CN103412221A - Transformer excitation surge current identification method based on time-frequency characteristic quantities - Google Patents

Transformer excitation surge current identification method based on time-frequency characteristic quantities Download PDF

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CN103412221A
CN103412221A CN2013103508675A CN201310350867A CN103412221A CN 103412221 A CN103412221 A CN 103412221A CN 2013103508675 A CN2013103508675 A CN 2013103508675A CN 201310350867 A CN201310350867 A CN 201310350867A CN 103412221 A CN103412221 A CN 103412221A
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CN103412221B (en
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束洪春
张兰兰
田开庆
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Kunming University of Science and Technology
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Abstract

The invention relates to a transformer excitation surge current identification method based on time-frequency characteristic quantities, and belongs to the technical field of transformer relay protection. The method comprises the steps: if the differential current of a transformer is larger than a setting value, decomposing data inside continuous N sliding time windows by utilizing the discrete wavelet decomposition, respectively calculating the total energy Ej of differential currents inside the sliding time windows and the energy sum Edi of frequency bands of the sliding time windows, obtaining the energy percentage Eij of each sub frequency band inside one time window, and forming a characteristic matrix WTF. A correlation coefficient of integrated time-frequency characteristics of the sum inside two adjacent time windows is calculated. When is smaller than , the is equal to 1, or the is equal to 0. By utilizing the correlation coefficient for representing the time-frequency characteristics of different current signals and the change rules of the different current signals, the integrated correlation coefficient is combined to form an excitation surge current identification criterion. According to the transformer excitation surge current identification method based on the time-frequency characteristic quantity, by starting from the analysis of the differential current time domain and the frequency domain, the time-frequency characteristic differences between fault currents and excitation surge currents inside different sliding time windows are effectively found out, and compared with a traditional surge current identification method by utilizing a single characteristic quantity, the transformer excitation surge current identification method based on the time-frequency characteristic quantities has the advantages of integrating multiple pieces of information such as the amplitude, the phase, the singularity and frequency distribution of the differential current and having higher reliability.

Description

A kind of discrimination method of transformer excitation flow based on the time-frequency characteristics amount
Technical field
The present invention relates to a kind of discrimination method of transformer excitation flow based on the time-frequency characteristics amount, belong to the transformer relay protecting technical field.
Background technology
Transformer is the indispensable visual plant of the different electric pressure networks of contact in electric system, and can its safe operation is directly connected to whole electric system the work of steady and continuous ground.Tranformer protection is of a great variety, and in the tranformer protection of numerous kinds, longitudinal differential protection can meet the central selectivity of relay protection, quick-action, sensitivity and reliability requirement preferably, is transformer main protection principal mode.Longitudinal difference protection utilizes the poor as poor stream of transformer primary side current and secondary side current, and poor stream surpasses certain setting valve, namely is judged as internal fault.In theory, the longitudinal difference protection extensively adopted at present can only be for the equipment be comprised of pure resistance.Yet; transformer has and the diverse characteristics such as bus, generator; its both sides are the saturable non-linear equipments that primary side and secondary side linked together by the iron core electromagnetic field; but not pure circuit structure; in the situations such as transformer during no-load closing, overexcitation, the recovery of external fault voltage, Kirchhoff's current law (KCL) (KCL) is not being set up, and the poor stream in transformer both sides will cause differential protection misoperation.Thereby no matter be traditional analog differential protection and protection of digital differential relaying instantly, all need effectively to identify excitation surge current, to guarantee that in the voltage rejuvenation after idle-loaded switching-on and external fault elimination, protection can misoperation.
The on-the-spot main secondary harmonic brake principle that adopts prevents the impact that excitation surge current brings to longitudinal difference protection, but along with the improvement of the ferromagnetic material of transformer, when saturated, the second harmonic composition significantly reduces, and now differential protection may misoperation; Be subjected to the impact of UHV (ultra-high voltage) long power transmission line shunt capacitance and distributed capacitance, when the inner generation of transformer catastrophic failure, the resonance between inductance and electric capacity can make the harmonic components in short-circuit current obviously increase, and likely causes differential protection deferred action.When current transformer was saturated, due to the excitation surge current generation inverse current of the progress of disease to secondary side, the waveform distortion, caused the interval angle that shoves to disappear, and makes and rely on the excitation surge current recognition technology of waveform interval angle feature to lose efficacy.Sampling value difference principle, waveform symmetry principle, waveform superposition principle, waveform correlation analysis method and waveform fitting are suggested in recent years.Wherein, the sampling value difference principle is the derivative of interrupted angle principle, and the waveform symmetry principle is the improvement of interrupted angle principle, and waveform superposition principle, waveform correlation analysis method and waveform fitting rule are the derivative improvement of waveform symmetry principle.The protection of above various principles is adjusted comparatively difficult, and in application, intelligence is according to actual conditions, and mode is by experiment set or revised, and has the hidden danger of erroneous judgement.Thereby at present excitation flow recognition method is of a great variety, but degree of perfection, applicability still remain to be improved.
