CN113805104A - Large power transformer magnetizing inrush current discrimination method based on wavelet analysis signal energy characteristics - Google Patents
Large power transformer magnetizing inrush current discrimination method based on wavelet analysis signal energy characteristics Download PDFInfo
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- 238000012850 discrimination method Methods 0.000 title description 8
- 238000000034 method Methods 0.000 claims abstract description 29
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical group [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims description 30
- 230000005389 magnetism Effects 0.000 claims description 24
- 238000000354 decomposition reaction Methods 0.000 claims description 13
- 238000004088 simulation Methods 0.000 claims description 11
- 238000010248 power generation Methods 0.000 abstract description 2
- 230000005284 excitation Effects 0.000 description 8
- 238000004804 winding Methods 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 7
- 230000005347 demagnetization Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000007547 defect Effects 0.000 description 3
- 238000003745 diagnosis Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000000696 magnetic material Substances 0.000 description 2
- 238000004904 shortening Methods 0.000 description 2
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- 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
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
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Abstract
The invention belongs to the technical field of power generation, and particularly relates to a large power transformer magnetizing inrush current judging method based on wavelet analysis signal energy characteristics.
Description
Technical Field
The invention belongs to the technical field of power generation, and particularly relates to a large power transformer magnetizing inrush current distinguishing method based on wavelet analysis signal energy characteristics.
Background
Residual magnetism exists in the iron core under the influence of the hysteresis effect of the iron core of the transformer, and the residual magnetism is the main reason for generating excitation inrush current when the large power transformer is switched on in a no-load mode. At present, the diagnosis and discrimination method for the no-load closing excitation inrush current of the large power transformer mainly comprises the following steps:
(1) the method is characterized in that a discontinuity angle discrimination method is used for discriminating internal faults of a transformer and excitation inrush current according to a discontinuity angle generated by excitation inrush current waveform as a discrimination basis, but the current value at the discontinuity angle of the excitation inrush current waveform of the transformer is extremely small or even zero, and extremely high requirements are provided for the precision of a digital-analog acquisition module of a microcomputer protection device; the method for judging the break angle is also limited by the saturation degree of the current transformer, if the current transformer is saturated, a reverse current is generated at the position of the break angle of the magnetizing inrush current waveform, and when the current value is too large, the break angle phenomenon is weakened or even disappears, so that a large error is brought to fault diagnosis and judgment.
(2) The waveform symmetry discrimination method is characterized by that in a waveform period a window function is used, and a forward differentiation method is adopted to obtain the derivative of current waveform in a period, and according to the symmetry of front and rear half-period waveforms the fault can be discriminated. However, the excitation inrush current waveform of a large power transformer is influenced by a plurality of factors such as a power grid voltage phase angle at the moment of switching-on, system impedance, a transformer winding connection form and the like, so that the judgment accuracy of the judgment method is not high, and the judgment threshold of the method needs to be determined according to a large amount of test data and past experience.
(3) The second harmonic identification method, the second discrimination method, judges the fault according to the content of the second harmonic (100 Hz) in the current waveform. The defects of the discrimination method are as follows: firstly, with the continuous improvement of the voltage grade of a power grid and the capacity of a transformer, when a large power transformer has short-circuit faults (including external short-circuit faults and transformer internal turn-to-turn short-circuit faults), the content of second harmonic in a fault current signal is increased, so that the judgment accuracy of the method is reduced; and secondly, the larger and larger transformer iron core is made of novel magnetic materials, the hysteresis effect of the transformer iron core is reduced by the novel magnetic materials, and the content of the second harmonic of a current signal is reduced when excitation inrush current is caused, so that wrong judgment can be caused.
(4) The method for judging the equivalent circuit has the defects that accurate equivalent leakage reactance parameters of the high-voltage side and the low-voltage side of a transformer are needed before judgment, the instantaneous admittance of a large power transformer is complex to calculate and difficult to set, and the judgment sensitivity is reduced due to the change of leakage inductance parameters.
