A distribution network operation safety risk assessment method considering typhoon and rainstorm combined disasters

Abstract

The present invention discloses a distribution network operation safety risk assessment method considering typhoon and rainstorm combined disasters, comprising the following steps: based on typhoon and rainstorm disaster forecast data, generating different typhoon paths as main scenes, and assigning weights to each main scene according to the probability of occurrence; considering micro-topography and rainfall intensity to evaluate wind speed in the main scene; V ( r ) and calculate the corrected wind speed according to the node i , j The failure probability of the distribution line between nodes and the failure probability caused by foreign objects hanging on the line are calculated to obtain the node i and j The total failure probability of the distribution lines between the two is calculated according to the amount of water accumulation in the distribution network area; the failure probability of the distribution equipment is generated by Monte Carlo simulation N Failure scenarios s Form a collection of fault scenarios S , and assign weights to each fault scenario; taking the minimum expected total load loss under all fault scenarios as the objective function, a risk assessment model is constructed, and the risk assessment model is solved in combination with the constraints.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method for assessing the operational safety risks of a distribution network taking into account combined disasters of typhoons and rainstorms. Background Art

[0002] As users' requirements for power supply continue to increase, the power grid needs to not only ensure reliable power supply under normal operation, but also ensure continuous power supply of the distribution network under extreme conditions. In recent years, due to the impact of global warming and climate oscillation, extreme weather events such as typhoons and rainstorms have occurred frequently, causing a variety of accidents such as tower collapse, line disconnection, and failure and shutdown of new energy units in the distribution system, which seriously threatens its safe and reliable operation. Therefore, accurate risk assessment of the distribution network before the extreme disaster has become an important basis for the pre-disaster prevention and post-disaster recovery strategy of the distribution network.

[0003] Risk assessment can be divided into typhoon wind speed calculation in the distribution network area, generation of fault scenarios and risk assessment models. Currently, there are many studies on the risk assessment of distribution networks under single disasters such as typhoons or rainstorms. However, typhoons often bring heavy rainfall, which has a significant impact on the risk of distribution networks. In addition, the current calculation of typhoon wind speed in the distribution network area takes into account the impact of micro-topography, but no refined geographical area model of the distribution network area has been constructed. Therefore, the impact of micro-topography on line wind speed in the distribution network area has not been accurately quantified. Another problem that currently affects the accuracy of distribution network assessment under disasters is the uncertainty of the typhoon path. The prediction error of the typhoon disaster prediction path is tens of kilometers, which increases the difficulty of risk assessment. The uncertainty of the fault scenario of the distribution network under typhoons also makes it difficult to accurately assess the risk of the distribution network. The inaccuracy of the generation of fault scenarios will cause great deviations in the risk assessment results of the distribution network under disasters. Summary of the invention

[0004] In view of this, the present invention provides a distribution network operation safety risk assessment method taking into account the combined disasters of typhoons and rainstorms, so as to at least solve the problem that the prior art does not comprehensively consider the disaster coupling and the impact of disasters on multiple links of distributed power sources, lines and distribution equipment, and has not yet formed an accurate typhoon and rainstorm combined disaster risk assessment method.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A distribution network operation safety risk assessment method considering typhoon and rainstorm combined disasters includes the following steps:

[0007] S1. Based on the typhoon and rainstorm disaster forecast data in the distribution network area with safety risks to be assessed, different typhoon paths are generated as main scenarios, and weights are assigned to each main scenario according to the probability of occurrence;

[0008] S2. Considering micro-topography and rainfall intensity, the wind speed in the main scene The influence of the wind speed is calculated, and the node is calculated based on the corrected wind speed. and The probability of failure of overhead lines between and nodes and between k Failure probability of a tower , combined with and Calculate the node , Failure probability of the distribution line between , obtain the failure probability caused by foreign objects hanging on the wire ,according to and Calculate the node and The total failure probability of the distribution lines between them; based on the typhoon and rainstorm disaster forecast data, the water surface elevation map of the distribution network area and the two-dimensional hydraulic model, the dynamic change process of the grid water volume in the distribution network area is simulated, and the failure probability of the distribution equipment is calculated based on the water volume in the distribution network area;

[0009] S3. Generate the total failure probability of the distribution line and the failure probability of the distribution equipment through Monte Carlo simulation N Failure scenarios s Form a collection of fault scenarios S , and assign weights to each fault scenario according to the probability of occurrence of each fault scenario, where the sum of the weights of the fault scenarios on the same main scenario is the weight of the main scenario;

[0010] S4. Taking into account the weight of each fault scenario and the adjustable output of distributed power sources, a risk assessment model is constructed with the minimum expected total load loss under all fault scenarios as the objective function, and the risk assessment model is solved in combination with the constraints.

[0011] Under the premise of considering the weight of each fault scenario and the adjustable output of distributed power sources, a risk assessment model is constructed with the minimum expected total load loss under all fault scenarios as the objective function, and the risk assessment model is solved in combination with the constraints.

[0012] Preferably, the specific contents of calculating the corrected wind speed in S2 include:

[0013] (1) Obtain the wind speed in the distribution network area where the safety risk is to be assessed based on the typhoon wind speed model ;

[0014] (2) Calculation of adjustment coefficient under the influence of micro-topography , according to the adjustment coefficient under the influence of micro-topography Calculate wind speed after micro-topography correction ;in:

[0015] ,

[0016] ,

[0017] In the formula, is the micro-topography coefficient, is the windward slope value, is the building height, It is the total height of a hillside or hilltop;

[0018] (3) Calculation of wind speed coefficient function and rainfall intensity coefficient function , combined with according to and Calculate the corrected wind speed for rainfall ;in:

[0019] ,

[0020] ,

[0021] ,

[0022] In the formula, Indicates the rainfall intensity during a typhoon.

[0023] Preferably, in S2, the node is calculated according to the corrected wind speed and The probability of failure of overhead lines between and nodes and between k Failure probability of a tower , combined with and Calculate the node , Failure probability of the distribution line between The specific contents include:

[0024] node and The probability of failure of overhead lines between for:

[0025] ,

[0026] In the formula, and are line fault multiplier coefficient and line fault summation coefficient respectively; is the design wind speed of the overhead line; is the length of the overhead line;

[0027] node and between k Failure probability of a tower :

[0028] ,

[0029] In the formula, is the tower fault multiplier coefficient; is the design wind speed of the tower;

[0030] Then the node and Failure probability of the distribution line between for:

[0031] ,

[0032] In the formula, K Indicates the number of line segments between distribution lines.

