CN110542831A - Fault traveling wave detection method based on variational modal decomposition and S transformation - Google Patents
Fault traveling wave detection method based on variational modal decomposition and S transformation Download PDFInfo
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- G—PHYSICS
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- 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
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- 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
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Abstract
the invention discloses a fault traveling wave detection method based on VMD-S conversion, which comprises the following steps: s1: acquiring a fault traveling wave, intercepting a traveling wave waveform of a certain time window, performing Kernel phase-mode conversion, and extracting a line-mode alpha component of the fault traveling wave; s2: applying Variational Modal Decomposition (VMD) to the line mode alpha component to obtain K inherent modal components (IMF), and then extracting the traveling wave waveform of the IMF1 component; s3: performing S transformation on the IMF1 component to obtain a time-frequency distribution matrix of the waveform; s4: taking a modulus value of the matrix element to obtain an S-mode matrix; s5: and (5) extracting the first instantaneous frequency in the S-mode transformation matrix obtained in the step (S4), and calibrating a first amplitude catastrophe point of the waveform as an initial traveling wave head. The invention has the advantages of simple principle, strong anti-interference capability, accurate calibration of the traveling wave head and the like.
Description
Technical Field
The invention mainly relates to the field of traveling wave detection, in particular to a fault traveling wave detection method based on variational modal decomposition and S transformation.
background
The distribution network is located the end of electric wire netting, directly links to each other with the user, because distribution lines branch is many, and the voltage grade is low, and the operational environment is complicated, how accurate discernment fault location shortens the power failure time, becomes the important factor that influences distribution network power supply reliability. The traveling wave positioning method is widely concerned by domestic and foreign scholars according to various characteristics of fault tolerance type, transition resistance and system operation mode influence.
the detection precision of a fault traveling wave head influences the accuracy of fault traveling wave positioning, the conventional wavelet analysis has a good effect on the detection of non-singular signals under a certain noise condition, but the decomposition result is related to the decomposition scale and the selection of a wavelet base, so that different decomposition effects are easily generated. The core Empirical Mode Decomposition (EMD) of the HHT transformation can generate aliasing phenomenon and end effect in the signal decomposition process, interfere the extraction of signal mutation quantity and cause large ranging error. Although the Ensemble Empirical Mode Decomposition (EEMD) and global local mean decomposition (ELMD) algorithms improve the EMD algorithm, aliasing between decomposed signals is reduced to a certain extent, but they still only have a suppressing effect, and cannot completely eliminate the influence of the decomposition method itself. The S transformation has good time-frequency resolution capability, can extract singular points of the frequency of the fault traveling wave signal, but for the fault traveling wave signal under the strong noise interference of the power distribution network, the capability of distinguishing the fault traveling wave signal by the S transformation is reduced, the singular points of the fault traveling wave signal are difficult to be effectively distinguished, and the arrival time of the fault traveling wave head is difficult to be accurately detected.
Disclosure of Invention
the technical problem to be solved by the invention is as follows: aiming at the technical problems in the prior art, the invention provides the fault traveling wave detection method based on variational modal decomposition and S transformation, which has the advantages of simple principle, strong anti-interference capability and accurate wave head calibration.
In order to solve the technical problems, the invention adopts the following technical scheme:
a fault traveling wave detection method based on variational modal decomposition and S transformation comprises the following steps:
S1: acquiring a fault traveling wave, intercepting a traveling wave waveform of a certain time window, performing Kernel phase-mode conversion, and extracting a line-mode alpha component of the fault traveling wave;
s2: applying Variational Modal Decomposition (VMD) to the line mode alpha component to obtain K inherent modal components (IMF), and then extracting the traveling wave waveform of the IMF1 component;
S3: performing S transformation on the IMF1 component to obtain a time-frequency distribution matrix of the waveform;
s4: taking a modulus value of the matrix element to obtain an S-mode matrix;
S5: and extracting the traveling wave waveform under the first instantaneous frequency component in the S-mode matrix, and calibrating a first amplitude catastrophe point of the waveform as an initial traveling wave head.
In the step S3, the IMF1 time-domain modal component u1(t) which is obtained in the step 2 and can reflect the original signal transformation trend most is extracted to perform S transformation.
