A method, apparatus, equipment and storage medium for fault location in power distribution networks

Abstract

This invention discloses a method, apparatus, equipment, and storage medium for fault location in a power distribution network. The method includes: acquiring report information from each line; constructing a fault section determination model and first constraints with the objective of minimizing the number of missed and false alarms in all reports; solving under each of the first constraints to obtain the faulty line; acquiring the number of UAV units, the faulty line, and the distance between two adjacent nodes within the faulty line; constructing an inspection optimization model and second constraints based on the number of units, the faulty line, and the distance, with the objective of minimizing the distance traveled by the UAV or minimizing the flight time of the UAV; solving under each of the second constraints to obtain the shortest path to all faulty lines; and conducting inspections according to the shortest path to determine the specific fault location in the power distribution network. By implementing this invention, errors caused by the calculation of electrical quantities can be reduced, improving the speed and accuracy of fault location.

Description

Technical Field

[0001] This invention relates to the field of power distribution network fault detection technology, and in particular to a method, apparatus, equipment and storage medium for locating power distribution network faults. Background Technology

[0002] In the context of dual carbon targets, improving the power grid's ability to cope with low-probability to high-risk extreme events is particularly important. According to the National Energy Administration's "Guiding Opinions on the Prevention and Response to Typhoon Disasters in Distribution Networks," enhancing grid resilience can be approached through five stages: pre-disaster preparedness, disaster prevention, disaster mitigation, post-disaster recovery, and post-disaster assessment. Quickly and reliably locating the precise location of a fault after it occurs enhances the power grid's ability to mitigate and recover from disasters, thereby improving its resilience.

[0003] In extreme weather conditions, when a large-scale fault occurs 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 over a large area, the electrical quantities are often affected by the combined effects of multiple faults, making it difficult to quickly determine the fault location based on the electrical quantities at this time. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and storage medium for fault location in power distribution networks, which eliminates the need to calculate electrical quantities when determining the fault location, reduces errors caused by the calculation of electrical quantities, and improves the speed and accuracy of fault location.

[0005] An embodiment of the present invention provides a method for locating faults in a power distribution network, comprising:

[0006] Obtain report information from each line; wherein the report information records whether the line is faulty;

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

[0008] Under each of the first constraints, the fault section determination model is solved to obtain the faulty line;

[0009] Obtain the number of drone units, the faulty line, and the distance between two adjacent nodes within the faulty line;

[0010] Based on the number of units, the faulty route, and the distance, an inspection optimization model and a second constraint condition for the inspection optimization model are constructed with the goal of minimizing the distance traveled by the UAV or minimizing the flight time of the UAV. The second constraint condition includes: unit path constraint, route start and end point constraint, path continuity constraint, and route sub-loop constraint.

[0011] Under each of the second constraints, the inspection optimization model is solved to obtain the shortest path to all faulty lines;

[0012] Conduct inspections along the shortest path to determine the specific location of faults in the power distribution network.

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

[0014] α1+α2=1

[0015]

[0016] In the formula, α1 represents the reporting weight of non-power outage faults, α2 represents the reporting weight of power outage faults, and f i This indicates the number of unreported non-power outage faults in the reported information, m i This indicates the number of false alarms for non-power outage faults in the reporting information, where Nf represents the total number of non-power outage fault reports in the reporting information, and f′ represents the number of false alarms for non-power outage faults in the reporting information. i This indicates the number of unreported power outage faults in the reported information, m′ i This indicates the number of false alarms for power outage faults in the reporting information, and Ns represents the total number of power outage fault reports in the reporting information.

