Power distribution network fault positioning method and device, equipment, storage medium and program product

Abstract

The invention discloses a power distribution network fault positioning method and device, equipment, a storage medium and a program product, and the method comprises the steps: obtaining the node state data of at least one network node in a power network, and constructing a power network state incidence matrix based on the node state data of each network node; and determining a power grid characteristic constraint condition of the power network state incidence matrix, and determining a fault section corresponding to the power network state incidence matrix according to the power grid characteristic constraint condition and a preset fault positioning model. The fault section can be positioned by analyzing the power network state incidence matrix, the fault positioning time is shortened, the preset fault positioning model can accurately identify fault features through the accurate boundary provided by the power grid characteristic constraint condition, the fault positioning precision is improved, and the fault positioning efficiency is improved. The power network state incidence matrix and the power grid characteristic constraint condition can be flexibly adjusted based on the dynamic change of the power network topology structure, and the adaptability of the method to the dynamic change of the network topology structure is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of power system fault location, and particularly to a fault location method, device, equipment, storage medium and program product for a distribution network. Background Art

[0002] In the daily operation of the power network, fault location is the cornerstone for ensuring the safe and stable operation of the power grid. As a key part of the power network, the network topology of the distribution network is often in dynamic change. Coupled with the diversity of load types, fault location in the power grid has become a relatively complex and difficult problem. Traditional fault location methods mainly rely on fixed network models and parameters, and it is difficult to accurately capture the dynamic characteristics of the distribution network, thus affecting the accuracy of fault location. In addition, with the continuous complexity of the power grid structure and the continuous expansion of its scale, the calculation efficiency of traditional fault location methods also cannot meet the actual needs. Therefore, providing a fault location method that can adapt to the dynamic changes of the power grid topology and ensure efficient and accurate fault location has become an urgent problem to be solved in the technical field of power system fault location. Summary of the Invention

[0003] Embodiments of the present invention provide a fault location method, device, equipment, storage medium and program product for a distribution network. The embodiments of the present invention shorten the fault location time of the distribution network, improve the fault location accuracy of the distribution network, and enhance the adaptability of the fault location method for the distribution network to changes in the network topology.

[0004] On the one hand, an embodiment of the present invention provides a fault location method for a distribution network, including:

[0005] Obtaining node status data of at least one network node in the power network, and constructing a power network status association matrix based on the node status data of each network node;

[0006] Determining the grid characteristic constraint conditions of the power network status association matrix, and determining the fault section corresponding to the power network status association matrix according to the grid characteristic constraint conditions and a preset fault location model.

[0007] On the other hand, an embodiment of the present invention provides a fault location device for a distribution network, including:

[0008] An association matrix construction module, configured to obtain node status data of at least one network node in the power network, and construct a power network status association matrix based on the node status data of each network node;

[0009] A fault section determination module, configured to determine the grid characteristic constraint conditions of the power network status association matrix, and determine the fault section corresponding to the power network status association matrix according to the grid characteristic constraint conditions and a preset fault location model.

[0010] On the other hand, an embodiment of the present invention provides a device, including:

[0011] At least one processor;

[0012] And a memory communicatively connected to the at least one processor;

[0013] Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the distribution network fault location method of any embodiment of the embodiments of the present invention.

[0014] On the other hand, an embodiment of the present invention provides a computer-readable storage medium, including:

[0015] Computer instructions for causing a processor to execute the distribution network fault location method of any embodiment of the embodiments of the present invention.

[0016] On the other hand, an embodiment of the present invention provides a computer program product, including:

[0017] A computer program that implements the distribution network fault location method of any embodiment of the embodiments of the present invention when executed by a processor.

[0018] In an embodiment of the present invention, in a power network, node state data that can characterize the operating conditions of at least one network node is obtained. The node state data of each network node can be used as matrix elements to construct a power network state correlation matrix that can describe the correlation relationship between each node in the power network. A preset fault location model for identifying faults in the power network and grid characteristic constraint conditions for providing boundaries and limitations for fault location are obtained. Among them, the grid characteristic constraint conditions can be determined according to the physical laws of power system operation and / or actual requirements. The power network state correlation matrix and the grid characteristic constraint conditions can be input into the preset fault location model, and the preset fault location model can output the fault section where a fault occurs in the power network by using the power network state correlation matrix and the grid characteristic constraint conditions. The embodiment of the present invention simplifies the power network structure by constructing the power network state correlation matrix, locates the fault section by analyzing the matrix elements in the power network state correlation matrix, shortens the distribution network fault location time, improves the efficiency of distribution network fault location, enables the preset distribution network fault location model to accurately identify fault characteristics through the precise boundaries provided by the grid characteristic constraint conditions, improves the accuracy of distribution network fault location, and can flexibly adjust the power network state correlation matrix and the grid characteristic constraint conditions based on the dynamic changes of the power network topology, enhancing the adaptability of the fault method to the dynamic changes of the power network topology and improving the robustness of the distribution network fault location method.

