Power distribution network traveling wave head fault calibration method, device, equipment and medium

Abstract

The invention provides a power distribution network traveling wave head fault calibration method. The method comprises the steps that initial fault voltage traveling waves of all measurement points in a power distribution network are collected; constructing a first phase-mode transformation matrix to perform phase-mode transformation on the initial fault voltage traveling wave, and selecting a fault voltage line-mode component of the initial fault voltage traveling wave; performing first characteristic mode decomposition on the fault voltage line mode component to obtain a square envelope spectrum Gini coefficient of the fault voltage line mode component; determining an optimal parameter combination corresponding to the Gini coefficient of the square envelope spectrum, performing second characteristic mode decomposition on the optimal parameter combination, calculating a kurtosis value of each initial fault traveling wave signal, and determining a wave head calibration mode component of the fault traveling wave signal; and carrying out differential operation on the wave head calibration mode component, introducing a differential energy operator, and determining the traveling wave head arrival time of the fault traveling wave signal. According to the technical scheme of the embodiment, the detection precision and reliability of the fault traveling wave head can be effectively improved, and the accuracy of power distribution network fault positioning is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network fault location, and in particular to a distribution network traveling wave head fault calibration method, device, equipment and medium. Background Art

[0002] At present, algorithms such as the classic wavelet method and Hilbert-Huang transform can make relatively accurate judgments on the wave head of the traveling wave. However, when there is a high-resistance grounding fault or a small initial phase angle in the distribution network, the amplitude of the fault traveling wave may be small, and the noise generated by the external environment may cause further interference to the distribution network. This series of complex situations will cause the characteristics of the traveling wave signal to be weak and difficult to distinguish, making it impossible for the traditional wave head calibration method to obtain accurate traveling wave arrival time. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a method, device, equipment and medium for calibrating a fault of a traveling wave head in a distribution network, which can effectively improve the positioning accuracy of the fault traveling wave head.

[0004] In a first aspect, an embodiment of the present invention provides a method for calibrating a traveling wave head fault in a distribution network, comprising:

[0005] Collect the initial fault voltage traveling wave at each measuring point in the distribution network;

[0006] Constructing a first phase mode transformation matrix, performing phase mode transformation on the initial fault voltage traveling wave according to the first phase mode transformation matrix, and selecting a fault voltage line mode component of the initial fault voltage traveling wave;

[0007] Performing a first characteristic mode decomposition on the fault voltage line mode component to obtain a square envelope spectrum Gini coefficient of the fault voltage line mode component;

[0008] Determine an optimal parameter combination corresponding to the square envelope spectrum Gini coefficient, perform a second characteristic mode decomposition on the optimal parameter combination, calculate the kurtosis value of each of the initial fault traveling wave signals, and confirm the wave head calibration modal component of the fault traveling wave signal according to the kurtosis value;

[0009] A differential operation is performed on the wave head calibration modal component, and a symmetric differential energy operator is introduced to determine the arrival time of the traveling wave head of the fault traveling wave signal.

[0010] In some embodiments of the present invention, constructing a phase transformation matrix includes:

[0011] Acquire a first parameter matrix of a three-phase line in the distribution network, and acquire first parameter information and second parameter information of the first parameter matrix;

[0012] Calculate a first eigenvalue, a second eigenvalue, and a third eigenvalue of the first parameter matrix according to the first parameter information and the second parameter information;

[0013] Assigning the first parameter matrix and the eigenvectors corresponding to the first eigenvalue, the second eigenvalue, and the third eigenvalue to obtain a first mode-phase transformation matrix and a second mode-phase transformation matrix;

[0014] Determine the constraint relationship between the first phase-mode transformation matrix and the second phase-mode transformation matrix, and decouple the modulus in the second phase-mode transformation matrix after the phase-mode transformation;

[0015] Obtaining a first inverse matrix of the first mode phase transformation matrix, selecting a first line mode component in the first inverse matrix, analyzing the first line mode component, and determining a satisfying condition of the second mode phase transformation matrix;

[0016] According to the first inverse matrix, the second phase mode transformation matrix and the satisfied condition, a first phase mode transformation matrix and a second inverse matrix of the first phase mode transformation matrix are constructed.

[0017] In some embodiments of the present invention, after constructing the first phase mode transformation matrix and the second inverse matrix of the first phase mode transformation matrix, the method further includes:

[0018] Determining a fault type of the three-phase circuit, and determining a fault additional network boundary condition according to the fault type;

[0019] The fault additional network boundary conditions are input into the first phase mode transformation matrix to obtain voltage traveling wave results under different fault types.

[0020] In some embodiments of the present invention, performing a first eigenmode decomposition on the fault voltage line mode component includes:

[0021] Obtaining the original signal, mode number and filter length of the fault voltage line mode component;

[0022] Initializing the filter group of the fault voltage line mode component according to the original signal, the number of modes and the filter length to obtain a decomposed modal signal, and calculating the local maximum value of the fault voltage line mode component according to the decomposed modal signal;

[0023] updating the filter coefficients of the filter according to the original signal, the decomposed modal signal and the local maximum, and initializing the number of iterations;

[0024] Acquire multiple modal components of the fault voltage line mode component, calculate the maximum correlation coefficient between every two modal components, and increase the number of iterations;

[0025] When the number of iterations reaches a preset number of iterations, constructing a second parameter matrix according to the maximum correlation coefficient, locking the first modal component and the second modal component having the maximum correlation coefficient according to the second parameter matrix, and removing the second modal component, wherein the first kurtosis information of the first modal component is greater than the first kurtosis information of the second modal component;

[0026] When the modal components reach a preset number, a final number of the modal components is output.

