Power distribution network disaster loss situation awareness method and system based on emergency communication under rain flood

Abstract

The application discloses a power distribution network disaster loss situation awareness method and system based on emergency communication under rain flood, establishes a power distribution network disaster loss situation awareness model under urban rainstorm waterlogging; a communication work point site selection model is constructed to determine possible parking points of mobile unmanned aerial vehicle stations and emergency communication vehicles; based on the possible parking points of the mobile unmanned aerial vehicle stations and the emergency communication vehicles, path constraints and time constraints are considered to determine a scheduling scheme and a transfer path of the mobile unmanned aerial vehicle stations and the emergency communication vehicles, and a vehicle-machine cooperative communication recovery model is constructed; based on the constructed power distribution network disaster loss situation awareness model under urban rainstorm waterlogging and the constructed vehicle-machine cooperative communication recovery model, a power distribution network disaster loss situation awareness improvement model under urban rainstorm waterlogging is constructed, and a situation awareness improvement strategy is obtained by solving the power distribution network disaster loss situation awareness improvement model under urban rainstorm waterlogging. The application improves the power distribution network disaster loss situation awareness capability and realizes fast recovery of the power distribution network after a disaster.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of disaster prevention and mitigation of power distribution network, and particularly relates to a power distribution network disaster damage situation awareness method and system based on emergency communication under rain flood. BACKGROUND

[0002] In recent years, extreme disasters have occurred more and more frequently, which seriously affect the safe operation of the power system. The burst rate of rainstorms is particularly significant, and the floodwaterlogging caused by rainstorms is one of the main disasters in summer. The waterlogging in local areas caused by long-time and high-intensity heavy rain seriously affects the normal operation of power distribution network equipment, and the rainstorm disaster often causes the communication equipment to be damaged, so that the communication equipment exists the conditions of false alarm and missed alarm. Considering the influence of rainstorm disaster on communication equipment, it is of great significance to establish a temporary emergency communication recovery strategy for post-disaster recovery of power distribution network.

[0003] At present, scholars have studied the influence mechanism of extreme weather on power distribution network, system measurement data fault location method, and fault complaint telephone positioning, and many have proposed communication recovery schemes through unmanned aerial vehicles and emergency communication vehicles. However, the existing methods for judging faults are all based on complete communication, but in the case of urban rainstorm waterlogging disasters, the original communication facilities will also be destroyed, and these methods for positioning the fault area according to the fault indicator may fail. The power company often confirms the power distribution network fault condition through the telephone complaint of the user, and in the case of urban rainstorm waterlogging, the extreme rainstorm disaster will damage the communication equipment, greatly affecting the usability and accuracy of the user fault complaint channel. At the same time, in order to solve the communication problems of the personnel in the disaster area and the material allocation problems, the unmanned aerial vehicle and emergency communication vehicle allocation strategy can also play an important role in the communication recovery of the power distribution network fault indication element.

[0004] Therefore, in view of the above problems, it is urgent to find a power distribution network disaster damage situation awareness method based on emergency communication under urban rain flood. SUMMARY

[0005] The technical problem to be solved by the application is to provide a power distribution network disaster damage situation awareness method and system based on emergency communication under rain flood, which solves the technical problems of communication problems of personnel in the disaster area and difficulties in material allocation under the condition of urban rainstorm waterlogging.

[0006] The application adopts the following technical scheme:

[0007] A power distribution network disaster damage situation awareness method based on emergency communication under rain flood comprises the following steps:

[0008] The system measurement data, user side feedback information and meteorological data are taken as input data, the system measurement data and user feedback information are used to determine the fault section of the distribution network through the causal logic of the feeder upstream and downstream; according to the position of the fault section in the distribution network and the meteorological data of the uncertain fault section, a disaster damage situation awareness model of the distribution network under urban rainstorm waterlogging is established based on the element vulnerability model, and a criterion of maximizing Bayesian posterior probability is used;

[0009] A communication work point location model is constructed to determine possible parking points of the mobile unmanned aerial vehicle station and the emergency communication vehicle; based on the possible parking points of the mobile unmanned aerial vehicle station and the emergency communication vehicle, a scheduling scheme and a transfer path of the mobile unmanned aerial vehicle station and the emergency communication vehicle are determined by considering path constraints and time constraints, and a vehicle-machine collaborative communication recovery model is constructed;

[0010] Based on the constructed disaster damage situation awareness model of the distribution network under urban rainstorm waterlogging and the constructed vehicle-machine collaborative communication recovery model, a disaster damage situation awareness improvement model of the distribution network under urban rainstorm waterlogging is constructed, and a situation awareness improvement strategy is obtained by solving the disaster damage situation awareness improvement model of the distribution network under urban rainstorm waterlogging.

[0011] Preferably, the establishment of the disaster damage situation awareness model of the distribution network under urban rainstorm waterlogging is specifically as follows:

[0012] Considering the completeness of information, a mapping relationship model of RFI fault alarm information and the disaster damage section of the distribution network is established, and the post-disaster disaster damage situation of the distribution network is determined based on system measurement data;

[0013] A direct failure model of the ring network high-voltage switch cabinet based on meteorological data is established, the reference failure rate of the ring network high-voltage switch cabinet is taken as the basis, the humidity and temperature of the high-voltage switch cabinet are taken as the covariates of the indirect failure model to improve the indirect failure model of the high-voltage switch cabinet; the indirect failure probability of the high-voltage switch cabinet at time t is determined by improving the indirect failure model; and the comprehensive failure probability P i (t) of the node under rainstorm waterlogging is obtained according to the independent event probability formula.

[0014] The user side feedback information is used to locate the fault of the distribution network.

[0015] The system measurement data, meteorological data and user side feedback information are fused to comprehensively analyze and judge the post-disaster fault scene scheme of the distribution network, and the disaster damage situation awareness model of the distribution network under rainstorm waterlogging is obtained.

[0016] Preferably, the determination of the post-disaster disaster damage situation of the distribution network based on system measurement data is specifically as follows:

[0017] A network description matrix [N] is established according to the topology of the distribution network, and the element N kl is determined according to the following rules:

[0018] If the feeder LSl is the start point of RFIk, N kl = 1;

[0019] If the feeder LSl is the end point of RFIk, N kl = -1;

[0020] If the feeder LSl is neither the start point nor the end point of RFIk, N kl = 0;

[0021] The fault identification vector [FS] = [N][F] T , the fault identification vector [F T ] of the T-shaped structure = [N T ]·[F] T ;

[0022] For the section LSk of the non-T-shaped structure, according to the value of the kth element in the fault identification vector [FS], if FS k ≥ 1, it is determined that there is a fault in the section k, otherwise it is determined that there is no fault in the section k;

[0023] For the T-shaped structure section, if the corresponding element FS k ≥ 1, and there is no -1 in the corresponding [F T ] element, it is determined that there is a fault in the T-shaped section, otherwise there is no fault.

[0024] Preferably, the comprehensive failure probability P i (t) of the node under the rainstorm waterlogging is specifically:

[0025] P i (t) = 1 - (1 - P i F (t))(1 - P i J (t))

[0026] Wherein, P i F (t) is the direct failure probability of the high-voltage switch cabinet, and P i J (t) is the indirect failure probability of the high-voltage switch cabinet.

