CN103728535A - Extra-high-voltage direct-current transmission line fault location method based on wavelet transformation transient state energy spectrum - Google Patents
Extra-high-voltage direct-current transmission line fault location method based on wavelet transformation transient state energy spectrum Download PDFInfo
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Abstract
The invention relates to an extra-high-voltage direct-current transmission line fault location method based on a wavelet transformation transient state energy spectrum, and belongs to the technical field of power system relay protection. When a direct-current line breaks down, according to two-pole direct-current voltage collected at a protection installation position (img file='dest_path_dest_path_image001. TIF'wi= '40' he='24'/) and (img file='dest_path_dest_path_image002. TIF'wi= '40' he='24'/), line mode voltage (img file='dest_path_dest_path_image003. TIF'wi= '48' he='24'/) is obtained, wavelet decomposition is carried out on line mode voltage (img file='dest_path_855728dest_path_image003. TIF'wi= '48' he='24'/) to obtain a high-frequency coefficient of the wavlet decomposition, the high-frequency signal wavelet energy sum (img file='dest_path_dest_path_image004. TIF'wi= '21' he='25'/) is solved by means of the high-frequency coefficient, normalization processing is carried out on (img file='dest_path_594008dest_path_image004. TIF'wi= '21' he='24'/) to obtain an input sample of a neural network, and the input sample in the neural network is trained to obtain a fault location result. By means of characteristics of a wavelet energy frequency band whose appearance is obvious and whose position is easy to determine, a fault position is looked for, and precision of fault location is improved; by means of non-linear fitting capacity of the neural network, extra-high-voltage direct-current grounded transmission line fault locating is carried out, the property of the sample is clear, the scale of a sample set is small, convergence efficiency is high, and the method is not prone to being influenced by system parameter conversion and transition resistance.
Description
Technical field
The present invention relates to a kind of extra high voltage direct current transmission line fault distance-finding method based on wavelet transformation transient state energy spectrum, belong to Relay Protection Technology in Power System field.
background technology
At present domesticly also do not carry out the research for the intelligent trouble diagnosis of extra-high voltage DC transmission system, external also very few to the Intelligent Fault Diagnose Systems of extra-high voltage DC transmission system, and also have certain distance from practical.Existing DC line range measurement system is to utilize the traveling wave method of transmission line travelling wave propagation characteristic mostly, and the time of propagating on transmission line of electricity by measurement row ripple is calculated fault distance, and traveling wave method can be divided into single-ended method and both-end method.Although travelling wave ranging method have advantages of be not subject to that current transformer is saturated, system oscillation and fault type etc. affect, and according to the related data of domestic and international actual motion, show, the DC line travelling wave ranging method of using at present is but subject to the impact of following factor:
(1) single-ended method investment is less, but subsequent reflection ripple is subject to undesired signal impact, is difficult to identification; Both-end distance measuring method need to be installed at circuit two ends distance measuring equipment, and needs time synchronized (gps clock) and communication port, and device is complicated, invests larger.
(2) the travelling wave signal random component that fault produces greatly, be easily disturbed, fleeting, not reproducible, be difficult to Measurement accuracy and seizure.
(3) row ripple is subject to the impact of DC line end smoothing reactor and DC filter, and near region exists dead band.
Therefore the impact that is subject to above-mentioned factor take transmission line travelling wave propagation characteristic as basic DC line range finding, distance accuracy is poor.
DC line monopolar grounding fault is during apart from difference, and the energy distribution of AC line mode voltage is different, and feature can be located in order to transmission line malfunction accordingly.The artificial neural network that obtains in recent years broad research has good robustness, anti-noise ability and fault-tolerant ability, and the failure modes based on artificial neural network and distance-finding method are not subject to the impact of system parameter variations.Therefore use neural network to carry out Fault Identification and localization of fault.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of extra high voltage direct current transmission line fault distance-finding method based on wavelet transformation transient state energy spectrum, the localization of fault of utilizing the method to realize has higher reliability and accuracy, effectively solved the difficulty that traveling wave fault location wave head identification difficulty and natural frequency method are accurately extracted natural frequency, and be not vulnerable to the impact of systematic parameter conversion and the impact of transition resistance, and reduced the dependence to high sample frequency.
Technical scheme of the present invention is: a kind of extra high voltage direct current transmission line fault distance-finding method based on wavelet transformation transient state energy spectrum, is characterized in that: after DC line breaks down, and the two poles of the earth DC voltage gathering according to protection installation place
with
obtain line mode voltage
; To line mode voltage
carry out wavelet decomposition, obtain the high frequency coefficient of wavelet decomposition; By high frequency coefficient obtain high-frequency signal wavelet energy and
; Right
after being normalized, obtain the input sample of neural network; In neural network, to input sample, training obtains the result of finding range.
Concrete steps are as follows:
(1) after DC line generation monopolar grounding fault, starting element starts immediately, now protects installation place to measure the two poles of the earth DC voltage and is respectively
with
, direct current two polar curve voltages are carried out to triumphant human relations boolean phase-model transformation and obtain independently line mode voltage
:
In formula:
,
be respectively positive and negative polar curve magnitude of voltage, k represents the 1st, 2,3 ... N sampled point, N=100.
(2) utilize db4 small echo to line mode voltage
carry out 7 layers of wavelet decomposition, obtain the high frequency coefficient of wavelet decomposition under different scale.