Based on the method, the large power transformer magnetizing inrush current judging method based on wavelet analysis signal energy characteristics is necessary to be researched.
Disclosure of Invention
Aiming at the defects and problems of the existing equipment, the invention provides a method for judging the magnetizing inrush current of a large power transformer based on wavelet analysis signal energy characteristics, which effectively solves the problems of low precision, complex operation and long judgment time of judging a fault transformer in the existing equipment.
The technical scheme adopted by the invention for solving the technical problems is as follows: a large power transformer magnetizing inrush current distinguishing method based on wavelet analysis signal energy characteristics comprises the following steps:
step 2: a current collecting device is arranged on the high-voltage side of the transformer, and current signals on the high-voltage side of the transformer are randomly collected to obtain a current signal set under the fault type;
and step 3: performing wavelet analysis on the acquired current signals one by one, and performing time-frequency domain analysis by using db10 wavelet 5-layer decomposition;
and 4, step 4: according to wavelet decomposition coefficients in different frequency domains, the characteristic energy value F, F = [ Ea, Ed1, Ed2, Ed3, Ed4 and Ed5 of each group of current signals under the fault type are obtained through function calculation]And calculating the Ea of each group of characteristic energy values and averaging to obtain the Ea representing the fault typeFlat plate；
And 5: modifying the residual magnetic quantity of the initial iron core of the transformer set in the simulation model, simulating a new fault type, repeating the steps 2-4, and obtaining Ea under different fault typesFlat plate；
Step 6: residual magnetic quantity and Ea of iron core under different fault typesFlat plateCurve fitting and polynomial fitting are carried out to obtain the residual magnetism of the iron core relative to EaFlat plateThe dynamic model of (1).
Further, the residual magnetic quantity of the initial iron core of the transformer is set in the simulation model in a mode of increasing progressively in sequence.
Furthermore, in step 4, the ranges of Ea and Ed5 under different fault types are counted, and the fault type is determined according to the different ranges of Ea and Ed 5.
Further, the method for calculating the characteristic energy value in the step 4 is calculated by adopting a werengy function in matlab.
The invention has the beneficial effects that: according to daily experience, iron core residual magnetism under different fault types is counted, initial iron core residual magnetism of a transformer is input into a simulation model, different fault types which occur daily are simulated, a current collecting device is arranged on the high-voltage side of the transformer, high-voltage side current signals of the transformer are randomly collected, time domain processing analysis can be well carried out on the signals by utilizing wavelet decomposition, energy characteristic values of wavelet decomposition signals during different faults are obtained by carrying out time domain analysis on the high-voltage side current signals of a large power transformer, and the fault types are distinguished according to the statistical ranges of Ea and Ed5 in the energy characteristic values; compared with the traditional frequency domain analysis, the time domain analysis is carried out on the high-voltage side current waveform of the transformer during the fault, and the actual condition of the current waveform change can be better and more accurately reflected. The method has the advantages of simple and uncomplicated practical mode, short discrimination time and high discrimination precision.
Meanwhile, Ea in a plurality of characteristic energy values under different iron core residual magnetism amounts is equalized by the method, and the Ea is used for evaluating the amount of the iron core residual magnetism amount, and the iron core residual magnetism amount and the Ea are usedFlat plateCurve fitting and polynomial fitting are carried out to obtain the residual magnetism of the iron core relative to EaFlat plateThe dynamic model can calculate the residual magnetism of the transformer winding by substituting the fundamental wave energy characteristic value of the current signal of the high-voltage side winding of the transformer into the fitting polynomial when the magnetizing inrush current occurs, so that the output direct current of the demagnetizer is set according to the residual magnetism to perform accurate demagnetization.
Therefore, the method and the device can accurately judge the fault type by carrying out current acquisition on the high-voltage side of the transformer and carrying out time-frequency domain analysis by using db10 wavelet 5-layer decomposition, have simple operation, short judging time and high precision, and can predict the residual magnetism of the transformer winding according to the fundamental wave energy characteristic value of the current signal of the transformer high-voltage side winding, thereby realizing the purpose of accurate demagnetization, providing convenience for people, shortening the judging time of faults and improving the processing efficiency of faults.