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

[0018] ∑x i ≥1

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

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

[0021]

[0022] In the formula, y downi This indicates whether a fault current actually exists in the i-th feeder terminal, flowing from upstream to downstream. downi When y is 1, it indicates that a fault has occurred in the downstream line of the i-th feeder terminal. downi When x is 0, it indicates that there is no fault in the downstream line of the i-th feeder terminal. jThis indicates the non-power outage type fault condition of the j-th line, y upi This indicates whether a fault current actually exists in the i-th feeder terminal, flowing from downstream to upstream. upi When y is 1, it indicates that there is a fault in the upstream line of the i-th feeder terminal. upi When C is 0, it indicates that there is no fault in the upstream line of the i-th feeder terminal. down C represents the set of downstream lines of the feeder terminal. up z represents the set of upstream lines of the feeder terminal. upi This indicates whether there is a report of fault current flowing from downstream to upstream at the i-th feeder terminal, when z upi When z is 1, it indicates that there is a report of fault current flowing from downstream to upstream at the i-th feeder terminal. downi This indicates whether there is a report of fault current flowing from upstream to downstream at the i-th feeder terminal, when z downi When f is 1, it indicates that there is a report of fault current flowing from upstream to downstream at the i-th feeder terminal. upi f represents the number of missed reports in the fault current reports flowing from downstream to upstream for the i-th feeder terminal. downi This represents the number of missed reports in the fault current reports flowing from upstream to downstream of the i-th feeder terminal, m. upi This represents the number of false alarm reports in the fault current reports flowing from upstream to downstream of the i-th feeder terminal, m. downi This represents the number of false alarms reported in the fault current reports flowing from downstream to upstream for the i-th feeder terminal. and To construct an auxiliary decision-making system, taking into account the influence of current direction;

[0023] The constraints for the non-power outage type fault error reporting are as follows:

[0024] f i =f upi ∪f downi

[0025] m i =m upi ∪m downi

[0026] The fault type relationship constraint is as follows:

[0027]

[0028] In the formula, b downi This indicates whether there is a power outage fault upstream of the i-th line, when b downi When b is 1, it indicates that there is a power outage fault upstream of the i-th line. downiWhen b is 0, it indicates that there is no power outage fault upstream of the i-th line. upi This indicates whether there is a power outage-type fault downstream of the i-th line, when b upi When b is 1, it indicates that there is a power outage fault downstream of the i-th line. upi When b is 0, it indicates that there is no power outage fault downstream of the i-th line. i This indicates the fault status of the i-th line;

[0029] The constraints for the power outage-type fault error reporting are as follows:

[0030] g i =b i ×BM i +m i ′ -f i ′

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

[0032] Furthermore, with the objective 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 objective is to minimize the distance traveled by the drone, then the inspection optimization model is:

[0034] f = minT total

[0035] T total =max(T) k )

[0036] In the formula, T total T represents the total time taken for all drone groups to experience all faulty lines. k This represents the time spent by the k-th drone group traversing the faulty line;

[0037] If the objective is to minimize the flight time of the drone, then the inspection optimization model is as follows:

[0038] f = minS total

[0039] S total =max(S) k )

[0040]

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

[0042] Furthermore, the unit path constraint is as follows:

[0043]

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

[0045]

[0046]

[0047] In the formula, This indicates that drone group k travels from drone station to node j. This indicates that drone group number k travels from node i to node j;

[0048] The path coherence constraint is:

[0049]

[0050] In the formula, Indicate whether drone group k0 reaches node i from other nodes. When the value is 0, it indicates that the drone group k0 does not reach node i. Indicates whether drone groups other than drone group k0 fly from node i to other nodes. When it is 1, it means that drone groups other than drone group k0 fly from node i to other nodes. When the value is 0, it indicates that the drone group k0 flies from node i to other nodes or that node i is the destination of the drone group k0;

[0051] The constraints of the line sub-circuit are:

[0052] u i -u j +Nl ij ≤N-1,1 <i≠j<n

[0053] In the formula, u i Let u represent the potential of node i. jLet l represent the potential of node j. ij Let N represent the faulty line between node i and node j, and let N be a very large number.

[0054] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments;

[0055] This invention provides a power distribution network fault location device, comprising:

[0056] The system includes a report information acquisition module, a fault section confirmation model construction module, a fault section confirmation model solution module, a data acquisition module, an inspection optimization model construction module, an inspection optimization model solution module, and a fault location determination module.

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

[0058] The fault section confirmation model construction module is used to construct 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 and false alarms in all reported 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 report constraint, fault type relationship constraint, and power outage type fault error report 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 faulty line.