[0027] In some embodiments of the present invention, the initializing operation of the filter bank of the fault voltage line mode component according to the original signal, the number of modes and the filter length includes:

[0028] Acquire the kurtosis information of the fault voltage line mode component, and update the filter bank with the kurtosis information as the objective function;

[0029] When the filter bank is updated, the frequency band information, sampling frequency and cutoff frequency of the original signal are obtained;

[0030] The frequency band information is evenly divided into K segments, and the cutoff frequency generates K filters according to a preset FIR function;

[0031] The upper limit frequency and the lower limit frequency of the original signal are calculated according to the sampling frequency, the cutoff frequency and the frequency band information after being divided into K bands.

[0032] In some embodiments of the present invention, after the filter bank of the fault voltage line mode component is initialized according to the original signal, the number of modes and the filter length, the method further comprises:

[0033] Acquire first quantity information of the modal component and second quantity information, length, sampling period and shift order of the filter;

[0034] Constructing a first constraint model and a second constraint model of the filter according to the first quantity information, the second quantity information, the length, the sampling period and the shift order;

[0035] Solving the first constraint model and the second constraint model to obtain a first decomposed modal matrix;

[0036] Acquire conjugate transpose operation information and intermediate variables of the filter coefficients, and construct a second decomposition modal matrix according to the conjugate transpose operation information and the intermediate variables;

[0037] Inputting the first decomposition modal matrix into the second decomposition modal matrix to obtain a filter coefficient matrix and a filter weight matrix;

[0038] A filter coefficient update value is obtained according to the filter coefficient matrix and the filter weighting matrix, wherein the filter coefficient update value is a filtered signal having the maximum first kurtosis information.

[0039] In some embodiments of the present invention, performing a differential operation on the wave head calibration modal component and introducing a symmetric differential energy operator includes:

[0040] Acquire any continuous signal of the wave head calibration modal component, define the wave head calibration modal component according to the continuous signal, and obtain an initial energy operator;

[0041] Acquire a first differential sequence of discrete signals in the wave head calibration modal component;

[0042] According to the first differential sequence, the original signal of the wave head calibration modal component is smoothed to obtain a second differential sequence;

[0043] The second difference sequence is introduced into the initial energy operator to obtain a symmetric difference energy operator.

[0044] In a second aspect, an embodiment of the present invention provides a distribution network traveling wave crest fault calibration device, comprising at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions executable by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the distribution network traveling wave crest fault calibration method as described in the first aspect above.

[0045] In a third aspect, an embodiment of the present invention provides an electronic device, comprising the distribution network traveling wave front fault calibration device as described in the second aspect above.

[0046] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the method for calibrating a traveling wave crest fault in a distribution network as described in the first aspect above.

[0047] The method for calibrating a traveling wave head fault in a distribution network according to an embodiment of the present invention has at least the following beneficial effects:

[0048] Collect the initial fault voltage traveling wave of each measuring point in the distribution network; construct a first phase mode transformation matrix, perform phase mode transformation on the initial fault voltage traveling wave according to the first phase mode transformation matrix, and select the fault voltage line mode component of the initial fault voltage traveling wave; perform the first characteristic mode decomposition on the fault voltage line mode component to obtain the square envelope spectrum Gini coefficient of the fault voltage line mode component; determine the optimal parameter combination corresponding to the square envelope spectrum Gini coefficient, perform the second characteristic mode decomposition on the optimal parameter combination, calculate the kurtosis value of each initial fault traveling wave signal, and confirm the wave head calibration modal component of the fault traveling wave signal according to the kurtosis value; perform differential operation on the wave head calibration modal component, and introduce the differential energy operator to determine the arrival time of the traveling wave head of the fault traveling wave signal. According to the technical solution of this embodiment, the detection accuracy and reliability of the fault traveling wave head can be effectively improved, which is helpful to enhance the accuracy of fault location in the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of a method for calibrating a traveling wave head fault in a distribution network provided by an embodiment of the present invention;

[0050] Figure 2 is a flow chart of constructing a phase transformation matrix provided by an embodiment of the present invention;

[0051] Figure 3 is a flowchart of constructing a first phase mode transformation matrix and a second inverse matrix of the first phase mode transformation matrix provided by an embodiment of the present invention;

[0052] Figure 4 is a flow chart of performing a first characteristic mode decomposition on a fault voltage line mode component provided by an embodiment of the present invention;

[0053] Figure 5 It is a flow chart of initializing the filter bank of the fault voltage line mode component according to the original signal, the number of modes and the filter length provided by an embodiment of the present invention;

[0054] Figure 6 It is a flow chart after initializing the filter bank of the fault voltage line mode component according to the original signal, the number of modes and the filter length provided by an embodiment of the present invention;

[0055] Figure 7 It is a flow chart of performing differential operation on wave head calibration modal components and introducing a symmetric differential energy operator provided by an embodiment of the present invention;

[0056] Figure 8 is a structural diagram of a distribution network traveling wave head fault calibration device provided by another embodiment of the present invention;

[0057] Fig. 9is a schematic diagram of a filter after initialization provided by an embodiment of the present invention;

[0058] Fig.10 It is a comparison diagram of the harmonic noise ratio, peak value and kurtosis information of different signal components provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0060] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0061] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0062] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0063] The embodiment of the present invention provides a method for calibrating the head fault of a distribution network traveling wave, including collecting the initial fault voltage traveling wave of each measuring point in the distribution network; constructing a first phase mode transformation matrix, performing phase mode transformation on the initial fault voltage traveling wave according to the first phase mode transformation matrix, and selecting the fault voltage line mode component of the initial fault voltage traveling wave; performing a first characteristic mode decomposition on the fault voltage line mode component to obtain the square envelope spectrum Gini coefficient of the fault voltage line mode component; determining the optimal parameter combination corresponding to the square envelope spectrum Gini coefficient, performing a second characteristic mode decomposition on the optimal parameter combination, calculating the kurtosis value of each initial fault traveling wave signal, and confirming the head calibration modal component of the fault traveling wave signal according to the kurtosis value; performing a differential operation on the head calibration modal component, and introducing a differential energy operator to determine the arrival time of the traveling wave head of the fault traveling wave signal. According to the technical solution of this embodiment, the detection accuracy and reliability of the fault traveling wave head can be effectively improved, which is helpful to enhance the accuracy of fault location in the distribution network.