Drawings
Fig. 1 is a schematic view of a current signal acquisition structure according to the present invention.
Fig. 2 is a wavelet exploded view of the magnetizing inrush current signal.
Fig. 3 is a wavelet exploded view of a turn-to-turn short circuit current signal.
Fig. 4 is a wavelet exploded view of a normal no-load closing current signal.
FIG. 5 is a fitting graph of the fundamental wave energy characteristic value Ea and the transformer core residual magnetism phi 0.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
Example 1: the embodiment aims to provide a large power transformer magnetizing inrush current judging method based on wavelet analysis signal energy characteristics, which is mainly used for fault judgment and processing of a transformer, and aims to solve the problems of low judging precision, complex operation and long judging time of a fault transformer in the existing equipment. The method has the advantages of simple and uncomplicated practical mode, short discrimination time and high discrimination precision.
When in implementation, the method comprises the following steps:
Step 2: a current collecting device is arranged on the high-voltage side of the transformer, and current signals on the high-voltage side of the transformer are randomly collected to obtain a current signal set under the fault type; as shown in fig. 1, the current signal is collected at the high voltage side of the transformer and used as the main parameter for determining the type of fault.
And step 3: wavelet analysis is carried out on the collected current signals one by one, time-frequency domain analysis is carried out by utilizing db10 wavelet 5-layer decomposition, simulation is carried out according to the simulated diagrams of figures 2-4, and the current waveforms of the high-voltage side of the transformer under the conditions of no-load closing excitation inrush current fault, low-voltage side winding turn-to-turn short circuit fault and normal no-fault no-load closing are obtained.
And 4, step 4: according to wavelet decomposition coefficients in different frequency domains, the characteristic energy value F, F = [ Ea, Ed1, Ed2, Ed3, Ed4 and Ed5 of each group of current signals under the fault type are obtained through function calculation]For each groupEa of the characteristic energy value, and calculating the average value to obtain Ea representing the fault typeFlat plate；
When the characteristic energy value is calculated, the characteristic energy value is calculated by adopting a werengy function in matlab, and the specific calculation mode is as follows: the wavelet signal energy characteristic value calculation procedure is as follows (taking the residual magnetism amount 0.3pu as an example):
load liciyongliu030.mat
x=liciyongliu;
[c,l]=wavedec(x,5,'db10');
[Ea,Ed]=wenergy(c,l)
where x is the transformer current signal and c and l are variables of wenergy.
And 5: modifying the initial residual magnetic quantity of the transformer iron core set in the simulation model, and setting the setting mode of the residual magnetic quantity of the iron core according to a sequentially increasing mode, such as 0, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80 and 0.90; simulating a new fault type, repeating the step 2-4, and obtaining Ea under different fault typesFlat plate；
Step 6: residual magnetic quantity and Ea of iron core under different fault typesFlat plateCurve fitting and polynomial fitting are carried out to obtain the residual magnetism of the iron core relative to EaFlat plateThe dynamic model of (1).
