Method and apparatus for fault determination of a power transmission line

Abstract

The application discloses a kind of fault determination method and device of transmission line, it is related to power system technical field.The method can include: obtaining the self-admittance matrix and mutual admittance matrix of multiple nodes in transmission line system;Based on self-admittance matrix and mutual admittance matrix, the complex domain voltage information and complex domain current information of fault node in transmission line system are determined;Complex domain voltage information and complex domain current information are converted, and the time domain waveform information of fault node is obtained;Calling neural network model analyzes time domain waveform information, and the position information of fault node is obtained.The application solves the technical problem that the accuracy of fault positioning of transmission line is low.

Description

Technical Field

[0001] This application relates to the field of power system technology, and more specifically, to a method and apparatus for determining faults in transmission lines. Background Technology

[0002] Currently, transmission lines are a crucial component of the power system; faults in transmission lines can lead to widespread power outages. Therefore, quickly and accurately locating the fault point on the transmission line is of paramount importance.

[0003] In related technologies, fault locations on transmission lines are determined by using single-ended traveling wave fault location methods. There are two main types of single-ended traveling wave fault location methods: one is an active detection type, but this method is only applicable to permanent short circuits; the other is based on the time difference between two consecutive arrivals of the traveling wave front at the measurement point to calculate the fault distance, but this method requires accurate identification of the traveling wave front and a very high sampling rate, thus resulting in the technical problem of low accuracy in fault location of transmission lines.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This application provides a method and apparatus for determining faults in transmission lines, which at least solves the technical problem of low accuracy in fault location of transmission lines.

[0006] According to one aspect of the embodiments of this application, a method for determining faults in a transmission line is provided. The method may include: acquiring self-admittance matrices and mutual admittance matrices of multiple nodes in a transmission line system, wherein each node includes at least a connection point and an endpoint of the transmission line; the self-admittance matrix represents the admittance characteristics of the node, and the mutual admittance matrix represents the admittance characteristics associated between the multiple nodes; determining complex-domain voltage information and complex-domain current information of a fault node in the transmission line system based on the self-admittance matrix and the mutual admittance matrix, wherein the complex-domain voltage information represents the voltage of the fault node in the complex frequency domain, and the complex-domain current information represents the current of the fault node in the complex frequency domain; converting the complex-domain voltage information and complex-domain current information to obtain time-domain waveform information of the fault node, wherein the time-domain waveform information represents the voltage waveform and current waveform of the fault node; and calling a neural network model to analyze the time-domain waveform information to obtain the location information of the fault node, wherein the neural network model is trained based on samples of the time-domain waveform information, and the location information represents the location where a fault occurs in the transmission line.

[0007] Optionally, the method further includes: obtaining the parallel admittance and series impedance of the transmission line, wherein the parallel admittance is used to represent the admittance characteristics of the transmission line per unit length, and the series impedance is used to represent the impedance characteristics of the transmission line per unit length; obtaining the self-admittance matrix and mutual admittance matrix of multiple nodes in the transmission line system, including: determining the self-admittance matrix and mutual admittance matrix based on the parallel admittance and series impedance.

[0008] Optionally, obtaining the parallel admittance and series impedance of the transmission line includes: using a frequency-varying parameter model of the transmission line system, performing polynomial vector fitting on the frequency-varying parameters of the transmission line to obtain the parallel admittance and series impedance, wherein the frequency-varying parameters are used to represent the line parameters that vary with frequency.

[0009] Optionally, the self-admittance matrix and mutual admittance matrix are determined based on the parallel admittance and series impedance, including: determining the self-admittance matrix and mutual admittance matrix based on the parallel admittance, series impedance, and transmission line length information, wherein the length information is used to represent the actual length of the transmission line.

[0010] Optionally, based on the self-admittance matrix and the mutual admittance matrix, the complex-domain voltage information and complex-domain current information of the fault node in the transmission line system are determined, including: based on the self-admittance matrix and the mutual admittance matrix, determining the impedance matrix of the transmission line system, wherein the impedance matrix is ​​used to represent the impedance characteristics of the transmission line system; and based on the impedance matrix, determining the complex-domain voltage information and complex-domain current information.

[0011] Optionally, the impedance matrix of the transmission line system is determined based on the self-admittance matrix and the mutual admittance matrix, including: determining the admittance matrix of the transmission line system based on the self-admittance matrix and the mutual admittance matrix, wherein the admittance matrix is ​​used to represent the admittance characteristics of the transmission line system; and transforming the admittance matrix to obtain the impedance matrix.

[0012] Optionally, based on the impedance matrix, the complex domain voltage information and complex domain current information are determined, including: adding a fault excitation current at the fault node based on the impedance matrix to obtain a fault excitation voltage, wherein the fault excitation current is used to represent the current fluctuation at the fault node, and the fault excitation voltage is used to represent the voltage fluctuation at the fault node; converting the fault excitation voltage to obtain complex domain short-circuit current information, wherein the complex domain short-circuit current information is used to represent the short-circuit current at the fault node in the complex frequency domain; and determining the complex domain voltage information and complex domain current information based on the impedance matrix and the complex domain short-circuit current information.

[0013] Optionally, the complex-domain voltage information and complex-domain current information are transformed to obtain the time-domain waveform information of the fault node, including: performing a numerical inverse Laplace transform on the complex-domain voltage information and complex-domain current information to obtain the time-domain waveform information.

[0014] Optionally, the method further includes: establishing an initial neural network model based on a residual convolutional neural network and a multi-head self-attention mechanism; training the initial neural network model based on time-domain waveform information samples to obtain a neural network model, wherein the time-domain waveform information samples are generated based on time-domain waveform information.

[0015] According to another aspect of the embodiments of this application, a fault determination device for a transmission line is also provided. The device may include: an acquisition unit, configured to acquire the self-admittance matrix and mutual admittance matrix of multiple nodes in a transmission line system, wherein each node includes at least a connection point and an endpoint of the transmission line, the self-admittance matrix represents the admittance characteristics of the node, and the mutual admittance matrix represents the admittance characteristics associated between the multiple nodes; a first determination unit, configured to determine the complex-domain voltage information and complex-domain current information of a fault node in the transmission line system based on the self-admittance matrix and the mutual admittance matrix, wherein the complex-domain voltage information represents the voltage of the fault node in the complex frequency domain, and the complex-domain current information represents the current of the fault node in the complex frequency domain; a conversion unit, configured to convert the complex-domain voltage information and complex-domain current information to obtain time-domain waveform information of the fault node, wherein the time-domain waveform information represents the voltage waveform and current waveform of the fault node; and a second determination unit, configured to call a neural network model to analyze the time-domain waveform information to obtain the location information of the fault node, wherein the neural network model is trained based on samples of the time-domain waveform information, and the location information represents the location where a fault occurs in the transmission line.

[0016] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, comprising: the storage medium including a stored program, wherein, when the program is running, it controls the device where the storage medium is located to execute any kind of transmission line fault determination method.

[0017] According to another aspect of the present invention, a processor is also provided for running a program, wherein the program executes the fault determination method for transmission lines according to the embodiments of the present invention.

[0018] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement any method for determining faults in a power transmission line.

[0019] According to another aspect of the embodiments of this application, a computer program product is also provided. This computer program product may include a computer program configured to execute any of the above-described methods for determining faults in transmission lines when running.

[0020] According to another aspect of the embodiments of this application, a computer program product is also provided. This computer program product may include a non-volatile computer-readable storage medium storing a computer program configured to execute any of the above-described transmission line fault determination methods during runtime.

[0021] According to another aspect of the embodiments of this application, a computer program is also provided. This computer program is configured to execute any of the above-described methods for determining faults in transmission lines when it is run.

