Power distribution network fault positioning method, apparatus and device, and storage medium

Abstract

The invention discloses a power distribution network fault positioning method, apparatus and device, and a storage medium. The method comprises the steps of obtaining report information of each line; constructing a fault section determination model and a first constraint condition by taking the minimum number of missing reports and false reports of all report information as a target; solving under each first constraint to obtain a fault line; obtaining the number of units of the unmanned aerial vehicle, a fault line and the distance between two adjacent nodes in the fault line; constructing an inspection optimization model and a second constraint condition according to the number of units, the fault line and the distance by taking the minimum journey experienced by the unmanned aerial vehicle as a target or the minimum flight time of the unmanned aerial vehicle as a target; solving under each second constraint to obtain the shortest path to all fault lines; routing inspection is carried out according to the shortest path, and the specific fault position of the power distribution network is determined. According to the invention, the error caused by the calculation of the electrical quantity can be reduced, and the fault positioning speed and accuracy can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network fault detection, and in particular to a distribution network fault locating method, device, equipment and storage medium. Background Art

[0002] Nowadays, in the context of the dual carbon goals, it is particularly important to improve the grid's ability to respond to low-probability, high-risk extreme events. According to the National Energy Administration's "Guiding Opinions on Distribution Network Prevention and Response to Typhoon Disasters", improving grid resilience can be done from five stages: normal preparation, pre-disaster prevention, disaster defense, post-disaster rescue, and post-event evaluation. Quickly and reliably locating the precise location of the fault after a fault occurs can improve the grid's ability to defend and rescue during a disaster, thereby improving the grid's resilience.

[0003] In extreme weather conditions, when large-scale faults occur in the distribution network, existing research mainly determines the fault location in the distribution network by calculating various electrical quantities. However, when multiple faults occur in a large area, the electrical quantities are often affected by multiple faults at the same time, making it difficult to quickly determine the fault location based on the electrical quantities at this time. Summary of the invention

[0004] The present invention provides a distribution network fault location method, device, equipment and storage medium, which can eliminate the need to calculate electrical quantities when determining the fault location, reduce errors caused by the calculation of electrical quantities, and improve the speed and accuracy of fault location.

[0005] An embodiment of the present invention provides a distribution network fault location method, comprising:

[0006] Obtaining report information of each line; wherein the report information records whether the line is faulty;

[0007] With the goal of minimizing the number of missed reports and false alarms in all report information, a fault section determination model and a first constraint condition of the fault section determination model are constructed; wherein the first constraint condition includes: non-power outage line fault constraint, non-power outage fault current constraint, non-power outage fault error reporting constraint, fault type relationship constraint and power outage fault error reporting constraint;

[0008] Under each first constraint, solving the fault section determination model to obtain a fault line;

[0009] Obtaining the number of drone units, the fault line, and the distance between two adjacent nodes in the fault line;

[0010] According to the number of units, faulty lines and distances, with the goal of minimizing the distance traveled by the UAV or minimizing the flight time of the UAV, a patrol optimization model and a second constraint condition of the patrol optimization model are constructed; wherein the second constraint condition includes: unit path constraint, line start and end point constraint, path continuity constraint and line sub-loop constraint;

[0011] Under each second constraint, solving the inspection optimization model to obtain the shortest path to all faulty lines;

[0012] Inspection is performed along the shortest path to determine the specific fault location of the distribution network.

[0013] Furthermore, the fault section determination model is:

[0014] α 1 +α 2 =1

[0015]

[0016] In the formula, α 1 represents the reporting weight of non-power outage faults, α 2 represents the reporting weight of power outage faults, f i Indicates the number of missed reports of non-power outage faults in the report information, m i represents the number of false positive reports of non-power outage faults in the report information, Nf represents the total number of reports of non-power outage faults in the report information, and f′ i Indicates the number of missed reports of power outage faults in the report information, m′ i It indicates the number of false positive reports of power outage faults in the report information, and Ns indicates the total number of reports of power outage faults in the report information.

[0017] Furthermore, the non-power outage line fault constraint is:

[0018] ∑x i ≥1

[0019] In the formula, x i Indicates the non-power outage fault condition of the i-th line;

[0020] The non-power outage fault current constraint is:

[0021]

[0022] In the formula, y downi Indicates whether there is actually a fault current flowing from upstream to downstream in the i-th feeder terminal. downi When y is 1, it means that the downstream line of the i-th feeder terminal has a fault. downiWhen x is 0, it means that there is no fault in the downstream line of the i-th feeder terminal. j Indicates the non-power failure condition of the jth line, y upi Indicates whether there is actually a fault current flowing from downstream to upstream in the i-th feeder terminal. upi When y is 1, it means that the upstream line of the i-th feeder terminal has a fault. upi When it is 0, it means that there is no fault in the upstream line of the i-th feeder terminal. down represents the set of downstream lines of the feeder terminal, C up represents the set of upstream lines of the feeder terminal, z upi Indicates whether there is a fault current flowing from downstream to upstream at the i-th feeder terminal. upi When it is 1, it indicates that there is a report of a fault current flowing from downstream to upstream at the i-th feeder terminal. downi Indicates whether there is a fault current flowing from upstream to downstream at the i-th feeder terminal. downi When it is 1, it indicates that there is a report of a fault current flowing from upstream to downstream at the i-th feeder terminal, f upi represents the number of missed reports in the fault current report flowing from downstream to upstream at the i-th feeder terminal, f downi represents the number of missed reports in the fault current report flowing from upstream to downstream at the i-th feeder terminal, m upi represents the number of false alarms in the fault current report flowing from upstream to downstream at the i-th feeder terminal, m downi represents the number of false alarms in the fault current report flowing from downstream to upstream at the i-th feeder terminal, and To build auxiliary decision-making, used to influence the direction of current flow;