[0064] It should be noted that the existing transformation matrices such as Clarke transformation matrix and Karen Bell transformation matrix are most commonly used in analyzing time domain problems, but their biggest drawback is that a single mode cannot be applied to all fault types.

[0065] The Clarke transformation formula is:

[0066]

[0067] When an AC phase-to-phase short circuit fault occurs in the system, the additional network boundary condition is: PB =0,u PA =-u PC , substituting into formula (5), the modulus value is always 0. When the system has a B-phase short circuit grounding fault, the fault additional network boundary condition is: PA =u PC = 0, substituting into the Clarke transformation formula, we can get the 2 modulus value to be always 0. PA 、u PB 、u PC It represents the voltage source at the fault point in the fault additional network. The voltage traveling wave transmitted to both sides of the line is generated by this voltage source.

[0068] Therefore, the 1 modulus obtained by Clarke transformation cannot reflect the AC phase-to-phase ungrounded short-circuit fault, and the 2 modulus cannot reflect the B phase short-circuit grounding fault.

[0069] The Karen Bell transformation is:

[0070]

[0071] When a phase C ground short circuit fault occurs in the system, the additional network boundary condition is: PA =u PB=0, and the modulus value is always 0 when the system has a B-phase short circuit grounding fault. The additional network boundary condition is: PA =u PC =0, substituting it into the Karenbell transformation formula, we can get the 2 modulus value to be always 0.

[0072] Therefore, the 1 modulus obtained by Karenberg transformation cannot reflect the C phase grounding short-circuit fault, and the 2 modulus cannot reflect the B phase short-circuit grounding fault.

[0073] From the above analysis, we can conclude that if the phase transformation matrix S -1 If a 0 element appears, the corresponding modulus cannot reflect the ground short circuit fault corresponding to the 0 element; if there are equal elements in the second or third row, the corresponding modulus cannot reflect the two-phase short circuit corresponding to the equal elements.

[0074] In order to solve the defect that a single modulus in the existing phase-mode transformation matrix cannot be applied to all fault types, the present invention assumes that the three-phase lines are identical and evenly transposed, and its parameter matrix is ​​P. The phase-mode transformation matrix is ​​constructed by diagonalizing the matrix P, thereby realizing a single modulus to detect all fault types.

[0075] The control method of the embodiment of the present invention is further described below based on the accompanying drawings.

[0076] Reference Figure 1 , Figure 1 A flow chart of a method for calibrating a traveling wave head fault in a distribution network provided by an embodiment of the present invention, the method for calibrating a traveling wave head fault in a distribution network includes but is not limited to the following steps:

[0077] Step S11, collecting the initial fault voltage traveling wave of each measuring point in the distribution network;

[0078] It should be noted that in the actual three-phase system of the distribution network, due to the coupling between phases, the three-phase lines will produce initial fault voltage traveling waves that affect each other. By collecting the initial fault voltage traveling waves, the initial fault data of the distribution network traveling wave head can be obtained.

[0079] Step S12, constructing a first phase mode transformation matrix, performing phase mode transformation on the initial fault voltage traveling wave according to the first phase mode transformation matrix, and extracting the fault voltage line mode component of the initial fault voltage traveling wave;

[0080] It should be noted that in a three-phase system, due to the coupling between phases, the initial fault voltage traveling waves generated by the three-phase line will affect each other. Therefore, before calibrating the wave head of the fault traveling wave, it is necessary to perform phase mode conversion on the initial fault voltage traveling wave to convert the initial fault voltage traveling wave into independent fault voltage line mode components.

[0081] Step S13, performing a first characteristic mode decomposition on the fault voltage line mode component to obtain a square envelope spectrum Gini coefficient of the fault voltage line mode component;

[0082] It should be noted that when a three-phase line fails, a traveling wave signal will be generated at the fault point and transmitted to each end of the line. The traveling wave signal transmitted along the three-phase line is composed of signals of different frequency bands. When detecting the initial fault traveling wave signal, the fault voltage line mode component needs to be decomposed into the first characteristic mode. Specifically, the first characteristic mode decomposition fully considers the impulse of the model, can effectively step the mutation characteristics of the fault traveling wave, and also has strong anti-interference and anti-noise capabilities. The first characteristic mode decomposition is achieved through an adaptive finite impulse response filter, thereby avoiding the limitations of the filter shape, bandwidth and center frequency in the traditional method, thereby achieving a more thorough signal decomposition.

[0083] Step S14, determining the optimal parameter combination corresponding to the square envelope spectrum Gini coefficient, performing a second characteristic mode decomposition on the optimal parameter combination, calculating the kurtosis value of each initial fault traveling wave signal, and confirming the wave head calibration modal component of the fault traveling wave signal according to the kurtosis value;

[0084] It should be noted that this embodiment initializes the length and the number of modes of the filter, calculates the square envelope spectral coefficient of each fault voltage modulus component after decomposition, finds the parameter combination corresponding to the maximum value of the square envelope spectral coefficient as the optimal parameter combination, performs a second characteristic mode decomposition on the optimal parameter combination, and thus calculates the kurtosis value of each initial fault traveling wave signal, and takes the component corresponding to the maximum kurtosis value as the wave head calibration modal component of the fault traveling wave signal.