Summary of the invention
The technical problem to be solved in the present invention is to improve transformer to identify correctly, rapidly the ability of excitation surge current, proposes a kind of discrimination method of transformer excitation flow based on the time-frequency characteristics amount.
Technical scheme of the present invention is: if the transformer differential electric current is greater than setting valve, utilizes discrete wavelet to decompose the data in its N continuous sliding window are decomposed, calculate respectively difference current gross energy in each sliding window E j With each frequency band energy and Ed i , each sub-band energy percentage in window while asking for E Ij , and form eigenmatrix W TF .Calculate in two windows when adjacent
Figure 792240DEST_PATH_IMAGE001
With
Figure 2013103508675100002DEST_PATH_IMAGE002
Comprehensive time-frequency characteristics related coefficient
Figure 361893DEST_PATH_IMAGE003
.When ρ J, j+1 < ρ Th The time, order ρ J, j+1 =1, otherwise ρ J, j+1 =0.Utilize related coefficient to characterize poor time-frequency characteristic and the Changing Pattern thereof that flows signal, and in conjunction with integrated correlation coefficient excitation inrush current distinguishing criterion.
Concrete steps are as follows:
(1), if the transformer differential electric current is greater than setting valve, utilizes discrete wavelet to decompose the data in its N continuous sliding window are decomposed.
(2) time-frequency characteristics matrix computations
Ask for each frequency band energy number percent in each sliding window: at first according to formula (1), calculate difference current gross energy in each sliding window E j Secondly according to formula (2) calculate in each sliding window each frequency band energy and Ed i Each sub-band energy percentage in window while finally asking for one according to formula (3) E Ij .
Figure 2013103508675100002DEST_PATH_IMAGE004
（1）
（2）
Figure 2013103508675100002DEST_PATH_IMAGE006
（3）
In formula j=1 K, K=400, sampled point in window during for each;
Figure 435602DEST_PATH_IMAGE007
Difference current amplitude for each sampled point; i=1 ... M, M=8 are that DWT decomposes the number of plies,
Figure 2013103508675100002DEST_PATH_IMAGE008
For after the difference current wavelet decomposition iLayer the nThe amplitude of point.
The time-frequency characteristics amount of each sliding window W TF, j As the formula (4), total time-frequency characteristics matrix W TF As the formula (5)
Figure 893128DEST_PATH_IMAGE009
（4）
Figure 2013103508675100002DEST_PATH_IMAGE010
（5）
(3) calculate comprehensive time-frequency characteristics related coefficient
Figure 317287DEST_PATH_IMAGE011
（6）
In formula Cov( W TF, j , W TF, j+1 ) be the special characteristic of field amount of time-frequency W TF, j , W TF, j+1 Covariance, Cov( W TF, j , W TF, j+1 )= E( W TF, j W TF, j+1 - E( W TF, j ) E( W TF, j+1 ,
Figure 2013103508675100002DEST_PATH_IMAGE012
,
Figure 716913DEST_PATH_IMAGE013
For the mean square deviation of time-frequency characteristics amount, wherein D( W TF, j )= E( W 2 TF, j )- E 2( W TF, j ), D( W TF, j+1 )= E( W 2 TF, j+1 )- E 2( W TF, j+1 ).
(4) integrated correlation coefficient of window when adjacent ρ J, j+1 Be less than threshold value ρ Th The time, order ρ J, j+1 =1, otherwise ρ J, j+1 =0.Introducing the normal distribution statistics analyzes and forms final criterion it.
(5) according to expectation value S size, differentiate whether be excitation surge current;
If its expectation value S<0.2, be judged to be internal fault current, protection outlet action; Otherwise be judged to be excitation surge current, the locking transformer differential protection.
