution cabinet:

[0072]

[0073] in, The average of the overall intensity. The standard deviation of the overall strength, The lower limit of standard deviation is the coefficient of variation.

[0074] Overall Intensity Average The strength of the sealant and the structural strength are combined with the same weight as the load effect to ensure that the load and strength are comparable within the same frame. Aging factor. As a multiplicative correction term, it simultaneously reduces the strength of both the sealing element and the structural element, reflecting the combined effect of switchgear aging on both failure modes. Standard deviation lower bound constraint This ensures that the strength uncertainty is not zero, thus allowing the interferometric calculation to retain a reasonable baseline failure probability under low load conditions.

[0075] S505, Based on the comprehensive strength model of the distribution cabinet and the hydraulic load effect, a load-strength function is constructed, so that when the function is positive, it indicates a reliable state and when it is negative, it indicates a failure tendency.

[0076] Specifically, given that the water depth and flow velocity have been determined by the flood evolution model, the load effect and comprehensive strength are interferometrically calculated to obtain the time-varying failure probability of the distribution cabinet:

[0077]

[0078] in, For functional purposes, Let Φ be the failure probability of distribution cabinet i in the k-th time period. The cumulative distribution function of the standard normal distribution. This represents the background failure probability (reflecting the baseline failure probability caused by non-flood factors, typically taken as 0.001 to 0.01). This is the upper limit of the failure probability (generally taken as 0.999 to 0.9999 to avoid deterministic failure).

[0079] The core idea of ​​the load-strength interference model is: when the load effect Exceeding overall strength When the overall strength is low, the distribution cabinet tends to fail; conversely, it tends to be reliable. Because the overall strength is uncertain, the failure probability is not a simple 0-1 judgment, but rather determined through a standard normal distribution function. calculate Exceed The probability of ( ). When ( / When it is large, As the value approaches 1, the failure probability approaches... ;when( / When smaller or negative, Approaching 0, the failure probability approaches Background failure probability Ensure that even under low load conditions, the distribution cabinet has a non-zero baseline failure probability; Ensure that the failure probability under high load conditions does not exceed the upper limit. By repeating the above calculation for each time period, the time-varying failure probability curve of each distribution cabinet during the entire flood process can be obtained, as shown in the attached figure. Figure 3 As shown. (Attached) Figure 3 The dynamic process of the failure probability of each distribution cabinet changing with the flood level is shown.

[0080] Step S6, cascading fault simulation.

[0081] In this embodiment, the cascading failure simulation specifically includes the following steps: S601 uses the Monte Carlo method to generate the initial fault state due to a disaster based on the time-varying failure probability of the distribution cabinet. For each disaster period k and each Monte Carlo sample r (r = 1, 2, ...), ... For each distribution cabinet i, generate a uniformly distributed random number in the range [0,1]. , and failure probability Compare: like ≤ If the distribution cabinet i is determined to be faulty in the r-th sample of the k-th time period; like > If so, then the distribution cabinet i is not in failure.

[0082] When a power distribution cabinet is determined to be faulty, the power distribution node corresponding to the power distribution cabinet is set as a topology fault node, and the branches connected to the fault node or disconnected from the power supply node are set to a shutdown state.

[0083] Among them, the number of Monte Carlo samples The number of samples should generally be no less than 500, but can be adjusted according to accuracy requirements. The larger the sample size, the narrower the confidence interval of the statistical results.

[0084] S602 takes node fault status, branch outage status, and transferable load as input, and uses the MATPOWER power flow calculation program in MATLAB to update bus type, node load, branch operating status, and power output constraints, and calculates the power flow of the distribution network. MATPOWER interface processing flow: (1) Bus type update: In the case data structure of MATPOWER, change the bus type of the load node disconnected due to the failure of the distribution cabinet from PQ node to isolated node, and set its active and reactive loads to zero.

[0085] (2) Branch status update: Set the “status” column of the out-of-service branch in the MATPOWER branch matrix to 0 so that it does not participate in power flow calculation.

[0086] (3) Handling of transferable loads: For nodes with a marked transferable load ratio, the load is allocated to adjacent lines according to the transferable ratio. When a node is disconnected due to the failure of the distribution cabinet, the transferable load is transferred through other lines, and the non-transferable load is disconnected.

[0087] (4) Power output constraint update: Update the output constraint range of the balancing node and PV node according to the number of power outage nodes and load changes.

[0088] (5) Power flow calculation: Call the runpf function of MATPOWER to perform power flow calculation using the Newton-Raphson method or the fast decoupling method to obtain the power flow of each branch. And node voltage. After the power flow calculation is completed, it is determined whether the limit has been exceeded based on the branch power flow results, and the branch tripping probability is determined accordingly.