[0060] The data acquisition module is used to acquire the number of drone units, the faulty line, and the distance between two adjacent nodes within the faulty line.

[0061] The inspection optimization model construction module is used to construct an inspection optimization model and a second constraint condition of the inspection optimization model based on 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; 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 faulty lines.

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

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

[0065] α1+α2=1

[0066]

[0067] In the formula, α1 represents the reporting weight of non-power outage faults, α2 represents the reporting weight of power outage faults, and f i This indicates the number of unreported non-power outage faults in the reported information, m i This indicates the number of false alarms for non-power outage faults in the reporting information, where Nf represents the total number of false alarms for non-power outage faults in the reporting information. i ′ This indicates the number of unreported power outage faults in the reported information, m i ′ This indicates the number of false alarms for power outage faults in the reporting information, and Ns represents the total number of power outage fault reports in the reporting information.

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

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

[0070] f = minT total

[0071] T total =max(T) k )

[0072] In the formula, T total T represents the total time taken for all drone groups to experience all faulty lines. k This represents the time spent by the k-th drone group traversing the faulty line;

[0073] If the objective is to minimize the flight time of the drone, then the inspection optimization model is as follows:

[0074] f = minS total

[0075] S total =max(S) k )

[0076]

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

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

[0079] 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. When the processor executes the computer program, it implements a power distribution network fault location method according to any embodiment of the present invention.

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

[0081] The present invention provides a storage medium including 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, it implements a power distribution network fault location method according to any embodiment of the present invention.

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

[0083] This invention provides a method, apparatus, device, and storage medium for fault location in a power distribution network. The method includes: firstly, acquiring report information from each line; wherein the report information records whether a line is faulty; then, with the objective of minimizing the number of missed and false alarms in all report information, constructing a fault section determination model and first constraints on the fault section determination model; wherein the first constraints include: constraints on non-outage type line faults, constraints on non-outage type fault currents, constraints on non-outage type fault false reports, constraints on fault type relationships, and constraints on outage type fault false reports; and then, under each of the first constraints, solving the fault section determination model to obtain the fault... The process involves: first, obtaining the number of drone units, the faulty line, and the distance between two adjacent nodes within the faulty line; then, based on the number of units, the faulty line, and the distance, constructing an inspection optimization model and its second constraints, with the goal of minimizing the distance traveled by the drone or minimizing the flight time; the second constraints include: unit path constraints, line origin-end point constraints, path continuity constraints, and line sub-loop constraints; then, solving the inspection optimization model under each second constraint to obtain the shortest path to all faulty lines; finally, conducting inspections along the shortest path to determine the specific fault location in the distribution network. Therefore, this invention establishes a fault section determination model by directly utilizing the report information of each line obtained from the power distribution network system. After solving the model, the faulty line is determined. Subsequently, based on the determined faulty line, the number of UAV units, and the distance between adjacent nodes within the faulty line, an inspection optimization model is established to determine the shortest path to all lines. Finally, the inspection is carried out according to the shortest path to determine the specific fault location. This makes it possible to determine the fault location without calculating electrical quantities, thus reducing the error caused by the calculation of electrical quantities and improving the speed and accuracy of fault location. Attached Figure Description

[0084] Figure 1 This is a flowchart illustrating a method for locating faults in a power distribution network according to an embodiment of the present invention.

[0085] Figure 2 This is a schematic diagram of the structure of a power distribution network fault location device provided in an embodiment of the present invention. Detailed Implementation

[0086] The technical solutions of this invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0087] like Figure 1 As shown, an embodiment of the present invention provides a method for locating faults in a power distribution network, comprising:

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

[0089] Specifically, faults are divided into power outage faults and non-power outage faults. The reporting information for power outage faults is obtained by the smart meters of each line in the distribution network, while the reporting information for non-power outage faults and lines without faults is obtained by the feeder terminals of the distribution network.

[0090] Step S102: With the goal of minimizing the number of missed and false alarms in all reported information, construct a fault section determination model and the 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 report constraint, fault type relationship constraint, and power outage type fault error report constraint.