In the step S5, the first instantaneous frequency in the S-mode transformation matrix obtained in the step S4 is extracted, and the first amplitude discontinuity point of the frequency is calibrated as the initial traveling wave head arrival time.
Compared with the prior art, the invention has the advantages that:
The invention relates to a fault traveling wave detection method based on variational modal decomposition and S-transformation, which aims at the defects of the traditional traveling wave detection method, decomposes intrinsic modal components IMF1 which can reflect the trend characteristics of traveling wave signals most on the basis of applying variational modal decomposition to process fault traveling wave line-mode components by virtue of good time-frequency localization characteristics of S-transformation, realizes effective separation of noise and signals, obtains frequency components from high to low, selects an amplitude time curve of a first instantaneous frequency component, further reduces the interference of the noise on the traveling wave signals, further accurately calibrates the time of a first amplitude catastrophe point in the frequency components as the arrival time of an initial traveling wave, and reduces the calibration error of the arrival time of the initial wave head caused by the dispersion characteristics of traveling wave transmission. The method can realize accurate identification and calibration of the initial traveling wave head under strong noise interference, and has better noise robustness.
drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
FIG. 2 is a schematic diagram of the experimental principle of the present invention in a specific application example.
Fig. 3 is a graph of EMD detection results after noise addition.
Fig. 4 is a graph of HHT detection results after noise addition.
Fig. 5 is a diagram of the detection result of S-transform after noise addition.
Fig. 6 is a graph of VMD decomposition results after noise addition.
fig. 7 is a diagram of VMD-S transform detection results after noise addition.
Detailed Description
The invention will be described in further detail below with reference to the drawings and specific examples.
As shown in fig. 1, the method for detecting fault traveling wave based on variational modal decomposition and S-transformation of the present invention includes the following steps:
S1: acquiring a fault traveling wave, intercepting a traveling wave waveform of a certain time window, performing Kernel phase-mode conversion, and extracting a line-mode alpha component of the fault traveling wave;
S2: applying Variational Modal Decomposition (VMD) to the line mode alpha component to obtain K inherent modal components (IMF), and then extracting the traveling wave waveform of the IMF1 component;
s3: performing S transformation on the IMF1 component to obtain a time-frequency distribution matrix of the waveform;
s4: taking a modulus value of the matrix element to obtain an S-mode matrix;
S5: and extracting the traveling wave waveform under the first instantaneous frequency component in the S-mode matrix, and calibrating a first amplitude catastrophe point of the waveform as an initial traveling wave head.
As a preferred embodiment, in step S1 of the present example, the preset time window of the fault traveling waveform is 1 ms.
As a preferred embodiment, in step S1 of this example, the three-phase voltage traveling wave signal is converted by using kelnbell phase-mode conversion, where the process of the kelnbell phase-mode conversion is as follows: wherein u alpha and u beta are line mode voltages, u0 is a zero mode voltage, and ua, ub and uc are phase voltages.
As a preferred embodiment, the specific steps in step S2 of this example are:
S201: assuming that an original signal u alpha (t) is divided into K modal components with limited bandwidth { uk (t) }, carrying out Fourier transform on ua (t), and moving a frequency spectrum to the center of the frequency spectrum to obtain a frequency spectrum signal
s202: initializing iteration times n of Lagrange multipliers corresponding to the central frequencies of K modal components, and setting initial values to be 0, wherein the initial values are expressed as initial iteration values of the kth modal component, and omega 1K is expressed as the initial iteration value of the central frequency of the kth modal component and is the initial iteration value of the Lagrange multiplier;
s203: with the increase of each iteration number n, updating each modal component by using an alternating direction multiplier method:
n←n+1
wherein c is a secondary penalty factor representing a bandwidth parameter; is a lagrange multiplier; i is more than or equal to 1 and less than or equal to K, i is not equal to K, when i is less than K, and when i is greater than K, the update value obtained after the kth modal component is subjected to n iterations is represented and used for the update calculation of the (n + 1) th iteration process, the update value obtained after the kth modal component is subjected to (n-1) th iteration is represented and used for the update calculation of the nth iteration process;
Updating the center frequency { omega n +1k } corresponding to each modal component, wherein the updating method comprises the following steps:
Representing an updated value obtained after n iterations of the center frequency corresponding to the kth modal component, and using the updated value for updating and calculating the (n + 1) th iteration process;
Updating Lagrange multipliers and the updating method thereof:
In the formula, gamma represents a noise tolerance function, represents an updated value of a Lagrange multiplier obtained after n iterations, and can be used for updating calculation in the (n + 1) th iteration process;
s204: when the iteration result meets the following conditions:
stopping iteration, otherwise, returning to the step S203 to continue iteration, wherein epsilon is the given precision of the discrimination condition; in the formula, K is more than or equal to 1 and less than or equal to K;
s205: and performing inverse Fourier transform on the obtained K frequency domain modal components, and obtaining a time domain modal component { uk (t) } by a real part.