[0019] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0021] Figure 1 is a flowchart of a method for locating faults in a distribution network provided in Embodiment 1 of the present invention;

[0022] Figure 2 is a flowchart of another method for locating faults in a distribution network provided in Embodiment 2 of the present invention;

[0023] Figure 3 Another flowchart of a method for locating faults in a distribution network provided in Embodiment 3 of the present invention;

[0024] Figure 4 A schematic diagram of a structural equation model provided in Embodiment 3 of the present invention;

[0025] Figure 5 A flowchart of an artificial hummingbird algorithm provided in Embodiment 3 of the present invention;

[0026] Figure 6 A schematic diagram of adding a virtual fault point to a fault line in a distribution network provided in Embodiment 3 of the present invention;

[0027] Figure 7 is a schematic diagram of the device structure for implementing the method for locating faults in a distribution network in an embodiment of the present invention;

[0028] Figure 8 is a block diagram of the device for executing the method for locating faults in a distribution network in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] In order to enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0031] Embodiment 1

[0032] Figure 1 FIG. 1 is a flowchart of a method for fault location in a distribution network according to Embodiment 1 of the present invention. The embodiments of the present invention are applicable to the situation of fault location in a power network with dynamically changing topological structures. This method can be executed by a distribution network fault location device, which can be implemented in the form of hardware and / or software, and the distribution network fault location device can be configured in a device. As Figure 1 shown, the method includes:

[0033] S101. Obtain node status data of at least one network node in the power network, and construct a power network status correlation matrix based on the node status data of each network node.

[0034] Among them, the node status data can be understood as a series of information that can characterize the operating conditions of network nodes. For example, the node status data can include information such as node voltage values, node current values, or node power values.

[0035] The power network status correlation matrix can be understood as a mathematical tool that can describe the correlation relationship between each node in the power network. It can be understood that the number of row vectors of the power network status correlation matrix is equal to the number of network nodes in the power network, and the column vectors of the power network status correlation matrix reflect the influence of the status of other nodes on the status of the reference node.

[0036] Specifically, in the power network, by obtaining the node status data of at least one network node that can characterize the operating conditions of the network nodes, the node status data of each network node can be used as matrix elements to construct a power network status correlation matrix that can describe the correlation relationship between each node in the power network. It can be understood that with the operation of the power system and the update of data, the matrix elements in the power network status correlation matrix can be continuously updated.

[0037] For example, the node status data may be obtained by installing a sensor on each network node, and obtaining the node status data collected by the sensor in a wired or wireless manner, or by manually carrying a portable device to measure the data.

[0038] S102, determining the grid characteristic constraint conditions of the power network state association matrix, and determining the fault section corresponding to the power network state association matrix according to the grid characteristic constraint conditions and a preset fault location model.

[0039] Among them, the grid characteristic constraints can be understood as a series of information used to provide boundaries and limitations for fault location, which can be determined based on the physical laws of power system operation and / or actual needs.

[0040] The preset fault location model can be understood as a pre-set mathematical model for identifying faults in the power network. It can be understood that the preset fault location model can be established based on the power system fault analysis theory and a large amount of historical data. In the preset fault location model, the changing characteristics and laws of the power network status data can be pre-set to locate the fault section.

[0041] The fault section can be understood as the specific line or node range where the fault occurs in the power network. The power network can be inspected and repaired in a targeted manner according to the fault section output by the preset fault location model, which can improve the fault handling efficiency of the power network.

[0042] Specifically, a preset fault location model for identifying faults in the power network and a power grid characteristic constraint condition for providing boundaries and limitations for fault location are obtained, wherein the power grid characteristic constraint condition can be determined based on the physical laws of power system operation and / or actual needs, and the power network state association matrix and the power grid characteristic constraint condition can be input into the preset fault location model. The preset fault location model can use the power network state association matrix and the power grid characteristic constraint condition to output the fault section where the fault occurs in the power network.

[0043] In an embodiment of the present invention, in a power network, node state data that can characterize the operating conditions of at least one network node is obtained. The node state data of each network node can be used as matrix elements to construct a power network state correlation matrix that can describe the correlation relationship between each node in the power network. A preset fault location model for identifying faults in the power network and grid characteristic constraints for providing boundaries and limitations for fault location are obtained. Among them, the grid characteristic constraints can be determined according to the physical laws of power system operation and / or actual requirements. The power network state correlation matrix and the grid characteristic constraints can be input into the preset fault location model, and the preset fault location model can output the fault section where a fault occurs in the power network by using the power network state correlation matrix and the grid characteristic constraints. In the embodiment of the present invention, the power network structure is simplified by constructing the power network state correlation matrix. By analyzing the matrix elements in the power network state correlation matrix, the fault section can be located, the fault location time of the distribution network is shortened, and the efficiency of the distribution network fault location is improved. Through the accurate boundaries provided by the grid characteristic constraints, the preset distribution network fault location model can accurately identify fault characteristics, improve the accuracy of the distribution network fault location, and can flexibly adjust the power network state correlation matrix and the grid characteristic constraints based on the dynamic changes of the power network topology, so that the adaptability of the fault method to the dynamic changes of the power network topology is enhanced, and the robustness of the distribution network fault location method is improved.

[0044] Embodiment 2

[0045] Figure 2 The flowchart of another distribution network fault location method provided by the second embodiment of the present invention. On the basis of the above embodiment, the second embodiment of the present invention provides another distribution network fault location method. As Figure 2 shown, the method includes:

[0046] S201. Taking network nodes as vertices and the power transmission relationship between network nodes as directed edges between vertices, a directed graph of the power network is established based on the vertices and directed edges.