[0022] In this embodiment, the self-admittance matrix and mutual admittance matrix of multiple nodes in a transmission line system are obtained. Each node includes at least the connection point and endpoint of the transmission line. The self-admittance matrix represents the admittance characteristics of a node, and the mutual admittance matrix represents the associated admittance characteristics between multiple nodes. Based on the self-admittance matrix and mutual admittance matrix, the complex-domain voltage information and complex-domain current information of the fault node in the transmission line system are determined. The complex-domain voltage information represents the voltage of the fault node in the complex frequency domain, and the complex-domain current information represents the current of the fault node in the complex frequency domain. The complex-domain voltage information and complex-domain current information are converted to obtain the time-domain waveform information of the fault node. The time-domain waveform information represents the voltage and current waveforms of the fault node. A neural network model is called to analyze the time-domain waveform information to obtain the location information of the fault node. The neural network model is trained based on samples of the time-domain waveform information. The location information represents the location of the fault in the transmission line, thereby improving the accuracy of fault location in transmission lines and solving the problem of low accuracy in fault location in transmission lines. Attached Figure Description

[0023] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0024] Figure 1 This is a flowchart illustrating a method for determining faults in a power transmission line according to an embodiment of this application.

[0025] Figure 2 This is a schematic diagram of the structure of a residual convolutional neural network according to an embodiment of this application;

[0026] Figure 3 This is a schematic diagram of a frequency-varying parameter model according to an embodiment of this application;

[0027] Figure 4 This is a schematic diagram of the structure of a fault determination device for a power transmission line according to an embodiment of this application;

[0028] Figure 5 This is a schematic block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] According to an embodiment of this application, a method embodiment for determining faults in a transmission line is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0032] Figure 1 This is a flowchart illustrating a method for determining faults in a power transmission line according to an embodiment of this application, as shown below. Figure 1 As shown, the method may include the following steps:

[0033] Step S102: Obtain the self-admittance matrix and mutual admittance matrix of multiple nodes in the transmission line system.

[0034] In the technical solution provided in step S102 above, the self-admittance matrix and mutual admittance matrix of multiple nodes in the transmission line system can be obtained. The transmission line system can be a T-connected hybrid transmission line system. The aforementioned nodes can at least include the connection points and endpoints of the transmission line. The self-admittance matrix can be used to represent the admittance characteristics of the nodes and can be called the complex domain self-admittance submatrix, which can be represented by y... s(s) is used to represent the mutual admittance matrix. This matrix can be used to represent the admittance characteristics associated with multiple nodes and can be called the line mutual admittance submatrix. It can be represented by y. m (s) is used to represent.

[0035] Optionally, by performing polynomial vector fitting based on rational function approximation on the frequency-varying parameters of the transmission line, the parallel admittance and series impedance of different types of transmission lines at discrete frequencies in a T-connected hybrid transmission line can be obtained. Furthermore, based on the obtained parallel admittance and series impedance, the complex domain self-admittance submatrix y of multiple nodes in the transmission line system can be determined. s (s) and the line mutual admittance submatrix y m (s).

[0036] Step S104: Based on the self-admittance matrix and the mutual admittance matrix, determine the complex domain voltage information and complex domain current information of the fault node in the transmission line system.

[0037] In the technical solution provided in step S104 above, after obtaining the self-admittance matrix and mutual admittance matrix of multiple nodes in the transmission line system, the complex domain voltage information and complex domain current information of the fault node in the transmission line system can be determined based on the obtained self-admittance matrix and mutual admittance matrix. The complex domain voltage information can be used to represent the voltage of the fault node in the complex frequency domain, and can be called the complex frequency fault transient voltage. The complex domain current information can be used to represent the current of the fault node in the complex frequency domain, and can be called the complex frequency fault transient current. It should be noted that the above-mentioned fault transient voltage and fault transient current are both fault component data.

[0038] Optionally, after obtaining the self-admittance and mutual admittance matrices of multiple nodes in the transmission line system, the admittance matrix of the transmission line system can be determined based on these matrices. Further transformation of the admittance matrix yields the impedance matrix of the transmission line system. Based on the impedance matrix, the complex domain expressions for the fault transient voltage and fault transient current at any point in the transmission line system can be determined.

[0039] Step S106: Convert the complex domain voltage information and complex domain current information to obtain the time domain waveform information of the fault node.

[0040] In the technical solution provided in step S106 above, after determining the complex-domain voltage information and complex-domain current information of the fault node in the transmission line system, the determined complex-domain voltage information and complex-domain current information can be converted to obtain the time-domain waveform information of the fault node. The time-domain waveform information can be used to represent the voltage waveform and current waveform of the fault node.

[0041] Optionally, after determining the complex domain voltage and complex domain current information of the fault node in the transmission line system, the complex domain expressions of the fault transient voltage and fault transient current can be quickly converted into time domain expressions using the numerical inverse Laplace transform, so as to accurately restore the morphological characteristics of the traveling wave surge.

[0042] Step S108: Call the neural network model to analyze the time-domain waveform information to obtain the location information of the fault node.

[0043] In the technical solution provided in step S108 above, after converting the complex-domain voltage information and complex-domain current information to obtain the time-domain waveform information of the fault node, a neural network model can be called to analyze the time-domain waveform information to obtain the location information of the fault node. The neural network model can be trained based on samples of the time-domain waveform information. The location information can be used to represent the location of the fault in the transmission line.

[0044] Optionally, a neural network model combining a residual convolutional neural network and a multi-head self-attention mechanism can be established. The training set is input into the residual convolutional neural network, and the network parameters are updated based on the loss function results. The trained residual convolutional neural network model is tested using a test set, the segmentation error is calculated, and the model's effectiveness is verified. By analyzing the time-domain waveform information using the aforementioned neural network model, the location information of the fault node can be obtained.

[0045] In this embodiment, the self-admittance matrix and mutual admittance matrix of multiple nodes in a transmission line system are obtained. Each node includes at least the connection point and endpoint of the transmission line. The self-admittance matrix represents the admittance characteristics of a node, and the mutual admittance matrix represents the associated admittance characteristics between multiple nodes. Based on the self-admittance matrix and mutual admittance matrix, the complex-domain voltage information and complex-domain current information of the fault node in the transmission line system are determined. The complex-domain voltage information represents the voltage of the fault node in the complex frequency domain, and the complex-domain current information represents the current of the fault node in the complex frequency domain. The complex-domain voltage information and complex-domain current information are converted to obtain the time-domain waveform information of the fault node. The time-domain waveform information represents the voltage and current waveforms of the fault node. A neural network model is called to analyze the time-domain waveform information to obtain the location information of the fault node. The neural network model is trained based on samples of the time-domain waveform information. The location information represents the location of the fault in the transmission line, thereby improving the accuracy of fault location in transmission lines and solving the problem of low accuracy in fault location in transmission lines.

[0046] The embodiments described above in this application will be further explained below.

[0047] In some embodiments of this application, the method further includes: obtaining the parallel admittance and series impedance of the transmission line, wherein the parallel admittance is used to represent the admittance characteristics of the transmission line per unit length, and the series impedance is used to represent the impedance characteristics of the transmission line per unit length; step S102, obtaining the self-admittance matrix and mutual admittance matrix of multiple nodes in the transmission line system, including: determining the self-admittance matrix and mutual admittance matrix based on the parallel admittance and series impedance.

[0048] In this embodiment, the parallel admittance and series impedance of the transmission line can be obtained. Based on the obtained parallel admittance and series impedance, the self-admittance matrix and mutual admittance matrix can be determined. The parallel admittance, which represents the admittance characteristic per unit length of the transmission line, can be called the unit-length parallel admittance and can be represented by y(s). The series impedance, which represents the impedance characteristic per unit length of the transmission line, can be called the unit-length series impedance and can be represented by z(s).