[0091] Specifically, a missed report means that the line should have received a report, but the corresponding report was not actually received. A false alarm means that the line did not experience a fault, and the report should have been "no fault," but the line's report was "faulty," or the line did experience a fault, and the report should have been "faulty," but the line's report was "no fault."

[0092] Specifically, "aiming to minimize the number of omissions and false alarms in all reported information" means that the most likely scenario is that the reported information contains the fewest errors.

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

[0094] α1+α2=1

[0095]

[0096] In the formula, α1 represents the reporting weight of non-power outage faults, α2 represents the reporting weight of power outage faults, and f i This indicates the number of unreported non-power outage faults in the reported information, m i This indicates the number of false alarms for non-power outage faults in the reporting information, where Nf represents the total number of non-power outage fault reports in the reporting information, and f′ represents the number of false alarms for non-power outage faults in the reporting information. i This indicates the number of unreported power outage faults in the reported information, m′ i This indicates the number of false alarms for power outage faults in the reporting information, and Ns represents the total number of power outage fault reports in the reporting information.

[0097] Specifically, the reporting weights α1 and α2 can be determined based on the actual situation, namely the reliability of reports of non-power outage faults and reports of power outage faults.

[0098] In this preferred embodiment, a fault segment determination model was constructed with the goal of minimizing the number of missed and false alarms in all reported information.

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

[0100] ∑x i ≥1

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

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

[0103] Preferably, the above-mentioned line fault constraint indicates that a fault must exist in the distribution network.

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

[0105]

[0106]

[0107] In the formula, y downi This indicates whether a fault current actually exists in the i-th feeder terminal, flowing from upstream to downstream. downi When y is 1, it indicates that a fault has occurred in the downstream line of the i-th feeder terminal. downi When x is 0, it indicates that there is no fault in the downstream line of the i-th feeder terminal. j This indicates the non-power outage type fault condition of the j-th line, y upi This indicates whether a fault current actually exists in the i-th feeder terminal, flowing from downstream to upstream. upi When y is 1, it indicates that there is a fault in the upstream line of the i-th feeder terminal. upi When C is 0, it indicates that there is no fault in the upstream line of the i-th feeder terminal. down C represents the set of downstream lines of the feeder terminal. up z represents the set of upstream lines of the feeder terminal. upi This indicates whether there is a report of fault current flowing from downstream to upstream at the i-th feeder terminal, when z upi When z is 1, it indicates that there is a report of fault current flowing from downstream to upstream at the i-th feeder terminal. downi This indicates whether there is a report of fault current flowing from upstream to downstream at the i-th feeder terminal, when zdowni When f is 1, it indicates that there is a report of fault current flowing from upstream to downstream at the i-th feeder terminal. upi f represents the number of missed reports in the fault current reports flowing from downstream to upstream for the i-th feeder terminal. downi This represents the number of missed reports in the fault current reports flowing from upstream to downstream of the i-th feeder terminal, m. upi This represents the number of false alarm reports in the fault current reports flowing from upstream to downstream of the i-th feeder terminal, m. downi This represents the number of false alarms reported in the fault current reports flowing from downstream to upstream for the i-th feeder terminal. These are auxiliary decision variables used to eliminate the influence of current direction;

[0108] Specifically, y downi and y upi Both are binary decision variables. (y) upi ∩~f upi ) indicates that the i-th feeder terminal actually has a fault current flowing from downstream to upstream and there is no missed alarm, (~y upi ∩m upi ) indicates that the i-th feeder terminal did not actually have a fault current flowing from downstream to upstream, and a false alarm occurred in the corresponding report; (y downi ∩~f downi ) indicates that the i-th feeder terminal actually has a fault current flowing from upstream to downstream and there is no missed alarm, (~y downi ∩m downi This indicates that the i-th feeder terminal did not actually have a fault current flowing from upstream to downstream, and a false alarm occurred in the corresponding report.