[0047] Among them, a network node refers to each component in the power network. By way of example, network nodes may include components such as sensors or power sources.

[0048] The power transmission relationship can be understood as an interaction relationship between network nodes. By way of example, the power transmission relationship may include: active power transmission relationship or reactive power transmission relationship, etc. By monitoring the power transmission relationship between network nodes, it can be judged whether the power network is in a normal operating state.

[0049] The directed graph of the power grid can be understood as a structure composed of vertices and directed edges. In this directed graph of the power grid, the network nodes in the power grid can be abstracted as vertices, and the power transmission relationship between network nodes can be abstracted as directed edges.

[0050] Specifically, all nodes in the power grid and the power transmission relationship between each pair of nodes can be determined. All nodes in the power grid can be abstracted as vertices, the power transmission relationship between each pair of nodes among all vertices can be abstracted as directed edges connecting each pair of vertices, and a directed graph of the power grid can be constructed based on all the abstracted vertices and the directed edges between each pair of vertices among all vertices.

[0051] It can be understood that in the directed graph of the power grid, the direction of the power transmission relationship can be used as the direction of the directed edge, and the magnitude of the power transmission can be used as the weight of the directed edge.

[0052] S202: Take any vertex in the directed graph of the power grid as the reference node and other vertices as non-reference nodes, and obtain the fault additional items of the reference node and the non-reference node status data of the non-reference nodes. Among them, the fault additional items at least include: fault current increment data, fault power increment data, and reference node status error.

[0053] Among them, the reference node can be understood as a reference node, which is a vertex arbitrarily selected from all vertices in the directed graph of the power grid. It can be understood that in subsequent fault analysis, the node status of this reference node can be associated with the node status of other nodes. For example, the node status of the reference node can be represented by the node status of all other nodes.

[0054] The non-reference node is relative to the reference node and corresponds to the network nodes of all other vertices except the reference node. It can be understood that in subsequent fault analysis, the relevant data of the non-reference nodes can be processed based on the reference node.

[0055] The fault additional items can be understood as a series of additional data items related to faults. For example, the fault additional items at least include: fault current increment data, fault power increment data, and reference node status error. Among them, the fault current increment data can be understood as the change amount of the reference node current when a fault occurs in the power grid, the fault power increment data can be understood as the change amount of the reference node power when a fault occurs in the power grid, and the reference node status error refers to the difference value between the actual operating status data and the calculated status data of the reference node.

[0056] The non-reference node status data can be understood as a series of information used to characterize the operating conditions of non-reference nodes. For example, the non-reference node status data can include information such as the node voltage value of the non-reference node, the node current value of the non-reference node, or the node power value of the non-reference node.

[0057] Specifically, in a directed graph of a power network, an arbitrarily selected vertex can be used as the reference node in the directed graph of the power network. All the remaining vertices except the reference node can be used as non-reference nodes. When a fault occurs in the power network, a series of fault additional terms related to the fault generated at the reference node and the non-reference node status data used to characterize the operating conditions of non-reference nodes generated at all non-reference nodes can be obtained. Subsequent operation steps can be based on the obtained fault additional terms and the non-reference node status data.

[0058] It can be understood that in the directed graph of a power network, all vertices can have the opportunity to be selected as the reference node for subsequent operations.

[0059] S203. Add the fault additional terms and the non-reference node status data to obtain the node status data of the reference node.

[0060] Specifically, obtain the fault additional terms of the reference node and the non-reference node status data of the non-reference nodes. The obtained fault additional terms and non-reference node status data can be added together, and the result data obtained by adding the fault additional terms and the non-reference node status data can be used as the node status data of the reference node.

[0061] S204. Construct a fault column vector with the fault additional terms as vector elements and construct a non-reference node status column vector with the non-reference node status data as vector elements.

[0062] Among them, the fault column vector is a vector formed by arranging the fault additional terms of each reference node in a column in a certain order, and is used to form the power network state association matrix of the power network.

[0063] The non-reference node status column vector refers to a vector formed by arranging the non-reference node status data in a column in a certain order, and is also used to form the power network state association matrix of the power network.

[0064] Specifically, the obtained fault additional terms of the reference node can be used as vector elements and arranged in a first column vector in a certain order. The first column vector can be used as the fault column vector. The obtained non-reference node status data of each non-reference node can be used as vector elements and arranged in a second column vector in a certain order. The second column vector can be used as the non-reference node status column vector.

[0065] For example, the steps of obtaining the fault column vector and the non-reference node state column vector may further include: all vertices in the directed graph of the power network can be used as reference nodes one by one, the fault additional terms of each reference node and the non-reference node state data of all non-reference nodes corresponding to each reference node are obtained, the fault additional terms of the obtained reference nodes can be arranged in a certain order to form a fault column vector, and the non-reference node state data of each obtained non-reference node can be arranged in a certain order to form a non-reference node state column vector.

[0066] S205. Construct the power network state correlation matrix of the reference node based on the fault column vector and the non-reference node state column vector.