[0033] Preferably, the failure probability caused by foreign matter hanging on the line is obtained in S2 The specific contents include:

[0034] (1) Integrate the factors that affect foreign matter hanging on the line into environmental parameters and line parameters ,in:

[0035] ,

[0036] ,

[0037] In the formula, is the line surrounding environment coefficient, Wind speed The normalized value of is the line span coefficient, , is the conductor density factor, , is the number of conductor loops;

[0038] (2) Obtain the environmental parameter membership function and the line parameter membership function respectively, determine the fuzzy segmentation number respectively, obtain the final fuzzy inference rule, and obtain the failure probability caused by foreign objects hanging on the line after defuzzification. .

[0039] Preferably, in S2, according to and Calculate the node and The specific contents of the total failure probability of the distribution lines between them include:

[0040] .

[0041] Preferably, the specific content of calculating the failure probability of the distribution equipment according to the water accumulation in the distribution network area in S2 includes:

[0042] Failure probability of power distribution equipment for:

[0043] ,

[0044] In the formula, for The amount of water accumulated in the area where the power distribution equipment is located at any given moment; It is the designed flood-proof height of the power distribution equipment. is the distribution equipment fault multiplier coefficient;

[0045] Then the failure probability of the power distribution equipment during the entire evaluation period is:

[0046] ,

[0047] In the formula, For the evaluation cycle, Any time period within the evaluation period.

[0048] Preferably, the specific contents of the dynamic change process of grid water accumulation in the distribution network area obtained by simulation based on typhoon and rainstorm disaster forecast data, water surface elevation map of the distribution network area and two-dimensional hydraulic model include:

[0049] A two-dimensional hydraulic model is used to simulate surface water accumulation. The two-dimensional hydraulic model is:

[0050] ,

[0051] In the formula, is the unit conversion factor, is the Manning coefficient; is the water flow rate; is the water surface elevation; is the rainfall intensity; It is the depth of water;

[0052] Divide the area where the power distribution equipment is located into grids, and take one of the grids C and its 8 neighbors. The only grids directly connected to grid C by edges are N, E, S, and W. The accumulated water in grid C only flows in the four directions of N, E, S, and W. The other grids adjacent to grid N are NW and NE, and the grids adjacent to grid S are SW and SE.

[0053] Find the gradient of the water surface elevation in the CE direction , whose lateral component and the longitudinal component for:

[0054] ,

[0055] ,

[0056] In the formula, is the water surface elevation, for The distance between two grids in the direction, for The distance between two grids in the direction, and They are and The water surface elevation of the grid;

[0057] According to the gradient The water flow rate is then calculated using a two-dimensional hydraulic model. , in a period of time The amount of water flowing from grid C into grid E is:

[0058] ,

[0059] A positive value indicates that the flow from C to E, and a negative value indicates that the flow from E to C. Similarly, and , and stipulates that along Axis or When the flow is in the positive direction of the axis, the flow is positive, and vice versa, the flow is negative;

[0060] Surface infiltration is considered in flow calculations and is:

[0061] ,

[0062] In the formula, is the infiltration flow rate, is the minimum infiltration rate, is the initial maximum infiltration rate, is the attenuation coefficient that describes the change of infiltration rate with time;

[0063] Considering the drainage of rainwater wells at the same time, the drainage capacity is:

[0064] ,

[0065] Where: is the drainage flow; is the drainage coefficient, which ranges from 0 to 1; is the cross-sectional area, i.e. the total area of ​​the openings on the rainwater manhole cover; for water depth;

[0066] Then the water depth at the next moment in grid C is:

[0067] ,

[0068] Then the distribution equipment in grid C is Water accumulation at the time .

[0069] Preferably, the objective function in S4 is:

[0070] ,

[0071] Where: For the evaluation cycle, For any time during the evaluation period, is the distribution network node index; It is a failure scenario The weight of It is a failure scenario Next Node exist The amount of load loss at the moment.

[0072] Preferably, the constraints in S4 include:

[0073] 1) Power balance constraints:

[0074] ,

[0075] ,

[0076] In the formula, Representation and Node Adjacent nodes where the distribution lines are not out of service A collection of; and For line exist Active and reactive currents at all times; and It indicates the active and reactive power transmitted from the upper transmission network to the distribution network; and Representation Node The active and reactive power sent by the distributed generation to the distribution network; and Representation Node exist Active and reactive power requirements at each moment;

[0077] 2) Voltage constraint:

[0078] ,

[0079] ,

[0080] ,

[0081] In the formula, and The nodes are and nodes exist The square of the voltage at the time; and are the resistance and reactance of the line respectively; For line exist The square term of the current transmitted at any moment; Indicates the line operation status as a 0-1 variable, 1 for normal operation and 0 for line failure and outage; and For Node The minimum and maximum voltages allowed; M For big M The coefficient of the variable in the method;

[0082] 3) Distributed power constraints:

[0083] ,

[0084] ,

[0085] ,

[0086] Where: and are the node sets of wind power and diesel generator respectively; Represents various types of distributed power sources and nodes The connection status; and They are respectively the upper limits of active and reactive output of distributed generation; and They are the upper and lower limits of the power factor of distributed power sources, and are the minimum and maximum values ​​of the power factor angle respectively;

[0087] 4) Line current and transmission capacity constraints:

[0088] ,

[0089] ,

[0090] Where: is the upper limit of the square term of the line current;

[0091] 5) Load reduction constraints:

[0092] ,

[0093] ,

[0094] Where: and They are the maximum values ​​of active and reactive power of the load respectively;

[0095] 6) Second-order cone constraint:

[0096] .