as a preferred embodiment, in step S3 of this example, the time-domain modal component u1(T) of IMF1 obtained in step 2 is extracted, the time-domain modal component u1(T) is sampled, and the discrete form u1(mT) of the obtained signal is represented in a sequence form, (m ═ 0,1,2, …, N-1), where T is a sampling interval and N is a number of sampling points. Obtaining a time-frequency distribution matrix under the discrete time-domain modal component, and the S transformation of the IMF1 time-domain modal component u1(mT) can be expressed as:
in the formula: l is a time parameter, l is 0,1,2 …, N-1; n is a frequency parameter, and N is 1,2 … and N/2. Is expressed as
the discrete signal u1(mT) can be S-transformed by the above formula to obtain a two-dimensional S matrix related to S transformation, and the matrix is shown as follows
In the formula, S (i, j) is represented as a sampling point j corresponding to a frequency i in the signal, that is, it represents that row elements in the matrix correspond to the frequency of signal sampling, and column elements in the matrix correspond to time points of signal sampling. The frequency difference between two adjacent rows is that the number of sampling points in the q-th row is N if the frequency of the q-th row is assumed to be N, and the actual sampling frequency is fs.
As a preferred embodiment, in step S4 of this example, each complex element in the S transformation matrix obtained in step 3 is subjected to modular value calculation to obtain an S-mode transformation matrix, and the S-mode transformation matrix is expressed as
as a preferred embodiment, in step S5 of this example, the first instantaneous frequency in the S-mode transformation matrix obtained in step S4 is extracted, and the first amplitude discontinuity of the frequency is calibrated to the initial traveling wave head arrival time.
as shown in fig. 2, in a specific application example, four feeder lines L1, L2, L3 and L4 are provided on a bus, L1 is a 20km overhead line, L2 is a 10km cable line, L3 is a hybrid line and consists of a 10km overhead line and a 5km cable line, and L4 is a 30km overhead line, and a traveling wave detection device is installed at the outlet of each feeder line. The experiment was carried out according to the 10.5kV distribution system shown in fig. 1, and the feeder parameters are shown in table 1.
TABLE 1
an A-phase grounding fault is arranged on a position, 10km away from a line bus, on an outgoing line L1, the fault transition resistance is 800 omega, the fault occurs at the moment of 0.2ms, the fault traveling wave signals with the time length of 1ms before and after the fault occurs are extracted and analyzed, and the sampling rate is set to be 10 MHz. Considering that the actual operating environment of the distribution line is poor, the signal is often interfered by various noises, so white noise with a signal-to-noise ratio of 40dB is added to the original traveling wave signal for interference, phase-mode conversion is performed on the mutually coupled fault signals by adopting a kelnbur conversion matrix, the fault signals are analyzed into three zero-mode components u0(t), a line-mode 1 component u α (t) and a line-mode 2 component u β (t) which are not affected by each other, and then HHT, S conversion and VMD-S conversion are respectively applied to perform wave head extraction on the fault traveling wave line 1 component u α (t), and the results are respectively shown in fig. 3 to fig. 7, wherein the VMD algorithm parameter is set to K4, τ is 2, and α is 4000. Only the fault traveling wave signal detection graph with 40dB white noise added is listed, limited to space.
As shown in fig. 4, the original fault traveling wave signal detected by HHT is completely submerged in the noise signal, and the calibration of the fault traveling wave head and the detection of the arrival time of the traveling wave at each end cannot be performed.