[0049] Optionally, by performing polynomial vector fitting based on rational function approximation on the frequency-varying parameters of the transmission line, the unit-length parallel admittance y(s) and unit-length series impedance z(s) of different types of transmission lines at discrete frequencies in a T-connected hybrid transmission line can be obtained. After obtaining the unit-length parallel admittance y(s) and unit-length series impedance z(s) of different types of transmission lines at discrete frequencies, the self-admittance matrix and mutual admittance matrix of each node in the system can be calculated.

[0050] In some embodiments of this application, obtaining the parallel admittance and series impedance of a transmission line includes: using a frequency-varying parameter model of the transmission line system to perform polynomial vector fitting on the frequency-varying parameters of the transmission line to obtain the parallel admittance and series impedance, wherein the frequency-varying parameters are used to represent the line parameters that change with frequency.

[0051] In this embodiment, using the frequency-varying parameter model of the transmission line system, polynomial vector fitting can be performed on the frequency-varying parameters of the transmission line to obtain the parallel admittance and series impedance. Optionally, a frequency-varying parameter model of the T-connected hybrid transmission line system can be established in electromagnetic transient simulation software to output the frequency-varying series impedance matrix and parallel admittance matrix of all transmission lines.

[0052] s=c+jω

[0053] Where s can be used to represent complex variables in the complex frequency domain, c can be used to represent the damping coefficient, j can be used to represent the imaginary unit in the complex domain, ω=(2m+1)π / (Ndt), N can be used to represent the total number of samples, N=T / dt, T can be used to represent the analytical calculation time length of this model, dt can be used to represent the analytical calculation step size, m=0,1,…N-1. Further, by performing polynomial vector fitting based on rational function approximation on the frequency-varying parameters of the transmission line, the unit-length parallel admittance y(s) and unit-length series impedance z(s) of different types of transmission lines at discrete frequencies in T-connected hybrid transmission lines can be obtained.

[0054] In some embodiments of this application, the self-admittance matrix and mutual admittance matrix are determined based on parallel admittance and series impedance, including: determining the self-admittance matrix and mutual admittance matrix based on parallel admittance, series impedance, and transmission line length information, wherein the length information is used to represent the actual length of the transmission line.

[0055] In this embodiment, after obtaining the parallel admittance and series impedance, the self-admittance matrix and mutual admittance matrix can be determined based on the parallel admittance, series impedance, and transmission line length information. The length information can be used to represent the actual length of the transmission line, and can be represented by the letter l.

[0056] Optionally, after obtaining the unit-length parallel admittance y(s) and unit-length series impedance z(s) of different types of transmission lines at discrete frequencies, the complex domain self-admittance submatrix y of each node in the system can be calculated based on the actual line length. s (s) and the line mutual admittance submatrix y m (s), including the target transmission line, the transmission line connected to the busbar on the back side of the target transmission line measurement end, and the transmission line connected to the busbar on the opposite side of the target transmission line measurement end. Since the transmission line is a three-phase AC transmission line, the dimension of each sub-matrix is ​​3×3, and the complex domain self-admittance sub-matrix y s (s) and the line mutual admittance submatrix y m The formula for calculating (s) is:

[0057]

[0058] Where l can represent the length of the transmission line, T can represent the transformation matrix (e.g., the Clarke transformation matrix), and γ(s) can represent the propagation coefficient per unit length of the line in complex domain form. The expression for γ(s) is:

[0059]

[0060] In some embodiments of this application, step S104, determining the complex domain voltage information and complex domain current information of the fault node in the transmission line system based on the self-admittance matrix and the mutual admittance matrix, includes: determining the impedance matrix of the transmission line system based on the self-admittance matrix and the mutual admittance matrix, wherein the impedance matrix is ​​used to represent the impedance characteristics of the transmission line system; and determining the complex domain voltage information and complex domain current information based on the impedance matrix.

[0061] In this embodiment, the impedance matrix of the transmission line system can be determined based on the acquired self-admittance matrix and mutual admittance matrix. Further, based on the determined impedance matrix, complex-domain voltage information and complex-domain current information can be determined. The impedance matrix, which can represent the impedance characteristics of the transmission line system, can be referred to as the complex-domain nodal impedance matrix.

[0062] Optionally, based on the obtained self-admittance matrix and mutual admittance matrix, the admittance matrix of the transmission line system can be determined first. Further inversion of the admittance matrix yields the complex domain nodal impedance matrix of the system.

[0063] In some embodiments of this application, determining the impedance matrix of a transmission line system based on the self-admittance matrix and the mutual admittance matrix includes: determining the admittance matrix of the transmission line system based on the self-admittance matrix and the mutual admittance matrix, wherein the admittance matrix is ​​used to represent the admittance characteristics of the transmission line system; and transforming the admittance matrix to obtain the impedance matrix.

[0064] In this embodiment, the admittance matrix of the transmission line system can be determined based on the obtained self-admittance matrix and mutual admittance matrix. Further transformation of the obtained admittance matrix yields the impedance matrix. The admittance matrix can be used to represent the admittance characteristics of the transmission line system.

[0065] Optionally, assuming there were N nodes before the system failure, and the failure point is taken as the new node after the failure, a system admittance matrix with (N+1)×(N+1) submatrices is constructed. Further, based on the determined complex domain self-admittance submatrix y of each node... s (s) and the line mutual admittance submatrix y m (s) If a system node has a power source or equivalent system impedance, or is connected to a load, construct the admittance matrix of its power source or equivalent system and the load. Summate this matrix with the self-admittance submatrix of the corresponding node in the system admittance matrix to obtain the admittance matrix of the system after the fault. Inverting the admittance matrix of the system after the fault yields the complex domain node impedance matrix of the system.

[0066] In some embodiments of this application, determining complex domain voltage information and complex domain current information based on the impedance matrix includes: adding a fault excitation current at the fault node based on the impedance matrix to obtain a fault excitation voltage, wherein the fault excitation current is used to represent the current fluctuation occurring at the fault node, and the fault excitation voltage is used to represent the voltage fluctuation occurring at the fault node; converting the fault excitation voltage to obtain complex domain short-circuit current information, wherein the complex domain short-circuit current information is used to represent the short-circuit current of the fault node in the complex frequency domain; and determining complex domain voltage information and complex domain current information based on the impedance matrix and the complex domain short-circuit current information.

[0067] In this embodiment, after determining the impedance matrix of the transmission line system, a fault excitation current can be added at the fault node based on the determined impedance matrix to obtain the fault excitation voltage. After obtaining the fault excitation voltage, the fault excitation voltage is transformed to obtain complex-domain short-circuit current information. Further, based on the determined impedance matrix and complex-domain short-circuit current information, complex-domain voltage information and complex-domain current information can be determined. The fault excitation current can be used to represent the current fluctuation occurring at the fault node. The fault excitation voltage can be used to represent the voltage fluctuation occurring at the fault node. The complex-domain short-circuit current information can be used to represent the short-circuit current of the fault node in the complex frequency domain, and can be referred to as the complex-domain short-circuit current at the fault port.

[0068] Optionally, after obtaining the impedance matrix, applying a fault excitation current at the fault node can determine the corresponding fault excitation voltage at each node. For the fault point, the time-domain model of the excitation voltage source during the fault is:

[0069] Δu fΦ (t)=U f sin[α(t-t0)+θ fΦ ]ε(t-t0)

[0070] Where, Δu fΦ (t) can be used to represent the time-domain model of the excitation voltage source during a fault, U f α can be used to represent the voltage amplitude at the fault point before the fault occurs, α can be used to represent the grid angular frequency, t0 can be used to represent the time of fault occurrence, and θ can be used to represent the time of fault occurrence. fΦ It can be used to represent the fault closing angle, and ε(t) can be used to represent the unit step signal.

