CN113960412A - Method and device for processing fault traveling wave signals of power distribution network - Google Patents

Method and device for processing fault traveling wave signals of power distribution network Download PDF

Info

Publication number
CN113960412A
CN113960412A CN202111220486.6A CN202111220486A CN113960412A CN 113960412 A CN113960412 A CN 113960412A CN 202111220486 A CN202111220486 A CN 202111220486A CN 113960412 A CN113960412 A CN 113960412A
Authority
CN
China
Prior art keywords
fault
traveling wave
modal
modal component
wave signal
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202111220486.6A
Other languages
Chinese (zh)
Inventor
潘姝慧
黄秉开
袁智勇
袁晓杰
雷金勇
李冠桥
白浩
钟振鑫
周长城
卓定明
郭琦
张胜强
余文辉
孙奇珍
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
CSG Electric Power Research Institute
Huizhou Power Supply Bureau of Guangdong Power Grid Co Ltd
Original Assignee
CSG Electric Power Research Institute
Huizhou Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by CSG Electric Power Research Institute, Huizhou Power Supply Bureau of Guangdong Power Grid Co Ltd filed Critical CSG Electric Power Research Institute
Priority to CN202111220486.6A priority Critical patent/CN113960412A/en
Publication of CN113960412A publication Critical patent/CN113960412A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/08—Locating faults in cables, transmission lines, or networks
    • G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
    • G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/08—Locating faults in cables, transmission lines, or networks
    • G01R31/088—Aspects of digital computing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y04S10/52—Outage or fault management, e.g. fault detection or location

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Theoretical Computer Science (AREA)
  • Locating Faults (AREA)

Abstract

The application discloses a method and a device for processing a power distribution network fault traveling wave signal, wherein the method comprises the following steps: acquiring a fault traveling wave signal to be processed; decomposing the fault traveling wave signal through a VMD decomposition processing algorithm to obtain a modal component; calculating a sample entropy value of the modal component by a time series complexity measurement method; filtering modal components of which the sample entropy values are larger than the sample entropy threshold value according to the comparison result of the sample entropy values and a preset sample entropy threshold value; based on the remaining modal components, a fault signal sequence is generated. The power distribution network fault traveling wave signal processing method is based on modal components obtained by decomposing fault traveling wave signals through a VMD decomposition processing algorithm, then sample entropy values of the modal components are calculated respectively, the modal components with the higher sample entropy values are filtered as non-stationary random noise, denoising of the fault traveling wave signals is achieved, traveling wave signals with higher precision are obtained, and therefore fault positioning precision is improved.