[0027] Preferably, the positioning of the power distribution network fault based on the user side feedback information is specifically:

[0028]

[0029] Wherein, is the node path passed through from the power supply end to the node k; is a binary variable of the state of node i at time t, is the normal work of node i at time t; d kBinary variable of feedback result for user k, d k = 1 if user k makes a fault complaint; Ω CF is the set of user feedback states.

[0030] Preferably, the integrated failure probability P i (t) of the distribution network node under rainwater flooding is established to maximize the Bayesian posterior probability as the objective function to determine the most likely failure scenario under rainwater disasters at time t, and a logarithmic function is used to linearize the objective function, and the disaster loss situation perception model of the distribution network under urban rainwater flooding is described as:

[0031]

[0032] wherein, is the system node, X i is a binary variable of node i state, p i F is the integrated failure probability of node i.

[0033] Preferably, in step S2, the objective function of the vehicle-machine cooperative communication recovery model is as follows:

[0034]

[0035] wherein, F obj1 is the normalized communication recovery time; F obj2 is the normalized distribution network disaster loss situation probability; β 1 , β 2 are the weight values of the two sub-objective functions, p k is a binary decision variable representing whether the discrete point k is selected as an emergency communication working point, and n is the number of discrete points.

[0036] Preferably, the constraints of the vehicle-machine cooperative communication recovery model are as follows:

[0037] Path constraint:

[0038]

[0039]

[0040] wherein, C N represents the set of all working points; N represents all vehicle working points including the warehouse; N0 represents the set of vehicle starting points, N + represents the set of vehicle arrival points; P is composed of <i,j,k> three points, i represents the set of unmanned aerial vehicle starting points, j represents the unmanned aerial vehicle reaching and taking communication recovery work working points; x (i,j) is a set of 0-1 variables; y (i,j,k) is a set of 0-1 variables; ui is a continuous variable;

[0041] Time constraints:

[0042]

[0043]

[0044] t′ k -(t′ j -τ′ ij )≤e+M(1-y ijk )

[0045]

[0046] where τ ij denotes the time taken by the vehicle to travel from work point (or warehouse) i to work point j (or warehouse); τ′ ij denotes the time taken by the UAV to travel from work point (or warehouse) i to work point j (or warehouse); s L denotes the operation time required by the vehicle staff to launch the UAV; s R denotes the time required by the vehicle staff to retrieve and replace the battery for the UAV, so that the UAV can complete launching again; e denotes the endurance of the UAV; t j is a continuous variable, denoting the time at which the vehicle arrives at work point (or warehouse) j; t′ j is the time at which the UAV arrives at j; it is stipulated that t j = t′ j = 0, indicating the initial time at which the vehicle and the UAV leave the warehouse;

[0047] Access order constraints:

[0048]

[0049] where p (i,j) is a set of 0-1 variables, i∈Cj∈{C:j≠i}, p (i,j) = 1 when i work point is accessed before j on the vehicle path;

[0050] Supplementary constraints:

[0051] t0= 0

[0052] t'0= 0

[0053]

[0054] The power distribution network fault location constraint is specifically:

[0055] Continuous time and discrete time stamp correspondence:

[0056]

[0057] where γ i,t is a binary variable indicating whether the communication of work point i is repaired at time t, γ i,t = 1 means that the communication of work point i is successfully repaired at time t; Δt is the whole model time interval; t is the discrete time parameter; v is the error value;

[0058] Power distribution network fault section location constraint:

[0059]

[0060] [Fs t ] = [N][F t ] T = [z 1,t , z 2,t , z 3,t ,..., z n,t ] T

[0061]

[0062] where, is the section communication recovery indication vector; is the customer communication recovery indication vector; C is the set of communication work points to be recovered; Ω station(i) is the set of RFI points covered by the communication work point i; Ω call(i) is the set of customer service center user points covered by the communication work point i; [N] is the network description matrix of the power distribution network; [F] is the true value of the fault indication vector received by the main station; [C F ] is the true value of the user feedback information vector; [Fs t ] is the generated fault indication vector at time t; is the actual fault vector received by the main station at time t; is the actual user feedback vector received by the main station at time t; is the indication vector of section k at time t, then there is a fault in the section; C LS is the set of sections; is the fault state of node i at time t; is the comprehensive failure probability of node i.

[0063] Preferably, the objective function of the communication work point location model is:

[0064]

[0065] Constraint condition:

[0066]

[0067] Among them, S UAV S is the set of discrete point coordinates; CN N is the set of coordinates of the communication requirement points; c For transmission capacity; p k The binary decision variable representing whether discrete point k is selected as an emergency communication work point; x c,k A binary variable representing whether communication demand point c is covered by discrete point k; (x c ,y c ),(x k ,y k ) represents the coordinates of the communication demand point c and the discrete point k; d k,c Let this represent the distance between communication node c and discrete point k, and the coverage capability of the base station.

[0068] Secondly, embodiments of the present invention provide a power distribution network disaster situation awareness system based on emergency communication under stormwater conditions, comprising:

[0069] The module takes system measurement data, user feedback information and meteorological data as input data. The system measurement data and user feedback information are used to infer the fault section of the distribution network through the causal logic of the upstream and downstream of the feeder. Based on the location of the fault section in the distribution network and the meteorological data of the uncertain fault section, a disaster loss situation perception model of the distribution network under urban rainstorm and waterlogging is established based on the component vulnerability model and the maximization of Bayesian posterior probability as the criterion.

[0070] The collaboration module constructs a communication work point location model to determine possible docking points for mobile unmanned aerial vehicle (UAV) stations and emergency communication vehicles. Based on the possible docking points of mobile UAV stations and emergency communication vehicles, and considering path constraints and time constraints, it determines the scheduling scheme and transfer path of mobile UAV stations and emergency communication vehicles, and constructs a vehicle-machine collaborative communication recovery model.

[0071] The output module, based on the constructed power distribution network disaster situational awareness model under urban rainstorm and flooding and the constructed vehicle-machine cooperative communication recovery model, constructs a power distribution network disaster situational awareness enhancement model under urban rainstorm and flooding, and solves the power distribution network disaster situational awareness enhancement model under urban rainstorm and flooding to obtain situational awareness enhancement strategies.

[0072] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for assessing the disaster situation of a power distribution network based on emergency communication during rainstorms.

[0073] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium comprising a computer program, which, when executed by a processor, implements the steps of the power distribution network disaster damage situation awareness method based on emergency communication under rainstorm described above.

[0074] In a fifth aspect, a chip comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the power distribution network disaster damage situation awareness method based on emergency communication under rainstorm described above when executing the computer program.

[0075] In a sixth aspect, an embodiment of the present application provides an electronic device comprising a computer program, which, when executed by the electronic device, implements the steps of the power distribution network disaster damage situation awareness method based on emergency communication under rainstorm described above.