[0023] The non-power failure type fault error reporting constraints are:

[0024] f i =f upi ∪f downi

[0025] m i =m upi ∪m downi

[0026] The fault type relationship constraint is:

[0027]

[0028] Where b downi Indicates whether there is a power outage fault upstream of the i-th line. downiWhen b is 1, it means that there is a power failure in the upstream of the i-th line. downi When it is 0, it means that there is no power outage fault upstream of the i-th line, b upi Indicates whether there is a power outage fault downstream of the i-th line. upi When b is 1, it indicates that there is a power failure in the downstream of the i-th line. upi When it is 0, it means that there is no power outage fault downstream of the i-th line, b i Indicates the fault condition of the i-th line;

[0029] The power outage fault error reporting constraints are:

[0030] g i =b i ×BM i +m i ′ -f i ′

[0031] In the formula, g i represents the number of power outage fault reports on the i-th line, BM i Indicates the number of power outage type fault reports that should be received.

[0032] Furthermore, with the goal of minimizing the distance traveled by the drone or minimizing the flight time of the drone, an inspection optimization model is constructed, including:

[0033] If the goal is to minimize the distance traveled by the drone, the inspection optimization model is:

[0034] f=minT total

[0035] T total =max(T k )

[0036] Where, T total represents the total time that all drone groups experience all faulty lines, T k represents the time taken by the kth drone group to experience the faulty line;

[0037] If the goal is to minimize the flight time of the drone, the inspection optimization model is:

[0038] f=minS total

[0039] S total =max(S k )

[0040]

[0041] In the formula, S total represents the total distance traveled by all drone groups through all faulty lines, S k represents the total distance traveled by the kth drone group through the faulty line, Indicates whether the kth group of drones fly from node i to node j, When it is 1, it means that the kth drone group flies from node i to node j. When it is 0, it means that the kth drone group will not fly from node i to node j, R ij represents the distance between node i and node j in each fault line, and n represents the total number of nodes.

[0042] Furthermore, the unit path constraint is:

[0043]

[0044] In the formula, s represents the total number of drone groups;

[0045]

[0046]

[0047] In the formula, It means that the k-th drone group is heading from the drone station to the node j. Indicates that drone group k is heading from node i to node j;

[0048] The path coherence constraint is:

[0049]

[0050] In the formula, Denotes the drone group k 0 Whether node i is reached from other nodes, when When it is 0, it means that the drone group k 0 Not reaching node i, Indicates that except for drone group k 0 Whether the drone group other than When it is 1, it means that except for drone group k 0 The drone groups other than When it is 0, it means that the drone group k 0 Fly from node i to other nodes or node i is a drone group k 0 The end point;

[0051] The line sub-loop constraints are:

[0052] u i -u j+Nl ij ≤N-1,1 <i≠j<n

[0053] In the formula, u i represents the potential of node i, u j represents the potential of node j, l ij represents the fault line between node i and node j, and N represents a large number.

[0054] Based on the above method embodiment, the present invention provides a corresponding device embodiment;

[0055] The present invention provides a distribution network fault location device, comprising:

[0056] Report information acquisition module, fault section confirmation model construction module, fault section confirmation model solution module, data acquisition module, inspection optimization model construction module, inspection optimization model solution module and fault location determination module;

[0057] The report information acquisition module is used to acquire the report information of each line; wherein the report information records whether the line is faulty;

[0058] The fault section confirmation model building module is used to build a fault section determination model and a first constraint condition of the fault section determination model with the goal of minimizing the number of missed reports and false reports in all report information; wherein the first constraint condition includes: non-power outage type line fault constraint, non-power outage type fault current constraint, non-power outage type fault error reporting constraint, fault type relationship constraint and power outage type fault error reporting constraint;

[0059] The fault section confirmation model solving module is used to solve the fault section determination model under each first constraint to obtain the fault line;

[0060] The data acquisition module is used to obtain the number of drone units, the fault line, and the distance between two adjacent nodes in the fault line;

[0061] The inspection optimization model building module is used to build an inspection optimization model and a second constraint condition of the inspection optimization model according to the number of units, fault lines and distances, with the goal of minimizing the distance traveled by the drone or minimizing the flight time of the drone; wherein the second constraint condition includes: unit path constraint, line start and end point constraint, path continuity constraint and line sub-loop constraint;

[0062] The inspection optimization model solving module is used to solve the inspection optimization model under each second constraint to obtain the shortest path to all fault lines;

[0063] The fault location determination module is used to perform inspection along the shortest path to determine the specific fault location of the distribution network.