Description

Method and device for processing fault traveling wave signals of power distribution network
Technical Field
The application relates to the technical field of traveling wave fault location, in particular to a power distribution network fault traveling wave signal processing method and device.
Background
With the continuous expansion of the capacity and the power grid area of each large power system in China, the operation management of the power distribution network is more complicated, and the problem of safety and stability of the power distribution network power system is increasingly prominent. The fault position can be rapidly and accurately judged after the fault of the distribution line, the line patrol burden can be reduced, the line can be timely repaired, the reliable power supply is ensured, and the comprehensive economic loss caused by power failure can be reduced. Therefore, the rapid and accurate fault location has very important significance for the safe and stable and economic operation of the power distribution network power system.
The traveling wave method is one of the most common power distribution network fault location methods at present, fault location of a power transmission line is realized according to a traveling wave transmission theory, the fault location accuracy is relatively high, but with the development of power distribution network automation, higher requirements are provided for the fault location accuracy, and how to improve the fault location accuracy becomes a technical problem which needs to be solved urgently by technical personnel in the field.
Disclosure of Invention
The embodiment of the application provides a power distribution network fault traveling wave signal processing method and device, which are used for achieving the purpose of improving fault positioning accuracy.
The application provides in a first aspect a method for processing a power distribution network fault traveling wave signal, comprising:
acquiring a fault traveling wave signal to be processed;
decomposing the fault traveling wave signal through a VMD decomposition processing algorithm to obtain a plurality of modal components;
calculating a sample entropy value of the modal component by a preset time series complexity measurement method;
filtering the modal component of which the sample entropy value is larger than the sample entropy threshold value according to the comparison result of the sample entropy value and the sample entropy threshold value;
and generating a fault signal sequence based on the residual modal components after filtering processing for fault positioning.
Preferably, after acquiring the fault traveling wave signal to be processed, the method further includes:
and extracting three-phase current of the power distribution network from the fault traveling wave signal, and converting the three-phase current in a Kerenbel conversion mode to obtain alpha mode current components so as to carry out decomposition processing through a VMD decomposition processing algorithm according to the alpha mode current components.
Preferably, the decomposing the fault traveling wave signal by using a VMD decomposition processing algorithm to obtain a plurality of modal components further includes:
calculating the instantaneous frequency mean value of each modal component, and constructing an instantaneous frequency mean value change curve of each modal component according to the number of decomposition layers corresponding to each modal component and the instantaneous frequency mean value;
determining the number of critical decomposition layers according to the curvature of each decomposition layer in the instantaneous frequency mean value change curve, and obtaining modal components corresponding to the number of critical decomposition layers according to the number of critical decomposition layers.
Preferably, after the generating the fault signal sequence based on the modal components remaining after the filtering, the method further includes:
calculating the maximum module value of the fault signal sequence through a wavelet transform processing mode based on the modal component contained in the fault signal sequence, and determining the fault traveling wave head of the fault signal sequence by comparing the attenuation characteristics of the maximum module values of the modal components of different decomposition layers.
Preferably, the decomposing the fault traveling wave signal by using a VMD decomposition processing algorithm to obtain a plurality of modal components further includes:
performing optimal solution solving on the modal component output by the VMD decomposition processing algorithm through a Lagrange multiplication operator and an alternating direction multiplier to obtain a first modal component and a second modal component, wherein the first modal component is obtained in the nth iteration, and the second modal component is obtained in the (n +1) th iteration;
and calculating a discrimination precision coefficient according to the difference value of the second modal component and the first modal component, and outputting the optimized modal component when the discrimination precision coefficient is smaller than a preset discrimination precision coefficient threshold value.
This application second aspect provides a distribution network trouble travelling wave signal processing apparatus, includes:
the traveling wave signal acquisition unit is used for acquiring a fault traveling wave signal to be processed;
the VMD decomposition processing unit is used for decomposing the fault traveling wave signal through a VMD decomposition processing algorithm to obtain a plurality of modal components;
the sample entropy calculation unit is used for calculating a sample entropy value of the modal component by a preset time series complexity measurement method;
the modal component filtering processing unit is used for filtering the modal component of which the sample entropy value is greater than the sample entropy threshold value according to the comparison result of the sample entropy value and the sample entropy threshold value;
and the fault sequence generation unit is used for generating a fault signal sequence based on the residual modal components after filtering processing so as to carry out fault positioning.
Preferably, the method further comprises the following steps:
and the signal decoupling processing unit is used for extracting the three-phase current of the power distribution network from the fault traveling wave signal, and converting the three-phase current in a Kerenbel conversion mode to obtain an alpha mode current component so as to carry out decomposition processing according to the alpha mode current component by a VMD decomposition processing algorithm.
Preferably, the method further comprises the following steps:
the instantaneous frequency mean value change curve construction unit is used for calculating the instantaneous frequency mean value of each modal component and constructing the instantaneous frequency mean value change curve of the modal component according to the decomposition layer number and the instantaneous frequency mean value corresponding to each modal component;
and the decomposition layer number determining unit is used for determining the critical decomposition layer number according to the curvature of each decomposition layer number in the instantaneous frequency mean value change curve and obtaining the modal component corresponding to the critical decomposition layer number according to the critical decomposition layer number.
Preferably, the method further comprises the following steps:
and the wave head calibration unit is used for calculating the maximum module value of the fault signal sequence in a wavelet transform processing mode based on the modal components contained in the fault signal sequence, and determining the fault traveling wave head of the fault signal sequence by comparing the attenuation characteristics of the maximum module values of the modal components with different decomposition layers.
Preferably, the method further comprises the following steps:
performing optimal solution solving on the modal component output by the VMD decomposition processing algorithm through a Lagrange multiplication operator and an alternating direction multiplier to obtain a first modal component and a second modal component, wherein the first modal component is obtained in the nth iteration, and the second modal component is obtained in the (n +1) th iteration;
and calculating a discrimination precision coefficient according to the difference value of the second modal component and the first modal component, and outputting the optimized modal component when the discrimination precision coefficient is smaller than a preset discrimination precision coefficient threshold value.
According to the technical scheme, the embodiment of the application has the following advantages:
the application provides a power distribution network fault traveling wave signal processing method, which comprises the following steps: acquiring a fault traveling wave signal to be processed; decomposing the fault traveling wave signal through a VMD decomposition processing algorithm to obtain a plurality of modal components; calculating a sample entropy value of the modal component by a preset time sequence complexity measurement method; filtering modal components of which the sample entropy values are larger than the sample entropy threshold value according to the comparison result of the sample entropy values and a preset sample entropy threshold value; and generating a fault signal sequence based on the residual modal components after filtering processing for fault positioning.
The power distribution network fault traveling wave signal processing method is based on modal components obtained by decomposing fault traveling wave signals through a VMD decomposition processing algorithm, then sample entropy values of the modal components are calculated respectively, the modal components with the higher sample entropy values are filtered out as non-stationary random noise, and the fault traveling wave signals are denoised, so that the fault traveling wave signals with higher precision are obtained, and the fault positioning precision is effectively improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic flowchart of an embodiment of a method for processing a traveling wave signal of a power distribution network fault according to the present application.
Fig. 2 is a schematic flowchart of a power distribution network fault traveling wave signal processing method according to a second embodiment of the present disclosure.
Fig. 3 is a schematic structural diagram of an embodiment of a power distribution network fault traveling wave signal processing apparatus provided in the present application.
Detailed Description
With the development of distribution network automation, a higher requirement is provided for the accuracy of fault location, and the applicant finds in the research on the conventional traveling wave method that some non-stationary random noise signals generally exist in fault traveling wave signals acquired by the conventional traveling wave method, and the noise signals interfere with the processing of the fault traveling wave signals, so that the improvement of the fault location accuracy is limited, and therefore, how to overcome the difficulties and improve the fault location accuracy becomes a technical problem that needs to be solved urgently by technical personnel in the field.
The embodiment of the application provides a power distribution network fault traveling wave signal processing method and device, which are used for achieving the purpose of improving fault positioning accuracy.
In order to make the objects, features and advantages of the present invention more apparent and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the embodiments described below are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, a method for processing a traveling wave signal of a power distribution network fault according to a first embodiment of the present application includes:
step 101, acquiring a fault traveling wave signal to be processed.
And 102, decomposing the fault traveling wave signal through a VMD decomposition processing algorithm to obtain a plurality of modal components.
It should be noted that, in this embodiment, based on the acquired fault traveling wave signal, the fault traveling wave signal is decomposed by the VMD decomposition processing algorithm to obtain a plurality of modal components, and it can be understood that the number of the modal components in this embodiment corresponds to the decomposition layer number parameter in the VMD decomposition processing algorithm.
VMD is a variation modal decomposition, which is implemented by decomposing a non-recursive real-valued function f (t) into k modal components μ with rich fault signalskThe VMD decomposition layer number is k, and the constraint model is shown as the following formula (3):