[0085] Step S15, performing a differential operation on the wave head calibration modal component, and introducing a symmetric differential energy operator to determine the arrival time of the traveling wave head of the fault traveling wave signal.

[0086] It should be noted that by introducing a symmetric differential energy operator to perform differential operation on the wave head calibration modal component, the instantaneous energy value of the wave head calibration modal component of the fault traveling wave is obtained, and the moment of the first peak on the instantaneous frequency spectrum is regarded as the arrival time of the initial fault traveling wave.

[0087] It should be noted that the phase-mode transformation matrix of this embodiment can realize effective detection of all fault types with a single modulus, ensuring that the positioning of the traveling wave is always effective under different fault conditions of the three-phase line, providing a reliable basis for fault detection, and improving the comprehensiveness of fault analysis; by adopting modal decomposition as a signal processing technology, the fault traveling wave signal of the main frequency band can be effectively extracted during the modal decomposition process, avoiding the common modal aliasing phenomenon in the signal processing process. Further, by adopting the square envelope spectral coefficient to optimize the parameter combination of modal decomposition, the problem of unsatisfactory decomposition effect caused by unreasonable parameter selection is avoided, so that the accuracy and reliability of signal decomposition are significantly improved. This embodiment uses the kurtosis criterion as the standard for extracting the calibration modal component of the fault traveling wave head, which can effectively identify and extract the fault characteristic components in the initial fault voltage traveling wave. After introducing the symmetric differential energy operator, it can quickly track the mutation of the signal and improve the system's response speed to sudden faults.

[0088] In addition, in one embodiment, referring to Figure 2 ,exist Figure 1 Step S12 of the illustrated embodiment also includes but is not limited to the following steps:

[0089] Step S21, obtaining a first parameter matrix of a three-phase line in a distribution network, and obtaining first parameter information and second parameter information of the first parameter matrix;

[0090] Step S22, calculating the first eigenvalue, the second eigenvalue and the third eigenvalue of the first parameter matrix according to the first parameter information and the second parameter information;

[0091] Step S23, assigning values ​​to the first parameter matrix and the eigenvectors corresponding to the first eigenvalue, the second eigenvalue and the third eigenvalue to obtain a first mode-phase transformation matrix and a second mode-phase transformation matrix;

[0092] Step S24, determining the constraint relationship between the first phase-mode transformation matrix and the second phase-mode transformation matrix, and decoupling the modulus in the second phase-mode transformation matrix after the phase-mode transformation;

[0093] Step S25, obtaining a first inverse matrix of the first mode phase transformation matrix, selecting a first line mode component in the first inverse matrix, analyzing the first line mode component, and determining a satisfying condition of the second mode phase transformation matrix;

[0094] Step S26, constructing the first phase mode transformation matrix and the second inverse matrix of the first phase mode transformation matrix according to the first inverse matrix, the second phase mode transformation matrix and satisfying the conditions.

[0095] It should be noted that in addition to the self-resistance, self-inductance Ls, self-conductance Gs, and self-capacitance Cs of the three-phase line, there is also a mutual coupling phenomenon between any two phases, which produces mutual resistance Rm, mutual inductance Lm, mutual conductance Gm, and mutual capacitance Cm between the phases.

[0096] In this embodiment, in a three-phase line, when the mutual coupling parameters between phases are the same, the relationship between the voltage and current of the three-phase line can be obtained, which is expressed by the following first formula and second formula:

[0097]

[0098]

[0099] in, Represents the three-phase voltage phasor, represents the three-phase current phasor, Z and Y are impedance matrix and admittance matrix respectively, Z s =R s +jωL s is the self-impedance, Y s =G s +jωC s is the self-admittance, Z m =R m +jωL m is the mutual impedance, Y m =G m +jωC m is the mutual admittance. It can be seen that the voltage and current of each phase in the line are not only related to the ground parameters of the line itself, but also to the mutual parameters and voltage and current of the other two phase lines. The phases are coupled and related to each other, which makes the calculation process more complicated. The decoupling method is used to convert the voltage and current with electromagnetic coupling into independent fault voltage line mode components. According to the phase mode transformation theory, the reason for the three-phase coupling is that the non-diagonal elements of the impedance matrix Z and the admittance matrix Y are not 0. Therefore, it is only necessary to introduce a specific matrix to diagonalize the impedance matrix Z and the admittance matrix Y. Among them, the voltage and current equations after decoupling are consistent with the case of single-phase conductors.

[0100] Furthermore, the first phase mode transformation matrix S and the second phase mode transformation matrix Q are introduced to transform the transformed voltage and It is called the modulus component, where a = 0, 1, 2. It represents the 0-mode, 1-mode and 2-mode respectively, and is expressed by the following third and fourth formulas:

[0101]

[0102]

[0103] Choose appropriate matrices S and Q so that S -1 ZQ and Q -1 If YS is a diagonal matrix, there is no coupling relationship between the obtained modulus components, and each modulus is independent of each other.

[0104] Furthermore, in order to solve the defect that a single modulus in the existing phase-mode transformation matrix cannot be applied to all fault types, in this embodiment, the three-phase lines are identical and evenly transposed. First, the first parameter matrix of the three-phase circuit in the distribution network is obtained, and the phase-mode transformation matrix is ​​constructed by diagonalizing the first parameter matrix, which is expressed by the following fifth formula:

[0105]

[0106] The first parameter information and the second parameter information of the first parameter matrix are obtained, wherein the first parameter information is the eigenvalue and the second parameter information is the eigenvector. Assume that the three eigenvalues ​​of the first parameter matrix are λ1, λ2, and λ3, respectively. According to the characteristic equation det(λI-P)=0, the eigenvalue of P is obtained as follows:

[0107]

[0108] Let the corresponding eigenvalue λ i The eigenvector of i =[s 1i ,s 2i ,s 3i ] T , where i = 1, 2, 3, and let the matrix S = [S1, S2, S3], then the matrix S is the first mode phase transformation matrix, and its inverse matrix S -1 is the second phase transformation matrix.