[0097] Preferably, the output power of the wind turbine in the distributed power source is affected by the wind speed, thereby affecting the overall output power of the distributed power source, wherein the specific content of calculating the output power of the wind turbine includes:

[0098] Active output power of wind turbine for:

[0099] ,

[0100] Where: is the rated power; is the wind speed of the wind turbine, that is and Represent the cut-in wind speed, rated wind speed and cut-out wind speed respectively. and Output power characteristic parameters:

[0101] ,

[0102] The reactive output power of a wind turbine is limited by the capacity of the inverter:

[0103] ,

[0104] Where: is the maximum reactive power output of the wind turbine, is the capacity of the inverter, is the active output power of the wind turbine.

[0105] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a distribution network operation safety risk assessment method considering typhoon and rainstorm combined disasters, which has the following beneficial effects:

[0106] The present invention first constructs a typhoon wind speed and rainfall model, uses micro-topography and rainfall intensity to correct the wind speed in the distribution network area, establishes a line failure probability model and a distribution network equipment waterlogging probability model, and through the above model, data such as typhoon wind speed, grid component failure rate during typhoons, and wind turbine output can be obtained to provide input parameters for fault scenario generation and risk assessment models; secondly, based on the above input parameters, the risk assessment index is obtained by iterative solution based on the distribution network Distflow model, and rapid calculation of the risk assessment index can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0108] Figure 1 It is a flow chart of a method for risk assessment of distribution network operation safety considering typhoon and rainstorm combined disasters disclosed by the present invention;

[0109] Figure 2 The environmental parameter membership function disclosed in the embodiment of the present invention;

[0110] Figure 3 The line parameter membership function disclosed in the embodiment of the present invention;

[0111] Figure 4 The grid and its neighborhood used for calculating the amount of accumulated water disclosed in the embodiment of the present invention;

[0112] Figure 5 A water surface elevation map of a distribution network area disclosed in an embodiment of the present invention;

[0113] Figure 6 A topological diagram of an improved IEEE 33-node distribution network system disclosed in an embodiment of the present invention;

[0114] Figure 7 is the typhoon path and the corresponding wind speed calculated value disclosed in the embodiment of the present invention; wherein Figure 7 (a) is the typhoon track map, Figure 7 (b) is the wind speed line graph;

[0115] Figure 8 is the load loss amount of each node in different cases disclosed in the embodiments of the present invention;

[0116] Fig. 9 The load loss amount and loss rate of the distribution network in Case 1 disclosed in the embodiment of the present invention;

[0117] Fig.10 The load loss amount and loss rate of the distribution network in Case 2 disclosed in the embodiment of the present invention;

[0118] Fig.11 The figure is a comparison of the total load loss of the distribution network in Cases 1 and 2 disclosed in the embodiments of the present invention. DETAILED DESCRIPTION

[0119] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0120] The present invention provides a method for evaluating the risk of power distribution network operation safety considering the combined disaster of typhoon and rainstorm. Figure 1 As shown, the following steps are included:

[0121] S1. Based on the typhoon and rainstorm disaster forecast data in the distribution network area with safety risks to be assessed, different typhoon paths are generated as main scenarios, and weights are assigned to each main scenario according to the probability of occurrence;

[0122] S2. Considering micro-topography and rainfall intensity, the wind speed in the main scene The influence of the wind speed is calculated, and the node is calculated based on the corrected wind speed. and The probability of failure of overhead lines between and nodes and between k Failure probability of a tower , combined with and Calculate the node , Failure probability of the distribution line between , obtain the failure probability caused by foreign objects hanging on the wire ,according to and Calculate the node and The total failure probability of the distribution lines between them; based on the typhoon and rainstorm disaster forecast data, the water surface elevation map of the distribution network area and the two-dimensional hydraulic model, the dynamic change process of the grid water volume in the distribution network area is simulated, and the failure probability of the distribution equipment is calculated based on the water volume in the distribution network area;

[0123] S3. Generate the total failure probability of the distribution line and the failure probability of the distribution equipment through Monte Carlo simulation N Failure scenarios s Form a collection of fault scenarios S , and assign weights to each fault scenario according to the probability of occurrence of each fault scenario, where the sum of the weights of the fault scenarios on the same main scenario is the weight of the main scenario;

[0124] S4. Taking into account the weight of each fault scenario and the adjustable output of distributed power sources, a risk assessment model is constructed with the minimum expected total load loss under all fault scenarios as the objective function, and the risk assessment model is solved in combination with the constraints.

[0125] It should be noted that:

[0126] After a disaster occurs, it will cause node failure and line outage. Since the failure caused by typhoons and rainstorms is uncertain information, this embodiment uses Monte Carlo simulation to evaluate the failure conditions and failure probabilities of each distribution network line and distribution equipment, and screen out N The goal is to minimize the expected total load loss under all typical scenarios under the premise of considering the weight of each typical scenario and the adjustable output of distributed power sources. To solve the distribution network risk assessment model, this embodiment calls the Gurobi solver to solve the risk assessment model.

[0127] In order to calculate the risk index of the distribution network more accurately, the present invention uses Monte Carlo simulation to sample and generate fault scenarios based on the sequential fault probability of the distribution lines and distribution cabinets. nodes, The distribution network consists of distribution lines, The operating status of the distribution network at any moment is composed of the status of the distribution equipment, i.e., nodes, and the distribution network lines. ,That Moment The status of the distribution line is shown as follows:

[0128] ,

[0129] In the formula is a random number uniformly distributed between 0 and 1 generated by Monte Carlo sampling. When the node or line fails, the line is in a fault state, otherwise it is in a normal working state. The generation of node status is similar to that of line status. When a node or line fails, since the disaster is ongoing, in order to ensure the safety of power grid operation and maintenance personnel, the line and node faults cannot be repaired during the entire risk assessment period.

[0130] In order to further implement the above technical solution, the specific contents of calculating the corrected wind speed in S2 include:

[0131] (1) Obtain the wind speed in the distribution network area where the safety risk is to be assessed based on the typhoon wind speed model ;

[0132] (2) Calculation of adjustment coefficient under the influence of micro-topography , according to the adjustment coefficient under the influence of micro-topography Calculate wind speed after micro-topography correction ;in:

[0133] ,

[0134] ,

[0135] In the formula, is the micro-topography coefficient, is the windward slope value, is the building height, It is the total height of a hillside or hilltop;

[0136] (3) Calculation of wind speed coefficient function and rainfall intensity coefficient function , combined with according to and Calculate the corrected wind speed for rainfall ;in:

[0137] ,

[0138] ,

[0139] ,

[0140] In the formula, Indicates the rainfall intensity during a typhoon, and the unit of rainfall intensity is mm / h.