[0071] Optionally, the time-domain expression above can be transformed to the complex domain, and based on the boundary conditions of single-phase ground fault, two-phase ground fault, three-phase inter-phase fault, and two-phase inter-phase fault, the complex domain composite sequence network under different fault types can be constructed using the symmetrical component transformation method, and the complex domain expression of the short-circuit current at the fault port can be derived:

[0072]

[0073] in, It can be used to represent the complex short-circuit current at the fault port. The superscript (type) can be used to indicate the fault type. Z ff(1) (s) can be used for positive-sequence complex-domain self-impedance in fault ports. θ (type) These can be used to represent parameters related to the fault type, and their expressions are shown in Table 1:

[0074] Table 1. Parameters related to fault types

[0075]

[0076] The fault type values ​​are as follows: AG can be used to indicate phase A grounding, BG can be used to indicate phase B grounding, CG can be used to indicate phase C grounding, AB can be used to indicate phase A and phase B short circuit, AC can be used to indicate phase A and phase C short circuit, BC can be used to indicate phase B and phase C short circuit, ABG can be used to indicate phase A and phase B short circuit to ground, ACG can be used to indicate phase A and phase C short circuit to ground, BCG can be used to indicate phase B and phase C short circuit to ground, and ABC can be used to indicate three-phase short circuit between phases A, B, and C.

[0077] After obtaining the impedance matrix of the system after the fault and the complex domain expression of the short-circuit current at the fault port, the complex domain expressions of the fault transient voltage and fault transient current at any point in the transmission system can be determined using the following formulas:

[0078]

[0079] in, This can be used to represent the complex domain expression of the fault transient voltage at node J. This can be used to represent the complex domain expression of the fault transient voltage at node K. The complex domain expression for the fault transient current at node J can be used. Nodes J and K can be used to represent the nodes at both ends of line JK, respectively. fJ (s) and Z fK (s) can be used to represent the mutual impedance between fault node f and node J, and between fault node f and node K in the complex domain nodal impedance matrix, respectively. s (s) and y m (s) can be used to represent the self-admittance of node J and the mutual admittance between line JK, respectively.

[0080] In some embodiments of this application, step S106, which converts the complex domain voltage information and complex domain current information to obtain the time-domain waveform information of the fault node, includes: performing a numerical inverse Laplace transform on the complex domain voltage information and complex domain current information to obtain the time-domain waveform information.

[0081] In this embodiment, after obtaining the complex-domain voltage and current information, a numerical inverse Laplace transform can be performed on the complex-domain voltage and current information to obtain time-domain waveform information. Optionally, after obtaining the complex-domain expressions for the fault transient voltage and fault transient current at the measuring point, the numerical inverse Laplace transform can be used to quickly convert the complex-domain expressions for the fault transient voltage and fault transient current into time-domain expressions, accurately restoring the morphological characteristics of the traveling wave surge. The complex-domain values ​​can be converted into time-domain values ​​using the following formula:

[0082]

[0083] Where f(kΔt) and F(c+jω) m ) can be used to represent numerical values ​​in the time domain and complex domain respectively, s=c+jω m c can be used to represent the damping coefficient, j can be used to represent the complex domain imaginary unit, w = (2m+1)pi / (Ndt), N can be used to represent the sample size, N = T / dt, T can be used to represent the analytical computation time of this model, dt can be used to represent the analytical computation step size, m, k = 0, 1, ..., N-1, σ m It can be used to represent the Hanning function, σ m The expression is as follows:

[0084]

[0085] In some embodiments of this application, the method further includes: establishing an initial neural network model based on a residual convolutional neural network and a multi-head self-attention mechanism; training the initial neural network model based on time-domain waveform information samples to obtain a neural network model, wherein the time-domain waveform information samples are generated based on time-domain waveform information.

[0086] It should be noted that this embodiment can automatically generate time-domain waveform information samples, that is, automatically generate waveform data samples. The fault distance is taken at intervals of 2 kilometers (km) along the entire length of the faulty line, and the fault resistance (which can be represented by R) is... f The values ​​(represented by θ) are 0, 50 ohms (Ω), 100 ohms, 150 ohms, and 200 ohms. The fault closing angle (which can be represented by θ) is... fΦThe values ​​for the fault conditions are 0 degrees (°), 30 degrees, 60 degrees, and 90 degrees. The fault type values ​​are AG, BG, CG, AB, BC, CA, ABG, BCG, CAG, and ABC. The above fault condition values ​​are iterated through one by one through complex domain analytical calculation to generate the time-domain waveforms of the three-phase fault transient voltage and current at the measuring point. The sampling frequency is 100 kHz, and the time window length is 5 milliseconds (ms) as the sample data.

[0087] In this embodiment, an initial neural network model combining a residual convolutional neural network and a multi-head self-attention mechanism is established. The three-phase fault transient voltage, transient current, zero-sequence voltage, and zero-sequence current are selected as inputs to the initial neural network model. Due to the significant differences in the input data, to improve the accuracy of network training and fully learn the fault characteristics, it is necessary to first normalize the sample data (i.e., time-domain waveform information samples).

[0088]

[0089] Among them, U abc It can be used to represent raw voltage data, U abc 'Can be used to represent normalized voltage sample data, I abc It can be used to represent raw current data, I abc 'Can be used to represent normalized current sample data, maxU abc maxI abc minU abc minI ab They can be used to represent custom values.

[0090] Optionally, the input dimensions differ depending on the fault type. When the fault type is a single-phase ground fault, the fault phase voltage, fault phase current, zero-sequence voltage, and zero-sequence current are taken as inputs, and the input dimension is 4. When the fault type is a two-phase-to-phase fault, both the fault phase voltage and fault phase current are two-dimensional, and there is no zero-sequence fault in a phase-to-phase short circuit, so the input dimension is 4. Similarly, the input dimension for a two-phase-to-phase ground fault is 6, and the input dimension for a three-phase-to-phase short circuit is 6. Figure 2 This is a schematic diagram of the structure of a residual convolutional neural network according to an embodiment of this application, such as... Figure 2As shown, the input sample data x is 500 in size and has an input dimension of 4 or 6, that is, x (500×4 or 500×6). The input data is processed sequentially by normalization, multi-head self-attention mechanism, gated recurrent unit (GRU), dimension transformation, and deep residual network (Residual Network152, ResNet152). The input data to the gated recurrent unit is x (500×4 or 500×6), the output data of the gated recurrent unit is x (500×100), the output data of the dimension transformation is x (5×100×100), and the output data of the deep residual network is y (1000). After y (1000) is processed by three regularizations, linear layers, and activation layers (ReLU), y (512), y (128), and y (16) are obtained respectively. Then, y (16) is processed by linear layers and activation layers (Sigmoid) to output y (1). Based on the aforementioned time-domain waveform information samples, the initial neural network model is further trained to obtain the neural network model, and the network parameters are updated according to the loss function results. The trained neural network model is then tested using a test set, and the segment error is calculated to verify the model's effectiveness.

[0091] In this embodiment, the self-admittance matrix and mutual admittance matrix of multiple nodes in a transmission line system are obtained. Each node includes at least the connection point and endpoint of the transmission line. The self-admittance matrix represents the admittance characteristics of a node, and the mutual admittance matrix represents the associated admittance characteristics between multiple nodes. Based on the self-admittance matrix and mutual admittance matrix, the complex-domain voltage information and complex-domain current information of the fault node in the transmission line system are determined. The complex-domain voltage information represents the voltage of the fault node in the complex frequency domain, and the complex-domain current information represents the current of the fault node in the complex frequency domain. The complex-domain voltage information and complex-domain current information are converted to obtain the time-domain waveform information of the fault node. The time-domain waveform information represents the voltage and current waveforms of the fault node. A neural network model is called to analyze the time-domain waveform information to obtain the location information of the fault node. The neural network model is trained based on samples of the time-domain waveform information. The location information represents the location of the fault in the transmission line, thereby improving the accuracy of fault location in transmission lines and solving the problem of low accuracy in fault location in transmission lines.