[0109] According to the properties of eigenvalues ​​and eigenvectors (P-λ i I)S i =0, and the three eigenvalues ​​are substituted into the sixth formula to obtain the following:

[0110]

[0111] Therefore, in order to achieve the decoupling of each modulus after the phase modulus transformation, each element in the matrix must satisfy the constraint relationship shown in the sixth formula above.

[0112] The first inverse matrix of the first mode phase transformation matrix is:

[0113]

[0114] No matter what kind of fault occurs in the three-phase line, its three-phase boundary conditions are never all 0. Further, in order to meet the requirement that a single modulus reflects all fault types and avoid the above defects, combined with the actual application of traveling wave positioning, this embodiment selects the line mode component for analysis. At this time, the phase mode transformation matrix satisfies the satisfying condition shown in the seventh formula below:

[0115]

[0116] Based on the above conditions, the first phase mode transformation matrix and its second inverse matrix of this embodiment are:

[0117]

[0118] In addition, in one embodiment, referring to Figure 3 ,exist Figure 2 After step S26 in the illustrated embodiment, the following steps are also included but not limited to:

[0119] Step S31, determining the fault type of the three-phase circuit, and determining the fault additional network boundary conditions according to the fault type;

[0120] Step S32, the fault additional network boundary conditions are input into the first phase mode transformation matrix to obtain voltage traveling wave results under different fault types.

[0121] It should be noted that when the system has a phase-to-phase short circuit fault, the additional network boundary condition is: PA =0,u PB =-u PC Substituting the boundary conditions into the first phase transformation matrix, we can get the voltage traveling waves of each module u0=0, u1=-2u PB 、u2=-2u PC . Where u PB 、u PC It represents the voltage source at the fault point under the fault additional network, and the voltage traveling wave transmitted to both sides of the line is generated by this voltage source. Similarly, according to the boundary conditions of the additional network under different fault types, combined with the first phase mode transformation matrix, the voltage traveling wave results of each modulus after decoupling by the first phase mode transformation matrix under different fault types can be obtained, as shown in Table 1.

[0122] Table 1 Voltage traveling wave modulus of the new phase mode conversion decoupling under different faults

[0123]

[0124]

[0125] It can be seen from Table 1 that when the phase mode transformation is used to analyze the voltage traveling waves of each modulus under different faults, the fault line mode component does not always remain zero under all fault types. Therefore, the first phase mode transformation matrix can realize the detection of all fault types with a single modulus.

[0126] It should be noted that when a three-phase line fails, the fault point will generate a fault traveling wave signal and transmit it to each end of the line. The traveling wave signal transmitted along the line is composed of signals in different frequency bands. When detecting the initial fault traveling wave signal, it is necessary to perform modal decomposition on the detected signal to obtain the fault traveling wave signal of the main frequency band.

[0127] In addition, in one embodiment, referring to Figure 4 ,exist Figure 1 Step S13 of the illustrated embodiment also includes but is not limited to the following steps:

[0128] Step S41, obtaining the original signal, mode number and filter length of the fault voltage line mode component;

[0129] Step S42, initializing the filter bank of the fault voltage line mode component according to the original signal, the number of modes and the filter length to obtain a decomposed mode signal;

[0130] Step S43, obtaining the local maximum point of the fault voltage line mode component, updating the filter coefficient of the filter according to the original signal, the decomposed modal signal and the local maximum point, and initializing the number of iterations;

[0131] Step S44, obtaining multiple modal components of the fault voltage line mode component, calculating the maximum correlation coefficient between every two modal components, and increasing the number of iterations;

[0132] Step S45, when the number of iterations reaches a preset number of iterations, constructing a second parameter matrix according to the maximum correlation coefficient, locking the first modal component and the second modal component with the maximum correlation coefficient according to the second parameter matrix, and removing the second modal component, wherein the first kurtosis information of the first modal component is greater than the first kurtosis information of the second modal component;

[0133] Step S46, when the modal components reach a preset number, output a final number of modal components.

[0134] It should be noted that, in this embodiment, the Hanning window is used to initialize the FIR filter group to provide direction for the decomposition of the fault voltage line mode component. The adaptive FIR filter is designed by iteratively updating the filter coefficients so that the filtered original signal is infinitely close to the deconvolution objective function. When performing the first eigenmode decomposition, the filter is updated according to the kurtosis information as the objective function.

[0135] It should be noted that the original signal of the fault voltage line mode component is obtained, and the initialization parameters are input, wherein the initialization parameters include the number of modes n and the filter length L, and the filter group of the fault voltage line mode component is initialized according to the original signal, the number of modes and the filter length, and the number of initialization iterations is set to i = 1, and the decomposed modal signal is obtained, that is, by decomposing the mode The decomposed signal, where k = 1, 2, ..., k, * is the convolution calculation, and the filter coefficients are updated according to the original signal, the decomposed mode and the local maximum point, where the local maximum point To decompose the mode The autocorrelation spectrum of moment, and initialize the number of iterations to i=i+1; confirm whether the current number of iterations has reached the maximum number of iterations. When the maximum number of iterations is reached, calculate the maximum correlation coefficient between every two modal components, construct a K*K matrix CC(k*k), lock the two modal components with the maximum correlation coefficient (i.e., the first modal component and the second modal component), and calculate the first kurtosis information and the second kurtosis information between the two modal components, then remove the modal component with smaller correlation kurtosis information from the two modal components, and set K=K-1; confirm whether the number of current modal components has reached the preset number of modal components, and when it has reached, output the final number of modal components.