[0141] It should be noted that:

[0142] Commonly used typhoon wind field models include the Holland model, the YAN MENG model and the Rankine model. In this embodiment, the more accurate Holland model is selected as the typhoon wind speed model.

[0143] The Holland model is a vector field centered on the eye of the typhoon, which changes as the typhoon moves inland. The typhoon wind field model can be represented by a vector field containing three variables: and equations to model. is the maximum sustained wind speed of the typhoon, Indicates the maximum wind circle radius of the typhoon, It is the distance from the eye of the typhoon to the outermost isobar. is a known typhoon constant, is the distance from the eye of the typhoon, is the reduction factor of the maximum sustained wind speed at the typhoon boundary. In this equation, it can be assumed that the area outside the boundary is not affected by the typhoon. The model is as follows:

[0144] ,

[0145] in,

[0146] ,

[0147] ,

[0148] ,

[0149] ,

[0150] ,

[0151] In the formula, It is the difference between the pressure of the outermost wind circle of the typhoon and the pressure of the center of the typhoon; is the Coriolis force parameter of the Earth's rotation; is the distance between the distribution network area and the typhoon center, which can be obtained from the longitude and latitude calculate; is the angular velocity of the Earth's rotation; The latitude of the typhoon when it made landfall.

[0152] The distribution network system studied in this embodiment is a 10kV low-voltage distribution network with a small geographical area. When calculating the wind speed, the distribution network is equivalent to a point, and the point coordinates are the center of the distribution network. It is assumed that at the same time, the wind speeds suffered by all components in the distribution network area are basically the same.

[0153] Different lines of the distribution network are located in different geographical locations. It is necessary to use micro-topography to correct the wind speed values ​​at each point in the typhoon wind field. Therefore, the adjustment coefficient of the typhoon under the influence of micro-topography is calculated. To complete the preliminary correction. When calculating the adjustment coefficient of the typhoon under the influence of micro-topography In the process of micro-topography, when the micro-topography is a peak or a slope, the micro-topography coefficient p Take 2.2 or 1.4 respectively; the values ​​of wind speed adjustment coefficient in mountainous areas are shown in Table 1:

[0154] Table 1 Wind speed adjustment coefficients in mountainous areas ,

[0155] Rainfall will affect the actual wind speed, and typhoons often bring heavy rainfall. Therefore, when calculating the wind speed in the distribution network area, in addition to considering the impact of micro-topography on wind speed, the wind speed must also be corrected based on rainfall.

[0156] Through the existing literature analysis of the effects of different rainfall intensities and ground roughness on typhoon wind speed, it can be seen that the effect of ground roughness on typhoon equivalent wind speed can be ignored, and the effect of rainfall on typhoon wind speed is mainly corrected by rainfall intensity, so the final corrected wind speed is obtained. , It represents the equivalent typhoon wind speed obtained after correction for micro-topography and rainfall intensity during the typhoon. The correction of wind speed is closer to the actual wind speed in the distribution network area.

[0157] In order to further implement the above technical solution, S2 calculates the node according to the corrected wind speed and The probability of failure of overhead lines between and nodes and between k Failure probability of a tower , combined with and Calculate the node , Failure probability of the distribution line between The specific contents include:

[0158] node and The probability of failure of overhead lines between for:

[0159] ,

[0160] In the formula, and are the line fault multiplier coefficient and the line fault summation coefficient respectively. , ; is the design wind speed of the overhead line; is the length of the overhead line;

[0161] node and between k Failure probability of a tower :

[0162] ,

[0163] In the formula, is the tower fault multiplier coefficient, which is 0.3 here; is the design wind speed of the tower;

[0164] Then the node and Failure probability of the distribution line between for:

[0165] ,

[0166] In the formula, K Indicates the number of line segments between distribution lines.

[0167] It should be noted that:

[0168] During typhoon weather, power outages in the distribution network are mainly caused by the destruction of distribution lines between nodes. Distribution lines are usually composed of "overhead lines + poles and towers" or cables. Since the cables are buried underground, the probability of failure is low during typhoon and rainstorm disasters. This embodiment does not consider this, and only builds fault models for overhead lines and poles and towers. In addition, this embodiment also considers the failure of distribution cabinets due to water accumulation during typhoon and rainstorm combined disasters.

[0169] The distribution lines between the nodes of the distribution network are usually composed of multiple towers and multiple overhead lines. Compared with the entire typhoon wind farm, the length of the overhead lines between adjacent distribution nodes is very short, so the multiple overhead lines between adjacent nodes can be regarded as bearing the same wind speed, and the failure probability is obtained. , in this embodiment, , is the model coefficient, here we take ; is the design wind speed of the overhead line, which is 30m / s in this embodiment; It can be seen that different line lengths and span division methods between towers will result in different overhead lines having different failure probabilities.

[0170] The failures of the towers between nodes are usually independent events, so the above Model, where is the model coefficient, and in this embodiment, the value is 0.3; is the design wind speed of the tower, which is 35m / s in this embodiment.

[0171] In order to further implement the above technical solution, the failure probability caused by foreign matter hanging on the line is obtained in S2 The specific contents include:

[0172] (1) Integrate the factors that affect foreign matter hanging on the line into environmental parameters and line parameters ,in:

[0173] ,

[0174] ,

[0175] In the formula, is the line surrounding environment coefficient, Wind speed The normalized value of is the line span coefficient, , is the conductor density factor, , is the number of conductor loops;

[0176] (2) Obtain the environmental parameter membership function and the line parameter membership function respectively, determine the fuzzy segmentation number respectively, obtain the final fuzzy inference rule, and obtain the failure probability caused by foreign objects hanging on the line after defuzzification. .