[0064] Furthermore, the fault section confirmation model is constructed as follows:

[0065] α 1 +α 2 =1

[0066]

[0067] In the formula, α 1 represents the reporting weight of non-power outage faults, α 2 represents the reporting weight of power outage faults, f i Indicates the number of missed reports of non-power outage faults in the report information, m i Nf represents the number of false positive reports of non-power outage faults in the report information, Nf represents the total number of reports of non-power outage faults in the report information, and f i ′ Indicates the number of missed reports of power outage faults in the report information, m i ′ It indicates the number of false positive reports of power outage faults in the report information, and Ns indicates the total number of reports of power outage faults in the report information.

[0068] Furthermore, the inspection optimization model is constructed as follows:

[0069] If the goal is to minimize the distance traveled by the drone, the inspection optimization model is:

[0070] f=minT total

[0071] T total =max(T k )

[0072] Where, T total represents the total time that all drone groups experience all faulty lines, T k represents the time taken by the kth drone group to experience the faulty line;

[0073] If the goal is to minimize the flight time of the drone, the inspection optimization model is:

[0074] f=minS total

[0075] S total =max(S k )

[0076]

[0077] In the formula, S total represents the total distance traveled by all drone groups through all faulty lines, S k represents the total distance traveled by the kth drone group through the faulty line, Indicates whether the kth group of drones fly from node i to node j, When it is 1, it means that the kth drone group flies from node i to node j. When it is 0, it means that the kth drone group will not fly from node i to node j, R ij Represents the distance between node i and node j in each fault line.

[0078] Based on the above method embodiment, the present invention provides a terminal device embodiment;

[0079] The present invention provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements a distribution network fault locating method described in any embodiment of the present invention when executing the computer program.

[0080] Based on the above method embodiment, the present invention provides a storage medium embodiment;

[0081] The present invention provides a storage medium, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, a distribution network fault location method described in any embodiment of the present invention is implemented.

[0082] The embodiments of the present invention have the following beneficial effects:

[0083] The present invention provides a distribution network fault location method, device, equipment and storage medium, the method comprising: first obtaining report information of each line; wherein the report information records whether the line is faulty; then, with the goal of minimizing the number of missed reports and false alarms in all report information, constructing a fault section determination model and a first constraint condition of the fault section determination model; wherein the first constraint condition includes: non-power outage type line fault constraint, non-power outage type fault current constraint, non-power outage type fault error reporting constraint, fault type relationship constraint and power outage type fault error reporting constraint; then, under each first constraint, the fault section determination model is solved to obtain the fault line; then obtain the number of drone units, the fault line and the distance between two adjacent nodes in the fault line; then, based on the number of units, the fault line and the distance, with the goal of minimizing the distance traveled by the drone or minimizing the flight time of the drone, construct an inspection optimization model and the second constraint condition of the inspection optimization model; wherein the second constraint condition includes: unit path constraint, line start and end point constraint, path continuity constraint and line sub-loop constraint; then under each second constraint, solve the inspection optimization model to obtain the shortest path to all fault lines; finally, perform inspection along the shortest path to determine the specific fault location of the distribution network. Therefore, the present invention directly uses the report information of each line obtained from the distribution network system to establish a fault section determination model, and determines the fault line after solving it. Then, according to the determined fault line, the number of drone units and the distance between adjacent nodes in the fault line, an inspection optimization model is established to determine the shortest path to all lines. Finally, inspections are performed along the shortest path to determine the specific fault location, so that the entire scheme does not need to calculate electrical quantities when determining the fault location, thereby reducing the error caused by the calculation of electrical quantities and improving the speed and accuracy of fault location. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 It is a flow chart of a distribution network fault location method provided by an embodiment of the present invention.

[0085] Figure 2 It is a structural schematic diagram of a distribution network fault locating device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0086] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings 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.

[0087] like Figure 1 As shown, a distribution network fault location method provided by an embodiment of the present invention includes:

[0088] Step S101: Acquire report information of each line; wherein the report information records whether the line is faulty;

[0089] Specifically, faults are divided into power outage type faults and non-power outage type faults. The report information corresponding to power outage type faults is obtained by the smart meters of each line in the distribution network, and the report information corresponding to non-power outage type faults and line fault-free faults is obtained by the feeder terminal of the distribution network.

[0090] Step S102: With the goal of minimizing the number of missed reports and false reports in all report information, a fault section determination model and a first constraint condition of the fault section determination model are constructed; wherein the first constraint condition includes: non-power outage type line fault constraint, non-power outage type fault current constraint, non-power outage type fault error reporting constraint, fault type relationship constraint and power outage type fault error reporting constraint;

[0091] Specifically, missed reports mean that the line should have report information, but the corresponding report information is not obtained. False reports mean that the line has no fault and the report information should be "no fault", but the report information of this line is "faulty", or the line has a fault and the report information should be "faulty", but the report information of this line is "no fault".

[0092] Specifically, "taking the minimum number of missed reports and false positives in all reported information as the goal" means that the most likely situation is that the reported information contains the least errors.