Figure BDA0003312402390000051
where min represents the minimum value, μkRepresenting the k-th modal component of the decomposition, ωkRepresents the center frequency of all modal components, Σ represents the sum sign, k represents the number of decomposition layers,
Figure BDA0003312402390000052
denotes partial derivative, δ, of ttIndicating the pulse function and j the imaginary unit.
And 103, calculating a sample entropy value of the modal component by a preset time sequence complexity measurement method.
The sample entropy is a time series complexity measurement method improved based on approximate entropy, and a range related to power system fault detection is introduced through the sample entropy, so that effective characteristic numbers are provided for detection and diagnosis of power system faults.
Assuming that the length of the original data is N time series, the specific steps are expressed as follows by { u (i):1 ≦ i ≦ N ]:
using the modal component data to construct a set of vectors for the m-dimensional space as: x (i) { x (i), x (i +1),.. x (i + m-1) }, wherein: i: 1 < i < N-m +1
Defining the distance between the vector sums as the largest difference of the corresponding elements of the two vectors, namely:
d[Xm(i),Xm(j)]＝maxk＝0,…,m-1(|x(i+k)-x(j+k)|)
for each { i: for 1. ltoreq. j. ltoreq.N-m, j. noteq.i } let the given allowable deviation be r, and count d [ X ≦ X ≠ i ]m(i),Xm(j)]The number of < r is denoted as Bi. And from this the ratio of this number to the total number of examples is calculated as:
Figure BDA0003312402390000061
meter B(m)(r) is:
Figure BDA0003312402390000062
add dimension to m +1, calculate Xm+1(i) And Xm+1(j) (j is more than or equal to 1 and less than or equal to N-m, j is not equal to i) the number of distances less than or equal to r is marked as AiObtaining:
Figure BDA0003312402390000063
Figure BDA0003312402390000064
since the actual process N cannot be ∞, under the condition that N is a finite value, the estimation is obtained:
Figure BDA0003312402390000065
the sample entropy method has reasonable statistical characteristics.
And 104, filtering modal components of which the sample entropy values are larger than the sample entropy threshold value according to the comparison result of the sample entropy values and the sample entropy threshold value.
It should be noted that, based on the modal components obtained in step 102, the sample entropy values of the modal components are respectively calculated by a preset time sequence complexity measurement method, and then the sample entropy values of the modal components are compared with a preset sample entropy threshold, so as to obtain a comparison result between the sample entropy values and the sample entropy threshold, and according to the comparison result, the modal component with a higher sample entropy value is regarded as non-stationary random noise to be filtered, so as to implement denoising of the fault traveling wave signal.
And 105, generating a fault signal sequence based on the residual modal components after filtering processing for fault positioning.
It should be noted that the signal is reconstructed into the fault traveling wave signal based on the modal component remaining after the filtering processing in step 104, so that the fault traveling wave signal with higher precision is obtained by the method of this embodiment to perform fault location analysis, and the fault location precision is improved.
The above content is a detailed description of an embodiment of the power distribution network fault traveling wave signal processing method provided by the present application, and the following content is a detailed description of another embodiment of the power distribution network fault traveling wave signal processing method provided by the present application.
Referring to fig. 2, based on the first embodiment, the method for processing traveling wave signals of power distribution network fault according to this embodiment includes:
further, after acquiring the fault traveling wave signal to be processed, the method further includes:
step 1001, extracting three-phase current of the power distribution network from the fault traveling wave signal, and performing conversion processing on the three-phase current in a Kerenbel transformation mode to obtain an alpha mode current component so as to perform decomposition processing through a VMD decomposition processing algorithm according to the alpha mode current component.
It should be noted that, when three-phase current information in the power transmission line is not completely independent and a coupling relationship exists between the three-phase current information, we should decouple the three-phase current information, vector information with the coupling relationship can be converted into independent modulus information without coupling through decoupling transformation, kelenbell transformation is to select a kelenbell transformation matrix as a conversion condition in the process of phase-mode transformation, and perform kelenbell transformation on three-phase current to decouple the three-phase current information to obtain three independent modulus components of α, β and 0 modulus, wherein the conversion process is shown in formula (1):
Figure BDA0003312402390000071
in the formula Ia、Ib、IcRespectively, phase current information of each phase in the line, Iα、IβAnd I0The three-phase current is subjected to Kerenbel conversion to obtain alpha mode beta mode and 0 mode component.
Further, in step 102, decomposing the fault traveling wave signal by using a VMD decomposition processing algorithm to obtain a plurality of modal components, and then:
step 1002, calculating an instantaneous frequency mean value of each modal component, and constructing an instantaneous frequency mean value change curve of the modal component according to the number of decomposition layers corresponding to each modal component and the instantaneous frequency mean value;
and 1003, determining the number of critical decomposition layers according to the curvature of each decomposition layer in the instantaneous frequency mean value change curve, and obtaining modal components corresponding to the number of critical decomposition layers according to the number of critical decomposition layers.