[0076] Compared with the prior art, the present application has at least the following beneficial effects:

[0077] A power distribution network disaster damage situation awareness method based on emergency communication under rainstorm, establishes a power distribution network disaster damage situation awareness strategy based on multi-source data fusion technology, first establishes a city rainstorm meteorological data impact model on power distribution network element devices, and obtains a power distribution network element vulnerability curve;Secondly, the logical causal relationship between RFI fault current information and fault section is established to determine the fault location;Finally, the logical causal relationship between user-side calls and fault sections is established, combined with meteorological data, measurement data, and user-side feedback data, based on a maximum Bayesian posterior probability model, a power distribution network fault point is given;An emergency communication recovery strategy based on vehicle-machine cooperation mode is established, first a vehicle, unmanned aerial vehicle working point site selection model is established, and the least working points are established under the premise of covering all communication demand points;Secondly, the path planning and time planning constraints of the vehicle-machine cooperation model are established, and combined with the power distribution network disaster damage situation awareness model, the power distribution network disaster damage situation developing dynamically with the rainstorm is obtained.

[0078] Further, a power distribution network disaster damage situation awareness model under city rainstorm waterlogging is established, which is obtained by fusing meteorological data, system measurement data and user-side feedback data, each data source can provide power distribution network disaster damage situation information from different time and space dimensions, which is beneficial to more accurately master the power distribution network disaster damage situation.

[0079] Further, a method for inferring a power distribution network fault scenario based on system measurement data is established, which can locate the fault section according to the collected fault indication data.

[0080] Further, in order to determine the unique system failure scenario, more basis needs to be supported as support, through the node comprehensive failure probability of the distribution network under the rainstorm waterlogging, the maximum Bayesian posterior probability is established as the objective function, and the most possible failure scenario can be determined. At the same time, it is noted that the objective function of the optimization problem is nonlinear and cannot be directly solved to obtain its result. The logarithmic function has the property of monotone increasing, so the logarithmic function is used to linearize the target.

[0081] Further, under the city rainstorm, rainwater accumulation often leads to road damage, the emergency communication vehicle is limited by the road condition, and the unmanned aerial vehicle is constrained by the endurance capacity, both of which cannot complete the communication recovery work alone. The vehicle-machine cooperative communication recovery objective function guarantees the maximization of the disaster damage situation awareness ability of the distribution network under the condition that the used time is the least.

[0082] Further, the constraint conditions of the scheduling problem of the vehicle-machine cooperative recovery emergency communication strategy include path constraint, time constraint, sequential access constraint and the like. Among them, the path constraint ensures that each communication node accesses according to the optimal path, avoiding the repetition and waste of the path selection of the emergency communication vehicle or the unmanned aerial vehicle; the time constraint ensures that the time of the vehicle and the unmanned aerial vehicle is coordinated, considering the required time of the communication recovery process; the sequential access constraint determines the access sequence of the working point, ensuring the sequential nature of the unmanned aerial vehicle path scheduling.

[0083] Further, in the optimal site selection problem of the emergency communication base station working point, as few candidate working points as possible are taken under the condition that the communication coverage area meets all the communication demand points.

[0084] It can be understood that the beneficial effects of the above-mentioned second aspect to the sixth aspect can be referred to the related description in the first aspect, which will not be repeated here.

[0085] In summary, the present application carries out the disaster damage situation awareness of the distribution network based on the emergency communication under the city rainstorm disaster. On the one hand, the information sources in multiple dimensions are considered, the running state of the distribution network is directly reflected through the system measurement data, the user side feedback information is used as direct and effective auxiliary information, and the influence result on the distribution equipment is obtained through the meteorological data, so that the distribution network running state awareness in the case of accurate information is realized. On the other hand, the destruction of the rainstorm to the original communication facilities is considered, the emergency communication vehicle and the unmanned aerial vehicle system recovery strategy are established, and the more accurate perception of the post-disaster running state of the distribution network is realized, the disaster damage situation of the distribution network is dynamically mastered, and the perception ability of the disaster damage situation of the distribution network is improved.

[0086] The technical solutions of the present application will be further described in detail below with the help of the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0087] Figure 1A flow chart of a disaster damage situation awareness method for a distribution network;

[0088] Figure 2 A state diagram of a line fault and RFI indicator;

[0089] Fig. 3 is a disaster damage situation diagram of an IEEE 33-node test system, wherein (a) the RFI indicator and user-side feedback are normal, (b) the RFI indicator and user-side feedback exist false negatives, and (c) is a false positive of the RFI indicator;

[0090] Figure 4 A geographic diagram of an IEEE 33-node test system;

[0091] Figure 5 An emergency communication site selection result of an IEEE 33-node test system;

[0092] Figure 6 A change trend diagram of a disaster damage situation awareness method for a distribution network;

[0093] Figure 7 A schematic diagram of a computer device provided by an embodiment of the present application;

[0094] Figure 8 A block diagram of a chip provided by an embodiment of the present application. DETAILED DESCRIPTION

[0095] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0096] In the description of the present application, it should be understood that the terms “include” and “contain” indicate the existence of described features, whole, steps, operations, elements and / or components, but do not exclude the existence or addition of one or more other features, whole, steps, operations, elements, components and / or sets thereof.

[0097] It should also be understood that the terms used in the present application specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, unless otherwise clearly indicated by the context, the singular forms “a”, “an” and “the” are intended to include the plural forms.

[0098] It should be further understood that the term "and / or" as used in the specification and in the claims, if any, means any combination of one or more of the associated listed items can be present, and includes all possible combinations, for example, A and / or B can mean: A alone, B alone, or A and B together. In addition, the character " / " in the present application generally represents an "or" relationship between the front and rear associated objects.

[0099] It should be understood that, although the terms first, second, third, etc. can be employed in the embodiments of the application to describe various ranges, etc., these ranges should not be limited to these terms. These terms are only used to distinguish one range from another. For example, a first range can also be referred to as a second range, and similarly, a second range can also be referred to as a first range, without departing from the scope of the embodiments of the application.

[0100] Depending on the context, the word "if" as used herein can be interpreted to mean "when" or "while" or "in response to determining" or "in response to detecting." Similarly, the phrase "if it is determined" or "if a stated condition or event occurs" can be interpreted to mean "when it is determined" or "in response to determining" or "when a stated condition or event occurs" or "in response to detecting a stated condition or event."

[0101] Various structural diagrams according to the disclosed embodiments of the application are shown in the accompanying drawings. These diagrams are not drawn to scale, in which certain details are exaggerated for the purpose of clarity, and certain details can be omitted. The shapes and relative sizes of the various regions, layers, and their relative positions shown in the drawings are merely exemplary, and in actuality, they can deviate due to manufacturing tolerances or technical limitations, and a person skilled in the art can additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0102] The application provides a power distribution network disaster damage situation awareness method based on emergency communication under rain flood, which fuses system measurement information, meteorological data and user side feedback information, sends remote fault indicator (RFI) and user feedback information to the dispatching control center through the cooperation of unmanned aerial vehicles and emergency communication vehicles, and obtains real-time power distribution network disaster damage situation.