[0093] In a preferred embodiment, the fault section determination model is:

[0094] α 1 +α 2 =1

[0095]

[0096] In the formula, α 1 represents the reporting weight of non-power outage faults, α 2 represents the reporting weight of power outage faults, f i Indicates the number of missed reports of non-power outage faults in the report information, m i represents the number of false positive reports of non-power outage faults in the report information, Nf represents the total number of reports of non-power outage faults in the report information, and f′ i Indicates the number of missed reports of power outage faults in the report information, m′ iIt indicates the number of false positive reports of power outage faults in the report information, and Ns indicates the total number of reports of power outage faults in the report information.

[0097] Specifically, the report weight α 1 and α 2 The size of can be determined based on the actual situation, that is, the reliability of reports on non-power outage type faults and reports on power outage type faults.

[0098] In this preferred embodiment, a fault section determination model is constructed with the goal of minimizing the number of missed reports and false alarms in all report information.

[0099] In another preferred embodiment, the non-power outage line fault constraint is:

[0100] ∑x i ≥1

[0101] In the formula, x i Indicates the non-power outage fault condition of the i-th line;

[0102] Specifically, x i is a binary decision variable.

[0103] Preferably, the above line fault constraint indicates that there must be a fault condition in the distribution network.

[0104] The non-power outage fault current constraint is:

[0105]

[0106]

[0107] In the formula, y downi Indicates whether there is actually a fault current flowing from upstream to downstream in the i-th feeder terminal. downi When y is 1, it means that the downstream line of the i-th feeder terminal has a fault. downi When x is 0, it means that there is no fault in the downstream line of the i-th feeder terminal. j Indicates the non-power failure condition of the jth line, y upi Indicates whether there is actually a fault current flowing from downstream to upstream in the i-th feeder terminal. upi When y is 1, it means that the upstream line of the i-th feeder terminal has a fault. upi When it is 0, it means that there is no fault in the upstream line of the i-th feeder terminal. down represents the set of downstream lines of the feeder terminal, C up represents the set of upstream lines of the feeder terminal, z upi Indicates whether there is a fault current flowing from downstream to upstream at the i-th feeder terminal.upi When it is 1, it indicates that there is a report of a fault current flowing from downstream to upstream at the i-th feeder terminal. downi Indicates whether there is a fault current flowing from upstream to downstream at the i-th feeder terminal. downi When it is 1, it indicates that there is a report of a fault current flowing from upstream to downstream at the i-th feeder terminal, f upi represents the number of missed reports in the fault current report flowing from downstream to upstream at the i-th feeder terminal, f downi represents the number of missed reports in the fault current report flowing from upstream to downstream at the i-th feeder terminal, m upi represents the number of false alarms in the fault current report flowing from upstream to downstream at the i-th feeder terminal, m downi represents the number of false alarms in the fault current report flowing from downstream to upstream at the i-th feeder terminal, is an auxiliary decision variable constructed to eliminate the influence of current direction;

[0108] Specifically, downi and upi are all binary decision variables. (y upi ∩~f upi ) indicates that there is actually a fault current flowing from downstream to upstream at the i-th feeder terminal and there is no missed report, (~y upi ∩m upi ) indicates that there is actually no fault current flowing from downstream to upstream at the i-th feeder terminal and a false alarm occurs in the corresponding report; (y downi ∩~f downi ) indicates that there is actually a fault current flowing from upstream to downstream at the i-th feeder terminal and there is no missed report, (~y downi ∩m downi ) indicates that there is actually no fault current flowing from upstream to downstream at the i-th feeder terminal and a false alarm occurs in the corresponding report.

[0109] Preferably, if there are distributed power sources in the distribution network, when two faults occur simultaneously upstream and downstream of the feeder terminal, if there is no false alarm at the feeder terminal, then z upi and z downi However, in reality, the feeder terminal will only report the fault current flowing from upstream to downstream or from downstream to upstream. In this case, z upi and z downi The main grid is prioritized, that is, it is assumed that the feeder terminal will report the fault current provided by the main grid instead of the fault current provided by the distributed generation, and then construct and These two binary decision variables.

[0110] Preferably, the first two formulas in the non-power outage type fault current constraint indicate the relationship between the fault current flowing through the feeder terminal and the fault location; the third and fourth formulas in the non-power outage type fault current constraint indicate the relationship between the report information obtained from the feeder terminal and the fault current flowing through the feeder terminal.

[0111] The non-power failure type fault error reporting constraints are:

[0112] f i =f upi ∪f downi

[0113] m i =m upi ∪m downi

[0114] The fault type relationship constraint is:

[0115]

[0116] Where b downi , b upi and b i are binary decision variables, b downi Indicates whether there is a power outage fault upstream of the i-th line. downi When b is 1, it means that there is a power failure in the upstream of the i-th line. downi When it is 0, it means that there is no power outage fault upstream of the i-th line, b upi Indicates whether there is a power outage fault downstream of the i-th line. upi When b is 1, it indicates that there is a power failure in the downstream of the i-th line. upi When it is 0, it means that there is no power outage fault downstream of the i-th line, b i Indicates the fault condition of the i-th line;

[0117] Preferably, the above fault type relationship constraint indicates the relationship between the power outage type fault and the fault location in the distribution network.