[0092] To facilitate a better understanding of the technical solutions of this application by those skilled in the art, a specific embodiment will now be described.

[0093] Currently, transmission lines, as a crucial component of the power system, bear the vital responsibility of ensuring a stable power supply. When a transmission line fails, it can lead to widespread power outages, severely impacting people's lives and social production. Therefore, quickly and accurately locating the fault point, reducing the difficulty for patrol personnel in troubleshooting, and shortening the fault repair time are extremely important. When a short circuit occurs in a transmission line, its fault traveling wave reflects back and forth between the fault point and the line endpoints, forming multiple sets of traveling wave surges in a very short time. The morphological characteristics of these fault traveling wave surges are closely related to the fault distance, fault transition resistance, fault type, and fault line parameters. Therefore, researching fault location methods based on fault traveling wave surge morphology recognition is of great significance, and the single-end traveling wave location method does not require data transmission through communication channels, offering greater convenience for engineering implementation.

[0094] In related technologies, there are two main types of single-ended traveling wave fault location methods: one is active detection, which is only applicable to permanent short circuits; the other calculates the fault distance based on the time difference between two consecutive arrivals of the traveling wave front at the measurement point. This method requires accurate identification of the traveling wave front and a very high sampling rate. However, when a short circuit occurs in a line, the fault traveling wave will reflect back and forth between the fault point and the busbars at both ends of the line, and will continuously attenuate during propagation, thus forming multiple surge features with different shapes in the early stage of the fault. These surges have differences in time and amplitude characteristics. Due to the frequency-varying characteristics of transmission lines, different surge types all have different shapes. These differences in characteristics are closely related to the fault distance and fault transition resistance. Existing single-ended traveling wave location methods only utilize the arrival time characteristics of the first two surges, which not only misses the characteristics of subsequent traveling wave surges, but also misses the differences in amplitude and shape characteristics of different traveling wave surge types. Furthermore, most existing fault location methods that utilize the morphological characteristics of traveling wave surges employ electromagnetic transient simulation calculations. This method requires technicians to generate a large number of waveform samples through simulation, which is very time-consuming and labor-intensive, resulting in low accuracy in fault location of transmission lines.

[0095] To address the aforementioned issues, this application proposes a fault segmentation method for overhead hybrid cable transmission lines with branches based on a multi-head self-attention mechanism residual network using complex-domain transient analytical computation. This method relates to a single-end fault location method based on fault traveling wave surge morphology identification using samples generated through complex-domain analytical computation. This embodiment fully utilizes the differences in amplitude, time, and morphological characteristics of traveling waves of different ranks for fault location, enhancing the robustness of fault location and improving accuracy.

[0096] This application employs a complex-domain analytical calculation method to establish a complex-domain mathematical model of the transmission line, and then establishes a complex-domain transfer function for the fault boundary and measurement point. Using different fault boundaries as input, the fault phasor frequency function of the corresponding complex-domain fault voltage or current at the measurement point is obtained. The corresponding fault transient time-domain waveform data is then obtained through numerical inverse Laplace transform. Since the computational efficiency of this analytical calculation method is far higher than that of electromagnetic transient simulation calculation, and the output waveform can accurately reconstruct the morphological characteristics of traveling wave surges in the early stages of a fault, the method of generating traveling wave surge morphology samples based on complex-domain analytical calculation can automatically generate a large number of waveform data samples of different types, locations, and transition resistances in a very short time, which is beneficial for training and machine learning of waveform morphology characteristics. Furthermore, a neural network model combining a residual neural network and a multi-head self-attention mechanism is established. Through learning from a large amount of waveform data, a single-ended fault location method based on traveling wave surge morphology recognition is formed.

[0097] Figure 3 This is a schematic diagram of a frequency-varying parameter model according to an embodiment of this application, such as... Figure 3 As shown, a frequency-varying parameter model of a T-connected hybrid transmission line system was established using electromagnetic transient simulation software. Lines 2, 3, and 4 are the target T-connected lines, with total lengths of 50km, 20km, and 10km, respectively. Line 5 is a transmission line connected to the busbar opposite the measurement end of the target transmission line, with a total length of 25km. Line 1 is a transmission line connected to the busbar behind the measurement end of the target transmission line, with a total length of 30km. Line 2 is 50km long and consists of a 20km overhead line, a 20km cable (shown by dashed lines in the figure), and a 10km overhead line, forming a hybrid line. Lines 3 and 4 are 20km and 10km overhead lines, respectively. The outer radius of the combined conductor of the overhead line of Line 4 is 0.0163454 m, while the outer radius of the combined conductor of the overhead lines of the other lines is 0.0203454 m. The simulation outputs the frequency-varying series impedance matrix and parallel admittance matrix of all transmission lines. The frequency-varying parameters of the transmission lines are subjected to polynomial vector fitting based on rational function approximation to obtain the unit length parallel admittance y(s) and unit length series impedance z(s) of different types of transmission lines at discrete frequencies in the T-connected hybrid transmission line. Wherein, s=c+jw, c can be used to represent the damping coefficient, the expression of c is c=log10(N2) / T, j can be used to represent the complex domain imaginary unit, w=(2m+1)pi / (Ndt), N can be used to represent the total number of samples, N=T / dt, T can be used to represent the analytical calculation time length of this model, dt is the analytical calculation step size, m=0,1,…N-1.

[0098] After obtaining the unit-length parallel admittance y(s) and unit-length series impedance z(s) for different types of transmission lines at discrete frequencies, the complex domain self-admittance submatrix y of each node in the system can be calculated based on the actual line length. s (s) and the line mutual admittance submatrix y m (s) includes the target transmission line, the transmission line connected to the busbar on the back side of the target transmission line measurement end, and the transmission line connected to the busbar on the opposite side of the target transmission line measurement end. Since it is a three-phase AC transmission line, the dimension of each sub-matrix is ​​3×3, and the complex domain self-admittance sub-matrix y can be expressed by the following formula. s (s) and the line mutual admittance submatrix y m (s):

[0099]

[0100] Where l can be used to represent the length of the transmission line, T can be used to represent the Clarke transformation matrix, and γ(s) can be used to represent the line propagation coefficient per unit length in complex domain form, the expression of which is as follows:

[0101]

[0102] Because a section of cable line exists in the middle of Line 2, dividing the overhead line into three parts, this line is considered to have 4 nodes. Before the system fault, there were 8 nodes. After the fault, the fault point is taken as the new node, and a 9*9 sub-matrix system admittance matrix is ​​constructed. Based on the obtained complex domain self-admittance sub-matrix y of each node... s (s) and the line mutual admittance submatrix y m (s) If a system node has the impedance of a power source or equivalent system, or is connected to a load, construct the admittance matrix of its power source or equivalent system and the load, sum the self-admittance submatrix of the corresponding node of the system admittance matrix to obtain the admittance matrix of the system after the fault, and inverse it to obtain the complex domain node impedance matrix of the system.

[0103] Using the impedance matrix obtained above, the corresponding fault excitation voltage at each node can be calculated when a fault excitation current is applied to the fault node. For the fault point, the time-domain model of the excitation voltage source during faulting is as follows:

[0104] Δu fΦ (t)=U f sin[α(t-t0)+θ fΦ ]ε(t-t0)

[0105] Where Uf can be used to represent the voltage amplitude at the fault point before the fault occurs, α can be used to represent the grid angular frequency, t0 can be used to represent the fault occurrence time, and θ fΦIt can be used to represent the fault closing angle, and ε(t) can be used to represent the unit step signal.