The VMD decomposes the acquired original input signal from a low frequency region to a high frequency region. If the signal is excessively decomposed, the modal component after the signal decomposition has internal discontinuity, especially in the high frequency part. This causes the overall average instantaneous frequency of the signal to be reduced even in the high frequency portion, and therefore, whether the over-resolution phenomenon occurs is determined by the instantaneous frequency average of the signal. The method specifically comprises the following steps: judging whether the instantaneous frequency of one component reaches a critical condition by calculating the instantaneous frequency of the component, wherein the critical value is the maximum value of the decomposition layer number k, and selecting a proper decomposition layer number k by using an instantaneous frequency judgment method as shown in the following formula (2):
Figure BDA0003312402390000081
in the formula (f)iRepresenting the instantaneous frequency value to be calculated, N representing the number of instantaneous frequencies of a certain component, j representing the jth of all sampling points, fijRepresents the instantaneous frequency value of the jth sample point,
Figure BDA0003312402390000082
indicating the sign of the summation.
Then, drawing a relation graph between the decomposition layer number and the instantaneous frequency mean value of the decomposed sub-signals IMF, calculating the instantaneous frequency mean value curvature at each decomposition layer number, and when the curvature of the instantaneous frequency mean value suddenly drops, considering that an over-decomposition phenomenon just occurs at the moment, wherein the decomposition number before the curvature drops is the proper number of the decomposition. The curvature quantitative analysis is carried out on the average instantaneous frequency to find out a critical k value, so that an accurate decomposition layer number is found, k modal components are extracted from the modal components obtained in the step 102 according to the critical decomposition layer number k value, or VMD decomposition processing is carried out again to obtain the k modal components, the situation that the VMD decomposition algorithm is over-decomposed or under-decomposed is avoided, and the accuracy of the signal is further improved.
Further, decomposing the fault traveling wave signal through a VMD decomposition processing algorithm, and after obtaining a plurality of modal components, further comprising:
and 1004, performing optimal solution solving on the modal component output by the VMD decomposition processing algorithm through a Lagrange multiplication operator and an alternate direction multiplier to obtain a first modal component and a second modal component.
The first modal component is a modal component obtained in the nth iteration, and the second modal component is a modal component obtained in the (n +1) th iteration.
Step 1005, calculating a discrimination precision coefficient according to the difference value between the second modal component and the first modal component, and outputting the optimized modal component when the discrimination precision coefficient is smaller than a preset discrimination precision coefficient threshold value.
It should be noted that, by introducing lagrange multiplier λ to solve the formula (3) mentioned in the first embodiment, the optimal solution is shown as the following formula:
Figure BDA0003312402390000091
wherein L represents the optimum value, μkRepresenting the k-th modal component of the decomposition, ωkDenotes the center frequency of all modal components, λ denotes the lagrangian multiplier, λ (t) denotes the function of the lagrangian multiplier λ as a function of t, α denotes a penalty factor, k denotes the number of decomposition layers,
Figure BDA0003312402390000092
denotes partial derivative, δ, of ttRepresenting the pulse function, f (t) representing the signal to be decomposed.
The problem is analyzed by using alternating direction multipliers (ADMMs), each mukThe optimal solution and the updated center frequency are shown in equations (5) and (6):
Figure BDA0003312402390000093
Figure BDA0003312402390000094
wherein
Figure BDA0003312402390000095
Wiener filtering representing the k-th component, wherek(t) is the real part of the wiener filter, which can be found by inverse Fourier transform, the symbol ^ is the value converted to the frequency domain by the solution found by Parseval/Plancherel Fourier equidistant transform,
Figure BDA0003312402390000096
the center frequency value of the corresponding component; ω represents the angular frequency of the wave,
Figure BDA0003312402390000097
the signal to be decomposed in the frequency domain, k represents the number of decompositions, i represents the ith number of decompositions, muk(ω) represents the modal component in the frequency domain,
Figure BDA0003312402390000098
representing the Lagrange multiplicative factor in the frequency domain, a representing the penalty factor, ωkRepresenting the central angular frequency.
In the steps, the specific steps of the VMD can be shown by referring to the following examples:
firstly, setting an initial value: will be provided with
Figure BDA0003312402390000099
Setting the initial value of n as 0, n being n +1, and k being the number of layers of VMD decomposition;
1) wherein the content of the first and second substances,
Figure BDA00033124023900000910
represents the initial values of the modal components in the frequency domain,
Figure BDA00033124023900000911
an initial value of the center frequency in the frequency domain is represented,
Figure BDA00033124023900000912
denotes a multiplier in the frequency domain and n denotes the number of iterations.
2) Updating parameters: updating mu using equations (5) and (6)k,ωk；
3) Using the pair of formula (7)
Figure BDA00033124023900000913
Updating:
Figure BDA00033124023900000914
in the formula (I), the compound is shown in the specification,
Figure BDA00033124023900000915
representing the lagrangian multiplier in the frequency domain, n representing the number of iterations, τ representing the update parameter, the symbol ^ being the value transformed into the frequency domain by the solution solved with the Parseval/Plancherel fourier equidistant transform,
Figure BDA0003312402390000101
representing the frequency domain lagrangian multiplication factor for the nth iteration, tau is a time constant,
Figure BDA0003312402390000102
represents the signal to be decomposed in the frequency domain, Σ represents the successive addition, k represents the number of decomposition layers,