[0109] Preferably, if distributed generation exists in the distribution network, and two faults occur simultaneously upstream and downstream of the feeder terminal, if the feeder terminal does not report a false alarm, then z upi and z downi Both are 1. However, in reality, the feeder terminal will only report fault current flowing from upstream to downstream or from downstream to upstream in one of the two directions. In this case, z upi and z downi The values ​​will not both be 1 simultaneously, so this situation needs to be excluded. Prioritizing the main network means assuming that the feeder terminal will prioritize reporting fault currents provided by the main network over those provided by distributed power sources, thereby constructing the necessary system. and These are two binary decision variables.

[0110] Preferably, the first two equations 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 equations 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 constraints for the non-power outage type fault error reporting are as follows:

[0112] f i =f upi ∪f downi

[0113] m i =m upi ∪m downi

[0114] The fault type relationship constraint is as follows:

[0115]

[0116] In the formula, b downi b upi and b i Both are binary decision variables, b downi This indicates whether there is a power outage fault upstream of the i-th line, when b downi When b is 1, it indicates that there is a power outage fault upstream of the i-th line. downi When b is 0, it indicates that there is no power outage fault upstream of the i-th line. upi This indicates whether there is a power outage-type fault downstream of the i-th line, when b upi When b is 1, it indicates that there is a power outage fault downstream of the i-th line. upi When b is 0, it indicates that there is no power outage fault downstream of the i-th line. i This indicates the fault status of the i-th line;

[0117] Preferably, the above-mentioned fault type relationship constraints indicate the relationship between power outage faults and fault locations in the distribution network.

[0118] The constraints for the power outage-type fault error reporting are as follows:

[0119] g i =b i ×BM i +m′ i -f′ i

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

[0121] Specifically, f′ i m′ i and g i They are all integer variables.

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

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

[0124] Step S103: Under each first constraint, solve the fault section determination model to obtain the faulty line;

[0125] Specifically, under each of the first constraints, in the process of solving the fault section determination model, the faulty line corresponding to the condition where the number of missed and false reports is minimized, that is, the current report information is as correct as possible, is taken as the optimal solution result.

[0126] Specifically, each first constraint is linearized, and the solver is used to solve the above-mentioned fault segment determination model under each first constraint.

[0127] Step S104: Obtain the number of drone units, the faulty line, and the distance between two adjacent nodes within the faulty line;

[0128] Preferably, the UAV inspection problem is treated as a special type of vehicle routing problem and solved through optimization methods. The UAV group is regarded as a vehicle group, the two ends of the faulty line are regarded as nodes, and the line itself is regarded as the path between nodes.

[0129] Step S105: Based on the number of units, the faulty line, and the distance, with the goal of minimizing the distance traveled by the UAV or minimizing the flight time of the UAV, 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.

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

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

[0132] f = minT total

[0133] T total =max(T) k )

[0134] In the formula, T total T represents the total time taken for all drone groups to experience all faulty lines. k This represents the time spent by the k-th drone group traversing the faulty line;

[0135] If the objective is to minimize the flight time of the drone, then the inspection optimization model is as follows:

[0136] f = minS total

[0137] S total =max(S) k )

[0138]

[0139] In the formula, S total S represents the total distance traveled by all drone groups along all faulty routes. k This represents the total distance traveled by the k-th drone group along the faulty route. Indicates whether the k-th group of drones flew from node i to node j. When it is 1, it means that the k-th drone group flies from node i to node j. When R is 0, it means that the k-th drone group will not fly from node i to node j. ij This represents the distance between node i and node j in each faulty 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 inspection optimization model described above requires determining the fault location as quickly as possible to restore the load. 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, based on the number of units, the faulty line, and the distance, an inspection optimization model is constructed with the goal of minimizing the distance traveled by the UAV or minimizing the flight time of the UAV.

[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, with a total of n nodes. The first expression in the above crew path constraint indicates that among all unmanned aerial vehicle (UAV) crews, exactly one crew starts from node i and flies to one of the nodes. The above crew path constraint guarantees that each node is traversed exactly once (except for the starting point).