[0177] It should be noted that:

[0178] In typhoon weather, strong winds often cause line outages due to tree branches hanging on wires and double-circuit line swinging and contact. Since such situations are extremely random, it is impossible to use a function method to fit the probability of such failures. The factors that affect foreign objects hanging on wires are used as input to establish a fuzzy mathematical model of the relevant failure rate. These factors include:

[0179] 1) Equivalent wind speed at the line location , take its normalized value ;

[0180] 2) Wire density factor , is the number of conductor loops;

[0181] 3) Line span coefficient ;

[0182] 4) Line surrounding environment coefficient , taking the open environment as the benchmark, the coefficient is 1.0~2.0 according to the residential areas, trees, and cross-lines near the line corridor;

[0183] The above factors are combined into two parameters, namely environmental parameters and line parameters .

[0184] The membership function of environmental parameters is as follows: Figure 2 As shown, the number of fuzzy segmentation is 4, that is, ( Small), ( medium), ( big), ( Very big).

[0185] The line parameter membership function is as follows: Figure 3 As shown, the fuzzy segmentation number is 3, that is, ( Small), ( medium), ( big).

[0186] The combination of 12 fuzzy inference rules is obtained. The distribution line is obtained by defuzzification. The failure probability of foreign body hanging wire As shown in Table 2:

[0187] Table 2 Fuzzy rule table of failure probability of foreign body hanging wire

[0188] In order to further implement the above technical solution, after the failure probability of foreign matter hanging on the line is corrected, the node at a certain time can be corrected Failure probability of the distribution line between ,node Failure probability of the distribution line route caused by wind speed Failure probability caused by foreign objects hanging on the wire Independent of each other, S2 is based on and Calculate the node and The specific contents of the total failure probability of the distribution lines between them include:

[0189] .

[0190] In order to further implement the above technical solution, the specific contents of calculating the failure probability of distribution equipment according to the water accumulation in the distribution network area in S2 include:

[0191] Failure probability of power distribution equipment for:

[0192] ,

[0193] In the formula, for The amount of water accumulated in the area where the power distribution equipment is located at any given moment; It is the designed flood-proof height of the power distribution equipment. is the distribution equipment fault multiplier coefficient, which is 0.15 here;

[0194] Then the failure probability of the power distribution equipment during the entire evaluation period is:

[0195] ,

[0196] In the formula, For the evaluation cycle, Any time period within the evaluation period.

[0197] It should be noted that:

[0198] This embodiment takes into account the impact of typhoon and rainstorm combined disasters on the failure probability of distribution equipment on the basis of considering the failure of distribution lines. The rainfall process during typhoons is a continuous process. When the rainfall is greater than the drainage capacity of the distribution network area, the water in the distribution network area caused by the rainfall is constantly accumulating. Therefore, as the typhoon and rainstorm process continues, the depth of the accumulated water continues to deepen, and the failure probability of the distribution equipment increases with the increase of the depth of the accumulated water.

[0199] In order to further implement the above technical solution, calculate The amount of water accumulated in the area where the power distribution equipment is located at any given moment The specific contents include:

[0200] A two-dimensional hydraulic model is used to simulate surface water accumulation. The two-dimensional hydraulic model is:

[0201] ,

[0202] In the formula, is the unit conversion factor, is the Manning coefficient; is the water flow rate; is the water surface elevation; is the rainfall intensity; It is the depth of water;

[0203] Divide the area where the power distribution equipment is located into grids, and take one of the grids C and its 8 neighbors. The only grids directly connected to grid C by edges are N, E, S, and W. The accumulated water in grid C only flows in the four directions of N, E, S, and W. The other grids adjacent to grid N are NW and NE, and the grids adjacent to grid S are SW and SE.

[0204] Find the gradient of the water surface elevation in the CE direction , whose lateral component and the longitudinal component for:

[0205] ,

[0206] ,

[0207] In the formula, is the water surface elevation, for The distance between two grids in the direction, for The distance between two grids in the direction, and They are and The water surface elevation of the grid;

[0208] According to the gradient The water velocity is calculated using the two-dimensional hydraulic model , in a period of time The amount of water flowing from grid C into grid E is:

[0209] ,

[0210] A positive value indicates that the flow from C to E, and a negative value indicates that the flow from E to C. Similarly, and , and stipulates that along Axis or When the flow is in the positive direction of the axis, the flow is positive, and vice versa, the flow is negative;

[0211] Surface infiltration is considered in flow calculations and is:

[0212] ,

[0213] In the formula, is the infiltration flow rate, is the minimum infiltration rate, is the initial maximum infiltration rate, is the attenuation coefficient that describes the change of infiltration rate with time;

[0214] Considering the drainage of rainwater wells at the same time, the drainage capacity is:

[0215] ,

[0216] Where: is the drainage flow; is the drainage coefficient, which ranges from 0 to 1; is the cross-sectional area, i.e. the total area of ​​the openings on the rainwater manhole cover; for water depth;

[0217] Then the water depth at the next moment in grid C is:

[0218] ,

[0219] Then the distribution equipment in grid C is Water accumulation at the time .

[0220] It should be noted that:

[0221] During the landing period of a typhoon, a large amount of rainfall is often brought. The rainfall brought by the typhoon is characterized by localization and high intensity. The severity of the rainstorm disaster is related to factors such as rainfall intensity, duration, and geographical environment. The existing risk assessment of the distribution network under typhoon disasters rarely considers the impact of the large amount of rainfall brought during the typhoon on the distribution network equipment. There are currently models that can simulate the cumulative rainfall of short-term rainstorms, but due to the small time scale of the simulated short-term rainstorm model, it cannot effectively reflect the rainfall intensity and rainfall during the period when the entire distribution network is affected by the typhoon. Therefore, this embodiment uses the real rainfall data of the area where the distribution network is located during the typhoon period when simulating rainstorm disasters.

[0222] Due to the combined effects of rainfall, topographic factors and urban drainage systems, the risk of failure of distribution network distribution cabinet equipment caused by heavy rain in low-lying areas of cities and areas with insufficient drainage capacity is directly related to the depth of water accumulation. The main reason for the failure of distribution equipment is that the height of flood control is set lower than the depth of water accumulation, and the equipment is affected and shut down. In order to determine the depth of water accumulation at the location of the distribution equipment, it is necessary to establish a heavy rain waterlogging model. First, two provisions are made for the model: first, the distribution network area is smaller than the rainfall coverage area, and the rainfall process in the distribution network area is the same; second, the distribution network is gridded according to the DEM data of the distribution network area.