[0118] The power outage fault error reporting constraints are:

[0119] g i =b i ×BM i +m′ i -f′ i

[0120] In the formula, g i represents the number of power outage fault reports on the i-th line, BM i Indicates the number of outage type reports that should be received.

[0121] Specifically, f′ i , m′ i and i All are integer variables.

[0122] Preferably, the above-mentioned power outage type fault error report constraint indicates the relationship between the number of power outage type fault reports received from the smart meter and the actual power outage situation.

[0123] In this preferred embodiment, non-power outage type line fault constraints, non-power outage type fault current constraints, non-power outage type fault error reporting constraints, fault type relationship constraints and power outage type fault error reporting constraints are constructed.

[0124] Step S103: Under the first constraints, the fault section determination model is solved to obtain the fault line;

[0125] Specifically, under each first constraint, in the process of solving the fault section determination model, the number of missed reports and false alarms in the report information is minimized, that is, the currently obtained report information is as correct as possible, and the corresponding fault line is taken as the optimal solution result.

[0126] Specifically, each first constraint is linearized, and a solver is used to solve the fault section determination model under each first constraint.

[0127] Step S104: obtaining the number of drone units, the fault line, and the distance between two adjacent nodes in the fault line;

[0128] Preferably, the drone inspection problem is regarded as a special vehicle routing problem and solved by optimization method. The drone group is regarded as a vehicle group, the two ends of the fault line are regarded as nodes, and the line itself is regarded as the path between nodes.

[0129] Step S105: According to the number of units, faulty lines and distances, with the goal of minimizing the distance traveled by the drone or minimizing the flight time of the drone, construct an inspection optimization model and a second constraint condition of the inspection optimization model; wherein the second constraint condition includes: unit path constraint, line start and end point constraint, path continuity constraint and line sub-loop constraint;

[0130] In a preferred embodiment, the inspection optimization model is constructed with the goal of minimizing the distance traveled by the drone or minimizing the flight time of the drone, including:

[0131] If the goal is to minimize the distance traveled by the drone, the inspection optimization model is:

[0132] f=minTtotal

[0133] T total =max(T k )

[0134] Where, T total represents the total time that all drone groups experience all faulty lines, T k represents the time taken by the kth drone group to experience the faulty line;

[0135] If the goal is to minimize the flight time of the drone, the inspection optimization model is:

[0136] f=minS total

[0137] S total =max(S k )

[0138]

[0139] In the formula, S total represents the total distance traveled by all drone groups through all faulty lines, S k represents the total distance traveled by the kth drone group through the faulty line, Indicates whether the kth group of drones fly from node i to node j, When it is 1, it means that the kth drone group flies from node i to node j. When it is 0, it means that the kth drone group will not fly from node i to node j, R ij represents the distance between node i and node j in each fault line, and n represents the total number of nodes.

[0140] Preferably, unlike the requirements of general vehicle routing problems, which often use cost or profit as the objective function, the above inspection optimization model requires that the fault location be determined as quickly as possible to restore the load as quickly as possible. Therefore, the purpose of the above inspection optimization model is to determine the precise location of all faults as quickly as possible.

[0141] In this preferred embodiment, an inspection optimization model is constructed based on the number of units, fault lines and distances, with the goal of minimizing the distance traveled by the drone or minimizing the flight time of the drone.

[0142] In another preferred embodiment, the unit path constraint is:

[0143]

[0144] In the formula, s represents the total number of drone groups;

[0145] Specifically, each node to be traversed is numbered, and there are n nodes in total. The first formula in the above crew path constraint indicates that among all drone crews, there is only one crew that starts from node i and flies to a certain node among all nodes. The above crew path constraint can ensure that each node is traversed and only traversed once (except the starting point).

[0146]

[0147] In the formula, It means that the k-th drone group is heading from the drone station to the node j. Indicates that drone group k is heading from node i to node j;

[0148] Specifically, i and j represent all nodes on the faulty line.

[0149] Preferably, since the information of the drone can be transmitted back in real time, it is not required for the drone to return to the warehouse after passing the last node, which is equivalent to allowing the kth group of drones to It means that each drone group does not need to return to the starting point after reaching the node of the end point. Then the third formula in the above group path constraint means that in all fault lines, there are s points that do not need to return to the starting point, and these s points are evenly distributed on the path of each drone group as the end point.

[0150] Preferably, the second formula in the above-mentioned crew path constraint can ensure that each drone crew starts from the starting point (drone site); the fourth and fifth formulas in the above-mentioned crew path constraint indicate that, except for the starting point, all other nodes will be traversed by the drone crew.

[0151] The path coherence constraint is:

[0152]

[0153] In the formula, Denotes the drone group k 0 Whether node i is reached from other nodes, when When it is 0, it means that the drone group k 0 Not reaching node i, Indicates that except for drone group k 0 Whether the drone group other than When it is 1, it means that except for drone group k 0 The drone groups other than When it is 0, it means that the UAV group k 0 Fly from node i to other nodes or node i is a drone group k 0 The end point;

[0154] Preferably, the above path coherence constraint is to ensure the coherence of the path.