[0106] Transforming the time-domain expression of the above equation into the complex domain, and based on the boundary conditions for single-phase grounding faults, two-phase-to-phase grounding faults, three-phase-to-phase faults, and two-phase-to-phase faults, the complex-domain composite sequence network under different fault types is constructed using the symmetrical component transformation method, and the complex-domain expression for the short-circuit current at the fault port is derived:

[0107]

[0108] The superscript (type) can be used to indicate the fault type, Z ff(1) (s) can be used to represent the positive-sequence complex-domain self-impedance in a faulty port. θ (type) These can be used to represent parameters related to the fault type, and their expressions are shown in Table 1.

[0109] Based on the impedance matrix of the system after the fault and the complex domain expression of the short-circuit current at the fault port obtained above, the complex domain expressions of the fault transient voltage and current at any point in the transmission system can be calculated using the following formulas:

[0110]

[0111] In this context, nodes J and K can represent the nodes at both ends of line JK, respectively, and ZfJ(s) and ZfK(s) can represent the mutual impedances between fault node f and node J, and between fault node f and node K in the complex domain node impedance matrix, respectively. s (s) and y m (s) can be used to represent the self-admittance of node J and the mutual admittance between line JK, respectively. In this embodiment, 2 is the observation node. If the line fault occurs between lines 2 and 3, then J is 2 and K is 9. If the line fault occurs in other line segments, the fault transient voltage U3 at node 3 needs to be calculated using the above formula. At this time, J is 2 and K is 3.

[0112] After obtaining the complex-domain expressions for the fault transient voltage and current at the measuring point, the inverse Laplace transform can be used to quickly convert the complex-domain expressions for the fault transient voltage and current into time-domain expressions, accurately restoring the morphological characteristics of the traveling wave surge. The complex-domain values ​​can be converted into time-domain values ​​using the following formula:

[0113]

[0114] Where f(kΔt) and F(c+jω) m ) can be used to represent numerical values ​​in the time domain and complex domain respectively, s=c+jω mc can be used to represent the damping coefficient, j can be used to represent the complex domain imaginary unit, w = (2m+1)pi / (Ndt), N can be used to represent the sample size, N = T / dt, T can be used to represent the analytical calculation time of this model, taken as 0.08 seconds (s), dt can be used to represent the analytical calculation step size, taken as 10 microseconds (µs), m, k = 0, 1, ..., N-1, σ m It can be used to represent the Hanning function, σ m The expression is as follows:

[0115]

[0116] Further automatic generation of waveform data samples, with fault distance values ​​taken at 2km intervals along the entire length of the faulty line, and fault resistance R. f The values ​​are 0, 50Ω, 100Ω, 150Ω, and 200Ω, with the fault closing angle θ. fΦ The values ​​are 0°, 30°, 60°, and 90°, and the fault type values ​​are AG, BG, CG, AB, BC, CA, ABG, BCG, CAG, and ABC. The above fault condition values ​​are iterated one by one through complex domain analytical calculation to generate the time domain waveforms of the three-phase fault transient voltage and current at the measuring point. The sampling frequency is 100kHz, and the time window length is 5ms as the sample data.

[0117] This embodiment establishes a neural network model combining a residual convolutional neural network and a multi-head self-attention mechanism. Three-phase fault transient voltage, transient current, zero-sequence voltage, and zero-sequence current are selected as inputs to the residual convolutional neural network, and a multi-head self-attention mechanism is used to address the multi-dimensional input problem. Data samples are divided into training and testing sets according to a certain ratio for machine learning. Due to the large differences in the input data, in order to improve the accuracy of network training and fully learn the fault characteristics, the sample data is normalized.

[0118]

[0119] Among them, U abc It can be used to represent raw voltage data, U abc 'Can be used to represent normalized voltage sample data, I abc It can be used to represent raw current data, I abc This can be used to represent normalized current sample data. In this embodiment, a custom value minU is defined. abc =-maxU abc ,minI ab =-maxI abc .

[0120] In this embodiment, the input dimensions differ depending on the fault type. When the fault type is a single-phase ground fault, the fault phase voltage, fault phase current, zero-sequence voltage, and zero-sequence current are taken as inputs, resulting in an input dimension of 4. When the fault type is a two-phase-to-phase fault, both the fault phase voltage and fault phase current are 2-dimensional, and there is no zero-sequence current in a phase-to-phase short circuit, therefore the input dimension is 4. Similarly, the input dimension for a two-phase-to-phase ground fault is 6; the input dimension for a three-phase-to-phase short circuit is 6. The specific structure of the residual convolutional neural network used is as follows: Figure 2 As shown in the diagram. It should be noted that this embodiment trains the model separately for different fault types: single-phase ground fault, two-phase short circuit, two-phase sum-to-short circuit to ground, and three-phase short circuit. The training set is then input into a residual convolutional neural network, and the network parameters are updated based on the loss function results. The trained residual convolutional neural network model is tested using a test set, the segmentation error is calculated, and the model's effectiveness is verified.

[0121] 100 fault transient voltage and current waveforms were randomly generated using an electromagnetic transient simulation model and sequentially input into the trained model. The model's segmented output values ​​were compared with the actual segment counts. The fault segment counts for line 2 were set to 0, for line 3 to 1, and for line 4 to 2. The results are shown in the table below:

[0122] Table 2 Fault transient voltage and current waveform data

[0123]

[0124]

[0125]

[0126]

[0127]

[0128] As can be seen from Table 2, except for the data results numbered 56, 66 and 79 which were misjudged, the model could accurately determine the segmentation results of the remaining data, with an accuracy rate of 97%, thus proving the effectiveness of the model.

[0129] In this embodiment, the self-admittance matrix and mutual admittance matrix of multiple nodes in a transmission line system are obtained. Each node includes at least the connection point and endpoint of the transmission line. The self-admittance matrix represents the admittance characteristics of a node, and the mutual admittance matrix represents the associated admittance characteristics between multiple nodes. Based on the self-admittance matrix and mutual admittance matrix, the complex-domain voltage information and complex-domain current information of the fault node in the transmission line system are determined. The complex-domain voltage information represents the voltage of the fault node in the complex frequency domain, and the complex-domain current information represents the current of the fault node in the complex frequency domain. The complex-domain voltage information and complex-domain current information are converted to obtain the time-domain waveform information of the fault node. The time-domain waveform information represents the voltage and current waveforms of the fault node. A neural network model is called to analyze the time-domain waveform information to obtain the location information of the fault node. The neural network model is trained based on samples of the time-domain waveform information. The location information represents the location of the fault in the transmission line, thereby improving the accuracy of fault location in transmission lines and solving the problem of low accuracy in fault location in transmission lines.

[0130] This application also provides a fault determination device for transmission lines. It should be noted that the fault determination device for transmission lines in this embodiment can be used with the fault determination method for transmission lines implemented in this invention.

[0131] Figure 4 This is a schematic diagram of the structure of a fault determination device for a transmission line according to an embodiment of this application, as shown below. Figure 4 As shown, the fault determination device 400 for the transmission line may include: an acquisition unit 402, a first determination unit 404, a conversion unit 406, and a second determination unit 408.

[0132] The acquisition unit 402 is used to acquire the self-admittance matrix and mutual admittance matrix of multiple nodes in the transmission line system. The nodes include at least the connection point and the endpoint of the transmission line. The self-admittance matrix is ​​used to represent the admittance characteristics of the node, and the mutual admittance matrix is ​​used to represent the admittance characteristics associated between multiple nodes.

[0133] The first determining unit 404 is used to determine the complex domain voltage information and complex domain current information of the fault node in the transmission line system based on the self-admittance matrix and the mutual admittance matrix. The complex domain voltage information is used to represent the voltage of the fault node in the complex frequency domain, and the complex domain current information is used to represent the current of the fault node in the complex frequency domain.