Figure BDA0003312402390000103
representing the modal components in the frequency domain of the (n +1) th iteration.
4) Iteration to convergence, for the set discrimination accuracy ∈>0, when equation (8) is satisfied, the iteration process ends, for μkK modal components mu can be obtained by carrying out inverse Fourier transformk(t), outputting the result, otherwise, returning to the step 2 and the step 3, and continuing to iterate until the condition (8) is met.
Figure BDA0003312402390000104
In the formula, k represents the number of decomposition layers,
Figure BDA0003312402390000105
representing the modal component at the (n +1) th iteration,
Figure BDA0003312402390000106
and representing the modal component in the nth iteration, wherein epsilon represents a discrimination precision threshold value.
Further, based on the modal components remaining after the filtering process, generating the fault signal sequence further includes:
step 1006, calculating a maximum modulus value of the fault signal sequence through a wavelet transform processing mode based on the modal component included in the fault signal sequence, and determining a fault traveling wave head of the fault signal sequence by comparing attenuation characteristics of the maximum modulus values of the modal components of different decomposition layers.
The obtained signal is decomposed into a plurality of modal components, the arrangement entropy value of each modal component is calculated, the modal component representing non-stationary random noise is reconstructed after the threshold value is set and judged, and the signal denoising can be realized.
And setting a modal component reconstruction fault signal by using the extracted load sample entropy to achieve the purpose of denoising, screening and reconstructing residual IMF finally to obtain a denoised data observation sequence, calculating a maximum modulus value by a wavelet transform method, and calibrating a wave head.
In addition, because the waveform upstream of the single-phase earth fault generates larger amplitude and lower frequency, the waveform downstream is just opposite to calculate the sample entropy values of each monitoring point upstream and downstream of the fault point, and after the sample entropy values are obtained, the section of the fault point can be judged according to the maximum absolute value of the difference between the sample entropies of two adjacent points.
The foregoing is a detailed description of a second embodiment of the power distribution network fault traveling wave signal processing method provided by the present application, and the following is a description of an embodiment of the power distribution network fault traveling wave signal processing apparatus provided by the present application.
Referring to fig. 3, a third embodiment of the present application provides a power distribution network fault traveling wave signal processing apparatus, including:
a traveling wave signal obtaining unit 201, configured to obtain a fault traveling wave signal to be processed;
the VMD decomposition processing unit 202 is configured to decompose the fault traveling wave signal through a VMD decomposition processing algorithm to obtain a plurality of modal components;
a sample entropy calculation unit 203, configured to calculate a sample entropy value of the modal component by using a preset time series complexity measurement method;
a modal component filtering processing unit 204, configured to filter a modal component whose sample entropy is greater than the sample entropy threshold according to a comparison result between the sample entropy and the sample entropy threshold;
and a fault sequence generating unit 205, configured to generate a fault signal sequence based on the modal components remaining after the filtering processing, so as to perform fault location.
Further, still include:
and the signal decoupling processing unit 206 is configured to extract three-phase currents of the power distribution network from the fault traveling wave signal, and perform conversion processing on the three-phase currents in a kelvin conversion manner to obtain α -mode current components, so as to perform decomposition processing according to the α -mode current components by using a VMD decomposition processing algorithm.
Further, still include:
an instantaneous frequency mean value change curve construction unit 207, configured to calculate an instantaneous frequency mean value of each modal component, and construct an instantaneous frequency mean value change curve of the modal component according to the number of decomposition layers corresponding to each modal component and the instantaneous frequency mean value;
the decomposition layer number determining unit 208 is configured to determine a critical decomposition layer number according to the curvature of each decomposition layer number in the instantaneous frequency mean change curve, and obtain a modal component corresponding to the critical decomposition layer number according to the critical decomposition layer number.
Further, still include:
and the wave head calibration unit 210 is configured to calculate a maximum modulus of the fault signal sequence in a wavelet transform processing manner based on the modal components included in the fault signal sequence, and determine the fault traveling wave head of the fault signal sequence by comparing attenuation characteristics of the maximum modulus of the modal components of different decomposition layers.
Further, still include:
and the modal component optimization unit 209 is configured to perform optimal solution solving on the modal component output by the VMD decomposition processing algorithm through a lagrange multiplier and an alternating direction multiplier to obtain a first modal component and a second modal component, where the first modal component is a modal component obtained in the nth iteration, the second modal component is a modal component obtained in the (n +1) th iteration, and then calculate a discrimination accuracy coefficient according to a difference between the second modal component and the first modal component, and when the discrimination accuracy coefficient is smaller than a preset discrimination accuracy coefficient threshold, output the optimized modal component.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the terminal, the apparatus and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed terminal, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims (10)