[0155] The line sub-loop constraints are:

[0156] u i -u j +Nl ij ≤N-1,1 <i≠j<n

[0157] In the formula, u i represents the potential of node i, u j represents the potential of node j, l ij represents the fault line between node i and node j, and N represents a large number.

[0158] Preferably, the above-mentioned line sub-loop constraint is to eliminate possible sub-loops, u is an integer decision variable, representing the potential of each node, and N in the line sub-loop constraint is a very large number, indicating that the potential of each node in the path is greater than the potential of the previous node, so that no sub-loop is generated.

[0159] In this preferred embodiment, unit path constraints, line start and end point constraints, path continuity constraints, and line sub-loop constraints are constructed.

[0160] Step S106: Under each second constraint, the inspection optimization model is solved to obtain the shortest path to all faulty lines;

[0161] Specifically, after linearizing each second constraint, the inspection optimization model is solved by the solver under each second constraint, and the solved l ij That is, the shortest path between each node, and then the shortest path of the entire fault line is obtained.

[0162] Step S107: perform inspection along the shortest path to determine the specific fault location of the distribution network.

[0163] Specifically, by checking according to the shortest path obtained, the specific fault location of the distribution network can be determined, and then the fault location can be reliably and quickly located after the disaster.

[0164] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0165] like Figure 2 As shown, an embodiment of the present invention provides a distribution network fault location device, comprising:

[0166] Report information acquisition module, fault section confirmation model construction module, fault section confirmation model solution module, data acquisition module, inspection optimization model construction module, inspection optimization model solution module and fault location determination module;

[0167] The report information acquisition module is used to acquire the report information of each line; wherein the report information records whether the line is faulty;

[0168] The fault section confirmation model building module is used to build a fault section determination model and a first constraint condition of the fault section determination model with the goal of minimizing the number of missed reports and false reports in all report information; wherein the first constraint condition includes: non-power outage type line fault constraint, non-power outage type fault current constraint, non-power outage type fault error reporting constraint, fault type relationship constraint and power outage type fault error reporting constraint;

[0169] The fault section confirmation model solving module is used to solve the fault section determination model under each first constraint to obtain the fault line;

[0170] The data acquisition module is used to obtain the number of drone units, the fault line, and the distance between two adjacent nodes in the fault line;

[0171] The inspection optimization model building module is used to build an inspection optimization model and a second constraint condition of the inspection optimization model according to the number of units, fault lines and distances, with the goal of minimizing the distance traveled by the drone or minimizing the flight time of the drone; wherein the second constraint condition includes: unit path constraint, line start and end point constraint, path continuity constraint and line sub-loop constraint;

[0172] The inspection optimization model solving module is used to solve the inspection optimization model under each second constraint to obtain the shortest path to all fault lines;

[0173] The fault location determination module is used to perform inspection along the shortest path to determine the specific fault location of the distribution network.

[0174] In a preferred embodiment, the fault section confirmation model is constructed as follows:

[0175] α 1 +α 2 =1

[0176]

[0177] In the formula, α 1 represents the reporting weight of non-power outage faults, α 2 represents the reporting weight of power outage faults, fi Indicates the number of missed reports of non-power outage faults in the report information, m i Nf represents the number of false positive reports of non-power outage faults in the report information, Nf represents the total number of reports of non-power outage faults in the report information, and f i ′ Indicates the number of missed reports of power outage faults in the report information, m i ′ It indicates the number of false positive reports of power outage faults in the report information, and Ns indicates the total number of reports of power outage faults in the report information.

[0178] In another preferred embodiment, the inspection optimization model is constructed as follows:

[0179] If the goal is to minimize the distance traveled by the drone, the inspection optimization model is:

[0180] f=minT total

[0181] T total =max(T k )

[0182] Where, T total represents the total time that all drone groups experience all faulty lines, T k represents the time taken by the kth drone group to experience the faulty line;

[0183] If the goal is to minimize the flight time of the drone, the inspection optimization model is:

[0184] f=minS total

[0185]

[0186] In the formula, S total represents the total distance traveled by all drone groups through all faulty lines, S k represents the total distance traveled by the kth drone group through the faulty line, Indicates whether the kth group of drones fly from node i to node j, When it is 1, it means that the kth drone group flies from node i to node j. When it is 0, it means that the kth drone group will not fly from node i to node j, R ij represents the distance between node i and node j in each fault line, and n represents the total number of nodes.

[0187] It should be noted that the device embodiments described above are merely schematic, wherein the modules described above as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without paying creative labor. The above schematic diagram is only an example of a distribution network fault locating device, and does not constitute a limitation on a distribution network fault locating device, and may include more or fewer components than shown in the figure, or a combination of certain components, or different components.

[0188] Based on the above method item embodiments, the present invention provides corresponding terminal device item embodiments.

[0189] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, a distribution network fault location method described in any embodiment of the present invention is implemented.