[0103] Referring to Figure 1 The application provides a power distribution network disaster damage situation awareness method based on emergency communication under rain flood, which comprises the following steps:

[0104] S1, based on a multi-source data fusion technology, a power distribution network disaster damage situation awareness model under urban rainstorm waterlogging is established;

[0105] S101, considering the information integrity, a mapping relationship model of RFI fault alarm information and power distribution network disaster damage section is established, and the post-disaster power distribution network disaster damage situation is preliminarily determined;

[0106] Select any node as the reference power supply, and assign an initial direction to each feeder section. Define the RFI state indication set as the fault indication vector, and the element in the fault indication vector depends on the direction of the RFI induced fault current. When the direction is the same as the reference direction, the corresponding element is 1; when the direction is opposite to the reference direction, the corresponding element is-1; when there is no fault current, the corresponding element is 0.

[0107] In order to obtain the operation state information of the power distribution network after the fault occurs, it is necessary to establish a network description matrix [N] according to the topology of the power distribution network, and the element N kl Determined according to the following rules:

[0108] If the feeder LSl is the starting point of RFIk, N kl =1;

[0109] If the feeder LSl is the end point of RFIk, N kl =-1;

[0110] If the feeder LSl is not the starting point and the end point of RFIk, N kl =0.

[0111] Define the fault discrimination vector [FS] = [N][F] T , the fault discrimination vector [F T ] of T-type structure = [N T ]·[F] T . For the section LSk of non-T-type structure, according to the value of the kth element in the fault discrimination vector [FS], if FS k ≥1, it is determined that there is a fault in the section k, otherwise it is determined that there is no fault in the section k. For T-type structure section, if its corresponding element FS k ≥1, and there is no-1 in its corresponding [F T ] element, it is determined that there is a fault in the T section, otherwise there is no fault.

[0112] S102, a mechanism model of the influence of rainstorm waterlogging on urban power distribution network is established, high-voltage switch cabinet and line are selected, the influence of rainstorm waterlogging on them is analyzed, and the component failure probability at different time periods after the disaster is calculated;

[0113] Firstly, a direct failure model of ring network high-voltage switch cabinet is established. In the process of urban rainstorm waterlogging, a time period [0, T] is composed of several Δt, and the direct failure rate of the equipment w in the time period is:

[0114]

[0115] In the formula, d w (t) is the depth of the position where the high-voltage switch cabinet is located at time t, D w is the design anti-flooding height of the power distribution station, D Bw is the cable joint height, ζ is the attenuation coefficient, and γ is the damping coefficient.

[0116] Based on the reference failure rate of the looped network high-voltage switch cabinet, the humidity and temperature of the high-voltage switch cabinet are taken as the covariants of the indirect failure model to improve the indirect failure model of the high-voltage switch cabinet:

[0117]

[0118] wherein β w is a shape parameter; η w is a proportional parameter; t e is a time conversion coefficient; X w,1 and X w,2 are the humidity and cable joint temperature in the switch cabinet; and α1 and α2 are covariant parameters.

[0119] The indirect failure probability of the high-voltage switch cabinet at time t is obtained by improving the indirect failure model:

[0120]

[0121] wherein n is the number of switch cabinets equipped for node i.

[0122] It is considered that the direct and indirect effects of extreme rainstorm flooding on power distribution equipment are independent of each other, and the comprehensive failure probability of the node under rainstorm flooding is obtained according to the independent event probability formula:

[0123] P i (t) = 1 - (1 - P i F (t))(1 - P i J (t))

[0124] S103, positioning the power distribution network fault based on user-side feedback information;

[0125] For the part of the user-side feedback power failure:

[0126]

[0127] wherein, is the node path passed through from the power supply end to node k; is a binary variable of the state of node i at time t, is a binary variable of the state of node i at time t, d k is a binary variable of the state of node i at time t, d k= 1 if user k has a complaint; Ω CF is the set of user feedback state; this equation constrains that at least one fault exists in the power distribution path of the fault user.

[0128] S104, determine the post-disaster fault scenario scheme of the power distribution network.

[0129] Let the decision variable composed of each node fault state be x; Let the system measurement and user complaint be accurate information, denoted as b0; Let the mapping relationship between system fault state and surviving measurement be f, then solving the city power distribution network disaster situation is to solve a group of equations:

[0130]

[0131] Among them, is the set of power distribution system node states; c is the current system measurement data, which is a group of parameters with values of ±1 / 0, given by the remote fault indicator, d is the current user feedback result, which is a group of parameters with values of 0 / 1,

[0132] It is more convenient to deduce the fault section through system measurement information and user feedback information, f(x) = b0 is equivalent to:

[0133] x = g1(c)

[0134] x = g2(d)

[0135] Specifically, it is expressed as:

[0136] [Fs] = [N][F] T = [z1, z2, z3,..., z n ] T

[0137]

[0138] Among them, is the fault binary variable of section k at time t, indicates that there is a fault in section k; C LS is the set of numbers of non-T structure sections; is the set of T structure section numbers.

[0139] Through the comprehensive failure probability P i (t) of the nodes of the power distribution network under rainstorm flooding, the maximum Bayesian posterior probability is established as the objective function to determine the most likely fault scenario at time t under rainstorm disaster, and the logarithmic function is used to linearize the objective function, and the model is described as:

[0140]

[0141] S2, construct the objective function and constraint condition of the power distribution network disaster damage situation awareness model under urban rainstorm waterlogging established in step S1;

[0142] S201, determine the model objective function;

[0143] The objective function after recovery is the case where the communication recovery time is the least, and the maximum power distribution network disaster damage situation awareness capability is guaranteed.

[0144] min F sum = β 1 F obj1 + β 2 F obj2

[0145]

[0146] Wherein, the objective function F obj1 is the normalized communication recovery time; F obj2 is the normalized power distribution network disaster damage situation probability; β 1 , β 2 are the weight values of the two sub-objective functions, and in the subsequent examples, β 1 = 0.9, β 1 = 0.1.

[0147] S202, determine the communication working point location model;

[0148] The objective function of the vehicle-machine cooperative emergency communication working point is:

[0149]

[0150] Constraint condition:

[0151]

[0152]

[0153] Wherein, S UAV is a discrete point coordinate set; S CN is a communication demand point coordinate set; N c is the transmission capacity; p k is a binary decision variable representing whether the discrete point k is selected as an emergency communication working point, if k point is selected as an emergency communication working point, p k = 1, if k point is not an emergency communication working point, p k = 0; x c,kA binary variable indicating whether the communication demand point c is covered by the discrete point k, taking value 1 if the communication demand point c is within the coverage of the discrete point k, and taking value 0 if the communication demand point c is not within the coverage of the discrete point k;(x c ,y c ) are the location coordinates of the communication demand point c and the discrete point k;(x k ,y k ) are the location coordinates of the communication demand point c and the discrete point k;(x k,c is a distance between the communication node c and the discrete point k and the base station coverage capability.

[0154] S203, establish a vehicle-machine cooperative planning constraint;

[0155] The scheduling problem of the vehicle-machine cooperative emergency communication strategy recovery includes path constraints, time constraints, and loop constraints.