[0146]

[0147] In the formula, This indicates that drone group k travels from drone station to node j. This indicates that drone group number k travels from node i to node j;

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

[0149] Preferably, since the drone's information can be transmitted and received in real time, it is not required that the drone return to the warehouse after passing through the last node. This means that the k-th group of drones can be allowed to... This means that each drone group does not need to return to the starting point after reaching the destination node. Therefore, the third equation in the above drone group path constraint means that there are a total of s points in all faulty routes that do not need to return to the starting point, and these s points are evenly distributed as destinations on the path of each drone group.

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

[0151] The path coherence constraint is:

[0152]

[0153] In the formula, Indicate whether drone group k0 reaches node i from other nodes. When the value is 0, it indicates that the drone group k0 does not reach node i. Indicates whether drone groups other than drone group k0 fly from node i to other nodes. When it is 1, it means that drone groups other than drone group k0 fly from node i to other nodes. When the value is 0, it indicates that the drone group k0 flies from node i to other nodes or that node i is the destination of the drone group k0;

[0154] Preferably, the above path coherence constraints are to ensure the coherence of the path.

[0155] The constraints of the line sub-circuit are:

[0156] u i -u j +Nl ij ≤N-1,1 <i≠j<n

[0157] In the formula, u i Let u represent the potential of node i. j Let l represent the potential of node j. ij Let N represent the faulty line between node i and node j, and let N be a very large number.

[0158] Preferably, the above-mentioned line subloop constraint is to eliminate possible subloops. u is an integer decision variable representing the potential of each node. N in the line subloop 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, which prevents the generation of subloops.

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

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

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

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

[0163] Specifically, by checking the shortest path obtained from the solution, the specific location of the fault in the distribution network can be determined, thus enabling reliable and rapid location of the fault after a disaster.

[0164] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments.

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

[0166] The system includes a report information acquisition module, a fault section confirmation model construction module, a fault section confirmation model solution module, a data acquisition module, an inspection optimization model construction module, an inspection optimization model solution module, and a fault location determination module.

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

[0168] The fault section confirmation model construction module is used to construct 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 and false alarms in all reported 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 report constraint, fault type relationship constraint, and power outage type fault error report 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 faulty line.

[0170] The data acquisition module is used to acquire the number of drone units, the faulty line, and the distance between two adjacent nodes within the faulty line.

[0171] The inspection optimization model construction module is used to construct an inspection optimization model and a second constraint condition of the inspection optimization model based on 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; 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 faulty lines.

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

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

[0175] α1+α2=1

[0176]

[0177] In the formula, α1 represents the reporting weight of non-power outage faults, α2 represents the reporting weight of power outage faults, and f i This indicates the number of unreported non-power outage faults in the reported information, m i This indicates the number of false alarms for non-power outage faults in the reporting information, where Nf represents the total number of false alarms for non-power outage faults in the reporting information. i ′ This indicates the number of unreported power outage faults in the reported information, mi ′ This indicates the number of false alarms for power outage faults in the reporting information, and Ns represents the total number of power outage fault reports in the reporting information.

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

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

[0180] f = minT total

[0181] T total =max(T) k )

[0182] In the formula, T total T represents the total time taken for all drone groups to experience all faulty lines. k This represents the time spent by the k-th drone group traversing the faulty line;

[0183] If the objective is to minimize the flight time of the drone, then the inspection optimization model is as follows:

[0184] f = minS total

[0185]

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

[0187] It should be noted that the device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without creative effort. The above schematic diagrams are merely examples of a distribution network fault location device and do not constitute a limitation on a distribution network fault location device. It may include more or fewer components than shown, or combine certain components, or use different components.

[0188] Based on the above method embodiments, the present invention provides corresponding terminal device 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. When the processor executes the computer program, it implements a power distribution network fault location method as described in any embodiment of the present invention.

[0190] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the device.

[0191] The aforementioned terminal devices can be computing devices such as desktop computers, laptops, handheld computers, and cloud servers. These devices may include, but are not limited to, processors and memory.

[0192] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the device, connecting various parts of the device via various interfaces and lines.

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

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

[0195] Another embodiment of the present invention provides a storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute a power distribution network fault location method according to any embodiment of the present invention.