[0067] Specifically, to obtain the fault column vector of the reference node and the non-reference node state column vector of the non-reference node, the obtained fault column vector and non-reference node state column vector can be arranged in a certain order to form a first row vector, and this first row vector can be used as the power network state correlation matrix.

[0068] S206. Construct the non-linear power grid constraint conditions of the power network state correlation matrix based on the preset operation requirements of the power network.

[0069] Among them, the non-linear power grid constraint conditions can be understood as a series of mathematical relationships used to restrict the relationships between node state data. For example, the non-linear power grid constraint conditions may include: the upper and lower limits of the branch current amplitude or the upper and lower limits of the node voltage amplitude.

[0070] Specifically, the preset operation requirements of the power network can be obtained, and based on the correlation between each matrix element in the power network state correlation matrix and the preset operation requirements, a series of mathematical relationships restricting the relationships between node state data can be listed, and this series of mathematical relationships can be used as the non-linear power grid constraint conditions.

[0071] S207. Perform equivalent relaxation on the non-linear power grid constraint conditions, and integrate the upper bound linear constraint conditions and the lower bound linear constraint conditions of the equivalently relaxed non-linear power grid constraint conditions into the power grid characteristic constraint conditions.

[0072] Among them, equivalent relaxation can be understood as an approximate processing method that can be used to convert non-linear constraints into linear constraints.

[0073] The upper bound linear constraint condition refers to a linear constraint condition obtained by performing equivalent relaxation on the non-linear power grid constraint conditions, and the upper bound linear constraint condition stipulates the upper limit value of a certain or certain state variables in the power network.

[0074] The lower-bound linear constraint condition refers to another linear constraint condition obtained after equivalently relaxing the non-linear power grid constraint condition, and the lower-bound linear constraint condition specifies the lower limit value of one or some state variables in the power network.

[0075] Specifically, to obtain the non-linear power grid constraint condition, an approximate processing method can be used to equivalently relax the obtained non-linear power grid constraint condition. After the non-linear power grid constraint condition is equivalently relaxed, two linear constraint conditions can be obtained, namely the upper-bound linear constraint condition and the lower-bound linear constraint condition. Among them, the upper-bound linear constraint condition specifies the upper limit value of one or some state variables in the power network, and the lower-bound linear constraint condition specifies the lower limit value of one or some state variables in the power network. The upper-bound linear constraint condition and the lower-bound linear constraint condition can be combined to obtain a combined constraint condition, and this combined constraint condition can be used as the power grid characteristic constraint condition.

[0076] S208. Based on the preset optimization requirement, determine the objective function corresponding to the power network state incidence matrix.

[0077] Among them, the objective function can be understood as a mathematical function, which can measure the gap between the operating state of the power network and the expected goal, and can make the optimization process have a clear direction and operability.

[0078] Specifically, based on the power network state incidence matrix, an objective function corresponding to the preset optimization requirement and capable of measuring the gap between the operating state of the power network and the expected goal can be determined. It can be understood that this objective function is transformed from the power network state incidence matrix.

[0079] S209. Combine the power grid characteristic constraint condition, and use the preset fault location model to determine the minimum objective function corresponding to the objective function, and output the power network node section corresponding to the minimum objective function. Among them, the construction algorithm of the preset fault location model at least includes: the artificial hummingbird algorithm.

[0080] Among them, the power network node section can be understood as the area containing the specific fault location, which is the definition of the specific physical range where the fault occurs in the power network.

[0081] Specifically, the preset fault location model can be used to optimize and solve the objective function. Under the constraints of power grid characteristics, the preset fault location model can be used to find the parameter value that makes the objective function reach the minimum value. The preset fault location model can output the specific location range of the fault in the power network according to the parameter value corresponding to the minimum objective function obtained by solving the preset fault location model. The output specific location range can be used as the power network node segment. Among them, the preset fault location model can be constructed based on the artificial hummingbird algorithm. The artificial hummingbird algorithm is a swarm intelligence optimization algorithm with a faster convergence speed and stronger global search capability. Constructing a preset fault location model based on the artificial hummingbird algorithm can improve the accuracy and efficiency of fault location.

[0082] S210: Taking the power network node section as the fault section of the power network.

[0083] Specifically, a power network node section in the power network is obtained, and the power network node section can be used as a fault section in the power network. The power network can be inspected and repaired specifically based on the fault section, thereby improving the fault handling efficiency of the power network.