[0190] Exemplarily, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more module elements may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the device;

[0191] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The device may include, but is not limited to, a processor, a memory;

[0192] The processor referred to herein may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The above-mentioned processor is the control center of the above-mentioned device, and uses various interfaces and lines to connect various parts of the entire device;

[0193] The above-mentioned memory can be used to store the above-mentioned computer programs and / or modules. The above-mentioned processor realizes various functions of the above-mentioned device by running or executing the computer programs and / or modules stored in the above-mentioned memory, and calling the data stored in the memory. The above-mentioned memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; in addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0194] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0195] Another embodiment of the present invention provides a storage medium, wherein the storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a distribution network fault locating method described in any embodiment of the present invention.

[0196] In this embodiment, the storage medium is a computer-readable storage medium, the computer program includes computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0197] Compared with the prior art, by implementing the above-mentioned embodiments of the present invention, it is not necessary to calculate electrical quantities when determining the fault location, thereby reducing the error caused by the calculation of electrical quantities and improving the speed and accuracy of fault location.

[0198] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A distribution network fault location method, characterized in that: include: Get report information for each line; The report information records whether the line is faulty; With the goal of minimizing the number of missed reports and false alarms in all report information, a fault section determination model and a first constraint condition of the fault section determination model are constructed; wherein the first constraint condition includes: non-power outage line fault constraint, non-power outage fault current constraint, non-power outage fault error reporting constraint, fault type relationship constraint and power outage fault error reporting constraint; Under each first constraint, solving the fault section determination model to obtain a fault line; Obtaining the number of drone units, the fault line, and the distance between two adjacent nodes in the fault line; According to the number of units, faulty lines and distances, with the goal of minimizing the distance traveled by the UAV or minimizing the flight time of the UAV, a patrol optimization model and a second constraint condition of the patrol optimization model are constructed; wherein the second constraint condition includes: unit path constraint, line start and end point constraint, path continuity constraint and line sub-loop constraint; Under each second constraint, solving the inspection optimization model to obtain the shortest path to all faulty lines; Inspection is performed along the shortest path to determine the specific fault location of the distribution network.

2. A distribution network fault location method according to claim 1, characterized in that: The fault section determination model is: α1+α2＝1 Where α1 represents the reporting weight of non-power outage faults, α2 represents the reporting weight of power outage faults, and f i Indicates the number of missed reports of non-power failure faults in the report information, m i represents the number of false positive reports of non-power outage faults in the report information, Nf represents the total number of reports of non-power outage faults in the report information, and f′ i Indicates the number of missed reports of power outage faults in the report information, m′ i It indicates the number of false positive reports of power outage faults in the report information, and Ns indicates the total number of reports of power outage faults in the report information.

3. A distribution network fault location method according to claim 2, characterized in that: The non-blackout line fault constraint is: ∑x i ≥1 In the formula, x i Indicates the non-power outage fault condition of the i-th line; The non-power outage fault current constraint is: z upi =(y upi ∩~f upi )∪(~y upi ∩m upi ) z downi =(y downi ∩~f downi )∪(~y downi ∩m downi ) In the formula, y downi Indicates whether there is actually a fault current flowing from upstream to downstream in the i-th feeder terminal. downi When y is 1, it means that the downstream line of the i-th feeder terminal has a fault. downi When x is 0, it means that there is no fault in the downstream line of the i-th feeder terminal. j Indicates the non-power failure condition of the jth line, y upi Indicates whether there is actually a fault current flowing from downstream to upstream in the i-th feeder terminal. upi When y is 1, it means that the upstream line of the i-th feeder terminal has a fault. upi When it is 0, it means that there is no fault in the upstream line of the i-th feeder terminal. down represents the set of downstream lines of the feeder terminal, C up represents the set of upstream lines of the feeder terminal, z upi Indicates whether there is a fault current flowing from downstream to upstream at the i-th feeder terminal. upi When it is 1, it indicates that there is a report of a fault current flowing from downstream to upstream at the i-th feeder terminal. downi Indicates whether there is a fault current flowing from upstream to downstream at the i-th feeder terminal. downi When it is 1, it indicates that there is a report of a fault current flowing from upstream to downstream at the i-th feeder terminal, f upi represents the number of missed reports in the fault current report flowing from downstream to upstream at the i-th feeder terminal, f downi represents the number of missed reports in the fault current report flowing from upstream to downstream at the i-th feeder terminal, m upi represents the number of false alarms in the fault current report flowing from upstream to downstream at the i-th feeder terminal, m downi represents the number of false alarms in the fault current report flowing from downstream to upstream at the i-th feeder terminal, and is an auxiliary decision variable constructed to eliminate the influence of current direction; The non-power failure type fault error reporting constraints are: f i =f upi ∪f downi m i =m upi ∪m downi The fault type relationship constraint is: b i =b upi ×b downi Where b downi Indicates whether there is a power outage fault upstream of the i-th line. downi When b is 1, it means that there is a power failure in the upstream of the i-th line. downi When it is 0, it means that there is no power outage fault upstream of the i-th line, b upi Indicates whether there is a power outage fault downstream of the i-th line. upi When b is 1, it indicates that there is a power failure in the downstream of the i-th line. upi When it is 0, it means that there is no power outage fault downstream of the i-th line, b i Indicates the fault condition of the i-th line; The power outage fault error reporting constraints are: g i =b i ×BM i +m′ i -f i ′ In the formula, g i represents the number of power outage fault reports on the i-th line, BM i Indicates the number of power outage type fault reports that should be received.