The following table is obtained by carrying out statistical calculation on the residual magnetism of each transformer iron core;
as can be seen from table 1, in this embodiment, different transformer core residual magnetic quantities are set, the fundamental wave energy value Ea and the low frequency energy value Ed5 corresponding to the current information are calculated, and curve fitting and polynomial fitting are performed on the above 10 sets of data, where the fitting procedure is as follows:
x=[72.7788 65.0842 64.7993 64.0456 52.1168 51.2705 61.4777 53.3713 57.5407 59.1372];
y=[0 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90];
p9=polyfit(x,y,9);
y9=polyval(p9,x);
p9=vpa(poly2sym(p9),9)
figure
plot(x,y,'bo');
hold on;
plot(x,y9,'g--');
legend ('raw data', 'polynomial fit of order 9');
xlabel('Ea');
ylabel('phi0');
a fitted curve of the fundamental wave energy characteristic value Ea and the residual magnetism phi0 of the transformer core;
the fitted polynomial is sorted into:
phi0=1.54305174e-8(Ea)9-7.79835635e-6(Ea)8+0.00174051531(Ea)7-0.225014882(Ea)6+18.5538304(Ea)5-1010.81336(Ea)4+36334.168(Ea)3-829398.512(Ea)2+10882044.6(Ea)-62299922.5
the residual magnetism quantity of the transformer winding can be calculated by substituting the fundamental wave energy characteristic value of the current signal of the high-voltage side winding of the transformer into the fitting polynomial according to the calculation of the fitting polynomial when the magnetizing inrush current occurs, so that the output direct current of the demagnetizer is set according to the residual magnetism quantity to carry out accurate demagnetization.
Therefore, in the present embodiment, Ea in a plurality of characteristic energy values of different residual amounts of the iron core is averaged and used as the evaluation of the residual amount of the iron core, and the residual amount of the iron core and Ea are usedFlat plateCurve fitting and polynomial fitting are carried out to obtain the residual magnetism of the iron core relative to EaFlat plateThe dynamic model can carry out accurate demagnetization according to the magnetizing inrush current, thereby providing convenience for people, shortening the fault distinguishing time and improving the fault processing efficiency.
Example 2: this example is substantially the same as example 1, except that: the present embodiment determines the type of the fault by Ea and Ed 5.
In step 4, the ranges of Ea and Ed5 under different fault types are counted, and the fault types are judged according to the different ranges of Ea and Ed 5.
The method comprises the following steps of carrying out time-frequency domain analysis on current signals under different fault conditions by utilizing db10 wavelet 5-layer decomposition, and obtaining characteristic energy values of different faults through function calculation according to wavelet decomposition coefficients under different frequency domains, wherein the wavelet decomposition energy characteristic values of different fault current signals are shown in table 1:
table 2 shows wavelet decomposition signal energy characteristic tables of different fault types, according to table 2, it can be determined that the large power transformer has magnetizing inrush current phenomena of different degrees if the reconstructed signal energy value Ea is (60-80) and the low-frequency energy value Ed5 is (14-30), and it can be determined that the transformer has turn-to-turn short circuit faults of different degrees if the reconstructed signal energy value Ea is (40-60) and the low-frequency energy value Ed5 is (30-60).
Through the correctness and the practicability of different fault model simulation verification and discrimination methods, when a large-scale power transformer breaks down, the current waveform data of the high-voltage side of the transformer in a fault recorder is led out, wavelet analysis is carried out on the led-out waveform, after wavelet coefficients of all layers are obtained, signal energy characteristics are calculated, and the fault type of the transformer is judged according to the energy characteristics.
Claims (4)
1. A large power transformer magnetizing inrush current distinguishing method based on wavelet analysis signal energy characteristics is characterized by comprising the following steps: the method comprises the following steps:
step 1, setting transformer initial iron core residual magnetism in a simulation model for simulating fault types of a transformer;
step 2: a current collecting device is arranged on the high-voltage side of the transformer, and current signals on the high-voltage side of the transformer are randomly collected to obtain a current signal set under the fault type;
and step 3: performing wavelet analysis on the acquired current signals one by one, and performing time-frequency domain analysis by using db10 wavelet 5-layer decomposition;
and 4, step 4: according to wavelet decomposition coefficients in different frequency domains, calculating to obtain characteristic energy values F, F = [ Ea, Ed1, Ed2, Ed3, Ed4 and Ed5 of each group of current signals under the fault type]For each group of characteristic energy value Ea, calculating the average value to obtain the value representing the faultType EaFlat plate；
And 5: modifying the residual magnetic quantity of the initial iron core of the transformer set in the simulation model, simulating a new fault type, repeating the steps 2-4, and obtaining Ea under different fault typesFlat plate；
Step 6: residual magnetic quantity and Ea of iron core under different fault typesFlat plateCurve fitting and polynomial fitting are carried out to obtain the residual magnetism of the iron core relative to EaFlat plateThe dynamic model of (1).