[0084] In an embodiment of the present invention, all nodes in the power network can be determined, and the power transmission relationship between each pair of nodes can be determined. All nodes in the power network can be abstracted as vertices, and the power transmission relationship between each pair of nodes among all vertices can be abstracted as a directed edge connecting each pair of vertices. A directed graph of the power network can be constructed based on all the abstracted vertices and the directed edges between each pair of vertices among all vertices. In the directed graph of the power network, an arbitrarily selected vertex can be used as a reference node in the directed graph of the power network, and all the remaining vertices except the reference node can be used as non-reference nodes. When a fault occurs in the power network, a series of fault additional terms related to the fault generated at the reference node, and non-reference node status data used to characterize the operating conditions of non-reference nodes generated at all non-reference nodes can be obtained. The obtained fault additional terms and non-reference node status data can be added together, and the resulting data obtained by adding the fault additional terms and non-reference node status data can be used as the node status data of the reference node. The obtained fault additional terms of the reference node can be used as vector elements and arranged in a certain order to form a first column vector, and this first column vector can be used as a fault column vector. The obtained non-reference node status data of each non-reference node can be used as vector elements and arranged in a certain order to form a second column vector, and this second column vector can be used as a non-reference node status column vector. The obtained fault column vector and non-reference node status column vector can be arranged in a certain order to form a row, resulting in a first row vector, and this first row vector can be used as a power network state correlation matrix. The preset operation requirements of the power network can be obtained, and based on the correlation between each matrix element in the power network state correlation matrix and the preset operation requirements, a series of mathematical relationships restricting the relationships between node status data can be listed, and this series of mathematical relationships can be used as non-linear power grid constraint conditions. An approximate processing method can be used to perform equivalent relaxation on the obtained non-linear power grid constraint conditions. After the equivalent relaxation of the non-linear power grid constraint conditions, two linear constraint conditions can be obtained, namely an upper bound linear constraint condition and a lower bound linear constraint condition. Among them, the upper bound linear constraint condition stipulates the upper limit value of a certain or certain state variables in the power network, and the lower bound linear constraint condition stipulates the lower limit value of a certain or certain state variables in the power network. The upper bound linear constraint condition and the lower bound linear constraint condition can be combined to obtain a combined constraint condition, and this combined constraint condition can be used as a power grid characteristic constraint condition. Based on the power network state correlation matrix, a target function corresponding to the preset optimization requirement and capable of measuring the gap between the operating state of the power network and the desired goal can be determined. It can be understood that this target function is transformed from the power network state correlation matrix. The preset fault location model can be used to optimize and solve the target function. Under the power grid characteristic constraint conditions, the preset fault location model can find the parameter values that minimize the target function, and the preset fault location model can output the specific location range of the fault in the power network according to the parameter values corresponding to the solved minimum target function.The specific position range of the output can be used as a node section of the power network. This node section of the power network can be regarded as a fault section in the power network, and the power network can be inspected and repaired specifically for this fault section, thereby improving the efficiency of fault handling in the power network. In the embodiment of the present invention, by abstracting network nodes as vertices and power transmission relationships as directed edges, a directed graph of the power network is established, simplifying the complex power network structure and enhancing the convenience of fault detection in the power network. By adding a fault additional term, it can be ensured that the power network state incidence matrix can accurately describe the actual operating state of the reference node, thereby improving the accuracy of distribution network fault location. By equivalently relaxing the grid characteristic constraint conditions into linear constraint conditions that are easier to solve, the process of optimizing the objective function of the preset fault location model is simplified, the solution speed of the preset fault location model is accelerated, and the efficiency of distribution network fault location is improved.

[0085] Based on the above embodiments, the embodiments of the present invention may further include the following steps:

[0086] Set multiple virtual fault points at equal intervals within the fault section, and use preset power system simulation software to collect the virtual electrical data of each virtual fault point; use the preset fault location model to identify the virtual electrical data, and output the accurate fault points of the power network based on the virtual electrical data.

[0087] Among them, a virtual fault point refers to a fault position point that does not actually exist within the fault section and can be used to accurately obtain the fault position in the power network.

[0088] Virtual electrical data refers to the simulation information obtained when using preset power system simulation software to simulate each virtual fault point. For example, the virtual electrical data may include simulation information such as voltage amplitude or current amplitude.

[0089] The accurate fault point can be understood as the specific position where a fault occurs in the power network. Based on this accurate fault point, the accurate position point where a fault actually occurs in the power network can be accurately obtained.

[0090] Specifically, to obtain the fault section in the power grid, multiple virtual fault points for accurately obtaining the fault location in the power grid can be set at equal intervals along the obtained fault section. A pre-selected preset power system simulation software can be used to simulate the power grid with virtual fault points. During the simulation process, the preset power system simulation software can collect the virtual electrical data of each virtual fault point. The obtained virtual electrical data can be input into the preset fault location model. The preset fault location model can identify the virtual electrical data and output the specific location where the fault occurs in the power grid, that is, output the accurate fault point where the fault occurs in the power grid. The multiple judgment mechanism of setting multiple virtual fault points in each fault section further improves the accuracy and reliability of the distribution network fault location.

[0091] Embodiment III

[0092] Figure 3 The flowchart of another distribution network fault location method provided by Embodiment III of the present invention. On the basis of the above embodiments, the present invention provides another distribution network fault location method. As Figure 3 shown, the method includes: abstracting each component in the power network system as a node in the directed graph G, abstracting the power transmission relationship between components as a directed edge in the directed graph. The graph G can be represented by (V, E), where V = {v1, v2,..., v k} represents all vertices, and E = {e ij} represents all edges connecting vertices. The attributes of the nodes and the weights of the directed edges can be set according to the actual operation situation and dispatching objectives of the power system; referring to the structural equation model, as Figure 4 shown, capturing the node state data y i of the power network node i to obtain the relevance of the network topology structure, where,

[0093]

[0094] where i and j respectively represent the node numbers, where i ≠ j; a i,j and b i are respectively weight coefficients, represents the state variable related to the node, where represents the real part of the voltage, represents the imaginary part of the voltage; ε i is the state error term of node i.