[0134] The conversion unit 406 is used to convert the complex domain voltage information and the complex domain current information to obtain the time domain waveform information of the fault node, wherein the time domain waveform information is used to represent the voltage waveform and current waveform of the fault node.

[0135] The second determining unit 408 is used to call a neural network model to analyze the time-domain waveform information and obtain the location information of the fault node. The neural network model is trained based on time-domain waveform information samples, and the location information is used to represent the location of the fault in the transmission line.

[0136] Optionally, the method further includes: a first acquisition unit, used to acquire the parallel admittance and series impedance of the transmission line, wherein the parallel admittance is used to represent the admittance characteristics of the transmission line per unit length, and the series impedance is used to represent the impedance characteristics of the transmission line per unit length; the acquisition unit 402 includes: a first determination module, used to determine the self-admittance matrix and the mutual admittance matrix based on the parallel admittance and series impedance.

[0137] Optionally, the first acquisition unit includes: a fitting module using a frequency-varying parameter model of the transmission line system, used to perform polynomial vector fitting on the frequency-varying parameters of the transmission line to obtain the parallel admittance and series impedance, wherein the frequency-varying parameters are used to represent the line parameters that change with frequency.

[0138] Optionally, the first determining module includes: a first determining submodule, used to determine the self-admittance matrix and the mutual admittance matrix based on the parallel admittance, series impedance, and transmission line length information, wherein the length information is used to represent the actual length of the transmission line.

[0139] Optionally, the first determining unit 404 includes: a second determining module, used to determine the impedance matrix of the transmission line system based on the self-admittance matrix and the mutual admittance matrix, wherein the impedance matrix is ​​used to represent the impedance characteristics of the transmission line system; and a third determining module, used to determine complex domain voltage information and complex domain current information based on the impedance matrix.

[0140] Optionally, the second determining module includes: a second determining submodule, used to determine the admittance matrix of the transmission line system based on the self-admittance matrix and the mutual admittance matrix, wherein the admittance matrix is ​​used to represent the admittance characteristics of the transmission line system; and to transform the admittance matrix to obtain the impedance matrix.

[0141] Optionally, the third determining module includes: an adding submodule, used to add a fault excitation current at the fault node based on the impedance matrix to obtain a fault excitation voltage, wherein the fault excitation current is used to represent the current fluctuation at the fault node, and the fault excitation voltage is used to represent the voltage fluctuation at the fault node; a conversion submodule, used to convert the fault excitation voltage to obtain complex domain short-circuit current information, wherein the complex domain short-circuit current information is used to represent the short-circuit current of the fault node in the complex frequency domain; and a third determining submodule, used to determine complex domain voltage information and complex domain current information based on the impedance matrix and the complex domain short-circuit current information.

[0142] Optionally, the conversion unit 406 includes a conversion module for performing a numerical inverse Laplace transform on the complex domain voltage information and the complex domain current information to obtain time domain waveform information.

[0143] Optionally, the method further includes: establishing a unit for establishing an initial neural network model based on a residual convolutional neural network and a multi-head self-attention mechanism; training the initial neural network model based on time-domain waveform information samples to obtain a neural network model, wherein the time-domain waveform information samples are generated based on time-domain waveform information.

[0144] In this device, the acquisition unit 402 acquires the self-admittance matrix and mutual admittance matrix of multiple nodes in the transmission line system. Each node includes at least the connection point and endpoint of the transmission line. The self-admittance matrix represents the admittance characteristics of a node, and the mutual admittance matrix represents the associated admittance characteristics between multiple nodes. Based on the self-admittance matrix and mutual admittance matrix, the first determination unit 404 determines the complex-domain voltage information and complex-domain current information of the fault node in the transmission line system. The complex-domain voltage information represents the voltage of the fault node in the complex frequency domain, and the complex-domain current information represents the current of the fault node in the complex frequency domain. The conversion unit 406 converts the complex-domain voltage information and complex-domain current information to obtain the time-domain waveform information of the fault node, which represents the voltage and current waveforms of the fault node. The second determining unit 408 calls the neural network model to analyze the time-domain waveform information and obtain the location information of the fault node. The neural network model is trained based on the time-domain waveform information samples, and the location information is used to represent the location of the fault in the transmission line. This achieves the technical effect of improving the accuracy of fault location in the transmission line, and solves the technical problem of low accuracy in fault location in the transmission line.

[0145] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, it controls the device where the non-volatile storage medium is located to execute any kind of transmission line fault determination method.

[0146] Specifically, the aforementioned storage medium is used to store program instructions for the following functions, thereby implementing the following functions:

[0147] The process involves obtaining the self-admittance matrix and mutual admittance matrix of multiple nodes in a transmission line system. Each node includes at least the connection point and endpoint of the transmission line. The self-admittance matrix represents the admittance characteristics of a node, and the mutual admittance matrix represents the associated admittance characteristics between multiple nodes. Based on the self-admittance and mutual admittance matrices, the complex-domain voltage and current information of the fault node in the transmission line system are determined. The complex-domain voltage information represents the voltage of the fault node in the complex frequency domain, and the complex-domain current information represents the current of the fault node in the complex frequency domain. The complex-domain voltage and current information are then transformed to obtain the time-domain waveform information of the fault node, which represents the voltage and current waveforms of the fault node. Finally, a neural network model is used to analyze the time-domain waveform information to obtain the location information of the fault node. The neural network model is trained based on samples of the time-domain waveform information, and the location information represents the location of the fault in the transmission line.

[0148] Optionally, in this embodiment, the storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specific examples of the storage medium include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0149] According to another aspect of the present invention, a processor is also provided for running a program, wherein the program executes the fault determination method for transmission lines according to the embodiments of the present invention.

[0150] In an exemplary embodiment of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements any of the above-described methods for determining faults in transmission lines.

[0151] Optionally, when executed by a processor, the computer program may perform the following steps:

[0152] The process involves obtaining the self-admittance matrix and mutual admittance matrix of multiple nodes in a transmission line system. Each node includes at least the connection point and endpoint of the transmission line. The self-admittance matrix represents the admittance characteristics of a node, and the mutual admittance matrix represents the associated admittance characteristics between multiple nodes. Based on the self-admittance and mutual admittance matrices, the complex-domain voltage and current information of the fault node in the transmission line system are determined. The complex-domain voltage information represents the voltage of the fault node in the complex frequency domain, and the complex-domain current information represents the current of the fault node in the complex frequency domain. The complex-domain voltage and current information are then transformed to obtain the time-domain waveform information of the fault node, which represents the voltage and current waveforms of the fault node. Finally, a neural network model is used to analyze the time-domain waveform information to obtain the location information of the fault node. The neural network model is trained based on samples of the time-domain waveform information, and the location information represents the location of the fault in the transmission line.

[0153] In an exemplary embodiment of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program configured to execute any of the above-described transmission line fault determination methods when run.

[0154] Optionally, when executed by a processor, the computer program may perform the following steps:

[0155] The process involves obtaining the self-admittance matrix and mutual admittance matrix of multiple nodes in a transmission line system. Each node includes at least the connection point and endpoint of the transmission line. The self-admittance matrix represents the admittance characteristics of a node, and the mutual admittance matrix represents the associated admittance characteristics between multiple nodes. Based on the self-admittance and mutual admittance matrices, the complex-domain voltage and current information of the fault node in the transmission line system are determined. The complex-domain voltage information represents the voltage of the fault node in the complex frequency domain, and the complex-domain current information represents the current of the fault node in the complex frequency domain. The complex-domain voltage and current information are then transformed to obtain the time-domain waveform information of the fault node, which represents the voltage and current waveforms of the fault node. Finally, a neural network model is used to analyze the time-domain waveform information to obtain the location information of the fault node. The neural network model is trained based on samples of the time-domain waveform information, and the location information represents the location of the fault in the transmission line.