[0156] Path constraints:

[0157]

[0158]

[0159] wherein C N ={1,2,...c} represents a set of all work points (excluding warehouses); N={0,1,...,c+1} represents all vehicle work points including warehouses, the starting point is 0, and the ending point is c+1; N0={0,1,...,c} represents a set of vehicle starting points, and the corresponding N + ={1,2,...,c+1} represents a set of vehicle arrival points; P is composed of three points <i,j,k>, i represents a set of unmanned vehicle starting points, i.e. j represents a work point where the unmanned vehicle arrives and performs communication recovery work, j∈{C':j≠i}, and k∈{N + :k≠j,k≠i,τ ij '+τ jk '≤e}; x (i,j) is a set of 0-1 variables, and the vehicle travels from i∈N0 to j∈N + , and x (i,j) =1 when i≠j; y (i,j,k) is a set of 0-1 variables, and the unmanned vehicle travels from i∈N0 to j∈C and returns to k∈{N + :(i,j,k)∈P}, and y (i,j,k) =1; u i is a continuous variable representing the position of the work point i in the vehicle access path, and has 1≤u i ≤c+2 to eliminate sub-loops.

[0160] Time constraints:

[0161]

[0162]

[0163] t′ k -(t′ j -τ′ ij )≤e+M(1-y ijk )

[0164]

[0165] where τ ij denotes the time taken by the vehicle to travel from work point (or warehouse) i to work point j (or warehouse), and has τ′ ij denotes the time taken by the UAV to travel from work point (or warehouse) i to work point j (or warehouse), and defines τ 0,c+1 ≡0; s L denotes the operation time required by the vehicle staff to launch the UAV; s R denotes the time required by the vehicle staff to retrieve and replace the battery for the UAV so that the UAV can complete the launch again; e denotes the endurance of the UAV; t j is a continuous variable, denoting the time when the vehicle arrives at work point (or warehouse) j; t' j is the time when the UAV arrives at j; it is stipulated that t j =t' j =0, indicating the initial time when the vehicle and the UAV leave the warehouse.

[0166] Access sequence constraint:

[0167]

[0168] where p (i,j) is a set of 0-1 variables, i∈Cj∈{C:j≠i}, p (i,j) =1, when i work point is accessed before j on the vehicle path.

[0169] Supplementary constraint:

[0170] t0=0

[0171] t'0=0

[0172]

[0173] S204, determining the power distribution network fault location constraint, which includes: continuous time and discrete time stamp correspondence and power distribution network fault section location constraint.

[0174] Continuous time and discrete time stamp correspondence:

[0175]

[0176] where γ i,t is a binary variable indicating whether the communication of work point i is repaired at time t, γ i,t = 1 means the communication of work point i is successfully repaired at time t; Δt is the whole model time interval; t is the discrete time parameter; ε is the error value.

[0177] Power distribution network fault section location constraints:

[0178]

[0179] [Fs t ] = [N][F t ] T = [z 1,t , z 2,t , z 3,t ,..., z n,t ] T

[0180]

[0181] where, is the section communication recovery indication vector; is the customer communication recovery indication vector; C is the set of communication work points to be recovered; Ω station(i) is the set of RFI points covered by communication work point i, for example, the recovery of communication work point 1 will make RFI2 and RFI5 recover communication, then Ω station(1) = [2, 5]; Similarly, Ω call(i) is the set of customer service center user points covered by communication work point i, for example, the recovery of communication work point 1 will make customer service center node 2 and customer service center node 5 recover information, then Ω call(1) = [2, 5]; [N] is the network description matrix of the power distribution network; [F] is the true value of the fault indication vector that the master station should receive; [C F ] is the true value of the user feedback information vector; [Fs t ] is the fault indication vector generated at time t, which is used to judge the state of the power distribution network section at time t, and its elements are z n,t ; F i,t T is the actual fault vector received by the master station at time t; C i,t T is the actual user feedback vector received by the master station at time t; is the indication vector of section k at time t, then there is a fault in the section; C LS is the set of sections; is the fault state of node i at time t; The comprehensive failure probability of the node i is synthesized.

[0182] S3, based on the objective function and constraint condition constructed in step S2, constructing a power distribution network disaster damage situation awareness improvement model under urban rainstorm waterlogging, and using a commercial solver Gurobi to solve the power distribution network disaster damage situation awareness improvement model under urban rainstorm waterlogging, using the obtained result to provide reference for rapid recovery of the power distribution network, and providing fault positioning reference for subsequent power distribution network recovery decision, saving repair time.

[0183] In another embodiment of the present application, a power distribution network disaster damage situation awareness system based on emergency communication under rain flood is provided, which can be used to implement the power distribution network disaster damage situation awareness method based on emergency communication under rain flood, and specifically, the power distribution network disaster damage situation awareness system based on emergency communication under rain flood comprises a construction module, a cooperation module and an output module.

[0184] The construction module takes system measurement data, user feedback information and meteorological data as input data, and determines the fault section of the power distribution network through the causal logic of the upstream and downstream of the feeder based on the system measurement data and the user feedback information;According to the position of the fault section in the power distribution network and the meteorological data of the uncertain fault section, a power distribution network disaster damage situation awareness model under urban rainstorm waterlogging is established based on the element vulnerability model, with the maximum Bayesian posterior probability as the criterion;

[0185] The cooperation module constructs a communication work point location model to determine the possible parking points of the mobile unmanned aerial vehicle station and the emergency communication vehicle;Based on the possible parking points of the mobile unmanned aerial vehicle station and the emergency communication vehicle, considering the path constraint and the time constraint, the scheduling scheme and the transfer path of the mobile unmanned aerial vehicle station and the emergency communication vehicle are determined, and a vehicle-machine cooperative communication recovery model is constructed;

[0186] The output module constructs a power distribution network disaster damage situation awareness improvement model under urban rainstorm waterlogging based on the constructed power distribution network disaster damage situation awareness model under urban rainstorm waterlogging and the constructed vehicle-machine cooperative communication recovery model, and solves the power distribution network disaster damage situation awareness improvement model under urban rainstorm waterlogging to obtain the situation awareness improvement strategy.

[0187] In another embodiment of the present application, a terminal device is provided, which comprises a processor and a memory, the memory being configured to store a computer program, the computer program comprising program instructions, and the processor being configured to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are particularly suitable for loading and executing one or more instructions to implement a corresponding method flow or corresponding function; the processor in the embodiments of the present application can be used for the operation of the power distribution network disaster damage situation awareness method based on emergency communication under rainstorm, including:

[0188] The system measurement data, user feedback information and meteorological data are taken as input data, the system measurement data and user feedback information are used to determine the fault section of the power distribution network through the causal logic of the upstream and downstream of the feeder; according to the position of the fault section in the power distribution network and the meteorological data of the uncertain fault section, a power distribution network disaster damage situation awareness model under urban rainstorm waterlogging is established based on the element vulnerability model, with the maximum Bayesian posterior probability as the criterion; a communication work point location model is constructed to determine the possible parking points of the mobile unmanned aerial vehicle station and the emergency communication vehicle; based on the possible parking points of the mobile unmanned aerial vehicle station and the emergency communication vehicle, the path constraint and the time constraint are considered to determine the scheduling scheme and the transfer path of the mobile unmanned aerial vehicle station and the emergency communication vehicle, and a vehicle-machine cooperative communication recovery model is constructed; based on the constructed power distribution network disaster damage situation awareness model under urban rainstorm waterlogging and the constructed vehicle-machine cooperative communication recovery model, a power distribution network disaster damage situation awareness improvement model under urban rainstorm waterlogging is constructed, and the situation awareness improvement strategy is obtained by solving the power distribution network disaster damage situation awareness improvement model under urban rainstorm waterlogging.