[0095] The formula (1) can be expressed in matrix form to obtain the node state correlation matrix:

[0096] Y = AY + BX + E (2)

[0097] Wherein, matrices A, B, and E are the matrix expressions corresponding to a i,j , b i , ε i respectively.

[0098] In the node state incidence matrix, new additional terms generated by the fault current are added to accurately describe the actual operating state of the system in a complex environment. It is necessary to expand the system state variables to

[0099] Therefore, Equation (2) is expressed as:

[0100] Y = AY + BX′ + E (3)

[0101] When a network fault occurs, in order to more accurately describe the actual operating state of the distribution network in a complex distribution network environment, an additional term Δz corresponding to the fault power is added to the measurement equation:

[0102]

[0103] Wherein, Δz P represents the active power component of the additional term, and Δz Q represents the reactive power component; represents the real part of the component of the additional term of the fault current, represents the imaginary part.

[0104] Therefore, Equation (3) can be further expressed as Equation (5) to obtain the power network state incidence matrix:

[0105] Y = AY + BX′ + E + Δz (5)

[0106] Based on the preset operating requirements of the power network, the objective function can be determined to minimize the node state error term:

[0107] min||E|| = min||Y - AY - BX′ - Δz|| (6)

[0108] The non-linear power grid constraint conditions are as follows:

[0109]

[0110] Wherein, I ij,max is the upper limit value of the branch current amplitude; U i,max and U i,min are the upper and lower limits of the node voltage amplitude; P ij and Q ij are the active and reactive powers of branch i, j; P ij,min , P ij，max are the upper and lower bounds of the active power of branch i, j respectively; S ij,max is the maximum value of the apparent power; PL,i Reactive power consumed by the load at node i; Q g,i Reactive power injected by the power source at node i, Q L,i Reactive power consumed by the load at node i.

[0111] The non-linear power grid constraint conditions can be rewritten using formula (8) involved in the equivalent relaxation method to obtain the power grid characteristic constraint condition formula (9):

[0112]

[0113] Where P = [P ij is the matrix representation of the active power of branch ij, Q = [Q ij is the matrix representation of the reactive power of branch ij, P = [P ij , Q = [Q ij , R = diag[R ij n×n 、X = diag[X ij n×n Represents the reactance matrix set, The first row of matrix A0 needs to be removed for the transformer node. Let [0 n ,I]A0 - I = A, where I is the n-dimensional identity matrix and 0n is the n-row 0 vector.

[0114]

[0115] Where D X+ 、H + Represent the non-negative elements of matrices D X 、H; D X- 、H_ represent the negative elements; Pg represents the power source matrix of the distribution network, Represents the maximum and minimum values of the power output of the power source; P L Represents the load matrix of each node of the distribution network.

[0116] In the embodiment of the present invention, l can be expressed as:

[0117]

[0118] Where Hessian matrix.

[0119] The derivation of the l boundary value can be expressed as:

[0120]

[0121] Where J ij+ 、J ij- Are J ij ​​matrices composed of non - negative elements and matrices composed of negative elements in; δ ij+ and δ ij- is δ ij matrices composed of non - negative and negative elements in. But l ij,min may take negative values, but so in actual simulation runs, l ij the lower - bound value should be max{0, l ij,min}; In addition, since the two eigenvalues of the Hessian matrix H ij are strictly positive and one is zero, it is positive semi - definite, so the boundary of l is convex.

[0122] Refer to the Artificial Hummingbird Algorithm (AHA). Using a preset fault location model, solve based on the power network state incidence matrix and power grid characteristic constraint conditions. The model input is the state data of each node. By comparing the node state errors of each node, the fault location of the distribution network is determined, and the specific location of the line fault is output. Among them, the AHA algorithm flow chart is as Figure 3 shown, mainly including the following steps: initialize the hummingbird position, calculate the fitness, update the position according to individual and neighbor experience, calculate the fitness again, select the optimal solution, judge the termination condition, if not reached, continue to iterate, and finally output the result.

[0123] In the artificial hummingbird algorithm, a chaotic - map Bernoulli initialization population method can be further added to solve the distribution network fault location model. Among them, the Bernoulli equation is as follows:

[0124]

[0125] In the formula, x(t) is the state variable at time t, and x(t + 1) is the state variable at time t + 1; λ is a constant parameter, where 0 ≤ λ ≤ 1, used as the threshold and scale factor of the piece - wise function.

[0126] The chaotic sequence generated by the chaotic map can generate a more diverse and uniform population, which can improve the solving ability of complex optimization models.

[0127] In an embodiment of the present invention, virtual fault points can be further set in each section and multiple judgments can be performed to achieve accurate positioning of the fault point of the distribution network: after successfully locating the node closest to the fault, in order to more accurately identify the exact location of the fault, the present invention implements more detailed division and processing of the branches directly connected to the node. This step aims to narrow the fault search range through detailed analysis. Specifically, the present invention introduces the concept of virtual fault points on the fault line of the distribution network. These virtual points are strategically set at key positions and divided according to equal spacing between the lines. The lines between the virtual fault points are then fault located section by section to achieve multiple state estimation, wherein the state estimation method each time is the same as the aforementioned method. So that the exact location of the fault can be checked and determined more systematically and step by step. In this way, not only the accuracy of fault location in the distribution network is improved, but also the efficiency and effect of fault detection are enhanced. The virtual fault point number is set to b(γ,η), which represents the ηth virtual fault point in the fault line γ, such as Figure 4 The figure shows a schematic diagram of adding a virtual fault point to a fault line in a distribution network.