During the difference current of described measuring transformer, time window is 20ms, and sample frequency is 20kHz, and in sliding window, the sampled point number is 50, and wavelet decomposition is 8 layers.
Principle of the present invention is:
1. the basic theories of wavelet transformation:
(1) continuous wavelet transform
If
Figure 2013103508675100002DEST_PATH_IMAGE014
Be a quadractically integrable function, if its Fourier transform
Figure 46263DEST_PATH_IMAGE015
Meet the admissibility condition, that is:
Figure 2013103508675100002DEST_PATH_IMAGE016
（1）
Claim
Figure 58213DEST_PATH_IMAGE014
Be a wavelet, or wavelet mother function.
By wavelet mother function
Figure 461512DEST_PATH_IMAGE014
Stretch and translation, can obtain the continuous wavelet basis function
Figure 782772DEST_PATH_IMAGE017
:
Figure 2013103508675100002DEST_PATH_IMAGE018
Figure 851616DEST_PATH_IMAGE019
（2）
In formula: a is contraction-expansion factor, or is called scale factor; B is shift factor.
For function arbitrarily Continuous wavelet transform be:
Figure 650944DEST_PATH_IMAGE021
（3）
In formula: Mean
Figure 987379DEST_PATH_IMAGE023
Conjugation.
(2) wavelet transform
Concept by continuous wavelet transform knows, the scale factor a of continuous wavelet transform and shift factor b are continuous variables.In actual applications, usually will
Figure 479540DEST_PATH_IMAGE017
In continuous variable a and b get and do the integer discrete form, will
Figure 455586DEST_PATH_IMAGE017
Be expressed as:
Figure DEST_PATH_IMAGE024
（4）
Corresponding function
Figure 370191DEST_PATH_IMAGE025
Wavelet transform can be expressed as:
Figure DEST_PATH_IMAGE026
（5）
Due to this discrete wavelet
Figure 826711DEST_PATH_IMAGE027
By wavelet function
Figure 161877DEST_PATH_IMAGE014
Warp Integral multiple is put, is contracted and through family of functions that the integer k translation generates
Figure 953116DEST_PATH_IMAGE029
,
Figure DEST_PATH_IMAGE030
.Therefore, this wavelet sequence after discrete is commonly referred to as discrete dyadic wavelet sequence.
2, related coefficient:
By signal f( t) and g( t) the strict difinition of cross correlation function as follows:
（6）
In formula, TIt is averaging time.Cross correlation function characterizes the time average of the product of two signals.
If f( t) and g( t) be the cycle to be T 0Periodic signal, following formula can be expressed as:
（7）
By the related function discretize, and the impact of eliminating signal amplitude, related operation is done to normalization.For discrete signal
Figure 708069DEST_PATH_IMAGE033
, its autocorrelation function can be expressed as:
Figure DEST_PATH_IMAGE034
（8）
In formula Cov( i( k), i( k+ 1)) be the special characteristic of field amount of time-frequency i( k), i( k+ j) covariance, Cov( i( k), i( k+ j))= E( i( k) i( k+ j)- E( i( k)) E( i( k+ j),
Figure 276454DEST_PATH_IMAGE035
,
Figure DEST_PATH_IMAGE036
For the mean square deviation of time-frequency characteristics amount, wherein D( i( k))= E( i 2( k))- E 2( i( k)), D( i( k+ j))= E( i 2( k+ j))- E 2( i( k+ j)). ρ K, k+j Value be between 0 to 1, when i( k) and i( k+ j) more approaching ρBe worth larger.
When j got 1, following formula can be expressed as:
Figure 804256DEST_PATH_IMAGE037
（9）
In formula, k=1,2,3...... N, N are the sampled data length in short time-window, coefficient of autocorrelation ρInterval be [1 ,+1] ,+1 means two signal 100% positive correlations ,-1 means two signal 100% negative correlation.
3, based on the transformer excitation flow of time-frequency characteristics amount, differentiate:
In different sliding windows, utilize wavelet transform to decompose difference current shown in accompanying drawing 2, wherein the sliding window moving process as shown in Figure 3, each the time window window length be a power frequency period (20ms).Decompose and to obtain shown in Figure 4 and Figure 5 internal fault current, excitation surge current time-frequency characteristics distribution plan in window when different, in figure xAxle be used for the sign time distribute, yThe axle frequency band distribution, zAxle characterizes the energy distribution under corresponding temporal frequency.
