CN105759167A  Wavelet neural networkbased distribution network singlephase short circuit line selection method  Google Patents
Wavelet neural networkbased distribution network singlephase short circuit line selection method Download PDFInfo
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 CN105759167A CN105759167A CN201610060984.1A CN201610060984A CN105759167A CN 105759167 A CN105759167 A CN 105759167A CN 201610060984 A CN201610060984 A CN 201610060984A CN 105759167 A CN105759167 A CN 105759167A
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 G01R31/08—Locating faults in cables, transmission lines, or networks
 G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
 G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors

 G—PHYSICS
 G01—MEASURING; TESTING
 G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
 G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
 G01R31/08—Locating faults in cables, transmission lines, or networks
 G01R31/088—Aspects of digital computing
Abstract
The invention discloses a wavelet neural networkbased distribution network singlephase short circuit line selection method, which belongs to the technical field of distribution network protection. The method comprises the following steps: 1) db4 wavelets are selected to serve as a wavelet packet basis function to decompose transient zerosequence current in each feeder line of the distribution network, and the sampling frequency is 10KHz; 2) the modulus maximum of the transient zerosequence current is calculated; 3) the modulus maximum obtained in the second step and the polarity of the modulus maximum are used for training the neural network; and 4) the BP neural network after the training in the third step is applied to the distribution network with singlephase short circuit, and a fault line is determined according to a different output result. The method of fault line selection by using the wavelet neural network disclosed by the invention has good reliability and practicability, a singlephase grounding fault line can be effectively eliminated, stable operation of the distribution network system is facilitated, and an important role is played in planning and applications of a distributed power supply.
Description
Technical field
The present invention relates to a kind of power distribution network singlephase short circuit selection method, belong to distribution protection technical field.
Background technology
Distributed power source refers generally to, for meeting power system and the specific requirement of user, be connected on the compact electrical generating systems near user side.Distributed power generation adopts clean energy resource or regenerative resource mostly, not only inexhaustible, nexhaustible, the impact of environment is also preferably minimized, thus obtains and widely popularize.Mostly concentrating on user side due to distributed power source, do not pass through upper level transformator directly to customer power supply, the installation scale of distributed power source is all smaller, builds and also disperses very much.
At present, the earthing mode of China's distribution network system is mainly neutral noneffective grounding, namely isolated neutral, through grounding through arc or high resistance ground, under this earthing mode, lowimpedance shortcircuit loop can not be constituted when power system is broken down, therefore earth current is only small, so system with non effectively earth ed neutral is also called small current neutral grounding system.In all faults of power distribution network, the probability that singlephase earth fault occurs is the highest, constitutes about about the 80% of total failare.
Compared with conventional electrical distribution net, after distributed power source is connected to the grid, bigger uncertain factor will be brought to electrical network.On the one hand, distributed power source has the reliability of better motility and Geng Gao, it is possible to be effectively ensured the power demands of user side, improves the quality of power supply；On the other hand, after accessing distributed power source, distribution net work structure there occurs the change of essence, single source network be progressively changed into multisource network.When, after power distribution network single phase ground fault fault, big system can provide short circuit current to trouble point as power supply, meanwhile, trouble point also will be provided short circuit current by distributed power source, change the distribution of power distribution network short circuit currents.
In existing selection method, zerosequence current amplitude method selects the feeder line of zerosequence current amplitude maximum to be faulty line；Zerosequence current real component method extracts zerosequence current real component and the polarity of each bar feeder line respectively, and the active power of fault feeder is bigger, and direction is with to perfect feeder line contrary；Negativesequence current method utilizes the negativesequence current of fault feeder substantially opposite with the negativesequence current phase place of normal feeder line, but basically identical with the phase place of faulted phase voltage selects faulty line；First halfwave route selection method utilizes transient state capacitance current contrary with polarity of voltage at initial time, and the feature that normal feeder line is all identical, construct route selection criterion；Fault signature component is carried out exponential term matching by Prony method, and then analyze its frequency spectrum, because fault signature reflects with the amplitude of voltage, frequency and phase place typically via fault current, and Prony method can therefrom extract high frequency and DC component, realizes route selection by calculating.But after addition distributed power source, faulty line all changes with the amplitude of electric current and phase angle in regular link, owing to distributed power source provides certain fault current, the zerosequence current of faulty line is not necessarily maximum, simultaneously because the effect of arc suppression coil, its direction is likely consistent with the zerosequence current in regular link, thus such as the earth fault detection for power of zerosequence current amplitude method, real component method etc be not suitable for.Simultaneously because the change of phase angle, first halfwave method can be influenced by impact in actual applications, and fault occurs when phase angle is only small, there will not be first halfwave, and the interference such as passage drift, outofbalance current also can affect the polarity of first halfwave.And Prony method is by the impact of amplitude with phase angle, it is possible to produce erroneous judgement.
For the power distribution network containing distributed power source; instantly the Main way studied is the gridconnected regulating strategy of distributed power source; and the impact that gridconnected rear distributed power source is on aspects such as distribution power flow, relay protection and the qualities of power supply, and about the power distribution network singlephase earthing fault route selection containing distributed power source research almost without.
Summary of the invention
The technical problem to be solved in the present invention is, not enough for prior art, propose a kind of based on wavelet neural network containing distributed power source power distribution network singlephase short circuit selection method, faulty line can be detected in time, being conducive to the stable operation of distribution network system, the planning simultaneously for distributed power source is also significant with application.
The present invention solves that the technical scheme that abovementioned technical problem proposes is: a kind of electrical power distribution net singlephase short circuit selection method based on wavelet neural network, perform step as follows:
1) choose db4 small echo as wavelet packet basis functions, the transient zerosequence current of each bar feeder line of described distribution network to be decomposed, sample frequency is 10KHz, and the resolution of described wavelet packet basis functions is 4, decomposes 16 frequency bands, the bandwidth of each frequency band is 312.5Hz
2) calculating the modulus maximum of described transient zerosequence current, computing formula is as follows,In formula(j, k) coefficient under subband, total n coefficient under each subband for WAVELET PACKET DECOMPOSITION；
3) utilize step 2) in the polarity of the modulus maximum that obtains and described modulus maximum train described neutral net, the relatively desired output of described neutral net and actual output, to the weight of synapse in described neutral net and in described neutral net neuronic threshold value be modified；
Described neutral net is BP neutral net；
4) by step 3) trained after BP Application of Neural Network in the power distribution network that singlephase short circuit occurs, with according to output result difference determine faulty line.
The improvement of technique scheme is, described BP neutral net composition is as follows:
Input vector P_{k}=(a_{1},a_{2},…,a_{n})；
Object vector T_{k}=(y_{1},y_{2},…,y_{q})；
Hidden layer input vector S_{k}=(s_{1},s_{2},…,s_{p}), output vector B_{k}=(b_{1},b_{2},…,b_{p})；
Output layer input vector L_{k}=(l_{1},l_{2},…,l_{q}), output vector C_{k}=(c_{1},c_{2},…,c_{q})；
Output layer is to the weight w of hidden layer_{ij}, i=1,2 ..., n, j=1,2 ..., p；
Hidden layer is to the weight v of output layer_{jt}, j=1,2 ..., p, t=1,2 ..., p；
Hidden layer output threshold θ_{j}, j=1,2 ..., p；
Output layer output threshold gamma_{j}, j=1,2 ..., p；
Parameter k=1,2 ..., m.
The improvement of technique scheme is, the training process of described BP neutral net is as follows:
(1) initial value is composed, to w_{ij}、v_{jt}、θ_{j}And γ_{j}Random assignment in interval (1,1)；
(2) one group of training sample is arbitrarily chosenAs the input of network, output vector；
(3) according to formula (312a), utilizew_{ij}And θ_{j}Calculate the input vector s of hidden layer neuron_{j}；According to formula (312b), utilize s_{j}Calculate the output vector b of hidden layer neuron_{j}；
b_{j}=f (s_{j}) (j=1,2 ..., p)
(312b)
(4) according to formula (313a), b is utilized_{j}、w_{ij}And γ_{j}Calculate the neuronic input vector L of output layer_{t}；According to formula (313b), utilize L_{t}Calculate the neuronic output vector C of output layer_{t}；
C_{t}=f (L_{t}) (t=1,2 ..., q)
(313b)
(5) by object vectorC is exported with reality_{t}Substitution formula (314), calculates the learning error of output layer
(6) by v_{jt}、d_{t}And b_{j}Substitution formula (315), calculates the learning error in intermediate layer
(7) willWith and b_{j}Substitution formula (316), to v_{jt}And γ_{j}It is modified；
T=1,2 ..., q；J=1,2 ..., p；0 < α < 1
(8) willAnd P_{k}=(a_{1},a_{2},…,a_{n}) substitute into formula (316), to w_{ij}And θ_{j}It is modified；
I=1,2 ..., n；J=1,2 ..., p；0 < β < 1
(9) all samples are continued training according to abovementioned steps, require until all inputs and output meet；
(10) again choosing one group of sample, after BP neural network learning, the error of comparing cell output and target sample, if error is in preset range, then network convergence, study terminates；Otherwise network is not restrained, and learns unsuccessfully.
This machine of technique scheme is: when BP neural metwork training, and the state of each feed line of described power distribution network is all as the input of input layer, input vector P=(x_{1},x_{2},x_{3},x_{4},…,x_{n}), wherein x_{i}(i=1,2,3,4 ..., n) represent the modulus maximum of zerosequence current wavelet package transforms on ith feeder line, therefore the nodes of input layer is n；The nodes of output layer is also n, output vector T=(y_{1},y_{2},y_{3},y_{4},…,x_{n}), y_{i}=1 (i=1,2,3,4 ..., n) represent ith feeder line singlephase short circuit, y_{i}=0 (i=1,2,3,4 ..., n) represent that ith is regular link.
The present invention adopts technique scheme to provide the benefit that: 1) signal that the present invention gathers is transient zerosequence current, than other faultsignals, initial instant in fault, the amplitude of zerosequence current and frequency are mainly determined by transient state capacitance current, not by the impact of neutral grounding mode.So extracting transient zerosequence current as the faultsignal of the method, it is effectively improved the route selection success rate of the method.
2) wavelet package transforms is the effective tool of time frequency analysis, by zerosequence current is carried out wavelet packet analysis, it is possible to accurately extract effective information therein, such as feature band, modulus maximum and polarity etc..BP neutral net has powerful learning capacity, pass through error back propagation, realizing the nonlinear mapping of input and output, characteristic quantity wavelet packet extracted hence with BP neutral net is trained, not by the impact of neutral grounding mode, fault moment and earth resistance.
3) when distributed power source is popularized gradually, also little for the research of the Singlephase Earthfault Selection in Distribution Systems containing distributed power source both at home and abroad, and utilize the method that wavelet neural network carries out failure line selection to have good reliability and practicality, and singlephase earth fault circuit can be got rid of timely and effectively, being conducive to the stable operation of distribution network system, the planning simultaneously for distributed power source is also significant with application.
Accompanying drawing explanation
Below in conjunction with accompanying drawing, the invention will be further described:
Fig. 1 is the schematic flow sheet of a kind of power distribution network singlephase short circuit selection method based on wavelet neural network of the embodiment of the present invention.
Detailed description of the invention
Embodiment
A kind of electrical power distribution net singlephase short circuit selection method based on wavelet neural network of the present embodiment, as it is shown in figure 1, it is as follows to perform step:
1) choosing db4 small echo as wavelet packet basis functions, the transient zerosequence current of each bar feeder line of distribution network to be decomposed, sample frequency is 10KHz, and the resolution of wavelet packet basis functions is 4, decomposes 16 frequency bands, and the bandwidth of each frequency band is 312.5Hz,
2) calculating the modulus maximum of transient zerosequence current, computing formula is as follows,In formula(j, k) coefficient under subband, total n coefficient under each subband for WAVELET PACKET DECOMPOSITION；
3) utilize step 2) in the polarity training neutral net of the modulus maximum that obtains and modulus maximum, the desired output of comparative neural network and actual output, to the weight of synapse in neutral net and in neutral net neuronic threshold value be modified；
Neutral net is BP neutral net；
4) by step 3) trained after BP Application of Neural Network in the power distribution network that singlephase short circuit occurs, with according to output result difference determine faulty line.
BP neutral net composition is as follows:
Input vector P_{k}=(a_{1},a_{2},…,a_{n})；
Object vector T_{k}=(y_{1},y_{2},…,y_{q})；
Hidden layer input vector S_{k}=(s_{1},s_{2},…,s_{p}), output vector B_{k}=(b_{1},b_{2},…,b_{p})；
Output layer input vector L_{k}=(l_{1},l_{2},…,l_{q}), output vector C_{k}=(c_{1},c_{2},…,c_{q})；
Output layer is to the weight w of hidden layer_{ij}, i=1,2 ..., n, j=1,2 ..., p；
Hidden layer is to the weight v of output layer_{jt}, j=1,2 ..., p, t=1,2 ..., p；
Hidden layer output threshold θ_{j}, j=1,2 ..., p；
Output layer output threshold gamma_{j}, j=1,2 ..., p；
Parameter k=1,2 ..., m.
The training process of BP neutral net is as follows:
(1) initial value is composed, to w_{ij}、v_{jt}、θ_{j}And γ_{j}Random assignment in interval (1,1)；
(2) one group of training sample is arbitrarily chosenAs the input of network, output vector；
(3) according to formula (312a), utilizew_{ij}And θ_{j}Calculate the input vector s of hidden layer neuron_{j}；According to formula (312b), utilize s_{j}Calculate the output vector b of hidden layer neuron_{j}；
b_{j}=f (s_{j}) (j=1,2 ..., p)
(312b)
(4) according to formula (313a), b is utilized_{j}、w_{ij}And γ_{j}Calculate the neuronic input vector L of output layer_{t}；According to formula (313b), utilize L_{t}Calculate the neuronic output vector C of output layer_{t}；
C_{t}=f (L_{t}) (t=1,2 ..., q)
(313b)
(5) by object vectorC is exported with reality_{t}Substitution formula (314), calculates the learning error of output layer
(6) by v_{jt}、d_{t}And b_{j}Substitution formula (315), calculates the learning error in intermediate layer
(7) willWith and b_{j}Substitution formula (316), to v_{jt}And γ_{j}It is modified；
T=1,2 ..., q；J=1,2 ..., p；0 < α < 1
(8) willAnd P_{k}=(a_{1},a_{2},…,a_{n}) substitute into formula (316), to w_{ij}And θ_{j}It is modified；
I=1,2 ..., n；J=1,2 ..., p；0 < β < 1
(9) all samples are continued training according to abovementioned steps, require until all inputs and output meet；
(10) again choosing one group of sample, after BP neural network learning, the error of comparing cell output and target sample, if error is in preset range, then network convergence, study terminates；Otherwise network is not restrained, and learns unsuccessfully.
When BP neural metwork training, the state of each feed line of power distribution network is all as the input of input layer, input vector P=(x_{1},x_{2},x_{3},x_{4},…,x_{n}), wherein x_{i}(i=1,2,3,4 ..., n) represent the modulus maximum of zerosequence current wavelet package transforms on ith feeder line, therefore the nodes of input layer is n；The nodes of output layer is also n, output vector T=(y_{1},y_{2},y_{3},y_{4},…,x_{n}), y_{i}=1 (i=1,2,3,4 ..., n) represent ith feeder line singlephase short circuit, y_{i}=0 (i=1,2,3,4 ..., n) represent that ith is regular link.
Utilize MATLAB/Simulink to set up a 10kV distribution network system containing distributed power source, have four feeder lines, respectively L1, L2, L3 and L4.Infinite bus system three phase mains equivalence replaces, U_{s}=10.5kV, f=50Hz.Article four, the line parameter circuit value of feeder line, length and load are in Table 1 and table 2.
Positive order parameter  Zero sequence parameter  
R(Ω/km)  0.01273  0.3864 
L(mH/km)  0.9337  4.1264 
C(nF/km)  12.74  7.751 
Table 1
Table 2
Such as neutral by arc extinction coil grounding, the overcompensation degree of arc suppression coil takes 10%, is computed, and the threephase ground capacitance of system is C_{0}=6.2008 × 10^{7}F.AgainThen U_{0}/ j ω L=1.1U_{0}J ω C, soGenerally, the resistance of arc suppression coil takes the 10% of reactance value, i.e. R_{L}=10%X_{L}=155.56 Ω.
In phantom, DG three phase mains represents, and sets active power and the reactive power of its output, the DG in Fig. 1 in advance.Access its capacity of distributed power source in distribution and be generally not very big, be generally 6MW and following, take 2MW at this.
1, isolated neutral
Breaking down during voltage zerocross, the A phase ground connection of feeder line L4, from bus 4.5km place, earth resistance 50 Ω, fault moment is 0.03s, and distributed power source is connected on L2, from bus 13.3km.
The feeder line L4 amplitude maximum broken down, and the sense of current and other threelines be contrary.After wavelet package transforms, at the energy respectively [1.333,0.5876,2.032,0.001340,0.007455] of (4,1) to (4,5) five frequency bands, so feature band is (4,3).On (4,3), the wavelet coefficient of each feeder line is similar with zerosequence current waveform, the zerosequence current I of L4_{4}Wavelet coefficient on (4,3) is maximum, and opposite polarity.Each feeder line wavelet modulus maxima is [0.3594,0.5141,0.3561 ,1.229] respectively, so faulty line is L4.
2, neutral by arc extinction coil grounding
The A phase ground connection of feeder line L1, from bus 7.7km place, earth resistance 120, fault moment is 0.027s, and distributed power source is connected on L4, from bus 15km.
After accessing arc suppression coil, when overcompensation, the zerosequence current steadystate value phase place of each feeder line is identical.After wavelet package transforms, at the energy respectively [57.87,86.57,91.91,4.074,19.52] of (4,1) to (4,5) five frequency bands, so feature band is (4,3).On (4,3), the zerosequence current I of the wavelet coefficient L1 of each feeder line_{1}Wavelet coefficient on (4,3) is maximum, and polarity is contrary with other feeder lines.Each feeder line wavelet modulus maxima is [8.181 ,3.921 ,2.580 ,1.717] respectively, so faulty line is L1.
Based on the route selection flow process of wavelet neural network, first have to through substantial amounts of simulated extraction training sample and object vector as data base.This power distribution network one has four feeder lines, therefore the nodes of input layer is 4, input vector P=(x_{1},x_{2},x_{3},x_{4}), wherein x_{i}(i=1,2,3,4) modulus maximum of zerosequence current wavelet package transforms on ith feeder line is represented；The nodes of output layer is also 5, output vector T=(y_{1},y_{2},y_{3},y_{4}), when ith feeder line generation singlephase earth fault, y_{i}(i=1,2,3,4) are 1, otherwise take 0.
Extracting 50 groups as training sample from emulation data, another extraction 30 groups is as test data.Table 3 is training sample and object vector, and table 4 is test data and route selection result.
Table 3
Table 4
The present invention is not limited to abovedescribed embodiment.All employings are equal to replaces the technical scheme formed, and all falls within the protection domain of application claims.
Claims (4)
1. the electrical power distribution net singlephase short circuit selection method based on wavelet neural network, it is characterised in that perform step as follows:
1) choose db4 small echo as wavelet packet basis functions, the transient zerosequence current of each bar feeder line of described distribution network to be decomposed, sample frequency is 10KHz, the resolution of described wavelet packet basis functions is 4, decomposes 16 frequency bands, and the bandwidth of each frequency band is 312.5Hz；
2) calculating the modulus maximum of described transient zerosequence current, computing formula is as follows,In formula(j, k) coefficient under subband, total n coefficient under each subband for WAVELET PACKET DECOMPOSITION；
3) utilize step 2) in the polarity of the modulus maximum that obtains and described modulus maximum train described neutral net, the relatively desired output of described neutral net and actual output, to the weight of synapse in described neutral net and in described neutral net neuronic threshold value be modified；
Described neutral net is BP neutral net；
4) by step 3) trained after BP Application of Neural Network in the power distribution network that singlephase short circuit occurs, with according to output result difference determine faulty line.