[0223] Currently, there are many computer numerical simulation methods for predicting waterlogging caused by heavy rains, such as the passive inundation method and the urban rainfall runoff model method. The above models are one-dimensional models and are not suitable for simulating two-dimensional surface waterlogging. This paper uses a two-dimensional hydraulic model to simulate surface waterlogging.

[0224] To simplify the calculation, a square calculation grid that coincides with the DEM grid is used in the two-dimensional hydraulic model calculation. Take a grid C and its 8 domains, such as Figure 4As shown. Assume that there are only four grids N, E, S, and W that are directly connected to C through edges. The accumulated water in the C grid can only flow in these four directions. The elevation gradient is obtained based on the water surface elevation map combined with the grid, and then the water flow velocity at the CN, CE, CS, and CW boundaries is calculated based on the elevation gradient combined with the two-dimensional hydraulic model according to the water depth of each grid at the initial time. Then the water flow rate is obtained by multiplying the water flow velocity by the cross-sectional area, and then the water depth variable of the C grid is calculated based on the water flow rate, so as to obtain the water depth at the next moment. The water surface elevation map is shown as follows: Figure 5 shown.

[0225] In order to further implement the above technical solution, the objective function in S4 is:

[0226] ,

[0227] Where: For the evaluation cycle, For any time during the evaluation period, is the distribution network node index; It is a failure scenario The weight of It is a failure scenario Next Node exist The amount of load loss at the moment.

[0228] It should be noted that:

[0229] The assessment period is the duration from the time the first power outage is caused by the typhoon to the time when the typhoon's level 7 wind circle leaves the target area.

[0230] In order to further implement the above technical solution, the constraints in S4 include:

[0231] 1) Power balance constraints:

[0232] ,

[0233] ,

[0234] In the formula, Representation and Node Adjacent nodes where the distribution lines are not out of service A collection of; and For line exist Active and reactive currents at all times; and It indicates the active and reactive power transmitted from the upper transmission network to the distribution network; and Representation Node The active and reactive power sent by the distributed generation to the distribution network; and Representation Node exist Active and reactive power requirements at each moment;

[0235] 2) Voltage constraint:

[0236] ,

[0237] ,

[0238] ,

[0239] In the formula, and The nodes are and nodes exist The square of the voltage at the time; and are the resistance and reactance of the line respectively; For line exist The square term of the current transmitted at any moment; Indicates the line operation status as a 0-1 variable, 1 for normal operation and 0 for line failure and outage; and For Node The minimum and maximum voltages allowed; M For big M The coefficient of the variable in the method;

[0240] 3) Distributed power constraints:

[0241] ,

[0242] ,

[0243] ,

[0244] Where: and are the node sets of wind power and diesel generator respectively; Represents various types of distributed power sources and nodes The connection status; and They are respectively the upper limits of active and reactive output of distributed generation; and They are the upper and lower limits of the power factor of distributed power sources, and are the minimum and maximum values ​​of the power factor angle respectively;

[0245] 4) Line current and transmission capacity constraints:

[0246] ,

[0247] ,

[0248] Where: is the upper limit of the square term of the line current;

[0249] 5) Load reduction constraints:

[0250] ,

[0251] ,

[0252] Where: and They are the maximum values ​​of active and reactive power of the load respectively;

[0253] 6) Second-order cone constraint:

[0254] .

[0255] In order to further implement the above technical solution, the output power of the wind turbine in the distributed power source is affected by the wind speed, thereby affecting the overall output power of the distributed power source. The specific contents of calculating the output power of the wind turbine include:

[0256] Active output power of wind turbine for:

[0257] ,

[0258] Where: is the rated power; is the wind speed of the wind turbine, that is ; and Represent the cut-in wind speed, rated wind speed and cut-out wind speed respectively. and Output power characteristic parameters:

[0259] ,

[0260] The reactive output power of a wind turbine is limited by the capacity of the inverter:

[0261] ,

[0262] Where: is the maximum reactive power output of the wind turbine, is the capacity of the inverter, is the active output power of the wind turbine.

[0263] It should be noted that:

[0264] A typhoon may cause the wind speed of a wind turbine to exceed the cut-out wind speed of the wind turbine. However, when the typhoon is far away from the wind turbine or the center of the typhoon just moves to the location of the wind turbine, the wind speed of the wind turbine is relatively low and the wind turbine can work normally. Take 5m / s, 12 m / s and 25m / s respectively.

[0265] In order to verify the feasibility and effectiveness of this embodiment, the following example test is used to illustrate:

[0266] This embodiment uses an improved IEEE 33-node distribution network system to perform example tests.

[0267] 1. Test system data

[0268] Assume that in this distribution network, Figure 6 As shown, nodes 16 and 19 are equipped with distributed wind power stations with a rated active output of 50KW, nodes 8 and 29 are equipped with diesel generators with a maximum active output of 200KW and 150KW, respectively, and nodes 12, 22 and 25 are equipped with diesel generators with a maximum active output of 100KW. In this embodiment, it is assumed that the distribution cabinet is installed on the distribution network node, and the distribution cabinet is equipped with a relay protection device. The failure of the distribution cabinet only affects the load at the node, and does not affect the power transmission on the normal line.

[0269] Regarding typhoon information, this embodiment selects a typhoon that occurred in the past as the research object, and assumes that a distribution network is about to be affected by the typhoon. Figure 7 (a) is shown. First, the typhoon forecast path and forecast rainfall are collected. In addition to the forecast path, two offset typhoon paths are selected as the main scenario. This method does not consider the impact of the typhoon path offset on the rainfall in the distribution network area. Secondly, the wind speed in the distribution network area is calculated every 1 hour (such as Figure 7 (b) According to the wind speed of each main scene, the micro-topography and rainfall intensity are used to calculate the fault conditions of the line and the distribution cabinet at each time. For each main scene, 10 typical sub-scenes are selected, totaling 30 sub-scenes.