In Fig. 4 ( a) figure and (b) difference current time-frequency characteristics distribution plan in window when figure is respectively adjacent under same operating, (c) figure is the difference current time-frequency characteristics distribution plan of 50 sliding windows of being separated by.After by Fig. 3-16, knowing transformer generation internal fault, its difference current time-frequency characteristics distributes basically identical in window when difference: mainly concentrate low-frequency band, and amplitude is substantially constant.In Fig. 3-17, in the time of the 1st in window ( aFigure) the excitation surge current frequency mainly is distributed in 7,8 layers and energy Ratios and is about 1:1; In the time of the 2nd, interior (b figure) frequency distribution of window changes, and mainly take 5,6,7,8 layers as main, and its energy Ratios is about 1:1:3:2; When during with the first two, window is compared the 50th in window the excitation surge current frequency content change more violently, mainly be distributed in the 4th, 5,6,7,8 layers, energy Ratios is about 1:1:1:1:1.
After transformer during no-load closing or failure removal, in voltage rejuvenation, the excitation surge current of generation is non-linear, the non-stationary signal consisted of the different frequency component; The poor stream of fault during power transformer interior fault is approximately the power frequency sinusoidal signal.Therefore the difference current data of each sliding window after can starting differential protection; through wavelet decomposition and by it, decompose to different frequency range; and then ask the time-frequency characteristics amount that obtains can be in time frequency window the abundant time-frequency characteristic of reaction signal, can be according to this excitation discriminatory criterion that shoves.
The invention has the beneficial effects as follows:
(1) the present invention starts with from the difference current time and frequency domain analysis, effectively searches out the time-frequency characteristics difference of the interior internal fault current of different sliding windows and excitation surge current.
(2) this patent is not the single features that merely utilizes difference current, but sinusoidal feature, waveform deflection time shaft one side of excitation surge current, the waveform of having analyzed all sidedly the internal fault current waveform have the features such as interval angle, merged the multiple information such as amplitude, phase place, singularity, frequency distribution of poor stream, reliability is higher.
(3) sample frequency of the present invention is 20kHz, meets current hardware condition, and easily realize at scene.
The accompanying drawing explanation
This aspect of Fig. 1 patent emulation schematic diagram;
Fig. 2 sliding window moves schematic diagram;
Fig. 3 difference current oscillogram, difference current oscillogram when wherein (a) figure is power transformer interior fault, the excitation surge current oscillogram when (b) figure is transformer during no-load closing;
Internal fault current time-frequency characteristics distribution plan in window when Fig. 4 is different, wherein ( a) figure and (b) difference current time-frequency characteristics distribution plan in window when figure is respectively adjacent under same operating, (c) figure is the difference current time-frequency characteristics distribution plan of 50 sliding windows of being separated by;
Excitation surge current time-frequency characteristics distribution plan in window when Fig. 5 is different, wherein ( a) figure and (b) difference current time-frequency characteristics distribution plan in window when figure is respectively adjacent under same operating, (c) figure is the difference current time-frequency characteristics distribution plan of 50 sliding windows of being separated by.
Embodiment
Below in conjunction with the drawings and specific embodiments, the invention will be further described.
One is based on the transformer excitation flow discrimination method of time-frequency characteristics amount, to be greater than setting valve when the transformer differential electric current, utilize discrete wavelet to decompose the data in its N continuous sliding window are decomposed, calculate respectively difference current gross energy in each sliding window E j With each frequency band energy and Ed i , each sub-band energy percentage in window while asking for E Ij , and form eigenmatrix W TF .Calculate in two windows when adjacent
Figure 686761DEST_PATH_IMAGE001
With Comprehensive time-frequency characteristics related coefficient
Figure 44110DEST_PATH_IMAGE003
.When ρ J, j+1 < ρ Th The time, order ρ J, j+1 =1, otherwise ρ J, j+1 =0.Utilize related coefficient to characterize poor time-frequency characteristic and the Changing Pattern thereof that flows signal, and in conjunction with integrated correlation coefficient excitation inrush current distinguishing criterion.
Concrete steps are as follows:
(1), if the transformer differential electric current is greater than setting valve, utilizes discrete wavelet to decompose the data in its N continuous sliding window are decomposed.