1. A power distribution network fault traveling wave signal processing method is characterized by comprising the following steps:
acquiring a fault traveling wave signal to be processed;
decomposing the fault traveling wave signal through a VMD decomposition processing algorithm to obtain a plurality of modal components;
calculating a sample entropy value of the modal component by a preset time series complexity measurement method;
filtering the modal component of which the sample entropy value is larger than the sample entropy threshold value according to the comparison result of the sample entropy value and the sample entropy threshold value;
and generating a fault signal sequence based on the residual modal components after filtering processing for fault positioning.
2. The method for processing the traveling wave signal of the power distribution network fault according to claim 1, wherein after acquiring the traveling wave signal of the fault to be processed, the method further comprises:
and extracting three-phase current of the power distribution network from the fault traveling wave signal, and converting the three-phase current in a Kerenbel conversion mode to obtain alpha mode current components so as to carry out decomposition processing through a VMD decomposition processing algorithm according to the alpha mode current components.
3. The method for processing the traveling wave signal of the power distribution network fault according to claim 1, wherein the decomposing the traveling wave signal of the fault by using a VMD decomposition processing algorithm further includes:
calculating the instantaneous frequency mean value of each modal component, and constructing an instantaneous frequency mean value change curve of each modal component according to the number of decomposition layers corresponding to each modal component and the instantaneous frequency mean value;
determining the number of critical decomposition layers according to the curvature of each decomposition layer in the instantaneous frequency mean value change curve, and obtaining modal components corresponding to the number of critical decomposition layers according to the number of critical decomposition layers.
4. The method for processing the traveling wave signal of the power distribution network fault according to claim 1, wherein after the generating of the fault signal sequence based on the modal components remaining after the filtering, the method further comprises:
calculating the maximum module value of the fault signal sequence through a wavelet transform processing mode based on the modal component contained in the fault signal sequence, and determining the fault traveling wave head of the fault signal sequence by comparing the attenuation characteristics of the maximum module values of the modal components of different decomposition layers.
5. The method for processing the traveling wave signal of the power distribution network fault according to claim 1, wherein the decomposing the traveling wave signal of the fault by using a VMD decomposition processing algorithm further includes:
performing optimal solution solving on the modal component output by the VMD decomposition processing algorithm through a Lagrange multiplication operator and an alternating direction multiplier to obtain a first modal component and a second modal component, wherein the first modal component is obtained in the nth iteration, and the second modal component is obtained in the (n +1) th iteration;
and calculating a discrimination precision coefficient according to the difference value of the second modal component and the first modal component, and outputting the optimized modal component when the discrimination precision coefficient is smaller than a preset discrimination precision coefficient threshold value.
6. A distribution network fault traveling wave signal processing device is characterized by comprising:
the traveling wave signal acquisition unit is used for acquiring a fault traveling wave signal to be processed;
the VMD decomposition processing unit is used for decomposing the fault traveling wave signal through a VMD decomposition processing algorithm to obtain a plurality of modal components;
the sample entropy calculation unit is used for calculating a sample entropy value of the modal component by a preset time series complexity measurement method;
the modal component filtering processing unit is used for filtering the modal component of which the sample entropy value is greater than the sample entropy threshold value according to the comparison result of the sample entropy value and the sample entropy threshold value;
and the fault sequence generation unit is used for generating a fault signal sequence based on the residual modal components after filtering processing so as to carry out fault positioning.
7. The traveling wave signal processing device for power distribution network faults as claimed in claim 6, further comprising:
and the signal decoupling processing unit is used for extracting the three-phase current of the power distribution network from the fault traveling wave signal, and converting the three-phase current in a Kerenbel conversion mode to obtain an alpha mode current component so as to carry out decomposition processing according to the alpha mode current component by a VMD decomposition processing algorithm.
8. The traveling wave signal processing device for power distribution network faults as claimed in claim 6, further comprising:
the instantaneous frequency mean value change curve construction unit is used for calculating the instantaneous frequency mean value of each modal component and constructing the instantaneous frequency mean value change curve of the modal component according to the decomposition layer number and the instantaneous frequency mean value corresponding to each modal component;
and the decomposition layer number determining unit is used for determining the critical decomposition layer number according to the curvature of each decomposition layer number in the instantaneous frequency mean value change curve and obtaining the modal component corresponding to the critical decomposition layer number according to the critical decomposition layer number.
9. The traveling wave signal processing device for power distribution network faults as claimed in claim 6, further comprising:
and the wave head calibration unit is used for calculating the maximum module value of the fault signal sequence in a wavelet transform processing mode based on the modal components contained in the fault signal sequence, and determining the fault traveling wave head of the fault signal sequence by comparing the attenuation characteristics of the maximum module values of the modal components with different decomposition layers.
10. The traveling wave signal processing device for power distribution network faults as claimed in claim 6, further comprising:
and the modal component optimization unit is used for performing optimal solution solving on the modal component output by the VMD decomposition processing algorithm through a Lagrange multiplier and an alternating direction multiplier to obtain a first modal component and a second modal component, wherein the first modal component is the modal component obtained in the nth iteration, the second modal component is the modal component obtained in the (n +1) th iteration, a discrimination precision coefficient is calculated according to the difference value between the second modal component and the first modal component, and when the discrimination precision coefficient is smaller than a preset discrimination precision coefficient threshold value, the optimized modal component is output.
CN202111220486.6A 2021-10-20 2021-10-20 Method and device for processing fault traveling wave signals of power distribution network Pending CN113960412A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111220486.6A CN113960412A (en) 2021-10-20 2021-10-20 Method and device for processing fault traveling wave signals of power distribution network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111220486.6A CN113960412A (en) 2021-10-20 2021-10-20 Method and device for processing fault traveling wave signals of power distribution network