4. A distribution network fault location method according to claim 3, characterized in that: With the goal of minimizing the distance traveled by the drone or minimizing the flight time of the drone, an inspection optimization model is constructed, including: If the goal is to minimize the distance traveled by the drone, the inspection optimization model is: f=minT total T total =max(T k ) Where, T total represents the total time that all drone groups experience all faulty lines, T k represents the time taken by the kth drone group to experience the faulty line; If the goal is to minimize the flight time of the drone, the inspection optimization model is: f=minS total S total =max(S k ) In the formula, S total represents the total distance traveled by all drone groups through all faulty lines, S k represents the total distance traveled by the kth drone group through the faulty line, Indicates whether the kth group of drones fly from node i to node j, When it is 1, it means that the kth drone group flies from node i to node j. When it is 0, it means that the kth drone group will not fly from node i to node j, R ij represents the distance between node i and node j in each fault line, and n represents the total number of nodes.

5. A distribution network fault location method according to claim 4, characterized in that: The unit path constraint is: In the formula, s represents the total number of drone groups; In the formula, It means that the k-th drone group is heading from the drone station to the node j. Indicates that drone group k is heading from node i to node j; The path coherence constraint is: In the formula, Indicates whether the drone group k0 reaches node i from other nodes. When it is 0, it means that the drone group k0 does not reach the node i. Indicates whether the drone group other than the drone group k0 flies from node i to other nodes. When it is 1, it means that the drone groups except the drone group k0 fly from node i to other nodes. When it is 0, it means that the drone group k0 flies from node i to other nodes or node i is the end point of the drone group k0; The line sub-loop constraints are: in i -in j +Nl ij ≤N-1, 1<i≠j <n In the formula, u i represents the potential of node i, u j represents the potential of node j, l ij represents the fault line between node i and node j, and N represents a large number.

6. A distribution network fault location device, characterized in that: include: Report information acquisition module, fault section confirmation model construction module, fault section confirmation model solution module, data acquisition module, inspection optimization model construction module, inspection optimization model solution module and fault location determination module; The report information acquisition module is used to acquire the report information of each line; The report information records whether the line is faulty; The fault section confirmation model building module is used to build a fault section determination model and a first constraint condition of the fault section determination model with the goal of minimizing the number of missed reports and false reports in all report information; wherein the first constraint condition includes: non-power outage type line fault constraint, non-power outage type fault current constraint, non-power outage type fault error reporting constraint, fault type relationship constraint and power outage type fault error reporting constraint; The fault section confirmation model solving module is used to solve the fault section determination model under each first constraint to obtain the fault line; The data acquisition module is used to obtain the number of drone units, the fault line, and the distance between two adjacent nodes in the fault line; The inspection optimization model building module is used to build an inspection optimization model and a second constraint condition of the inspection optimization model according to the number of units, fault lines and distances, with the goal of minimizing the distance traveled by the drone or minimizing the flight time of the drone; wherein the second constraint condition includes: unit path constraint, line start and end point constraint, path continuity constraint and line sub-loop constraint; The inspection optimization model solving module is used to solve the inspection optimization model under each second constraint to obtain the shortest path to all fault lines; The fault location determination module is used to perform inspection along the shortest path to determine the specific fault location of the distribution network.

7. A distribution network fault location device according to claim 6, characterized in that: The fault section confirmation model is constructed as follows: α1+α2＝1 Where α1 represents the reporting weight of non-power outage faults, α2 represents the reporting weight of power outage faults, and f i Indicates the number of missed reports of non-power failure faults in the report information, m i represents the number of false positive reports of non-power outage faults in the report information, Nf represents the total number of reports of non-power outage faults in the report information, and f′ i Indicates the number of missed reports of power outage faults in the report information, m′ i It indicates the number of false positive reports of power outage faults in the report information, and Ns indicates the total number of reports of power outage faults in the report information.

8. A distribution network fault location device according to claim 7, characterized in that: The inspection optimization model is constructed as follows: If the goal is to minimize the distance traveled by the drone, the inspection optimization model is: f=minT total T total =max(T k ) Where, T total represents the total time that all drone groups experience all faulty lines, T k represents the time taken by the kth drone group to experience the faulty line; If the goal is to minimize the flight time of the drone, the inspection optimization model is: f=minS total S total =max(S k ) In the formula, S total represents the total distance traveled by all drone groups through all faulty lines, S k represents the total distance traveled by the kth drone group through the faulty line, Indicates whether the kth group of drones fly from node i to node j, When it is 1, it means that the kth drone group flies from node i to node j. When it is 0, it means that the kth drone group will not fly from node i to node j, R ij represents the distance between node i and node j in each fault line, and n represents the total number of nodes.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a distribution network fault location method as claimed in any one of claims 1 to 5 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is executed, the device where the storage medium is located is controlled to execute a distribution network fault location method according to any one of claims 1 to 5.

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