(2) time-frequency characteristics matrix computations
Ask for each frequency band energy number percent in each sliding window: at first according to formula (1), calculate difference current gross energy in each sliding window E j Secondly according to formula (2) calculate in each sliding window each frequency band energy and Ed i Each sub-band energy percentage in window while finally asking for one according to formula (3) E Ij .
Figure 295094DEST_PATH_IMAGE004
（1）
（2）
（3）
In formula j=1 K, K=400, sampled point in window during for each;
Figure 412982DEST_PATH_IMAGE007
Difference current amplitude for each sampled point; i=1 ... M, M=8 are that DWT decomposes the number of plies,
Figure 338213DEST_PATH_IMAGE008
For after the difference current wavelet decomposition iLayer the nThe amplitude of point.
The time-frequency characteristics amount of each sliding window W TF, j As the formula (4), total time-frequency characteristics matrix W TF As the formula (5)
（4）
Figure 951914DEST_PATH_IMAGE010
（5）
(3) calculate comprehensive time-frequency characteristics related coefficient
Figure 49314DEST_PATH_IMAGE011
（6）
In formula Cov( W TF, j , W TF, j+1 ) be the special characteristic of field amount of time-frequency W TF, j , W TF, j+1 Covariance, Cov( W TF, j , W TF, j+1 )= E( W TF, j W TF, j+1 - E( W TF, j ) E( W TF, j+1 , ,
Figure 286577DEST_PATH_IMAGE013
For the mean square deviation of time-frequency characteristics amount, wherein D( W TF, j )= E( W 2 TF, j )- E 2( W TF, j ), D( W TF, j+1 )= E( W 2 TF, j+1 )- E 2( W TF, j+1 ).
(4) integrated correlation coefficient of window when adjacent ρ J, j+1 Be less than threshold value ρ Th The time, order ρ J, j+1 =1, otherwise ρ J, j+1 =0.Introducing the normal distribution statistics analyzes and forms final criterion it.
(5) according to expectation value S size, differentiate whether be excitation surge current;
If its expectation value S<0.2, be judged to be internal fault current, protection outlet action; Otherwise be judged to be excitation surge current, the locking transformer differential protection.
During the difference current of described measuring transformer, time window is 20ms, and sample frequency is 20kHz, and in sliding window, the sampled point number is 50, and wavelet decomposition is 8 layers.
Embodiment 1: in analogue system shown in Figure 1, transformer is three single-phase three-winding transformers, adopts the Yd11 connection.High pressure winding access 110kV system is transformer primary side, and middle pressure winding and the cascade of low pressure winding form the transformer secondary.Transmission line of electricity is simulated by 5 sections π type equivalent electrical circuit, and every segment length is 4km.The transformer simulation system parameters is as shown in table 1, and the magnetization curve parameter is as shown in table 2.
Table 1 simulation system parameters
Table 2 magnetizing parameters
Figure 779745DEST_PATH_IMAGE039
When the inner generation of transformer 30% turn-to-turn fault:
(1) the transformer differential electric current is greater than setting valve, utilizes discrete wavelet to decompose the data in its continuous 400 sliding windows are decomposed.
(2) calculate the time-frequency characteristics matrix in different sliding windows W TF
Comprehensive time-frequency characteristics related coefficient when (3) calculating is adjacent in window
Figure DEST_PATH_IMAGE040
, the integrated correlation coefficient of window when adjacent ρ J, j+1 Be less than threshold value ρ Th The time, order ρ J, j+1 =1, otherwise ρ J, j+1 =0.
(4) utilize normal distribution counterweight postpone Analyze, expectation S=0.01<0.2, be judged to be internal fault, protection outlet action.
Embodiment 2: analogue system and transformer parameter are with embodiment 1.
Transformer during no-load closing, switching angle are 45 °, carry out counting statistics, expectation value S=0.43 by the method that embodiment 1 is identical > 0.2, correctly identify excitation surge current, protection blocking.。
The above is explained in detail the specific embodiment of the present invention by reference to the accompanying drawings, but the present invention is not limited to above-mentioned embodiment, in the ken that those of ordinary skills possess, can also under the prerequisite that does not break away from aim of the present invention, change.