2. The method for discriminating the magnetizing inrush current of the large power transformer based on the wavelet analysis signal energy characteristics according to claim 1, wherein: and setting the residual magnetic quantity of the initial iron core of the transformer in the simulation model according to a mode of sequentially increasing.
3. The method for discriminating the magnetizing inrush current of the large power transformer based on the wavelet analysis signal energy characteristics according to claim 1, wherein: in step 4, the ranges of Ea and Ed5 under different fault types are counted, and the fault types are judged according to the different ranges of Ea and Ed 5.
4. The method for discriminating the magnetizing inrush current of the large power transformer based on the wavelet analysis signal energy characteristics according to claim 1, wherein: and 4, calculating the characteristic energy value in the step 4 by adopting a werengy function in matlab.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115047240A (en) * | 2022-05-17 | 2022-09-13 | 国网湖北省电力有限公司黄冈供电公司 | Transformer magnetizing inrush current discrimination method using wavelet detail component change characteristics |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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) |
| CN103018601A (en) * | 2012-12-05 | 2013-04-03 | 江南大学 | Primary fault diagnosis method of converter in wind turbine system |
| CN204882843U (en) * | 2015-08-19 | 2015-12-16 | 国网四川省电力公司阿坝供电公司 | Transformer remanence detects and demagnetization device |
| CN108107384A (en) * | 2017-12-18 | 2018-06-01 | 国网新疆电力公司检修公司 | 750kV auto-transformers directly hinder remanent magnetism amount computational methods after experiment |
| CN108663599A (en) * | 2018-05-07 | 2018-10-16 | 太原理工大学 | Fault line selection method for single-phase-to-ground fault based on transient high-frequency component correlation analysis |
| CN110828101A (en) * | 2018-08-14 | 2020-02-21 | 广州供电局有限公司 | Power transformer demagnetization and remanence measurement device |
-
2021
- 2021-08-16 CN CN202110934169.4A patent/CN113805104A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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) |
| CN103018601A (en) * | 2012-12-05 | 2013-04-03 | 江南大学 | Primary fault diagnosis method of converter in wind turbine system |
| CN204882843U (en) * | 2015-08-19 | 2015-12-16 | 国网四川省电力公司阿坝供电公司 | Transformer remanence detects and demagnetization device |
| CN108107384A (en) * | 2017-12-18 | 2018-06-01 | 国网新疆电力公司检修公司 | 750kV auto-transformers directly hinder remanent magnetism amount computational methods after experiment |
| CN108663599A (en) * | 2018-05-07 | 2018-10-16 | 太原理工大学 | Fault line selection method for single-phase-to-ground fault based on transient high-frequency component correlation analysis |
| CN110828101A (en) * | 2018-08-14 | 2020-02-21 | 广州供电局有限公司 | Power transformer demagnetization and remanence measurement device |
Non-Patent Citations (4)
| Title |
|---|
| 王晓翀等: "基于小波能量的变压器励磁涌流识别方法", 《电力科学与工程》 * |
| 王晓翀等: "基于小波能量的变压器励磁涌流识别方法", 《电力科学与工程》, vol. 26, no. 8, 31 August 2010 (2010-08-31) * |
| 金燕等: "基于小波能量矩的变压器励磁涌流识别", 《机电工程》 * |
| 金燕等: "基于小波能量矩的变压器励磁涌流识别", 《机电工程》, vol. 30, no. 5, 30 May 2013 (2013-05-30) * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115047240A (en) * | 2022-05-17 | 2022-09-13 | 国网湖北省电力有限公司黄冈供电公司 | Transformer magnetizing inrush current discrimination method using wavelet detail component change characteristics |
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Application publication date: 20211217 |