[0156] In an exemplary embodiment of this application, a computer program is also provided, which is configured to execute the steps of any of the above method embodiments at runtime.

[0157] Optionally, when executed by a processor, the computer program may perform the following steps:

[0158] The process involves obtaining the self-admittance matrix and mutual admittance matrix of multiple nodes in a transmission line system. Each node includes at least the connection point and endpoint of the transmission line. The self-admittance matrix represents the admittance characteristics of a node, and the mutual admittance matrix represents the associated admittance characteristics between multiple nodes. Based on the self-admittance and mutual admittance matrices, the complex-domain voltage and current information of the fault node in the transmission line system are determined. The complex-domain voltage information represents the voltage of the fault node in the complex frequency domain, and the complex-domain current information represents the current of the fault node in the complex frequency domain. The complex-domain voltage and current information are then transformed to obtain the time-domain waveform information of the fault node, which represents the voltage and current waveforms of the fault node. Finally, a neural network model is used to analyze the time-domain waveform information to obtain the location information of the fault node. The neural network model is trained based on samples of the time-domain waveform information, and the location information represents the location of the fault in the transmission line.

[0159] An electronic device is provided according to an embodiment of this application, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the above-described transmission line fault determination methods.

[0160] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0161] Figure 5 This is a schematic block diagram of an electronic device according to an embodiment of this application. The electronic 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 electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present application described and / or claimed herein.

[0162] like Figure 5As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. The RAM 503 may also store various programs and data required for the operation of the device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0163] Multiple components in device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0164] The computing unit 501 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as a method for determining faults in a transmission line. For example, in some embodiments, the method for determining faults in a transmission line may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the method for determining faults in a transmission line described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform a fault determination method for transmission lines by any other suitable means (e.g., by means of firmware).

[0165] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0166] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0167] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0168] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, 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 sound input, voice input, or tactile input).

[0169] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0170] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0171] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0172] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0173] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0174] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0175] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0176] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0177] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for determining faults in a power transmission line, characterized in that, include: Obtain the self-admittance matrix and mutual admittance matrix of multiple nodes in a transmission line system, wherein the nodes include at least the connection point and the endpoint of the transmission line, the self-admittance matrix is ​​used to represent the admittance characteristics of the node, and the mutual admittance matrix is ​​used to represent the admittance characteristics associated between the multiple nodes; Based on the self-admittance matrix and the mutual admittance matrix, the complex domain voltage information and complex domain current information of the fault node in the transmission line system are determined, wherein the complex domain voltage information is used to represent the voltage of the fault node in the complex frequency domain, and the complex domain current information is used to represent the current of the fault node in the complex frequency domain. The complex domain voltage information and the complex domain current information are converted to obtain the time domain waveform information of the fault node, wherein the time domain waveform information is used to represent the voltage waveform and current waveform of the fault node; The neural network model is called to analyze the time-domain waveform information to obtain the location information of the fault node. The neural network model is trained based on time-domain waveform information samples, and the location information is used to represent the location of the fault in the transmission line. The determination of complex-domain voltage and complex-domain current information of a fault node in the transmission line system based on the self-admittance matrix and the mutual admittance matrix includes: determining the impedance matrix of the transmission line system based on the self-admittance matrix and the mutual admittance matrix, wherein the impedance matrix represents the impedance characteristics of the transmission line system; adding a fault excitation current at the fault node based on the impedance matrix to obtain a fault excitation voltage, wherein the fault excitation current represents the current fluctuation at the fault node, and the fault excitation voltage represents the voltage fluctuation at the fault node; converting the fault excitation voltage to obtain complex-domain short-circuit current information, wherein the complex-domain short-circuit current information represents the short-circuit current of the fault node in the complex frequency domain; and determining the complex-domain voltage and complex-domain current information based on the impedance matrix and the complex-domain short-circuit current information.

2. The method according to claim 1, characterized in that, The method further includes: Obtain the parallel admittance and series impedance of the transmission line, wherein the parallel admittance is used to represent the admittance characteristic of the transmission line per unit length, and the series impedance is used to represent the impedance characteristic of the transmission line per unit length. Obtaining the self-admittance matrix and mutual admittance matrix of multiple nodes in a transmission line system includes: determining the self-admittance matrix and the mutual admittance matrix based on the parallel admittance and the series impedance.

3. The method according to claim 2, characterized in that, Obtaining the parallel admittance and series impedance of the transmission line includes: Using the frequency-varying parameter model of the transmission line system, polynomial vector fitting is performed on the frequency-varying parameters of the transmission line to obtain the parallel admittance and the series impedance, wherein the frequency-varying parameters are used to represent the line parameters that vary with frequency.

4. The method according to claim 2, characterized in that, Determining the self-admittance matrix and the mutual admittance matrix based on the parallel admittance and the series impedance includes: Based on the parallel admittance, the series impedance, and the length information of the transmission line, the self-admittance matrix and the mutual admittance matrix are determined, wherein the length information is used to represent the actual length of the transmission line.

5. The method according to claim 1, characterized in that, Based on the self-admittance matrix and the mutual admittance matrix, the impedance matrix of the transmission line system is determined, including: Based on the self-admittance matrix and the mutual admittance matrix, the admittance matrix of the transmission line system is determined, wherein the admittance matrix is ​​used to represent the admittance characteristics of the transmission line system; The impedance matrix is ​​obtained by transforming the admittance matrix.

6. The method according to any one of claims 1 to 5, characterized in that, The complex-domain voltage information and the complex-domain current information are converted to obtain the time-domain waveform information of the fault node, including: The complex domain voltage information and the complex domain current information are subjected to inverse numerical Laplace transform to obtain the time domain waveform information.

7. The method according to any one of claims 1 to 5, characterized in that, The method further includes: An initial neural network model is established based on residual convolutional neural networks and multi-head self-attention mechanisms. The initial neural network model is trained based on the time-domain waveform information samples to obtain the neural network model, wherein the time-domain waveform information samples are generated based on the time-domain waveform information.

8. A fault determination device for a power transmission line, characterized in that, include: An acquisition unit is used to acquire the self-admittance matrix and mutual admittance matrix of multiple nodes in a transmission line system, wherein the nodes include at least the connection point and the endpoint of the transmission line, the self-admittance matrix is ​​used to represent the admittance characteristics of the node, and the mutual admittance matrix is ​​used to represent the admittance characteristics associated between the multiple nodes. The first determining unit is used to determine the complex domain voltage information and complex domain current information of the fault node in the transmission line system based on the self-admittance matrix and the mutual admittance matrix, wherein the complex domain voltage information is used to represent the voltage of the fault node in the complex frequency domain, and the complex domain current information is used to represent the current of the fault node in the complex frequency domain. A conversion unit is used to convert the complex domain voltage information and the complex domain current information to obtain the time domain waveform information of the fault node, wherein the time domain waveform information is used to represent the voltage waveform and current waveform of the fault node; The second determining unit is used to call a neural network model to analyze the time-domain waveform information to obtain the location information of the fault node, wherein the neural network model is trained based on time-domain waveform information samples, and the location information is used to represent the location where the fault occurs in the transmission line; The first determining unit is further configured to perform the following steps: determining the impedance matrix of the transmission line system based on the self-admittance matrix and the mutual admittance matrix, wherein the impedance matrix represents the impedance characteristics of the transmission line system; adding a fault excitation current at the fault node based on the impedance matrix to obtain a fault excitation voltage, wherein the fault excitation current represents the current fluctuation at the fault node and the fault excitation voltage represents the voltage fluctuation at the fault node; converting the fault excitation voltage to obtain complex domain short-circuit current information, wherein the complex domain short-circuit current information represents the short-circuit current of the fault node in the complex frequency domain; and determining the complex domain voltage information and the complex domain current information based on the impedance matrix and the complex domain short-circuit current information.

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