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

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

[0198] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for locating faults in a power distribution network, characterized in that, include: Obtain report information from each route; The report information records whether the line is faulty; With the goal of minimizing the number of missed and false alarms in all reported information, a fault section determination model and a first constraint condition for the fault section determination model are constructed. The first constraint condition includes: non-power outage type line fault constraint, non-power outage type fault current constraint, non-power outage type fault false report constraint, fault type relationship constraint, and power outage type fault false report constraint. Under each of the first constraints, the fault section determination model is solved to obtain the faulty line; Obtain the number of drone units, the faulty line, and the distance between two adjacent nodes within the faulty line; Based on the number of units, the faulty route, and the distance, an inspection optimization model and a second constraint condition for the inspection optimization model are constructed with the goal of minimizing the distance traveled by the UAV or minimizing the flight time of the UAV. The second constraint condition includes: unit path constraint, route start and end point constraint, path continuity constraint, and route sub-loop constraint. Under each of the second constraints, the inspection optimization model is solved to obtain the shortest path to all faulty lines; Conduct inspections along the shortest path to determine the specific location of faults in the power distribution network.

2. The method for locating faults in a power distribution network according to claim 1, characterized in that, The fault section determination model is as follows: α1+α2＝1 In the formula, α1 represents the reporting weight of non-power outage faults, α2 represents the reporting weight of power outage faults, and f i This indicates the number of unreported non-power outage faults in the reported information, m i This indicates the number of false alarms for non-power outage faults in the reporting information, where Nf represents the total number of non-power outage fault reports in the reporting information, and f′ represents the number of false alarms for non-power outage faults in the reporting information. i This indicates the number of unreported power outage faults in the reported information, m′ i This indicates the number of false alarms for power outage faults in the reporting information, and Ns represents the total number of power outage fault reports in the reporting information.

3. The method for locating faults in a power distribution network according to claim 2, characterized in that, The non-power outage type line fault constraint is as follows: ∑x i ≥1 In the formula, x i This indicates the non-power outage type fault condition of the i-th line; The non-power outage type 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 This indicates whether a fault current actually exists in the i-th feeder terminal, flowing from upstream to downstream. downi When y is 1, it indicates that a fault has occurred in the downstream line of the i-th feeder terminal. downi When x is 0, it indicates that there is no fault in the downstream line of the i-th feeder terminal. j This indicates the non-power outage type fault condition of the j-th line, y upi This indicates whether a fault current actually exists in the i-th feeder terminal, flowing from downstream to upstream. upi When y is 1, it indicates that there is a fault in the upstream line of the i-th feeder terminal. upi When C is 0, it indicates that there is no fault in the upstream line of the i-th feeder terminal. down C represents the set of downstream lines of the feeder terminal. up z represents the set of upstream lines of the feeder terminal. upi This indicates whether there is a report of fault current flowing from downstream to upstream at the i-th feeder terminal, when z upi When z is 1, it indicates that there is a report of fault current flowing from downstream to upstream at the i-th feeder terminal. downi This indicates whether there is a report of fault current flowing from upstream to downstream at the i-th feeder terminal, when z downi When f is 1, it indicates that there is a report of fault current flowing from upstream to downstream at the i-th feeder terminal. upi f represents the number of missed reports in the fault current reports flowing from downstream to upstream for the i-th feeder terminal. downi This represents the number of missed reports in the fault current reports flowing from upstream to downstream of the i-th feeder terminal, m. upi This represents the number of false alarm reports in the fault current reports flowing from upstream to downstream of the i-th feeder terminal, m. downi This represents the number of false alarms reported in the fault current reports flowing from downstream to upstream for the i-th feeder terminal. and These are auxiliary decision variables used to eliminate the influence of current direction; The constraints for the non-power outage type fault error reporting are as follows: f i =f upi ∪f downi m i =m upi ∪m downi The fault type relationship constraint is as follows: b i =b upi ×b downi In the formula, b downi This indicates whether there is a power outage fault upstream of the i-th line, when b downi When b is 1, it indicates that there is a power outage fault upstream of the i-th line. downi When b is 0, it indicates that there is no power outage fault upstream of the i-th line. upi This indicates whether there is a power outage-type fault downstream of the i-th line, when b upi When b is 1, it indicates that there is a power outage fault downstream of the i-th line. upi When b is 0, it indicates that there is no power outage fault downstream of the i-th line. i This indicates the fault status of the i-th line; The constraints for the power outage-type fault error reporting are as follows: g i =b i ×BM i +m′ i -f i ′ In the formula, g i BM represents the number of outage-type fault reports on the i-th line. i This indicates the number of outage-related fault reports that should be received.