[0136] Further, when the current number of iterations does not reach the maximum number of iterations, the modal signal is reacquired; when the number of modal components does not reach the maximum number of iterations, the modal signal is reacquired.

[0137] In addition, in one embodiment, referring to Figure 5 ,exist Figure 4 Step S42 of the illustrated embodiment also includes but is not limited to the following steps:

[0138] Step S51, obtaining kurtosis information of the fault voltage line mode component, and updating the filter bank with the kurtosis information as the objective function;

[0139] Step S52, when the filter bank is updated, the frequency band information, sampling frequency and cutoff frequency of the original signal are obtained;

[0140] Step S53, the frequency band information is evenly divided into K segments, and the cutoff frequency generates K filters according to a preset FIR function;

[0141] Step S54, calculating the upper limit frequency and the lower limit frequency of the original signal according to the sampling frequency, the cutoff frequency and the frequency band information after being divided into K bands.

[0142] It should be noted that when the filter is updated, the frequency band of the original signal is first evenly divided into K segments. Then, the upper and lower cutoff frequencies f u and f l Make the following allocation:

[0143]

[0144] Among them, f s is the sampling frequency of the original signal, and the FIR function is used to obtain the cutoff frequency [f l , f u ] segment to generate K FIR filters.

[0145] like Fig. 9 As shown in Figure 1, a group of K FIR filters uniformly distributed over the entire frequency band are initialized by the Hanning window to form an FIR filter bank. #n indicates Fig. 9 The position of the nth filter in . Adaptive decomposition of fault modes is achieved through filter updating and mode selection.

[0146] In addition, in one embodiment, referring to Figure 6 ,exist Figure 4 After step S42 of the illustrated embodiment, the following steps are also included but not limited to:

[0147] Step S61, obtaining first quantity information of modal components and second quantity information, length, sampling period and shift order of filters;

[0148] Step S62, constructing a first constraint model and a second constraint model of the filter according to the first quantity information, the second quantity information, the length, the sampling period and the shift order;

[0149] Step S63, solving the first constraint model and the second constraint model to obtain a first decomposed modal matrix;

[0150] Step S64, obtaining conjugate transposition operation information and intermediate variables of the filter coefficients, and constructing a second decomposition modal matrix according to the conjugate transposition operation information and the intermediate variables;

[0151] Step S65, inputting the first decomposition modal matrix into the second decomposition modal matrix to obtain a filter coefficient matrix and a filter weighting matrix;

[0152] Step S66, obtaining a filter coefficient update value according to the filter coefficient matrix and the filter weight matrix, wherein the filter coefficient update value is a filter signal having the maximum first kurtosis information.

[0153] It should be noted that the initialized filter group will cause noise interference in the decomposed modal components, so the filtering function of the filter should be further improved. Specifically, there are different signal interferences in the distribution network fault signal. Fig.10 As shown in the figure, the test signal contains a fault mutation signal (signal #1), a random pulse interference signal (signal #2), a harmonic signal (signal #3) and a noise signal (signal #4). The three indicators of harmonic noise ratio, kurtosis and kurtosis information are used to evaluate the mutation of the signal. The indicators of the four signals are calculated respectively and the results are normalized. Under the premise of considering the mutation of the signal, the fault mutation signal (signal #1) has the largest correlation kurtosis value. Therefore, the modal component containing fault information is extracted with the correlation kurtosis value as the objective function, which can exclude the interference of other frequency band signals and noise.

[0154] Furthermore, for the original signal x(n) with a marking length of N, the characteristic mode decomposition of the original signal needs to satisfy the constrained optimization problem shown in the following first constraint model and second constraint model:

[0155] The first constraint model is:

[0156]

[0157] The second constraint model is:

[0158]

[0159] Among them, u k (n) is the Kth decomposition mode, f k is the Kth filter, whose length is L, T S is the sampling period, and M is the shift order.

[0160] The first constraint model and the second constraint model are solved to obtain a first decomposition modal matrix, which is expressed by the following seventh formula:

[0161] u k =Xf k ;

[0162] Among them, u k It is expressed by the following eighth formula:

[0163]

[0164]

[0165] The relevant kurtosis of the decomposed mode is defined and expressed by the following ninth formula:

[0166]

[0167] Where H is the conjugate transpose operation, W M It is used as an intermediate variable to control the weighted correlation matrix.

[0168] Furthermore, W M It is expressed by the following tenth formula:

[0169]

[0170] Substituting the first decomposed modal matrix into the ninth formula, we get the eleventh formula, which is as follows:

[0171]

[0172] Among them, R XX is the filter coefficient matrix R XWX is the filter weight matrix.

[0173] Maximizing the filter coefficients is equivalent to solving R XWX f k =R XX f k The eigenvector of the largest eigenvalue λ in λ. Therefore, during the iteration, the kth filter coefficient will be passed through R XWX f k =R XX f k The solution of λ is updated to keep approaching the set target, that is, the filtered signal with the maximum correlation kurtosis.

[0174] In addition, in one embodiment, referring to Figure 7 ,exist Figure 1 Step S15 of the illustrated embodiment also includes but is not limited to the following steps:

[0175] Step S71, obtaining any continuous signal of the wave head calibration modal component, defining the wave head calibration modal component according to the continuous signal, and obtaining an initial energy operator;

[0176] Step S72, obtaining a first differential sequence of discrete signals in the wave head calibration modal component;

[0177] Step S73, smoothing the original signal of the wave head calibration modal component according to the first differential sequence to obtain a second differential sequence;

[0178] Step S74, bringing the second difference sequence into the initial energy operator to obtain a symmetric difference energy operator.