As shown in fig. 5, the fault traveling wave signal after being interfered by the noise can detect a signal singular point after S conversion, but because the interference of the noise signal is large, the S conversion cannot effectively reduce the interference degree of the noise signal to the original signal, and compared with the original signal, the initial traveling wave head under the S conversion is not easily calibrated, is easily submerged by the noise, and cannot be regarded as reliable data of the arrival time of the wave head.
As can be seen from fig. 6, the fault traveling wave signal after being interfered by the noise can be effectively denoised under the VMD decomposition, and compared with the original fault signal, the mode 1 component IMF1 can reflect the change trend of the original fault signal most, so that the mode 1 component IMF1 is subjected to S transformation to obtain a time-amplitude curve, such as fig. 7, and the arrival time of the fault initial traveling wave can be determined according to the abrupt change point of the first instantaneous frequency. Compared with S transformation, VMD-S transformation has better noise robustness, more obvious singular points and better detection effect.
Under the condition of strong noise interference, the original traveling wave signal can generate a plurality of burrs. Actual data can be submerged in noise, the actual situation of signals cannot be reflected, the extraction of a traveling wave head cannot be carried out, and the traveling wave detection effect is influenced. The existing traveling wave detection methods such as HHT and S conversion have respective defects. Compared with HHT and S transformation, the method is easier to distinguish the first frequency mutation point of the fault traveling wave signal under noise interference, and has better noise robustness.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (3)
1. A fault traveling wave detection method based on variational modal decomposition and S transformation comprises the following steps:
s1: acquiring a fault voltage traveling wave, intercepting a voltage traveling wave waveform of a preset time window, performing Kernel phase-mode conversion, and extracting a line-mode alpha component of the voltage traveling wave;
S2: : applying Variational Modal Decomposition (VMD) to the line mode alpha component to obtain K inherent modal components (IMF), and then extracting the traveling wave waveform of the IMF1 component;
S3: performing S transformation on the IMF1 component to obtain a time-frequency distribution matrix of the waveform;
S4: taking a modulus value of the matrix element to obtain an S-mode matrix;
S5: and extracting the traveling wave waveform under the first instantaneous frequency component in the S-mode matrix, and calibrating a first amplitude catastrophe point of the waveform as an initial traveling wave head.
2. The method for detecting traveling wave fault based on variational modal decomposition and S-transform of claim 1, wherein in step S3, IMF1 time-domain modal component u1(t) which is obtained in step 2 and can reflect most of the original signal transformation trend is extracted for S-transform.
3. the method for detecting the traveling wave fault based on the variational modal decomposition and the S-transform of claim 1, wherein in the step S5, the first instantaneous frequency in the S-mode transform matrix obtained in the step S4 is extracted, and the first amplitude discontinuity of the frequency is calibrated as the arrival time of the initial traveling wave head.
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| CN112710925A (en) * | 2020-12-22 | 2021-04-27 | 三峡大学 | High-permeability active power distribution network fault location method based on improved VMD and S transformation |
| CN112748362A (en) * | 2020-12-22 | 2021-05-04 | 国网河南省电力公司电力科学研究院 | Small current ground fault detection method based on combination of VMD and grey correlation degree |
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| CN114019325A (en) * | 2021-11-02 | 2022-02-08 | 国网江苏省电力有限公司常州供电分公司 | Cable double-end positioning method and device |
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| CN112710925A (en) * | 2020-12-22 | 2021-04-27 | 三峡大学 | High-permeability active power distribution network fault location method based on improved VMD and S transformation |
| CN112748362A (en) * | 2020-12-22 | 2021-05-04 | 国网河南省电力公司电力科学研究院 | Small current ground fault detection method based on combination of VMD and grey correlation degree |
| CN112748362B (en) * | 2020-12-22 | 2022-04-26 | 国网河南省电力公司电力科学研究院 | Small current ground fault detection method based on combination of VMD and grey correlation degree |
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| CN113484683B (en) * | 2021-07-14 | 2022-11-08 | 贵州电网有限责任公司 | Power distribution network fault positioning system and method based on transient information |
| CN114019325A (en) * | 2021-11-02 | 2022-02-08 | 国网江苏省电力有限公司常州供电分公司 | Cable double-end positioning method and device |
| CN114019325B (en) * | 2021-11-02 | 2023-11-14 | 国网江苏省电力有限公司常州供电分公司 | Cable double-end positioning method and device |
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Application publication date: 20191206 |