[0089] S603, calculate the branch over-limit ratio based on power flow calculation results:

[0090] in, Let e ​​be the cross-limit ratio of branch e in the k-th time period. Calculate the apparent power (MVA) for the power flow of branch e. The rated capacity (MVA) of branch e.

[0091] S604 determines the branch tripping probability based on the over-limit ratio, and divides it into three cases: (1) When the branch road has not exceeded the limit ( ≤ 1), set the branch tripping probability to zero:

[0092] (2) When a branch exceeds the limit but does not reach the hard trip limit ratio ( ), calculate the branch tripping probability based on the degree of over-limit:

[0093] in, Let e ​​be the tripping probability of branch e in the k-th time period. This is the probability of tripping at the moment of exceeding the limit (generally taken as 0.01 to 0.05, reflecting the initial tripping risk at the moment of exceeding the limit). It is the tripping probability growth coefficient (generally taken as 0.5 to 1.0, reflecting the rate at which the tripping probability increases with the degree of over-limit). The over-limit ratio for hard tripping is typically set to 1.3 to 1.5, meaning that the tripping is forced when the rated capacity is exceeded by 30% to 50%. =1.

[0094] (3) When the branch reaches the hard trip over-limit ratio ( Set the branch tripping probability to 1:

[0095] S605 updates the network topology status based on the branch tripping probability and repeats the power flow calculation until the preset conditions are met. Specifically, when a branch is determined to have tripped, that branch is placed in an out-of-service state, and the MATPOWER power flow calculation is re-executed. The redistributed power flow may cause new branches to exceed limits and trip, forming a cascading failure propagation. The above process is repeated iteratively until no new tripped branches appear or the maximum number of iterations is reached, at which point the cascading failure propagation process for that Monte Carlo sample ends.

[0096] The above iterative process reflects the cascading failure evolution mechanism from "initial failure due to disaster" to "power flow redistribution" and then to "branch over-limit tripping".

[0097] Step S7: Risk indicator statistics and output.

[0098] In this embodiment, the statistical analysis and output of the risk indicators specifically includes the following steps: Statistical analysis is performed on the cascading failure simulation samples obtained from S6, outputting risk indicators such as node power outage probability, expected load shedding, and expected power shortage. (This is combined with the appendix...) Figure 4 The summary results of the key risk indicators shown are as follows: S701, for each disaster period and each Monte Carlo sample, the node outage status is statistically analyzed to obtain the node outage probability:

[0099] in, Let be the probability of power outage at node j in the k-th time period. For Monte Carlo sample size, This is a power outage state variable.

[0100] S702, calculate the load shedding amount for each disaster period and each Monte Carlo sample to obtain the expected load shedding amount:

[0101] in, Let be the expected load shedding (MW) for the k-th time period. Let represent the load shedding amount (MW) for the r-th sample in the k-th time period.

[0102] S703, calculate the cumulative value of the expected load shedding to obtain the expected power shortage:

[0103] Where EENS represents the expected power shortage (MWh), and K represents the total number of disaster periods. The statistical step size for risk indicators (h) is generally consistent with or an integer multiple of the disaster evolution step size Δtd.

[0104] S704, Key Risk Node Ranking: The nodes are sorted according to their cumulative power outage probability, expected power shortage contribution, or comprehensive risk score, and a list of key risk nodes is output.

[0105] Appendix Figure 4 The summary results of the key risk indicators shown include: the cumulative power outage probability, expected load shedding, and cumulative power shortage EENS for each node during the entire flood process, as well as the ranking of the key nodes that contribute the most to equipment risk and power shortage.

[0106] The figure compares four key indicators under "moderate" and "extreme" flood scenarios: equipment risk and peak failure probability (vertical axis) represent the probability of node power outage; operational consequences and peak load shedding represent the expected load shedding; and cumulative power shortage and EENS (vertical axis) represent the expected power shortage. This visually reflects the significant increase in water level, power outage probability, load shedding, and power shortage as the disaster escalates from moderate to extreme. Identifying these key nodes provides a quantitative basis for prioritizing flood control upgrades to the distribution network, allocating emergency repair resources, and developing pre-disaster reinforcement plans.

[0107] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0108] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0109] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0110] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0111] Finally, the above description is merely 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 assessing the risk of cascading failures in a power distribution network considering the hydraulic load effect of distribution cabinets, characterized in that, Includes the following steps: Based on the water exchange process and the principle of water conservation, combined with the installation elevation of the distribution cabinet and the Manning roughness coefficient, the water depth and flow velocity at the distribution cabinet location are obtained. The hydraulic load acting on the distribution cabinet is calculated based on the water depth and flow velocity at the specified points, and the hydraulic load effect is obtained. A comprehensive strength model for the distribution cabinet is established, and the failure probability of the distribution cabinet is calculated based on the load-strength interference relationship between the hydraulic load effect and the comprehensive strength model. Based on the failure probability, the fault status of the distribution network node is generated, and the simulation results are obtained by combining power flow calculation and branch over-limit tripping mechanism to simulate cascading failures. The risk indicators of the distribution network are obtained from the simulation results.

2. The method for assessing the risk of cascading faults in a power distribution network considering the hydraulic load effect of the distribution cabinet, as described in claim 1, is characterized in that... Based on the water exchange process and the principle of water conservation, combined with the installation elevation of the distribution cabinet and the Manning roughness coefficient, the point water depth and point flow velocity of the distribution cabinet are obtained, including: The water exchange process is the dynamic inflow and outflow process of water in the flood storage area; Establish the water level-water surface area relationship based on the topographic features of the flood storage area, and construct the water storage capacity-water level function based on the water level-water surface area relationship; Calculate the total discharge capacity by combining the orifice discharge process and the weir flow process; The water storage is updated based on the difference between the inflow and the total outflow and the principle of water conservation. The flood level is then calculated back based on the updated water storage. The water level process is generated iteratively, and the difference between the water level process and the installation elevation of the distribution cabinet is used as the water depth at the distribution cabinet location. Based on the water level process, combined with the installation elevation of the distribution cabinet and the Manning roughness coefficient, the point flow velocity of the distribution cabinet is obtained.

3. The method for assessing the risk of cascading faults in a power distribution network considering the hydraulic load effect of the distribution cabinet, as described in claim 1, is characterized in that... The hydraulic load acting on the distribution cabinet is calculated based on the water depth and flow velocity at the specified location, resulting in the hydraulic load effect, including: Based on the water depth and flow velocity at the specified locations, the structural force load acting on the distribution cabinet is calculated in conjunction with the width of the distribution cabinet facing the current, and the structural force load is normalized to obtain the structural load effect. The sealing load effect corresponding to the sealing action is determined based on the water depth at the specified location, and the structural load effect is combined with the sealing load effect to obtain the normalized load effect of the distribution cabinet, namely the hydraulic load effect.

4. The method for assessing the risk of cascading faults in a power distribution network considering the hydraulic load effect of the distribution cabinet, as described in claim 1, is characterized in that... The establishment of the comprehensive strength model for the power distribution cabinet includes: Based on the sealing performance parameters, structural load-bearing parameters, and service life of the distribution cabinet, the strength of the sealing element, the strength of the structural element, and the aging correction factor are determined respectively, and a comprehensive strength model of the distribution cabinet is constructed based on the sealing element strength, structural strength, and aging correction factor.

5. The method for assessing the risk of cascading faults in a power distribution network considering the hydraulic load effect of the distribution cabinet, as described in claim 4, is characterized in that... The calculation of the distribution cabinet failure probability using the load-strength interference relationship includes: Based on the comprehensive strength model of the power distribution cabinet and the hydraulic load effect, a load-strength function is constructed. Using the overall strength as a random variable, the time-varying failure probability of the distribution cabinet is calculated based on the probability distribution interference relationship between the load effect and the overall strength. The distribution cabinet tends to fail when the function value is negative.

6. The method for assessing the risk of cascading failures in a power distribution network considering the hydraulic load effect of the distribution cabinet, as described in claim 1, is characterized in that... The failure probability generates the fault state of the distribution network node, including: Based on the failure probability of the distribution cabinet, a random sampling method is used to generate the failure status of the distribution cabinet; Based on the fault status of the power distribution cabinet, the power distribution node corresponding to the failed power distribution cabinet is converted into a network topology fault node, and the outage status of the associated branch and the fault status of the node are determined according to the node connectivity.

7. The method for assessing the risk of cascading failures in a power distribution network considering the hydraulic load effect of the distribution cabinet, as described in claim 6, is characterized in that... The simulation of cascading faults by combining power flow calculation and branch over-limit tripping mechanism includes: Based on the branch operation status and node fault status, and in conjunction with the load transfer situation, the power flow calculation of the distribution network is performed to obtain the calculation results; The branch over-limit ratio is generated based on the calculation results. When the over-limit ratio is greater than 1, the branch tripping probability is determined according to the over-limit ratio. The network topology is updated based on the branch tripping probability, and the power flow calculation is repeated until the preset conditions are met.

8. The method for assessing the risk of cascading faults in a power distribution network considering the hydraulic load effect of the distribution cabinet, as described in claim 1, is characterized in that... The risk indicators include at least one of the following: The results of the ranking of node power outage probability, expected load shedding, expected power shortage, and key risk nodes.

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