Claims (3)

1. discrimination method of the transformer excitation flow based on the time-frequency characteristics amount, it is characterized in that: if the transformer differential electric current is greater than setting valve, utilize discrete wavelet to decompose the data in its N continuous sliding window are decomposed, calculate respectively difference current gross energy in each sliding window E j With each frequency band energy and Ed i , each sub-band energy percentage in window while asking for E Ij , and form eigenmatrix W TF Calculate in two windows when adjacent
Figure 2013103508675100001DEST_PATH_IMAGE001
With
Figure 365124DEST_PATH_IMAGE002
Comprehensive time-frequency characteristics related coefficient
Figure 2013103508675100001DEST_PATH_IMAGE003
When ρ J, j+1 < ρ Th The time, order ρ J, j+1 =1, otherwise ρ J, j+1 =0, utilize the integrated correlation coefficient of normal distribution counterweight postpone to add up, according to the size of expectation value S, differentiate excitation surge current.
2. the discrimination method of the transformer excitation flow based on the time-frequency characteristics amount according to claim 1, is characterized in that, concrete steps are as follows:
(1), if the transformer differential electric current is greater than setting valve, utilizes discrete wavelet to decompose the data in its N continuous sliding window are decomposed;
(2) time-frequency characteristics matrix computations:
Ask for each frequency band energy number percent in each sliding window: at first according to formula (1), calculate difference current gross energy in each sliding window E j Secondly according to formula (2) calculate in each sliding window each frequency band energy and Ed i Each sub-band energy percentage in window while finally asking for one according to formula (3) E Ij
Figure 344581DEST_PATH_IMAGE004
（1）
Figure 2013103508675100001DEST_PATH_IMAGE005
（2）
Figure 186635DEST_PATH_IMAGE006
（3）
In formula j=1 K, K=400, sampled point in window during for each;
Figure 2013103508675100001DEST_PATH_IMAGE007
Difference current amplitude for each sampled point; i=1 ... M, M=8 are that DWT decomposes the number of plies,
Figure 644161DEST_PATH_IMAGE008
For after the difference current wavelet decomposition iLayer the nThe amplitude of point;
The time-frequency characteristics amount of each sliding window W TF, j As the formula (4), total time-frequency characteristics matrix W TF As the formula (5)
Figure 2013103508675100001DEST_PATH_IMAGE009
（4）
Figure 520851DEST_PATH_IMAGE010
（5）
(3) calculate comprehensive time-frequency characteristics related coefficient:
Figure 2013103508675100001DEST_PATH_IMAGE011
（6）
In formula Cov( W TF, j , W TF, j+1 ) be the special characteristic of field amount of time-frequency W TF, j , W TF, j+1 Covariance, Cov( W TF, j , W TF, j+1 )= E( W TF, j W TF, j+1 - E( W TF, j ) E( W TF, j+1 ,
Figure 405630DEST_PATH_IMAGE012
,
Figure 2013103508675100001DEST_PATH_IMAGE013
For the mean square deviation of time-frequency characteristics amount, wherein D( W TF, j )= E( W 2 TF, j )- E 2( W TF, j ), D( W TF, j+1 )= E( W 2 TF, j+1 )- E 2( W TF, j+1 );
(4) integrated correlation coefficient of window when adjacent ρ J, j+1 Be less than threshold value ρ Th The time, order ρ J, j+1 =1, otherwise ρ J, j+1 =0, introduce the normal distribution statistics it is analyzed and forms final criterion;
(5) according to expectation value S size, differentiate whether be excitation surge current:
If its expectation value S<0.2, be judged to be internal fault current, protection outlet action; Otherwise be judged to be excitation surge current, the locking transformer differential protection.
3. the discrimination method of the transformer excitation flow based on the time-frequency characteristics amount according to claim 1, it is characterized in that: during the difference current of described measuring transformer, time window is 20ms, and sample frequency is 20kHz, in sliding window, the sampled point number is 50, and wavelet decomposition is 8 layers.