[0270] 2. Example Analysis

[0271] This example uses two different cases to verify the effectiveness of the solution, as shown below:

[0272] Case 1: The impact of rainfall is not considered when conducting risk assessment;

[0273] Case 2: Use the method in this embodiment to conduct risk assessment of distribution network;

[0274] 1. Comparison of risk assessment results of pre-distribution power grid in Case 1-2

[0275] This embodiment introduces two evaluation indicators: the amount of load loss in the distribution network and distribution network load loss rate The calculation formulas for each indicator are as follows:

[0276] ,

[0277] ,

[0278] In the formula, It is a collection of 30 sub-scenes; It's a scene The weight of It's a scene Next Node exist The amount of load loss during the period; Is a node exist The power demand during the period; the load loss node under each scheme in one of the scenarios when the distribution network leaves the typhoon level 7 wind circle is as follows: Figure 8 As shown:

[0279] Comparing Case 1 and Case 2, in Case 1, if only the impact of typhoon on the distribution network is considered, since line 1-2 is a cable line, it is less affected by the typhoon disaster and the line will not be shut down, so the load demand on node 2 can be fully met and no load loss will occur. However, in Case 2, considering the combined disaster of typhoon and rainstorm, due to the deep water depth at the location of node 2, the distribution cabinet fails and fails, affecting the transmission of power from the distribution cabinet to the lower distribution station area, resulting in a complete loss of load on node 2. For node 13, in Case 1, although line 7-8 had been shut down before that moment, due to the output of the distributed power source at node 12, the load loss of node 13 at the moment of leaving the level 7 wind circle was 12.8KW. When considering the effect of rainstorm, due to the failure of the distribution cabinet at node 13, the load loss increased to 33KW.

[0280] The load loss and load loss rate of the distribution network under each scheme are as follows: Fig. 9 and 10 As shown:

[0281] Depend on Fig. 9 and 10It can be seen that as the typhoon approaches the area where the distribution network is located, the number of distribution network outage lines increases, and the load loss of the distribution network increases. However, considering the change over time, the load demand decreases, so the total load loss of the distribution network shows a trend of first increasing and then decreasing. Similar to the total load loss, the total load loss rate of the distribution network also shows a trend of first increasing and then decreasing. However, due to the role of the distribution cabinet being shut down due to heavy rain and water accumulation during the typhoon, the total load loss rate in Case 2 decreases after reaching the highest value, but the load loss rate is 43.89%, which is still maintained above 40%, while the total load loss rate in Case 1 will drop to 39.58%.

[0282] Comparison of total load loss in distribution networks between Case 1 and Case 2 Fig.11 As shown:

[0283] Depend on Fig.11 It can be seen that due to the cumulative effect of water accumulation, the impact of heavy rain on the load loss of the distribution network is concentrated after 19:00 in the evening. When considering the impact of heavy rain during typhoons, the load loss of the distribution network increases significantly, which shows the necessity of more accurate risk assessment of the distribution network considering the combined heavy rain disaster during typhoons. The risk assessment of the distribution network considering the combined disaster of typhoon and heavy rain can provide more accurate initial values ​​for the subsequent distribution network risk prevention and control strategy, and provide a basis for obtaining a better risk prevention and control strategy.

[0284] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A distribution network operation safety risk assessment method considering typhoon and rainstorm combined disasters, characterized in that: The following steps are involved: S1. Based on the typhoon and rainstorm disaster forecast data in the distribution network area with safety risks to be assessed, different typhoon paths are generated as main scenarios, and weights are assigned to each main scenario according to the probability of occurrence; S2. Considering micro-topography and rainfall intensity, the wind speed in the main scene The influence of the wind speed is calculated, and the node is calculated based on the corrected wind speed. and The probability of failure of overhead lines between and nodes and between k Failure probability of a tower , combined with and Calculate the node , Failure probability of the distribution line between , obtain the failure probability caused by foreign objects hanging on the wire ,according to and Calculate the node and Total failure probability of distribution lines between ; Based on the typhoon and rainstorm disaster forecast data, the water surface elevation map of the distribution network area and the two-dimensional hydraulic model, the dynamic change process of the grid water volume in the distribution network area is simulated, and the failure probability of the distribution equipment is calculated based on the water volume in the distribution network area; S3. Generate the total failure probability of the distribution line and the failure probability of the distribution equipment through Monte Carlo simulation N Failure scenarios s Form a collection of fault scenarios S , and assign weights to each fault scenario according to the probability of occurrence of each fault scenario, where the sum of the weights of the fault scenarios on the same main scenario is the weight of the main scenario; S4. Taking into account the weight of each fault scenario and the adjustable output of distributed power sources, a risk assessment model is constructed with the minimum expected total load loss under all fault scenarios as the objective function, and the risk assessment model is solved in combination with the constraints.

2. A distribution network operation safety risk assessment method considering typhoon and rainstorm combined disasters according to claim 1, characterized in that: The specific contents of calculating the corrected wind speed in S2 include: (1) Obtain the wind speed in the distribution network area where the safety risk is to be assessed based on the typhoon wind speed model ; (2) Calculation of adjustment coefficient under the influence of micro-topography , according to the adjustment coefficient under the influence of micro-topography Calculate wind speed after micro-topography correction ;in: ， ， In the formula, is the micro-topography coefficient, is the windward slope value, is the building height, It is the total height of a hillside or hilltop; (3) Calculation of wind speed coefficient function and rainfall intensity coefficient function , combined with according to and Calculate the corrected wind speed for rainfall ;in: ， ， ， In the formula, Indicates the rainfall intensity during a typhoon.

3. A distribution network operation safety risk assessment method considering typhoon and rainstorm combined disasters according to claim 1, characterized in that: S2 calculates the node according to the corrected wind speed and The probability of failure of overhead lines between and nodes and between k Failure probability of a tower , combined with and Calculate the node , Failure probability of the distribution line between The specific contents include: node and The probability of failure of overhead lines between for: ， In the formula, and are line fault multiplier coefficient and line fault summation coefficient respectively; is the design wind speed of the overhead line; is the length of the overhead line; node and between k Failure probability of a tower : ， In the formula, is the tower fault multiplier coefficient; is the design wind speed of the tower; Then the node and Failure probability of the distribution line between for: ， In the formula, K Indicates the number of line segments between distribution lines.