[0189] Please refer to Figure 7, the terminal device is a computer device, the computer device 60 of this embodiment includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61, and the computer program 63, when executed by the processor 61, implements the fluid composition calculation method in the reservoir stimulation wellbore in the embodiment. To avoid repetition, details are not repeated here. Alternatively, the computer program 63, when executed by the processor 61, implements the functions of each model / unit in the power distribution network disaster situation awareness system based on emergency communication under rainstorm in the embodiment, and to avoid repetition, details are not repeated here.

[0190] The computer device 60 can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The computer device 60 can include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art can understand that the computer device 60 can include more or fewer components, or combine certain components, or include different components, such as an input / output device, a network access device, a bus, and the like. Figure 7 The computer device 60 is only an example and does not constitute a limitation on the computer device 60, and can include more or fewer components than shown, or combine certain components, or different components, for example, the computer device can also include an input / output device, a network access device, a bus, and the like.

[0191] The processor 61 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic components, discrete hardware components, or the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0192] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or a memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, and the like.

[0193] Further, the memory 62 can include both an internal storage unit and an external storage device of the computer device 60. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0194] Referring now to the drawings Figure 8 The terminal device 600 is in the form of an electronic device, which can be in the form of a general purpose computing device. Components of the electronic device can include, but are not limited to, at least one processing unit 610, at least one memory unit 620, a bus 630 that connects the various platform components including the memory unit 620 and the processing unit 610, a display unit 640, and the like.

[0195] The memory unit stores program code that can be executed by the processing unit 610 such that the processing unit 610 performs the steps described in the above method section of this specification in accordance with the various example embodiments of this application. For example, the processing unit 610 can perform the steps shown in Figure 1 .

[0196] The memory unit 620 can include a readable medium in the form of volatile memory units, such as a random access memory (RAM) 6201 and / or a cache memory unit 6202, and can further include a read-only memory (ROM) 6203.

[0197] The memory unit 620 can further include a program / utility 6204 having a set of programs / modules 6205, which can include an operating system, one or more application programs, other programs, and programmatic data, each or some combination thereof, which can include implementation of a network environment.

[0198] The bus 630 can be representative of one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration bus, a processor or local bus using any of a variety of bus structures, and the like.

[0199] The electronic device 600 can also communicate with one or more external devices 700 such as a keyboard or pointing device, a Bluetooth device, or a database, and / or one or more devices that enable a user to interact with the electronic device 600 and / or one or more devices (e.g., a router, a modem, a server, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication can occur via an input / output (I / O) interface 650. Still yet, the electronic device 600 can communicate with one or more networks, such as a local area network (LAN), a wide area network (WAN), and / or the Internet, through a network adapter 660. The network adapter 660 can be communicatively coupled to the other components of the electronic device 600 via a bus 630. It should be appreciated that the bus 630 can be one of any suitable type, including a bus system, a message bus, a PCI bus, a HyperTransport bus, a USB bus, and the like. It is worthy to note that any of the devices, components, or modules described herein can be implemented using hardware, software, firmware, or any combination thereof and can be implemented as one or more computer programs running on a system such as the electronic device 600 described herein.

[0200] In another embodiment of the present application, the present application also provides a storage medium, specifically a computer readable storage medium, which is a memory device in a terminal device, used for storing programs and data. It can be understood that the computer readable storage medium herein can include a built-in storage medium in the terminal device, and of course can also include an extended storage medium supported by the terminal device. The computer readable storage medium provides a storage space, which stores an operating system of the terminal. In addition, one or more instructions adapted to be loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more computer programs. It should be noted that the computer readable storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory.

[0201] The one or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the power distribution network disaster state perception method based on emergency communication in rainstorm flood in the above embodiments. The one or more instructions stored in the computer readable storage medium are loaded and executed by the processor to implement the following steps:

[0202] The system measurement data, user feedback information and meteorological data are taken as input data, the system measurement data and user feedback information are used to determine the fault section of the distribution network through the causal logic of the feeder upstream and downstream; according to the position of the fault section in the distribution network and the meteorological data of the uncertain fault section, a disaster damage situation awareness model of the distribution network under urban rainstorm waterlogging is established based on the element vulnerability model, and the criterion is to maximize the Bayesian posterior probability; a communication work point location model is constructed to determine the possible parking points of the mobile unmanned aerial vehicle station and the emergency communication vehicle; based on the possible parking points of the mobile unmanned aerial vehicle station and the emergency communication vehicle, the path constraint and the time constraint are considered, the scheduling scheme and the transfer path of the mobile unmanned aerial vehicle station and the emergency communication vehicle are determined, and a vehicle-machine cooperative communication recovery model is constructed; based on the constructed disaster damage situation awareness model of the distribution network under urban rainstorm waterlogging and the constructed vehicle-machine cooperative communication recovery model, a disaster damage situation awareness improvement model of the distribution network under urban rainstorm waterlogging is constructed, and the situation awareness improvement strategy is obtained by solving the disaster damage situation awareness improvement model of the distribution network under urban rainstorm waterlogging.

[0203] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art without creative labor based on the embodiments in the present application belong to the scope of protection of the present application.

[0204] Taking the IEEE 33-node test system as an example, a design verification example is designed to verify the effectiveness of the method.

[0205] Please refer to Figure 2 , after the urban rainstorm waterlogging disaster occurs, the line fault position and fault indicator data are as shown in Figure 2 ; it is assumed that all fault indicators are normal, and the RFI gives an indication alarm information according to the fault current information, [F] = [10 0 -1-1 0 0].

[0206] Firstly, the network description matrix [N] of the power distribution system is obtained:

[0207]

[0208] Then the fault discrimination matrix under this scenario is:

[0209] [FS] = [-1 2 -1 0 0 1 -1 0]

[0210] According to the discrimination rule, section 2 and section 6 are possible fault areas, and both sections are T structure sections, which need to be further discriminated.

[0211] For the 2, 4, 6 sections of the T structure, select the corresponding row in the network description matrix to obtain the fault description matrix:

[0212]

[0213] According to the formula [F T ]=[N T ]·[F] T , the fault description matrix of the T structure is generated:

[0214]

[0215] In [F T ], it is indicated that the number of rows of section 2 and section 6 contains elements greater than 1 and does not contain element-1, so it can be preliminarily judged that the fault occurs in section 2 and section 6.