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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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CN104993455A (en) * 2015-07-28 2015-10-21 株洲南车时代电气股份有限公司 Traction transformer over current protection method
CN106842023A (en) * 2017-01-22 2017-06-13 浙江大学 The method for diagnosing faults of electric rotating machine
CN109031020A (en) * 2018-07-09 2018-12-18 北京四方继保自动化股份有限公司 A kind of transformer inrush current identification method that this base of a fruit of logic-based returns
CN109066587A (en) * 2018-08-01 2018-12-21 西南交通大学 Converter power transformer differential protection fault judgment method based on wavelet energy entropy
CN109586249A (en) * 2018-12-12 2019-04-05 国网河北省电力有限公司电力科学研究院 Method for Identifying Transformer Inrush Current and device
CN112039021A (en) * 2020-09-08 2020-12-04 河南理工大学 Transformer excitation inrush current identification method based on differential waveform parameters
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009041976A (en) * 2007-08-07 2009-02-26 Kansai Electric Power Co Inc:The Fault point locating method and system
CN101567552A (en) * 2009-06-03 2009-10-28 昆明理工大学 Recognition method of magnetizing inrush current and internal short circuit of power transformer by utilizing morphological structure
US20110216450A1 (en) * 2010-03-08 2011-09-08 National Formosa University Transformer failure analysis system
CN102510044A (en) * 2011-11-04 2012-06-20 上海电力学院 Excitation inrush current identification method based on wavelet transformation and probabilistic neural network (PNN)
CN102570392A (en) * 2012-01-17 2012-07-11 上海电力学院 Method for identifying exciting inrush current of transformer based on improved probability neural network

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009041976A (en) * 2007-08-07 2009-02-26 Kansai Electric Power Co Inc:The Fault point locating method and system
CN101567552A (en) * 2009-06-03 2009-10-28 昆明理工大学 Recognition method of magnetizing inrush current and internal short circuit of power transformer by utilizing morphological structure
US20110216450A1 (en) * 2010-03-08 2011-09-08 National Formosa University Transformer failure analysis system
CN102510044A (en) * 2011-11-04 2012-06-20 上海电力学院 Excitation inrush current identification method based on wavelet transformation and probabilistic neural network (PNN)
CN102570392A (en) * 2012-01-17 2012-07-11 上海电力学院 Method for identifying exciting inrush current of transformer based on improved probability neural network

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
孙向飞 等: "励磁涌流导致变压器差动保护误动分析与对策", 《云南电力技术》, vol. 40, no. 5, 31 October 2012 (2012-10-31) *
崔恒荣 等: "基于小波算法的变压器涌流和内部故障电流识别", 《低压电器》, no. 21, 31 December 2007 (2007-12-31) *
李海锋 等: "电力变压器励磁涌流判别的自适应小波神经网络方法", 《中国电机工程学报》, vol. 25, no. 7, 30 April 2005 (2005-04-30) *
赵立新 等: "励磁涌流的参数化时频分析", 《长春理工大学学报（自然科学版）》, vol. 35, no. 2, 30 June 2012 (2012-06-30) *

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104749519A (en) * 2015-03-12 2015-07-01 云南电网公司西双版纳供电局 Correlation analysis based on-load voltage regulating transformer tapping switch operating state judgment method
CN104993455A (en) * 2015-07-28 2015-10-21 株洲南车时代电气股份有限公司 Traction transformer over current protection method
CN106842023A (en) * 2017-01-22 2017-06-13 浙江大学 The method for diagnosing faults of electric rotating machine
CN106842023B (en) * 2017-01-22 2019-05-21 浙江大学 The method for diagnosing faults of rotating electric machine
CN109031020A (en) * 2018-07-09 2018-12-18 北京四方继保自动化股份有限公司 A kind of transformer inrush current identification method that this base of a fruit of logic-based returns
CN109066587A (en) * 2018-08-01 2018-12-21 西南交通大学 Converter power transformer differential protection fault judgment method based on wavelet energy entropy
CN109586249A (en) * 2018-12-12 2019-04-05 国网河北省电力有限公司电力科学研究院 Method for Identifying Transformer Inrush Current and device
CN109586249B (en) * 2018-12-12 2020-08-11 国网河北省电力有限公司电力科学研究院 Method and device for judging excitation inrush current of transformer
CN112039021A (en) * 2020-09-08 2020-12-04 河南理工大学 Transformer excitation inrush current identification method based on differential waveform parameters
CN114167117A (en) * 2021-12-02 2022-03-11 合肥工业大学 Method for identifying differential protection excitation inrush current of double-winding transformer
CN116683411A (en) * 2023-08-01 2023-09-01 华北电力大学 Alternating current line protection method and system and electronic equipment
CN116683411B (en) * 2023-08-01 2023-09-29 华北电力大学 Alternating current line protection method and system and electronic equipment

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