4. A distribution network operation safety risk assessment method considering typhoon and rainstorm combined disasters according to claim 3, characterized in that: Obtain the failure probability caused by foreign objects hanging on the wire in S2 The specific contents include: (1) Integrate the factors that affect foreign matter hanging on the line into environmental parameters and line parameters ,in: ， ， In the formula, is the line surrounding environment coefficient, Wind speed The normalized value of is the line span coefficient, , is the conductor density factor, , is the number of conductor loops; (2) Obtain the environmental parameter membership function and the line parameter membership function respectively, determine the fuzzy segmentation number respectively, obtain the final fuzzy inference rule, and obtain the failure probability caused by foreign objects hanging on the line after defuzzification. .

5. A distribution network operation safety risk assessment method considering typhoon and rainstorm combined disasters according to claim 4, characterized in that: According to S2 and Calculate the node and The specific contents of the total failure probability of the distribution lines between them include: 。 6. A distribution network operation safety risk assessment method considering typhoon and rainstorm combined disasters according to claim 1, characterized in that: The specific contents of calculating the failure probability of distribution equipment according to the water accumulation in the distribution network area in S2 include: Failure probability of power distribution equipment for: ， In the formula, for The amount of water accumulated in the area where the power distribution equipment is located at any given moment; It is the designed flood-proof height of the power distribution equipment. is the distribution equipment fault multiplier coefficient; Then the failure probability of the power distribution equipment during the entire evaluation period is: ， In the formula, For the evaluation cycle, Any time period within the evaluation period.

7. A method for evaluating the risk of distribution network operation safety considering typhoon and rainstorm combined disasters according to claim 6, characterized in that: The dynamic change process of grid water accumulation in the distribution network area is simulated based on typhoon and rainstorm disaster forecast data, water surface elevation map of the distribution network area and two-dimensional hydraulic model, and the specific contents include: A two-dimensional hydraulic model is used to simulate surface water accumulation. The two-dimensional hydraulic model is: ， In the formula, is the unit conversion factor, is the Manning coefficient; is the water flow rate; is the water surface elevation; is the rainfall intensity; It is the depth of water; Divide the area where the power distribution equipment is located into grids, and take one of the grids C and its 8 neighbors. The only grids directly connected to grid C by edges are N, E, S, and W. The accumulated water in grid C only flows in the four directions of N, E, S, and W. The other grids adjacent to grid N are NW and NE, and the grids adjacent to grid S are SW and SE. Find the gradient of the water surface elevation in the CE direction , whose lateral component and the longitudinal component for: ， ， In the formula, is the water surface elevation, for The distance between two grids in the direction, for The distance between two grids in the direction, and They are and The water surface elevation of the grid; According to the gradient The water flow rate is then calculated using a two-dimensional hydraulic model. , in a period of time The amount of water flowing from grid C into grid E is: ， A positive value indicates that the flow from C to E, and a negative value indicates that the flow from E to C. Similarly, and , and stipulates that along Axis or When the flow is in the positive direction of the axis, the flow is positive, and vice versa, the flow is negative; Surface infiltration is considered in flow calculations and is: ， In the formula, is the infiltration flow rate, is the minimum infiltration rate, is the initial maximum infiltration rate, is the attenuation coefficient that describes the change of infiltration rate with time; Considering the drainage of rainwater wells at the same time, the drainage capacity is: ， Where: is the drainage flow; is the drainage coefficient, which ranges from 0 to 1; is the cross-sectional area, i.e. the total area of ​​the openings on the rainwater manhole cover; for water depth; Then the water depth at the next moment in grid C is: ， Then the distribution equipment in grid C is Water accumulation at the time .

8. A distribution network operation safety risk assessment method considering typhoon and rainstorm combined disasters according to claim 1, characterized in that: The objective function in S4 is: ， Where: For the evaluation cycle, For any time during the evaluation period, is the distribution network node index; It is a failure scenario The weight of It is a failure scenario Next Node exist The amount of load loss at the moment.

9. A distribution network operation safety risk assessment method considering typhoon and rainstorm combined disasters according to claim 1, characterized in that: The constraints in S4 include: 1) Power balance constraints: ， ， In the formula, Representation and Node Adjacent nodes where the distribution lines are not out of service A collection of; and For line exist Active and reactive currents at all times; and It indicates the active and reactive power transmitted from the upper transmission network to the distribution network; and Representation Node The active and reactive power sent by the distributed generation to the distribution network; and Representation Node exist Active and reactive power requirements at each moment; 2) Voltage constraint: ， ， ， In the formula, and The nodes are and nodes exist The square of the voltage at the time; and are the resistance and reactance of the line respectively; For line exist The square term of the current transmitted at any moment; Indicates the line operation status as a 0-1 variable, 1 for normal operation and 0 for line failure and outage; and For Node the minimum and maximum voltages allowed; M For big M The coefficient of the variable in the method; 3) Distributed power constraints: ， ， ， Where: and are the node sets of wind power and diesel generator respectively; Represents various types of distributed power sources and nodes The connection status; and They are respectively the upper limits of active and reactive output of distributed generation; and They are the upper and lower limits of the power factor of distributed power sources, and are the minimum and maximum values ​​of the power factor angle respectively; 4) Line current and transmission capacity constraints: ， ， Where: is the upper limit of the square term of the line current; 5) Load reduction constraints: ， ， Where: and They are the maximum values ​​of active and reactive power of the load respectively; 6) Second-order cone constraint: 。 10. A distribution network operation safety risk assessment method considering typhoon and rainstorm combined disasters according to claim 9, characterized in that: The output power of wind turbines in distributed power sources is affected by wind speed, which in turn affects the overall output power of distributed power sources. The specific contents of calculating the output power of wind turbines include: Active output power of wind turbine for: ， Where: is the rated power; is the wind speed of the wind turbine, that is and Represent the cut-in wind speed, rated wind speed and cut-out wind speed respectively. and Output power characteristic parameters: ， The reactive output power of a wind turbine is limited by the capacity of the inverter: ， Where: is the maximum reactive power output of the wind turbine, is the capacity of the inverter, is the active output power of the wind turbine.

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