[0216] Table 1 is the parameters of the extreme rain indirect failure model, and the indirect failure rate and failure probability of the distribution equipment are calculated:

[0217] Table 1 Indirect failure model parameters

[0218]

[0219] According to the above model, the comprehensive failure probability of each high-voltage switch cabinet is calculated, and the comprehensive failure probability of the node at time t=60min is selected, as shown in Table 2:

[0220] Table 2 Node comprehensive failure rate

[0221]

[0222] According to the fault section determined by the system measurement information, considering the user fault complaint constraint, adding a constraint condition to the disaster loss model, maximizing the Bayesian posterior probability, considering complete information, RFI and user missing report, RFI false alarm, and designing an example to obtain the disaster loss situation of the distribution network and the objective function P, the disaster loss situation is shown in Figure 3:

[0223] 1) RFI indicator and user side feedback are normal, the fault indication vector uploaded by RFI is [F]=[1 0 0-1-1 00], the fault nodes are 6, 19, 26, and the objective function value p t =-10.7232;

[0224] 2) There are missed reports on the RFI indicator and the user side. The fault indication vector [F] = [1 0 0 0 0 0 0], there is no user feedback information, and the fault nodes are 2, 3, 6, and 26. t = -13.7443;

[0225] 3) False alarms exist in the RFI. The fault indication vector [F] = [1 0 011 0 0], and the fault nodes are 3, 7, 27, 31, p. t = -14.0111.

[0226] It can be seen that, under conditions of sufficient information, the power distribution network disaster situation awareness model based on multi-source data fusion technology can accurately identify fault nodes; however, in the absence of communication, false alarms and missed alarms of RFI, as well as the failure of user information, will all lead to a weakening of the ability to perceive the power distribution network disaster situation.

[0227] IEEE 33-node test system, such as Figure 4 As shown, the system has 7 RFIs, the entire power distribution feeder is divided into 8 sections by RFIs, and there are 4 customer service centers responsible for collecting complaint call information from all 33 nodes. First, based on the optimal addressing model, the best candidate communication working point of the system was calculated. Then, according to the vehicle-machine collaborative emergency communication recovery strategy, the optimal communication recovery scheduling strategy was calculated. Finally, the disaster situation of the power distribution network under urban rainstorm flooding that changes with the progress of the rainstorm was obtained.

[0228] 70×65 discrete points were evenly distributed on the plane as candidate locations for emergency communication work points. The RFI coordinates and customer service center coordinates are shown in Tables 3 and 4. These points were used as input to obtain 6 work point locations, with a coverage area of ​​10 and a channel capacity of 10. Table 5 shows the results of the communication work point location selection. Figure 5 Each communication work point and its coverage area are plotted.

[0229] Table 3 RFI Coordinates

[0230]

[0231] Table 5 Site Selection Results

[0232]

[0233] It can be seen that work points S1 and S2 cover a higher proportion of RFIs, while work point S4 covers a higher proportion of customer service centers. The starting warehouse location is set to (30, 30), based on... Figure 5The communication working points S1 to S6 are shown, the emergency communication vehicle working point set N and the unmanned aerial vehicle working point set C are determined, it is assumed that the driving speed of the emergency communication vehicle and the flight speed of the unmanned aerial vehicle are the same, and both are 1 unit / min, the time for the emergency communication vehicle staff to launch the unmanned aerial vehicle and recover the unmanned aerial vehicle is set to 1 min, and the endurance time of the unmanned aerial vehicle is set to 60 min. max = 120 min, the time interval Δt = 10 min, a single emergency communication vehicle and a single unmanned aerial vehicle are used.

[0234] From the IEEE 33-node example diagram shown in Figure 4 , node 1 is a substation node, there are 7 RFI in the system, the entire distribution feeder is divided into 8 sections by RFI, there are 2 distributed power sources in the system, located at node 18 and node 22 respectively, with a power of 1.65 MW and 1.5 MW respectively, the node comprehensive failure probability in table 2 is used, the RFI and user false reporting are considered, an emergency communication recovery example is designed, the total time is 46.64 min, and the communication recovery solving result is shown in table 6 and table 7.

[0235] Table 6 Vehicle unmanned aerial vehicle path result

[0236]

[0237] Table 7 RFI / user information recovery process

[0238]

[0239] It can be seen from the above results that the communication recovery preferentially selects the communication working point covering more RFI to obtain the real-time maximum distribution network disaster situation, and the user side is used as a supplement to the distribution network disaster situation, and the recovery priority is low. After recovery, the distribution network disaster situation p(t) is as shown in Figure 6 .

[0240] It can be seen that at 33.48 minutes, the false reported RFI4 and RFI5 are all recovered, which greatly improves the distribution network disaster situation awareness ability; at 37.61 minutes, the user information is recovered, which plays a supplementary role for the distribution network situation.

[0241] In summary, the rain flood based emergency communication based distribution network disaster situation awareness method and system fuses system measurement information, meteorological data, user feedback information and other multi-source data, uses the emergency communication vehicle and unmanned aerial vehicle mobile base station to send remote fault indication equipment and user feedback information to the dispatching control center, determines the most possible distribution network fault scene based on the maximum Bayesian posterior probability, better improves the distribution network disaster situation awareness ability, and realizes the rapid recovery of the distribution network after the disaster.

[0242] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be realized in the form of hardware or software. In addition, the specific name of each functional unit and module is only for the convenience of mutual distinction, and does not limit the protection scope of the present application. The specific working process of the unit and module in the above system can refer to the corresponding process in the foregoing method embodiment, which will not be described here.

[0243] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0244] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present application can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0245] In the embodiments provided by the present application, it should be understood that the disclosed apparatus / terminal and method can be implemented by other ways. For example, the above-mentioned apparatus / terminal embodiments are only schematic, and the division of the modules or units is only a logical function division, and there can be another division way in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0246] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0247] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0248] The integrated module / unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer-readable storage medium. The computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms. The computer-readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the computer-readable medium can include or exclude contents according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0249] The present application is described with reference to flowcharts and / or block diagrams according to the methods, devices, and computer program products of embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks

[0250] These computer program instructions can also be stored in a computer-readable storage medium that can guide the computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer-readable storage medium produce a manufactured product including instruction devices that implement the functions specified in the flowcharts and / or block diagrams. Figure 1one or more processes and / or blocks Figure 1 the function specified in the one or more blocks.

[0251] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operational steps are performed on the computer or other programmable data processing devices to generate a computer-implemented process, so that the instructions executed on the computer or other programmable data processing devices provide a process for implementing the flowchart Figure 1 one or more processes and / or blocks Figure 1 the function specified in the one or more blocks.

[0252] The above is only to illustrate the technical idea of the present application, and cannot limit the protection scope of the present application. Any modification made according to the technical idea of the present application on the basis of the technical scheme falls within the protection scope of the claims of the present application.