[0128] Embodiment 4

[0129] Figure 7 A fourth embodiment of the present invention provides a device capable of executing a distribution network fault location method, such as Figure 7 As shown, the device comprises:

[0130] The association matrix construction module 301 is used to obtain node status data of at least one network node in the power network, and to construct a power network status association matrix based on the node status data of each network node;

[0131] The fault section determination module 302 is used to determine the grid characteristic constraint conditions of the power network state association matrix, and determine the fault section corresponding to the power network state association matrix according to the grid characteristic constraint conditions and a preset fault location model.

[0132] In an embodiment of the present invention, in a power network, node state data that can characterize the operating conditions of at least one network node is obtained. The node state data of each network node can be used as matrix elements to construct a power network state correlation matrix that can describe the correlation relationship between each node in the power network. A preset fault location model for identifying faults in the power network and grid characteristic constraint conditions for providing boundaries and limitations for fault location are obtained. Among them, the grid characteristic constraint conditions can be determined according to the physical laws of power system operation and / or actual requirements. The power network state correlation matrix and the grid characteristic constraint conditions can be input into the preset fault location model, and the preset fault location model can output the fault section where a fault occurs in the power network by using the power network state correlation matrix and the grid characteristic constraint conditions. In the embodiment of the present invention, the power network structure is simplified by constructing the power network state correlation matrix. The fault section can be located by analyzing the matrix elements in the power network state correlation matrix, shortening the fault location time of the distribution network and improving the efficiency of the distribution network fault location. Through the precise boundaries provided by the grid characteristic constraint conditions, the preset fault location model can accurately identify fault characteristics, improving the accuracy of the distribution network fault location. The power network state correlation matrix and the grid characteristic constraint conditions can be flexibly adjusted based on the dynamic changes of the power network topology structure, enhancing the adaptability of the fault method to the dynamic changes of the power network topology structure and improving the robustness of the distribution network fault location method.

[0133] Based on the above embodiment, the correlation matrix construction module 301 of the embodiment of the present invention is specifically used for:

[0134] Taking network nodes as vertices and the power transmission relationship between network nodes as directed edges between vertices, and establishing a directed graph of the power network based on the vertices and directed edges;

[0135] Taking any vertex in the directed graph of the power network as a reference node and other vertices as non-reference nodes, obtaining the fault additional term of the reference node and the non-reference node state data of the non-reference nodes;

[0136] Adding the fault additional term and the non-reference node state data to obtain the node state data of the reference node, where the fault addition at least includes: fault current increment data, fault power increment data, and reference node state error.

[0137] Based on the above embodiment, the correlation matrix construction module 301 of the embodiment of the present invention is further specifically used for:

[0138] Taking the fault additional term as vector elements to construct a fault column vector and taking the non-reference node state data as vector elements to construct a non-reference node state column vector;

[0139] Construct the power network state correlation matrix of the reference node based on the fault column vector and the non-reference node state column vector.

[0140] Based on the above embodiments, the fault section determination module 302 of the embodiments of the present invention is specifically configured to:

[0141] Construct the non-linear power grid constraint conditions of the power network state correlation matrix based on the preset operation requirements of the power network;

[0142] Perform equivalent relaxation on the non-linear power grid constraint conditions, and integrate the upper bound linear constraint conditions and the lower bound linear constraint conditions of the equivalent relaxation of the non-linear power grid constraint conditions into the power grid characteristic constraint conditions.

[0143] Based on the above embodiments, the fault section determination module 302 of the embodiments of the present invention is further specifically configured to:

[0144] Determine the objective function corresponding to the power network state correlation expression based on the preset optimization requirements;

[0145] Combined with the power grid characteristic constraint conditions, use the preset fault location model to determine the minimum objective function corresponding to the objective function, and output the power network node section corresponding to the minimum objective function;

[0146] Take the power network node section as the fault section of the power network, where the construction algorithm of the preset fault location model at least includes: the artificial hummingbird algorithm.

[0147] Based on the above embodiments, the fault section determination module 302 of the embodiments of the present invention is further specifically configured to:

[0148] Set a plurality of virtual fault points at equal intervals within the fault section, and use the preset power system simulation software to collect the virtual electrical data of each virtual fault point;

[0149] Use the preset fault location model to identify the virtual electrical data, and output the exact fault point of the power network based on the virtual electrical data.

[0150] Embodiment Five

[0151] The embodiments of the present invention provide a device for executing the distribution network fault location method, a computer-readable storage medium, and a computer program product.

[0152] Figure 8The structural schematic diagram of a device that can be used to implement the embodiments of the present invention is shown. The device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown in the embodiments of the present invention, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the embodiments of the present invention described and / or claimed herein.