Publications (1)

Publication Number Publication Date
CN113960412A true CN113960412A (en) 2022-01-21

Family

ID=79465599

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111220486.6A Pending CN113960412A (en) 2021-10-20 2021-10-20 Method and device for processing fault traveling wave signals of power distribution network

Country Status (1)

Country Link
CN (1) CN113960412A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117196123A (en) * 2023-11-06 2023-12-08 深圳市粤能电气有限公司 Data control method, device and equipment for digital production of power distribution equipment
CN117290788A (en) * 2023-11-27 2023-12-26 南昌航空大学 Power distribution network fault identification method and system based on improved wavelet transformation algorithm

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117196123A (en) * 2023-11-06 2023-12-08 深圳市粤能电气有限公司 Data control method, device and equipment for digital production of power distribution equipment
CN117196123B (en) * 2023-11-06 2024-03-19 深圳市粤能电气有限公司 Data control method, device and equipment for digital production of power distribution equipment
CN117290788A (en) * 2023-11-27 2023-12-26 南昌航空大学 Power distribution network fault identification method and system based on improved wavelet transformation algorithm
CN117290788B (en) * 2023-11-27 2024-02-02 南昌航空大学 Power distribution network fault identification method and system based on improved wavelet transformation algorithm

Similar Documents

Publication Publication Date Title
Bayer et al. An iterative wavelet threshold for signal denoising
Swami et al. Linear modeling of multidimensional non-Gaussian processes using cumulants
CN113960412A (en) Method and device for processing fault traveling wave signals of power distribution network
Upendar et al. Statistical decision-tree based fault classification scheme for protection of power transmission lines
Song et al. Weighted kurtosis-based VMD and improved frequency-weighted energy operator low-speed bearing-fault diagnosis
Lima et al. A novel method for power quality multiple disturbance decomposition based on Independent Component Analysis
CN116049629B (en) Voltage transformer error state prediction method, system, equipment and medium
CN114879085B (en) Single-phase earth fault identification method and device, electronic equipment and medium
CN114781430A (en) Partial discharge signal denoising method
CN113687257A (en) Power supply health state dynamic evaluation method and device
Fu et al. An improved VMD-based denoising method for time domain load signal combining wavelet with singular spectrum analysis
CN110311392B (en) Electric power system oscillation mode and mode identification method based on SPDMD
Chen et al. A signal-enhancement algorithm for the quantification of NMR data in the time domain
Dan et al. An automatic real-time cable modal frequency identification and tracking algorithm by combining recursive band-pass filter and recursive Hilbert transform
Beylkin et al. Nonlinear inversion of a band-limited Fourier transform
CN117290679A (en) Running state detection method and device of current transformer and electronic equipment
Casciola et al. The regularizing properties of anisotropic radial basis functions
CN115169418A (en) Switch cabinet partial discharge identification method and system based on improved wavelet threshold method
CN112817056B (en) Magnetotelluric signal denoising method and system
CN111611870B (en) Fault feature identification method, system, device and medium for cold tandem mill
CN114186590A (en) Power distribution network single-phase earth fault identification method based on wavelet and deep learning
CN110244144B (en) Lightning arrester state monitoring method and alternating current data acquisition method
CN114910742B (en) Single-phase fault grounding monitoring method and monitoring system, electronic equipment and storage medium
Khorasani et al. Decreasing Cramer–Rao lower bound by preprocessing steps
Farhoodnea et al. Identification of multiple harmonic sources in power systems using independent component analysis and mutual information

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination