CN103163430A - Resonant grounding system fault line selection method by combining complex wavelets with ANN (artificial neural network) - Google Patents
Resonant grounding system fault line selection method by combining complex wavelets with ANN (artificial neural network) Download PDFInfo
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Abstract
The invention relates to a resonant grounding system fault line selection method by combining complex wavelets with an ANN (artificial neural network), and belongs to the technical field of power system relay protection. The method includes the steps: immediately starting a fault line selection device and recording faults when a momentary value of bus zero-sequence voltage is out-of-limit to acquire zero-sequence transient current of each feeder line; and transforming and decomposing the zero-sequence transient current of a time window by the aid of the complex wavelets after each feeder line is faulted for 5ms, dividing frequency bands, selecting the frequency band with the maximum energy sum of all feeder lines as a characteristic frequency band according to the maximum energy sum principle, selecting a phase corresponding to the center frequency of the energy sum characteristic frequency band of each feeder line in the characteristic frequency band as a training sample set, determining the number of nodes of an input layer, an output layer and a hidden layer, selecting a transfer function and a learning rule, setting proper neural network parameters, acquiring a fault line selection network by training and adaptively selecting fault lines. A large amount of simulation indicates that the resonant grounding system fault line selection method is accurate and reliable in line selection.
Description
Technical field
The present invention relates to the Relay Protection Technology in Power System field, specifically a kind of resonant earthed system failure line selection new method of utilizing multiple small echo and ANN combination.
Background technology
The power distribution network broad covered area, and directly provide the electricity consumption service in the face of the user for it.Account for 80% of distribution network failure according to the statistics singlephase earth fault.The power distribution network resonant earthed system is the neutral by arc extinction coil grounding system, belongs to small current neutral grounding system.Single-phase grounded malfunction in grounded system of low current can affect the healthy phases voltage-to-ground and cause its rising, and voltage raises to produce the insulation of grid equipment and destroys; Intermittent arcing ground particularly, can cause arc overvoltage, this voltage is by destroying system insulation and then developing into alternate or the multipoint earthing short circuit, cause system overvoltage, thereby damage equipment, destroy system safety operation, therefore must find accurately, fast faulty line and in time fault be got rid of.During neutral by arc extinction coil grounding system generation singlephase earth fault, carry out route selection if use power frequency steady-state quantity colony amplitude comparison phase comparing method, due to the impact that is subjected to the arc suppression coil compensation effect, ground current is faint, may cause the route selection mistake.And after fault, the fault transient state current amplitude much larger than steady-state current, and is not affected by arc suppression coil, and the selection method Billy who therefore utilizes transient has more advantage with the selection method of steady-state quantity.But no matter utilize steady-state quantity or transient route selection, all exist some fault signature apparent in view, some fault signature is fuzzyyer, disturbing factor is larger on some fault signature impact, some fault signature is affected the problems such as less, therefore utilize single route selection criterion also the situation of falsely dropping can occur.
Multiple small echo is that a series of female small echos are the basis function of plural number, and its Wavelet Transform Parameters is also plural number, can obtain simultaneously signal amplitude information and phase information thus.Multiple small echo series commonly used has multiple Gauss, multiple Shannon, multiple Morlet and complex frequency B spline wavelets.That multiple Gauss wavelet has is nonopiate, the character of biorthogonal, non-tight and Perfect Reconstruction.The advantage of multiple small echo maximum is that it can extract the phase information of signal.
The ANN artificial neural network is a kind of self-adaptation nonlinear dynamic system, and he is interconnected by a large amount of simple neurons and forms.The principal character of artificial neural network is: large-scale information parallel processing capability and distributed information storage function, extremely strong self-study, association, fault-tolerant ability and good adaptivity, self-organization are the nonlinear system of many inputs and many outputs.
Summary of the invention
The objective of the invention is to propose a kind of malfunction route selection method for resonant grounded system that utilizes multiple small echo and ANN combination, overcome the deficiency of existing malfunction route selection method for resonant grounded system.
The present invention utilizes the malfunction route selection method for resonant grounded system of multiple small echo and ANN combination, carries out as follows:
(1) when bus residual voltage instantaneous value is out-of-limit, namely
The time, fault line selection device starts immediately and records ripple, obtains each feeder line zero sequence transient current, all feeder line zero sequence transient currents of window when recording after fault 5ms; Wherein,
Be the bus rated voltage,
=0.15;
(2) each zero sequence transient current is carried out the multiple Gauss Wavelet Transform in 20 rank, decomposing the number of plies is 256 layers; Choose the decomposition result that decomposition scale is the 4-203 layer, divide according to frequency band of 20 yardsticks, obtain the energy of 10 frequency bands of each feeder line; According to energy and maximum principle, choose all feeder line energy in 10 frequency bands and maximum frequency band as feature band; Ask for the energy of each feeder line in feature band
, extract phase place corresponding to each feeder line feature band centre frequency;
(3) design BP neural network, the BP neural network is divided into three layers, and topological structure is
*
*
Wherein, ground floor is that input layer contains
Individual node, the energy of each feeder line that will try to achieve by multiple wavelet transformation
With phase place corresponding to each feeder line feature band centre frequency
As sample attribute; The second layer is that hidden layer contains
Individual node, adopting the logsig function is logarithm sigmoid transport function; The 3rd layer contains for output layer
Individual node, adopting the purelin function is pure linear transfer function;
mBe resonant earthed system feeder line quantity,
(4) carry out ANN (Artificial Neural Network) training, the following fault signature of analog simulation: choose respectively the trouble spot at 1/3l, the 1/2l of every feeder line, 2/3l place and bus place, fault resistance is chosen for respectively arc furnace load and constant transition resistance 20 Ω, 100 Ω, 500 Ω, the initial phase angle of fault is got respectively 0 °, 30 °, 60 °, 90 °, and load is chosen for respectively firm power load and arc furnace load; By step (1), (2) described method, extract respectively the fault transient amount
With
As sample set, train in input BP neural network after normalized, obtain satisfactory failure line selection network, the error performance target is 1
(5) with the fault transient amount of power distribution network to be selected
With
The sample attribute input neural network carries out renormalization to the Output rusults of neural network and processes, obtain 1 *
Failure line selection network output matrix
[
,
,
...,
,
]; Wherein,
Be resonant earthed system feeder line quantity;
(6) according to failure line selection network output matrix
YCarry out failure line selection: when
≈ 1, and other value approximates at 0 o'clock, judges the
The bar feeder line breaks down,
Be the feeder line numbering; When
≈ 1, and other value approximates at 0 o'clock, judges that bus breaks down;
Be resonant earthed system feeder line quantity.
Principle of the present invention is:
One, the extraction of characteristic quantity
1, when power distribution network bus residual voltage instantaneous value is out-of-limit, namely
The time, fault line selection device starts immediately and records ripple, obtains each feeder line zero sequence transient current, all feeder line zero sequence transient currents of window when recording after fault 5ms; Wherein,
Be the bus rated voltage,
=0.15;
2, one of transient characteristic quantity is selected: because the amplitude (or energy) of transient state component in feature band of each feeder line distributes and PHASE DISTRIBUTION all exists mapping relations with the distribution characteristics of the transient state component of fault feeder, for the impact of reduce disturbance signal, the frequency band of choosing all feeder line energy and maximum according to energy and maximum principle is feature band; In order to strengthen the reliability of route selection, simultaneously in the selected characteristic frequency band phase place corresponding to the energy of each feeder line and feature band centre frequency as the characteristic quantity of route selection.
3, multiple wavelet transformation: the advantage of multiple small echo maximum is that it can extract amplitude information and the phase information of signal simultaneously, this is to be the basis function of plural number because multiple small echo is a series of female small echos, its Wavelet Transform Parameters is also plural number, can obtain simultaneously signal amplitude information and phase information thus.Multiple small echo series commonly used has multiple Gauss, multiple Shannon, multiple Morlet and complex frequency B spline wavelets.
Being transformed to of continuous complex wavelet:
（1）
In formula,
SBe the flexible parameter of the yardstick of wavelet transformation,
uBe the time migration parameter, t is the time, and C is constant.
The simple information of multiple wavelet transformation comprises respectively real part (RWT), imaginary part (IWT), amplitude (MWT) and phase place (PHWT), and amplitude and phase place are defined as:
Continuous wavelet decomposes the corresponding relation of the number of plies and frequency:
Wherein,
aBe to decompose the number of plies, △ is sampling interval;
F cBe wavelet center frequency, the Hz of unit;
F aThe pseudo frequency corresponding with decomposing the number of plies, the Hz of unit.Adopt the multiple Gauss wavelet in 20 rank herein.
4, characteristic quantity calculates: for model shown in Figure 1, to after fault during 5ms all feeder line zero sequence transient currents of window carry out the multiple Gauss Wavelet Transform in 20 rank, decomposing the number of plies is 256 layers, choose the decomposition result that decomposition scale is the 4-203 layer, divide according to frequency band of 20 yardsticks, obtain the energy of 10 frequency bands of each feeder line.
According to energy and maximum principle, choose all feeder line energy in 10 frequency bands and maximum frequency band as feature band.Ask for the energy of each feeder line in feature band
, extract phase place corresponding to each feeder line feature band centre frequency
, wherein
Be resonant earthed system feeder line quantity.
The realization of two, route selection function
1, neural network: neural network is to interconnect by a large amount of simple neurons the self-adaptation nonlinear dynamic system that forms.The principal character of artificial neural network is: large-scale parallel processing and the storage of distributed information; Extremely strong self-study, association and fault-tolerant ability; Good self-adaptation and self-organization; The nonlinear system of many inputs, many outputs.
2, the design of BP neural network: for system shown in Figure 1 and selected characteristic quantity design BP neural network, being divided into is three layers, and topological structure is
*
*
, wherein ground floor is that input layer contains
Individual node, the energy of each feeder line that will try to achieve by multiple wavelet transformation
The phase place corresponding with the feature band centre frequency
As sample attribute.The second layer is that hidden layer contains
Individual node, adopting the logsig function is logarithm sigmoid transport function; The 3rd layer for output layer contains 7 nodes, and adopting the purelin function is pure linear transfer function.
3, ANN (Artificial Neural Network) training: be set as follows fault signature: choose respectively the trouble spot at 1/3l, the 1/2l of every feeder line, 2/3l place and bus place; Fault resistance is chosen for respectively arc furnace load and constant transition resistance (20 Ω, 100 Ω, 500 Ω); The initial phase angle of fault is got respectively 0 °, and 30 °, 60 °, 90 °; Load is chosen for respectively firm power load and arc furnace load, extracts respectively the fault transient amount by A, B method
With
As sample set, train in input BP neural network after normalized, obtain satisfactory failure line selection network, the error performance target is 1
4, route selection criterion: with the fault transient amount of power distribution network to be selected
With
The sample attribute input neural network, the Output rusults of neural network obtains faulty line after renormalization is processed.
The output quantity of this failure line selection network is 1 *
Matrix:
[
,
,
...,
,
]; When
≈ 1, and other value approximates at 0 o'clock, judges the
The bar feeder line breaks down,
Be the feeder line numbering.When
≈ 1, and other value approximates at 0 o'clock, judges that bus breaks down.
The present invention compared with prior art has following advantage:
1, this method has overcome and uses single route selection criterion to cause the problem of route selection mistake;
2, amplitude and the phase place of the disposable extraction zero-sequence current of multiple wavelet transformation energy of the method use, simplified computation process;
3, the method as the route selection criterion, has reduced the impact of undesired signal with the characteristic quantity in feature band; Feeder line transient state energy and phase place corresponding to feature band centre frequency have been increased the reliability of route selection simultaneously as the ANN training sample set.
4, the method ANN(artificial neural network) when training considered the impacts such as fault angle, transition resistance and load characteristic, so the route selection effect do not affect by above-mentioned factor, accurately and reliably, belongs to intelligent route selection method.
Along with the transformation and upgrade of power distribution network, cable is used in a large number, causes the distribution distributed capacitance to increase, and causes the ground connection capacity current to surpass the regulation of operating standard, requires the neutral point must be through grounding through arc.The present invention is applicable to the resonant earthed system by line-the cable joint line forms.
Description of drawings
Fig. 1 is resonant earthed system singlephase earth fault realistic model of the present invention;
Fig. 2 is the energy under the embodiment of the present invention 1 each frequency band of all feeder lines;
Fig. 3 is the energy that the embodiment of the present invention 1 is levied each feeder line in frequency band;
Fig. 4 is the embodiment of the present invention 1 each feeder line feature band centre frequency corresponding phase;
Fig. 5 is the embodiment of the present invention 1 neural network structure.
Embodiment
Below in conjunction with the drawings and specific embodiments, the invention will be further described.
Embodiment 1: 110kV/35kV resonant earthed system singlephase earth fault realistic model as shown in Figure 1, and it has 6 feeder lines, and the Z-shaped transformer neutral point is by arc suppression coil resistance in series ground connection.Overhead feeder
=15km,
=18km,
=30km ， Xian – cable mixing feeder line
=17km, its overhead feeder 12km, cable 5km, cable feeder line
=6km,
=8km.Wherein, overhead feeder is JS1 bar type, and LGJ-70 type wire, span 80m, cable feeder line are YJV23-35/95 type cable.G in this electrical network is infinitely great power supply; T is main-transformer, and no-load voltage ratio is 110 kV/35kV, and connection set is
/ d11;
It is the zigzag transformer; L is arc suppression coil; R is the damping resistance of arc suppression coil.Feeder line adopts overhead transmission line, overhead line-cable hybrid line and three kinds of circuits of cable line.
Suppose feeder line
Apart from bus 5km place's generation singlephase earth fault, fault moment is 0.023ms, and transition resistance is 20 Ω, and sample frequency is 10kHz.According to preceding method, when choosing 5ms after fault, the zero-sequence current data of each feeder line of window are carried out multiple wavelet transformation, try to achieve the energy and as shown in Figure 2 under each frequency band of all feeder lines.
Corresponding frequency band is 2.75 ~ 0.478kHz,
Corresponding frequency band is 0.458 ~ 0.256kHz, other frequency bands can the like.Know frequency band by Fig. 2
Corresponding energy and maximum are feature band under this fault condition according to energy and selected this frequency band of maximum principle.The calculated characteristics frequency band
The energy of interior each feeder line
And each feeder line feature band centre frequency corresponding phase
, as shown in Figure 3, Figure 4.Fault feeder and perfect feeder line at feature band
Under energy and phase place corresponding to feature band centre frequency have obvious difference.The above explanation by special case uses energy and the phase place corresponding to feature band centre frequency of each feeder line under multiple wavelet transformation extraction feature band to carry out the route selection accurate and effective as characteristic quantity.
With vectorial P=[
,
,
,
,
,
,
,
,
,
,
,
] sample attribute builds the BP neural network as input layer, contains 12 nodes; It is logarithm sigmoid transport function that the hidden layer of network adopts the logsig function, contains 12 nodes; It is pure linear transfer function that output layer adopts the purelin function, contains 7 nodes.Neural network structure figure as shown in Figure 5.Be set as follows the various faults feature: choose respectively the trouble spot at 1/3l, the 1/2l of every feeder line, 2/3l place and bus place; Fault resistance is chosen for respectively arc furnace load and constant transition resistance (20 Ω, 100 Ω, 500 Ω); The initial phase angle of fault is got respectively 0 °, and 30 °, 60 °, 90 °; Load is chosen for respectively firm power load and arc furnace load, and extracts respectively the fault transient amount
With
As sample set, train in input BP neural network after normalized, obtain satisfactory failure line selection neural network, the error performance target is made as 1
, the neural network output vector is
[
,
,
...,
].The Output rusults of neural network obtains faulty line after renormalization is processed.The route selection criterion is, when
≈ 1, and other value approximates at 0 o'clock, judges the
The bar feeder line breaks down,
Be the feeder line numbering.When
≈ 1, and other value approximates at 0 o'clock, judges that bus breaks down.
Now suppose feeder line
Singlephase earth fault occurs, and fault distance bus distance is respectively 2km, 6 km, 14 km, corresponding transition resistance and the initial phase angle of fault are respectively 150 Ω, 500 Ω, 100 Ω and 0 °, 15 °, 55 °.Zero sequence transient current under each fault condition is carried out the multiple Gauss Wavelet Transform in 20 rank, and decomposing the number of plies is 256 layers; Choose the decomposition result that decomposition scale is the 4-203 layer, divide according to frequency band of 20 yardsticks, obtain the energy of 10 frequency bands of each feeder line; According to energy and maximum principle, choose all feeder line energy in 10 frequency bands and maximum frequency band as feature band; Ask for the energy of each feeder line in feature band
, extract phase place corresponding to each feeder line feature band centre frequency
With the fault transient amount of trying to achieve
With
In the failure line selection neural network that the above-mentioned training of sample attribute input obtains, the neural network output vector of trying to achieve
[
,
,
...,
] result as shown in table 1.As shown in Table 1
≈ 1, and other value approximates 0, judges thus feeder line
Break down, judged result is consistent with hypothesis, and route selection is correct.
Table 1 feeder line
Route selection result when breaking down
Embodiment 2: for resonant earthed system as shown in Figure 1, suppose feeder line
Singlephase earth fault occurs, and fault distance bus distance is respectively 2km, 5 km, 9.5km, corresponding transition resistance and the initial phase angle of fault are respectively 50 Ω, 250 Ω, 300 Ω and 25 °, 40 °, 75 °.Zero sequence transient current under each fault condition is carried out the multiple Gauss Wavelet Transform in 20 rank, and decomposing the number of plies is 256 layers; Choose the decomposition result that decomposition scale is the 4-203 layer, divide according to frequency band of 20 yardsticks, obtain the energy of 10 frequency bands of each feeder line; According to energy and maximum principle, choose all feeder line energy in 10 frequency bands and maximum frequency band as feature band; Ask for the energy of each feeder line in feature band
, extract phase place corresponding to each feeder line feature band centre frequency
With the fault transient amount of trying to achieve
With
In the failure line selection neural network that sample attribute input embodiment 1 training obtains, the neural network output vector of trying to achieve
[
,
,
...,
] result as shown in table 2.As shown in Table 2
≈ 1, and other value approximates 0, judges thus feeder line
Break down, judged result is consistent with hypothesis, and route selection is correct.
Table 2 feeder line
Route selection result when breaking down
Embodiment 3: for resonant earthed system as shown in Figure 1, suppose bus generation singlephase earth fault, and the transition resistance of fault and the corresponding initial phase angle of fault are respectively 100 Ω, 200 Ω, 500 Ω and 15 °, 35 °, 75 °.Zero sequence transient current under each fault condition is carried out the multiple Gauss Wavelet Transform in 20 rank, and decomposing the number of plies is 256 layers; Choose the decomposition result that decomposition scale is the 4-203 layer, divide according to frequency band of 20 yardsticks, obtain the energy of 10 frequency bands of each feeder line; According to energy and maximum principle, choose all feeder line energy in 10 frequency bands and maximum frequency band as feature band; Ask for the energy of each feeder line in feature band
, extract phase place corresponding to each feeder line feature band centre frequency
With the fault transient amount of trying to achieve
With
The failure line selection neural network that sample attribute input embodiment 1 training obtains, the neural network output vector of trying to achieve
[
,
,
...,
] result as shown in table 3.As shown in Table 3
≈ 1, and other value approximates 0, judges that thus bus breaks down, and judged result is consistent with hypothesis, and route selection is correct.
Route selection result when table 3 bus breaks down
The above is illustrated embodiments 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 skilled in the art possess, can also make a variety of changes under the prerequisite that does not break away from aim of the present invention.
Claims (1)
1. one kind is utilized the malfunction route selection method for resonant grounded system of answering small echo and ANN combination, it is characterized in that by following process implementation:
(1) when bus residual voltage instantaneous value is out-of-limit, namely
The time, fault line selection device starts immediately and records ripple, obtains each feeder line zero sequence transient current, all feeder line zero sequence transient currents of window when recording after fault 5ms; Wherein,
Be the bus rated voltage,
=0.15;
(2) each zero sequence transient current is carried out the multiple Gauss Wavelet Transform in 20 rank, decomposing the number of plies is 256 layers; Choose the decomposition result that decomposition scale is the 4-203 layer, divide according to frequency band of 20 yardsticks, obtain the energy of 10 frequency bands of each feeder line; According to energy and maximum principle, choose all feeder line energy in 10 frequency bands and maximum frequency band as feature band; Ask for the energy of each feeder line in feature band
, extract phase place corresponding to each feeder line feature band centre frequency
(3) design BP neural network, the BP neural network is divided into three layers, and topological structure is
*
*
Wherein, ground floor is that input layer contains
Individual node, the energy of each feeder line that will try to achieve by multiple wavelet transformation
With phase place corresponding to each feeder line feature band centre frequency
As sample attribute; The second layer is that hidden layer contains
Individual node, adopting the logsig function is logarithm sigmoid transport function; The 3rd layer contains for output layer
Individual node, adopting the purelin function is pure linear transfer function;
mBe resonant earthed system feeder line quantity,
(4) carry out the ANN training, the following fault signature of analog simulation: choose respectively the trouble spot at 1/3l, the 1/2l of every feeder line, 2/3l place and bus place, fault resistance is chosen for respectively arc furnace load and constant transition resistance 20 Ω, 100 Ω, 500 Ω, the initial phase angle of fault is got respectively 0 °, 30 °, 60 °, 90 °, and load is chosen for respectively firm power load and arc furnace load; By step (1), (2) described method, extract respectively the fault transient amount
With
As sample set, train in input BP neural network after normalized, obtain satisfactory failure line selection network, the error performance target is 1
(5) with the fault transient amount of power distribution network to be selected
With
The sample attribute input neural network, the Output rusults of neural network obtains faulty line after renormalization is processed, and the output quantity of failure line selection network is 1 *
Matrix,
[
,
,
...,
,
]; Wherein,
Be resonant earthed system feeder line quantity;
The output quantity matrix of the failure line selection network that (6) obtains according to step (5) carries out failure line selection; When
≈ 1, and other value approximates at 0 o'clock, judges the
The bar feeder line breaks down,
Be the feeder line numbering; When
≈ 1, and other value approximates at 0 o'clock, judges that bus breaks down;
Be resonant earthed system feeder line quantity.
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Cited By (13)
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|---|---|---|---|---|
| CN103424668A (en) * | 2013-08-05 | 2013-12-04 | 昆明理工大学 | Arc light ground fault continuous route selection method utilizing principal component analysis of zero-sequence current of feeder line and evidence theoretical integration |
| CN103454559A (en) * | 2013-09-02 | 2013-12-18 | 国家电网公司 | Power distribution network single-phase earth fault zone positioning method and positioning device |
| CN103631985A (en) * | 2013-09-02 | 2014-03-12 | 国家电网公司 | Simulation impedance model of electric arc furnace piecewise linearity |
| CN104569684A (en) * | 2015-01-14 | 2015-04-29 | 上海和伍新材料科技有限公司 | Fault electric arc detection method based on electric arc spectrum signals |
| CN104865499A (en) * | 2015-05-11 | 2015-08-26 | 昆明理工大学 | Super-high voltage direct-current power transmission line region internal and external fault identification method |
| CN106443334A (en) * | 2016-09-18 | 2017-02-22 | 昆明理工大学 | Zero sequence current difference polarity comparison based power distribution network fault line selection method |
| CN108279364A (en) * | 2018-01-30 | 2018-07-13 | 福州大学 | Wire selection method for power distribution network single phase earthing failure based on convolutional neural networks |
| CN108663600A (en) * | 2018-05-09 | 2018-10-16 | 广东工业大学 | A kind of method for diagnosing faults, device and storage medium based on power transmission network |
| CN109061397A (en) * | 2018-10-11 | 2018-12-21 | 南方电网科学研究院有限责任公司 | Line fault area identification method |
| CN112240965A (en) * | 2019-07-17 | 2021-01-19 | 南京南瑞继保工程技术有限公司 | Grounding line selection device and method based on deep learning algorithm |
| CN113866572A (en) * | 2021-09-06 | 2021-12-31 | 西安理工大学 | Direct-current fault arc detection and positioning method under condition of access of multiple power electronic devices |
| CN114814468A (en) * | 2022-06-20 | 2022-07-29 | 南京工程学院 | Intelligent line selection method considering high-proportion DG access for single-phase earth fault of power distribution network |
| CN115291039A (en) * | 2022-08-09 | 2022-11-04 | 贵州大学 | Single-phase earth fault line selection method of resonance earthing system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101162838A (en) * | 2007-11-29 | 2008-04-16 | 昆明理工大学 | Low current neutral grounding system fault route selecting method by wavelet package decompose and correlation analysis |
| CN101545943A (en) * | 2009-05-05 | 2009-09-30 | 昆明理工大学 | Method for fault line selection of cable-wire mixed line of electric distribution network by using wavelet energy relative entropy |
| CN101924354A (en) * | 2010-04-19 | 2010-12-22 | 昆明理工大学 | Artificially neural network routing method for distribution network failure by using S-transforming energy sampling property |
-
2013
- 2013-03-29 CN CN2013101071086A patent/CN103163430A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101162838A (en) * | 2007-11-29 | 2008-04-16 | 昆明理工大学 | Low current neutral grounding system fault route selecting method by wavelet package decompose and correlation analysis |
| CN101545943A (en) * | 2009-05-05 | 2009-09-30 | 昆明理工大学 | Method for fault line selection of cable-wire mixed line of electric distribution network by using wavelet energy relative entropy |
| CN101924354A (en) * | 2010-04-19 | 2010-12-22 | 昆明理工大学 | Artificially neural network routing method for distribution network failure by using S-transforming energy sampling property |
Non-Patent Citations (1)
| Title |
|---|
| 束洪春等: "基于零序电流小波变换系数均方根值的故障选线ANN方法", 《电力科学与技术学报》 * |
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