2. the electrical power distribution net singlephase short circuit selection method based on wavelet neural network as claimed in claim 1, it is characterised in that described BP neutral net composition is as follows:
Input vector P_{k}=(a_{1},a_{2},…,a_{n})；
Object vector T_{k}=(y_{1},y_{2},…,y_{q})；
Hidden layer input vector S_{k}=(s_{1},s_{2},…,s_{p}), output vector B_{k}=(b_{1},b_{2},…,b_{p})；
Output layer input vector L_{k}=(l_{1},l_{2},…,l_{q}), output vector C_{k}=(c_{1},c_{2},…,c_{q})；
Output layer is to the weight w of hidden layer_{ij}, i=1,2 ..., n, j=1,2 ..., p；
Hidden layer is to the weight v of output layer_{jt}, j=1,2 ..., p, t=1,2 ..., p；
Hidden layer output threshold θ_{j}, j=1,2 ..., p；
Output layer output threshold gamma_{j}, j=1,2 ..., p；
Parameter k=1,2 ..., m.
3. the electrical power distribution net singlephase short circuit selection method based on wavelet neural network as claimed in claim 2, it is characterised in that the training process of described BP neutral net is as follows:
(1) initial value is composed, to w_{ij}、v_{jt}、θ_{j}And γ_{j}Random assignment in interval (1,1)；
(2) one group of training sample is arbitrarily chosenAs the input of network, output vector；
(3) according to formula (312a), utilizew_{ij}And θ_{j}Calculate the input vector s of hidden layer neuron_{j}；According to formula (312b), utilize s_{j}Calculate the output vector b of hidden layer neuron_{j}；
b_{j}=f (s_{j}) (j=1,2 ..., p)
(312b)
(4) according to formula (313a), b is utilized_{j}、w_{ij}And γ_{j}Calculate the neuronic input vector L of output layer_{t}；According to formula (313b), utilize L_{t}Calculate the neuronic output vector C of output layer_{t}；
C_{t}=f (L_{t}) (t=1,2 ..., q)
(313b)
(5) by object vectorC is exported with reality_{t}Substitution formula (314), calculates the learning error of output layer
(6) by v_{jt}、d_{t}And b_{j}Substitution formula (315), calculates the learning error in intermediate layer
(7) willWith and b_{j}Substitution formula (316), to v_{jt}And γ_{j}It is modified；
T=1,2 ..., q；J=1,2 ..., p；0 < α < 1
(8) willAnd P_{k}=(a_{1},a_{2},…,a_{n}) substitute into formula (316), to w_{ij}And θ_{j}It is modified；
I=1,2 ..., n；J=1,2 ..., p；0 < β < 1
(9) all samples are continued training according to abovementioned steps, require until all inputs and output meet；
(10) again choosing one group of sample, after BP elearning, the error of comparing cell output and target sample, if error is in preset range, then network convergence, study terminates；Otherwise network is not restrained, and learns unsuccessfully.
4. as claimed in claim 2 or claim 3 based on the electrical power distribution net singlephase short circuit selection method of wavelet neural network, it is characterised in that: during training, the state of each feed line of described power distribution network is all as the input of input layer, input vector P=(x_{1},x_{2},x_{3},x_{4},…,x_{n}), wherein x_{i}(i=1,2,3,4 ..., n) represent the modulus maximum of zerosequence current wavelet package transforms on ith feeder line, therefore the nodes of input layer is n；The nodes of output layer is also n, output vector T=(y_{1},y_{2},y_{3},y_{4},…,x_{n}), y_{i}=1 (i=1,2,3,4 ..., n) represent ith feeder line singlephase short circuit, y_{i}=0 (i=1,2,3,4 ..., n) represent that ith is regular link.
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