[0179] It should be noted that the filter length L, the frequency band number parameter K and the mode number parameter n, as input parameters of modal decomposition, play an important role in the decomposition performance. Among them, L is the filter length, which is an important parameter of the FIR filter. If the filter length is too short, it is easy to cause edge effects and may not fully capture the characteristics of the signal; if the filter length is too long, it may cause distortion of the results and further increase the computational burden. The frequency band number parameter K is used to determine the frequency band number divided for the initialization filter. First, the larger K is, the heavier the computational burden is. Secondly, K ≥ n is another important constraint to ensure that n modes can be decomposed. Finally, the selection of the frequency band number parameter K will affect the decomposition performance to a certain extent. Therefore, for the optimal selection of the frequency band number parameter K, a trade-off can be made between decomposition performance and computational burden. The mode number parameter n determines the number of decomposed modes. FMD with relevant kurtosis information as the objective function is more inclined to extract modal components containing fault information. The mode selection strategy further eliminates modal mixing. Too large a mode number parameter n may generate redundant modes, and too small a mode number parameter n will lose potential fault information.

[0180] Based on the above reasons, in order to avoid unsatisfactory decomposition effect of FMD caused by unreasonable parameter selection, this embodiment proposes to use the differential energy operator to determine the optimal parameter combination of eigenmode decomposition, and its mathematical model is:

[0181]

[0182] Where S = abs[FFT(Hilbert(x))], S k is the kth amplitude of the signal envelope spectrum arranged in ascending order, PSP1 is the l1 norm of S, and N is the length of the signal.

[0183] After the above steps, the eigenmode decomposition decomposes a signal into several narrowband IMF components with descending center frequencies. Compared with the classic EMD decomposition, the eigenmode decomposition has obvious advantages, effectively avoiding the mode aliasing phenomenon that occurs during the EMD decomposition process, and has stronger noise resistance for the decomposition of noise-interfered signals. In addition, in terms of determining the number of decomposed modal components, the symmetric differential energy operator is introduced to determine the optimal parameter combination for the eigenmode decomposition.

[0184] Furthermore, the present invention uses the kurtosis criterion as a standard for extracting the fault traveling wave head calibration modal component, which is mathematically described as:

[0185]

[0186] Among them, σ represents the standard deviation; μ represents the mean deviation; and x represents the fault signal.

[0187] Kurtosis information is a dimensionless parameter. When the system is operating normally, the kurtosis value conforms to the normal distribution. However, when the system fails, due to the appearance of the impulse signal, the probability density of the signal will increase and the kurtosis value will increase. The larger the kurtosis value of the modal component, the more fault characteristic components it contains. Therefore, the kurtosis information is used as the criterion for selecting the sensitive modal components in the final decomposition mode of the characteristic mode decomposition, so as to effectively extract the modal components that are most conducive to calibrating the fault traveling wave head.

[0188] The present invention introduces a symmetric differential energy operator in the detection of traveling wave heads:

[0189] For any continuous signal x(t), its traditional initial energy operator is defined as:

[0190]

[0191] The first difference sequence of the discrete signal x(n) is defined as:

[0192]

[0193] Difference sequence y d (n) is actually the smoothing of the original signal x(n). From the above formula, we can get y d The second difference sequence of (n):

[0194]

[0195] y d (n) Substitute the discrete form expression of the initial energy operator into the improved energy operator, namely the symmetric difference energy operator:

[0196]

[0197] The symmetric differential energy operator is improved on the basis of the Teager energy operator. For each instant, only three adjacent sampling points need to be calculated. The calculation is simple and efficient, and signal mutations can be quickly tracked. In addition, since the differential sequence is used to smooth the signal during the calculation process, the influence of energy fluctuations near the mutation point can be avoided. The obtained energy spectrum is smoother, and noise interference can be avoided to a certain extent, thereby accurately calibrating the position of the traveling wave head.

[0198] like Figure 8 As shown, Figure 8 The present invention also provides a distribution network traveling wave head fault calibration device, including:

[0199] The processor 801 may be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0200] The memory 802 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 802, and the processor 801 calls and executes the distribution network traveling wave head fault calibration method of the embodiment of this application;

[0201] Input / output interface 803, used to implement information input and output;

[0202] The communication interface 804 is used to realize the communication interaction between the present apparatus and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0203] A bus 805 that transmits information between the various components of the device (e.g., the processor 801, the memory 802, the input / output interface 803, and the communication interface 804);

[0204] The processor 801 , the memory 802 , the input / output interface 803 and the communication interface 804 are connected to each other in communication within the device via a bus 805 .

[0205] An embodiment of the present application also provides an electronic device, including the distribution network traveling wave head fault calibration device as described above.

[0206] An embodiment of the present application also provides a storage medium, which is a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned distribution network traveling wave head fault calibration method is implemented.

[0207] As a non-transient computer-readable storage medium, the memory can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and are implemented to be located in one place, or may also be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0208] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically include computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0209] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above-mentioned implementation mode. Technical personnel familiar with the field can also make various equivalent deformations or substitutions under the shared conditions without violating the spirit of the present invention. These equivalent deformations or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A method for calibrating a traveling wave head fault in a distribution network, characterized in that: include: Collect the initial fault voltage traveling wave at each measuring point in the distribution network; Constructing a first phase mode transformation matrix, performing phase mode transformation on the initial fault voltage traveling wave according to the first phase mode transformation matrix, and selecting a fault voltage line mode component of the initial fault voltage traveling wave; Performing a first characteristic mode decomposition on the fault voltage line mode component to obtain a square envelope spectrum Gini coefficient of the fault voltage line mode component; Determine an optimal parameter combination corresponding to the square envelope spectrum Gini coefficient, perform a second characteristic mode decomposition on the optimal parameter combination, calculate the kurtosis value of each of the initial fault traveling wave signals, and confirm the wave head calibration modal component of the fault traveling wave signal according to the kurtosis value; A differential operation is performed on the wave head calibration modal component, and a symmetric differential energy operator is introduced to determine the arrival time of the traveling wave head of the fault traveling wave signal.