4. The method for locating faults in a power distribution network according to claim 3, characterized in that, An inspection optimization model is constructed with the objective of minimizing the distance traveled by the drone or minimizing the flight time of the drone, including: If the objective is to minimize the distance traveled by the drone, then the inspection optimization model is: f=minT total T total =max(T k ) In the formula, T total T represents the total time taken for all drone groups to experience all faulty lines. k This represents the time spent by the k-th drone group traversing the faulty line; If the objective is to minimize the flight time of the drone, then the inspection optimization model is as follows: f=minS total S total =max(S k ) In the formula, S total S represents the total distance traveled by all drone groups along all faulty routes. k This represents the total distance traveled by the k-th drone group along the faulty route. Indicates whether the k-th group of drones flew from node i to node j. When it is 1, it means that the k-th drone group flies from node i to node j. When R is 0, it means that the k-th drone group will not fly from node i to node j. ij This represents the distance between node i and node j in each faulty line, and n represents the total number of nodes.

5. The method for locating faults in a power distribution network 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, This indicates that drone group k travels from drone station to node j. This indicates that drone group number k travels from node i to node j; The path coherence constraint is: In the formula, Indicate whether drone group k0 reaches node i from other nodes. When the value is 0, it indicates that the drone group k0 does not reach node i. Indicates whether drone groups other than drone group k0 fly from node i to other nodes. When it is 1, it means that drone groups other than drone group k0 fly from node i to other nodes. When the value is 0, it indicates that the drone group k0 flies from node i to other nodes or that node i is the destination of the drone group k0; The constraints of the line sub-circuit are: in i -in j +Nl ij ≤N-1, 1<i≠j <n In the formula, u i Let u represent the potential of node i. j Let l represent the potential of node j. ij Let N represent the faulty line between node i and node j, and let N be a very large number.

6. A power distribution network fault location device, characterized in that, include: The system includes a report information acquisition module, a fault section confirmation model construction module, a fault section confirmation model solution module, a data acquisition module, an inspection optimization model construction module, an inspection optimization model solution module, and a fault location determination module. The report information acquisition module is used to acquire report information for each line; The report information records whether the line is faulty; The fault section confirmation model construction module is used to construct 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 and false alarms in all reported 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 report constraint, fault type relationship constraint, and power outage type fault error report constraint. The fault section confirmation model solving module is used to solve the fault section determination model under each first constraint to obtain the faulty line. The data acquisition module is used to acquire the number of drone units, the faulty line, and the distance between two adjacent nodes within the faulty line. The inspection optimization model construction module is used to construct an inspection optimization model and a second constraint condition of the inspection optimization model based on 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; 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 faulty lines. The fault location determination module is used to perform inspections along the shortest path to determine the specific fault location in the power distribution network.

7. A power distribution network fault location device according to claim 6, characterized in that, The fault section confirmation model is constructed as follows: α1+α2＝1 In the formula, α1 represents the reporting weight of non-power outage faults, α2 represents the reporting weight of power outage faults, and f i This indicates the number of unreported non-power outage faults in the reported information, m i This indicates the number of false alarms for non-power outage faults in the reporting information, where Nf represents the total number of non-power outage fault reports in the reporting information, and f′ represents the number of false alarms for non-power outage faults in the reporting information. i This indicates the number of unreported power outage faults in the reported information, m′ i This indicates the number of false alarms for power outage faults in the reporting information, and Ns represents the total number of power outage fault reports in the reporting information.

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

9. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement a power distribution network fault location method as described in any one of claims 1 to 5.

10. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to perform a power distribution network fault location method as described in any one of claims 1 to 5.

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