Claims

1. A method for power distribution grid damage state awareness based on emergency communication under storm flood, characterized in that, The method comprises the following steps: The system measurement data, user feedback information and meteorological data are taken as input data, the system measurement data and the user feedback information are used to determine a fault section of the power distribution network through a causal logic of upstream and downstream feeders; according to a position of the fault section in the power distribution network and meteorological data of the fault section, a power distribution network disaster damage situation awareness model under urban rainstorm waterlogging is established based on a component vulnerability model and a criterion of maximizing a Bayesian posterior probability, and specifically, the power distribution network disaster damage situation awareness model under urban rainstorm waterlogging is established based on the component vulnerability model and the criterion of maximizing the Bayesian posterior probability. A mapping relationship model of the RFI fault alarm information and the disaster damage section of the power distribution network is established in consideration of perfect information, and a post-disaster disaster damage situation of the power distribution network is determined based on the system measurement data; A direct failure model of the ring network high-voltage switch cabinet based on meteorological data is established, a reference failure rate of the ring network high-voltage switch cabinet is taken as a basis, and an ambient humidity and temperature in the high-voltage switch cabinet are taken as covariates of an indirect failure model to improve the indirect failure model of the high-voltage switch cabinet; By improving the indirect failure model At the moment, determine the indirect failure probability of the high-voltage switch cabinet; According to the independent event probability formula, the comprehensive failure probability of the node under the rainstorm waterlogging is obtained ; The power distribution network fault is located based on the user feedback information; The system measurement data, the meteorological data and the user feedback information are fused to comprehensively analyze and judge a post-disaster fault scene scheme of the power distribution network, and a power distribution network disaster damage situation awareness model under rainstorm waterlogging is obtained; Comprehensive failure probability of node under rainstorm waterlogging Specifically: wherein, Pd is the direct failure probability of the high-voltage switchgear, Pind is the indirect failure probability of the high-voltage switchgear; By rainstorm waterlogging under the distribution network node comprehensive failure probability , the maximum Bayesian posterior probability is established as the objective function to determine the most likely failure scenario under rainstorm disasters at any time, and the logarithmic function is used for linearization. The disaster loss situation awareness model of urban rainstorm waterlogging distribution network is described as: wherein, is the state of each node of the system, is a binary variable for the state of node i, is the overall failure probability of node i; A communication work point location model is constructed to determine possible parking points of the mobile unmanned aerial vehicle station and the emergency communication vehicle; a scheduling scheme and a transfer path of the mobile unmanned aerial vehicle station and the emergency communication vehicle are determined based on the possible parking points of the mobile unmanned aerial vehicle station and the emergency communication vehicle, path constraints and time constraints are considered, a vehicle-aircraft collaborative communication recovery model is constructed, and an objective function of the vehicle-aircraft collaborative communication recovery model is as follows: wherein, is the normalized communication recovery time; is the normalized distribution grid disaster situation probability; , are weight values of two sub-objective functions, respectively, is a binary decision variable representing whether a discrete point is selected as an emergency communication working point, is the number of discrete points, is the objective function of the communication system; Based on the constructed power distribution network disaster damage situation awareness model under urban rainstorm waterlogging and the constructed vehicle-aircraft collaborative communication recovery model, a power distribution network disaster damage situation awareness improvement model under urban rainstorm waterlogging is constructed, and a situation awareness improvement strategy is obtained by solving the power distribution network disaster damage situation awareness improvement model under urban rainstorm waterlogging; An objective function of the communication work point location model is as follows: Constraint conditions are as follows: wherein, is the number of discrete points, is the set of discrete point coordinates; is the set of communication demand point coordinates; is the transmission capacity; is the set of discrete points characterizing the discrete points is a binary decision variable indicating whether a communication demand point is covered by a discrete point is a binary variable indicating whether a communication demand point is covered by a discrete point is the location coordinate of a communication demand point is the location coordinate of a discrete point is a variable indicating the distance of a communication node from a discrete point is the base station coverage capacity.

2. A storm-flood based emergency communication based power distribution grid damage state estimation system characterized in that, The method comprises the following steps: The system measurement data, user feedback information and meteorological data are taken as input data, the system measurement data and the user feedback information are used to determine a fault section of the power distribution network through a causal logic of upstream and downstream feeders; according to a position of the fault section in the power distribution network and meteorological data of the fault section, a power distribution network disaster damage situation awareness model under urban rainstorm waterlogging is established based on a component vulnerability model and a criterion of maximizing a Bayesian posterior probability, and specifically, the power distribution network disaster damage situation awareness model under urban rainstorm waterlogging is established based on the component vulnerability model and the criterion of maximizing the Bayesian posterior probability. A mapping relationship model of the RFI fault alarm information and the disaster damage section of the power distribution network is established in consideration of perfect information, and a post-disaster disaster damage situation of the power distribution network is determined based on the system measurement data; A direct failure model of the ring network high-voltage switch cabinet based on meteorological data is established, a reference failure rate of the ring network high-voltage switch cabinet is taken as a basis, and an ambient humidity and temperature in the high-voltage switch cabinet are taken as covariates of an indirect failure model to improve the indirect failure model of the high-voltage switch cabinet; By improving the indirect failure model At the moment, determine the indirect failure probability of high-voltage switch cabinet; According to the independent event probability formula, the comprehensive failure probability of the node under the rainstorm waterlogging is obtained ; The power distribution network fault is located based on the user feedback information; The system measurement data, the meteorological data and the user feedback information are fused to comprehensively analyze and judge a post-disaster fault scene scheme of the power distribution network, and a power distribution network disaster damage situation awareness model under rainstorm waterlogging is obtained; Comprehensive failure probability of node under rainstorm waterlogging Specifically: wherein, Pd is the direct failure probability of the high-voltage switchgear, Pind is the indirect failure probability of the high-voltage switchgear; By rainstorm waterlogging under the distribution network node comprehensive failure probability , the maximum Bayesian posterior probability is established as the objective function to determine the most likely failure scenario under rainstorm disaster at any time, and the logarithmic function is used for linearization. The disaster loss situation awareness model of urban rainstorm waterlogging distribution network is described as: wherein, is the system state, is a binary variable for the state of node i, is the overall failure probability of node i; The cooperative module constructs a communication work point location model, determines possible parking points of the mobile unmanned aerial vehicle station and the emergency communication vehicle, determines a scheduling scheme and a transfer path of the mobile unmanned aerial vehicle station and the emergency communication vehicle based on the possible parking points of the mobile unmanned aerial vehicle station and the emergency communication vehicle and considering path constraints and time constraints, constructs a vehicle-machine cooperative communication recovery model, and the objective function of the vehicle-machine cooperative communication recovery model is as follows: wherein, is the number of discrete points, is an objective function of the communication system, is a normalized communication recovery time; is a normalized distribution state probability of the power distribution network disaster, , are weight values of two sub-objective functions, respectively, is a binary decision variable representing whether a discrete point is selected as an emergency communication working point, is the number of discrete points; The output module constructs a power distribution network disaster damage situation awareness improvement model based on the constructed power distribution network disaster damage situation awareness model under urban rainstorm waterlogging and the constructed vehicle-machine cooperative communication recovery model, and solves the power distribution network disaster damage situation awareness improvement model under urban rainstorm waterlogging to obtain a situation awareness improvement strategy. The objective function of the communication work point location model is as follows: The constraint condition is as follows: wherein, is a set of discrete point coordinates; is a set of communication demand point coordinates; is a transmission capacity; is a binary decision variable representing whether a discrete point is selected as an emergency communication working point; is a binary variable representing whether a communication demand point is covered by a discrete point ; is a location coordinate of a communication demand point with a discrete point ; is a distance of a communication node from a discrete point and a base station coverage capacity.

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