[0153] As Figure 8 shown, the device includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can execute various appropriate actions and processes according to the computer program stored in the ROM 12 or the computer program loaded from the storage unit 18 into the RAM 13. In the RAM 13, various programs and data required for the operation of the device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0154] Multiple components in the device are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the device to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0155] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit, a graphics processing unit, various dedicated artificial intelligence computing chips, various processors running machine learning model algorithms, a digital signal processor, and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for locating faults in a distribution network.

[0156] In some embodiments, the distribution network fault location method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed onto the device via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps based on the distribution network fault location method may be performed. Alternatively, in other embodiments, processor 11 may be configured for the distribution network fault location method by any other suitable means (e.g., by means of firmware).

[0157] The various embodiments of the systems and techniques described above in the embodiments of the present invention can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays, application specific integrated circuits, application specific standard products, systems on a chip, programmable logic devices loaded, computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs being executable and / or interpretable on a programmable system including at least one programmable processor, the programmable processor may be a dedicated or general purpose programmable processor, receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0158] The computer programs for implementing the methods of the embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, a dedicated computer, or other programmable data processing device, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs may be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0159] In the context of embodiments of the present invention, a computer-readable storage medium may be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium may be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0160] In order to provide interaction with a user, the systems and techniques described herein can be implemented on a device having: a display device (e.g., a cathode ray tube or a liquid crystal display monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including: acoustic input, voice input, or tactile input).

[0161] The systems and techniques described herein can be implemented in a computing system that includes backend components (such as, for example, a data server), or a computing system that includes middleware components (such as, for example, an application server), or a computing system that includes frontend components (such as, for example, a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (such as, for example, a communication network). Examples of communication networks include: local area networks, wide area networks, blockchain networks, and the Internet.

[0162] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The client-server relationship is created by computer programs that run on respective computers and have a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and virtual private server services.

[0163] It should be understood that various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0164] The above specific embodiments do not constitute a limitation on the protection scope of the embodiments of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for fault location in a distribution network, characterized in that, The method includes: Obtaining node status data of at least one network node in the power network, and constructing a power network status association matrix based on the node status data of each of the network nodes; Determining grid characteristic constraint conditions of the power network status association matrix, and determining a fault section corresponding to the power network status association matrix according to the grid characteristic constraint conditions and a preset fault location model.

2. The method according to claim 1, wherein The obtaining node status data of at least one network node in the power network includes: Taking the network nodes as vertices and taking the power transmission relationship between the network nodes as directed edges between the vertices, and establishing a directed graph of the power network based on the vertices and the directed edges; Taking any vertex in the directed graph of the power network as a reference node and taking other vertices as non-reference nodes, and obtaining a fault additional item of the reference node and non-reference node status data of the non-reference nodes, where the fault additional item at least includes: fault current increment data, fault power increment data, and reference node status error; Adding the fault additional item and the non-reference node status data to obtain the node status data of the reference node.

3. The method according to claim 2, wherein The constructing a power network status association matrix based on the node status data of each of the network nodes includes: Taking the fault additional item as a vector element to construct a fault column vector and taking the non-reference node status data as a vector element to construct a non-reference node status column vector; Constructing a power network status association matrix of the reference node based on the fault column vector and the non-reference node status column vector.

4. The method according to claim 1, wherein The determining grid characteristic constraint conditions of the power network status association matrix includes: Constructing non-linear grid constraint conditions of the power network status association matrix based on preset operation requirements of the power network; Performing equivalent relaxation on the non-linear grid constraint conditions, and integrating an upper bound linear constraint condition and a lower bound linear constraint condition of the equivalently relaxed non-linear grid constraint conditions into the grid characteristic constraint conditions.

5. The method according to claim 1, wherein The determining a fault section corresponding to the power network status association matrix according to the grid characteristic constraint conditions and a preset fault location model includes: Determining an objective function corresponding to the power network status association matrix based on a preset optimization requirement; Combining the grid characteristic constraint conditions, using a preset fault location model to determine a minimum objective function corresponding to the objective function, and outputting a power network node section corresponding to the minimum objective function, where a construction algorithm of the preset fault location model at least includes: artificial hummingbird algorithm; Taking the power network node section as the fault section of the power network.

6. The method according to claim 5, wherein The determining a fault section corresponding to the power network status association matrix according to the grid characteristic constraint conditions and a preset fault location model further includes: Setting a plurality of virtual fault points at equal intervals in the fault section, and using a preset power system simulation software to collect virtual electrical data of each of the virtual fault points; Identifying the virtual electrical data by using the preset fault location model, and outputting an exact fault point of the power network based on the virtual electrical data.

7. A distribution network fault location device, characterized in that, The device includes: An association matrix construction module, configured to obtain node status data of at least one network node in the power grid, and construct a power grid status association matrix based on the node status data of each of the network nodes; A fault section determination module, configured to determine the grid characteristic constraint conditions of the power grid status association matrix, and determine the fault section corresponding to the power grid status association matrix according to the grid characteristic constraint conditions and a preset fault location model.

8. A device, characterized in that, The device includes: At least one processor; And a memory communicatively connected to the at least one processor; Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the distribution network fault location method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores: Computer instructions, which are used to implement the distribution network fault location method according to any one of claims 1-6 when executed by a processor.

10. A computer program product, characterized in that, The computer program product includes a computer program, which implements the distribution network fault location method according to any one of claims 1-6 when executed by a processor.

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