2. The method for calibrating a traveling wave head fault in a distribution network according to claim 1, characterized in that: The step of constructing a phase transformation matrix comprises: Acquire a first parameter matrix of a three-phase line in the distribution network, and acquire first parameter information and second parameter information of the first parameter matrix; Calculate a first eigenvalue, a second eigenvalue, and a third eigenvalue of the first parameter matrix according to the first parameter information and the second parameter information; Assigning the first parameter matrix and the eigenvectors corresponding to the first eigenvalue, the second eigenvalue, and the third eigenvalue to obtain a first mode-phase transformation matrix and a second mode-phase transformation matrix; Determine the constraint relationship between the first phase-mode transformation matrix and the second phase-mode transformation matrix, and decouple the modulus in the second phase-mode transformation matrix after the phase-mode transformation; Obtaining a first inverse matrix of the first mode phase transformation matrix, selecting a first line mode component in the first inverse matrix, analyzing the first line mode component, and determining a satisfying condition of the second mode phase transformation matrix; According to the first inverse matrix, the second phase mode transformation matrix and the satisfied condition, a first phase mode transformation matrix and a second inverse matrix of the first phase mode transformation matrix are constructed.

3. The method for calibrating a traveling wave head fault in a distribution network according to claim 2, characterized in that: After constructing the first phase mode transformation matrix and the second inverse matrix of the first phase mode transformation matrix, the method further includes: Determining a fault type of the three-phase circuit, and determining a fault additional network boundary condition according to the fault type; The fault additional network boundary conditions are input into the first phase mode transformation matrix to obtain voltage traveling wave results under different fault types.

4. The method for calibrating a traveling wave head fault in a distribution network according to claim 1, characterized in that: The performing a first characteristic mode decomposition on the fault voltage line mode component comprises: Obtaining the original signal, mode number and filter length of the fault voltage line mode component; Initializing the filter group of the fault voltage line mode component according to the original signal, the number of modes and the filter length to obtain a decomposed modal signal, and calculating the local maximum value of the fault voltage line mode component according to the decomposed modal signal; updating the filter coefficients of the filter according to the original signal, the decomposed modal signal and the local maximum value, and initializing the number of iterations; Acquire multiple modal components of the fault voltage line mode component, calculate the maximum correlation coefficient between every two modal components, and increase the number of iterations; When the number of iterations reaches a preset number of iterations, constructing a second parameter matrix according to the maximum correlation coefficient, locking the first modal component and the second modal component having the maximum correlation coefficient according to the second parameter matrix, and removing the second modal component, wherein the first kurtosis information of the first modal component is greater than the first kurtosis information of the second modal component; When the modal components reach a preset number, a final number of the modal components is output.

5. The method for calibrating a traveling wave head fault in a distribution network according to claim 4, characterized in that: The initializing operation of the filter group of the fault voltage line mode component according to the original signal, the number of modes and the filter length includes: Acquire the kurtosis information of the fault voltage line mode component, and update the filter bank with the kurtosis information as the objective function; When the filter bank is updated, the frequency band information, sampling frequency and cutoff frequency of the original signal are obtained; The frequency band information is evenly divided into K segments, and the cutoff frequency generates K filters according to a preset FIR function; The upper limit frequency and the lower limit frequency of the original signal are calculated according to the sampling frequency, the cutoff frequency and the frequency band information after being divided into K bands.

6. The method for calibrating a traveling wave head fault in a distribution network according to claim 4, characterized in that: After the filter group of the fault voltage line mode component is initialized according to the original signal, the number of modes and the filter length, the method further includes: Acquire first quantity information of the modal component and second quantity information, length, sampling period and shift order of the filter; Constructing a first constraint model and a second constraint model of the filter according to the first quantity information, the second quantity information, the length, the sampling period and the shift order; Solving the first constraint model and the second constraint model to obtain a first decomposed modal matrix; Acquire conjugate transpose operation information and intermediate variables of the filter coefficients, and construct a second decomposition modal matrix according to the conjugate transpose operation information and the intermediate variables; Inputting the first decomposition modal matrix into the second decomposition modal matrix to obtain a filter coefficient matrix and a filter weight matrix; A filter coefficient update value is obtained according to the filter coefficient matrix and the filter weighting matrix, wherein the filter coefficient update value is a filtered signal having the maximum first kurtosis information.

7. The method for calibrating a traveling wave head fault in a distribution network according to claim 1, characterized in that: The differential operation is performed on the wave head calibration modal component, and a symmetric differential energy operator is introduced, including: Acquire any continuous signal of the wave head calibration modal component, define the wave head calibration modal component according to the continuous signal, and obtain an initial energy operator; Acquire a first differential sequence of discrete signals in the wave head calibration modal component; According to the first differential sequence, the original signal of the wave head calibration modal component is smoothed to obtain a second differential sequence; The second difference sequence is introduced into the initial energy operator to obtain a symmetric difference energy operator.

8. A device for calibrating traveling wave head faults in a distribution network, characterized in that: It includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the distribution network traveling wave head fault calibration method as described in any one of claims 1 to 7.

9. An electronic device, characterized in that: It includes the distribution network traveling wave head fault calibration device as described in claim 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the distribution network traveling wave crest fault calibration method as described in any one of claims 1 to 7.

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