(3) obtain the 3rd yardstick to the high-frequency signal wavelet energy under seven foot degree and
(I=3,4,5,6,7):
In formula:
for energy signal;
for sample sequence length, N=100;
for
the high frequency coefficient that layer wavelet decomposition obtains.
(4) choose that (3) step obtains
e 3 ,
e 4 ,
e 5 ,
e 6 ,
e 7 with, it is done to normalized, the data that normalization is obtained, as the input vector of neural network, obtain the input sample of neural network.
(5) sample of the neural network obtaining in (4) step is sent in the neural network of fault localization and trains, utilize the neural network training to carry out fault localization, draw range finding result.
The input sample selection rule of described neural network is as follows:
(1) along circuit whole process, choose trouble spot, choose fault distance change step and be
=10km;
(2) fault resstance: 0≤
≤ 100 Ω, choose fault resstance step-length and are:
=10 Ω.
The neural network of described fault localization adopts BP neural network model, and network topology structure is 5 × 40 × 1, and ground floor is input layer; The second layer is hidden layer, and node number is 40, and transport function is transig; The 3rd layer is output layer, and transport function is trainlm, and training algorithm is selected adaptive learning rate algorithm, and maximum frequency of training is elected as 1000000 times, and objective function error is set as 1e
-7.
Principle of the present invention is:
1. DC power transmission line range measurement principle
During DC line generation monopolar grounding fault, the energy distribution of AC line mode voltage is different, and feature can be located in order to transmission line malfunction accordingly.Consider different fault ground resistance, the fault transient process of abort situation, using the transient state energy after AC line route mode voltage wavelet transformation as training sample, select suitable neural network parameter structure BP network model, the neural network that training obtains is carried out localization of fault.
2. the basic theories of wavelet transformation
Wavelet analysis has good local character on time domain and frequency domain simultaneously, can adopt meticulous gradually sampling step length to different frequency contents, focus on any details of signal, this is all effective to detecting high and low frequency signal, for transmission line malfunction location provides one more meticulous effective analytical approach.
If φ (t) is a quadractically integrable function, if its Fourier transform ψ (ω) meets admissibility condition, that is:
(3)
Claim that φ (t) is a wavelet, or wavelet mother function.
Wavelet mother function φ (t) is stretched and translation, can obtain continuous wavelet basis function φ a, b(t):
In formula: a is contraction-expansion factor, or be called scale factor; B is shift factor.
For function f (t) ∈ L2(R arbitrarily) continuous wavelet transform (Continuous Wavelet Transform, CWT) be:
In formula:
represent
conjugation.
Wavelet multiresolution analysis is exactly the process of input signal sequence being carried out to binary channels filtering, and the output of wave filter corresponds respectively to low frequency general picture and the high frequency details of input signal.Utilize " two extract " to repeat down the low frequency part after each decomposition, that is: this grade of input signal of each fraction stem-butts cutting off resolves into the rough approximation of a low frequency and the detail section of a high frequency, and every grade of output sampling rate can reduce by half again.To sample frequency, be
f s discrete signal carry out multi-scale wavelet transformation, what j yardstick was corresponding is the signal at frequency band [fs/2j+1, fs/2j].
After transmission line of electricity breaks down, the frequency characteristic temporal evolution of fault-signal and changing, wavelet transformation has equidistant characteristics, and the wavelet transformation of fault-signal keeps energy conservation, and energy equates in time domain and wavelet field.Through wavelet multiresolution analysis, can obtain being distributed in the fault-signal of different frequency bands, the energy of each band signal comprises abundant failure message, can be used for localization of fault.
The signal wavelet energy now defining under certain yardstick is this yardstick wavelet conversion coefficient square integration along time shaft, and expression formula is as follows:
In formula:
it is j layer signal wavelet energy; Window data width when N is;
be j layer wavelet conversion coefficient.The wavelet energy spectral sequence of wavelet transform is
Wavelet Energy Spectrum has reflected the energy level of each yardstick of fault-signal, and the signal wavelet energy under low yardstick represents high-frequency signal wavelet energy, and signal wavelet energy under high yardstick represents low frequency signal wavelet energy.
3.BP neural network model
Artificial neural network (Artificial Neural Network, be called for short ANN) refer to the nonlinear system by a large amount of simple computation unit (neuron) formation, simulating to a certain extent information processing, storage and the retrieval capability of biological neuron, is that one has highly intelligentized mathematical tool.ANN has good adaptivity, self-organization and fault-tolerance, has the abilities such as stronger study, memory, association, identification and classification.ANN topological structure is divided into three layers: input layer, output layer and hidden layer.Input layer is accepted the input signal arriving from external environment condition, produces output after activation functions effect, and this output is used as the input of hidden layer, and this process is sustained until meet certain specified conditions or output to the external world from output layer.
BP neural network is the multilayer feedforward neural network based on error backpropagation algorithm (BP algorithm).BP network structure comprises input node, output node, one or more layers hidden node, and wherein hidden node adopts Sigmoid type transport function conventionally, and output layer node adopts Purelin type transport function.Fig. 3 has provided three layers of BP network model with single hidden layer, wherein,
for input signal,
for output signal,
for hidden layer output;
expression is from being input to the connection weights of hidden layer,
the connection weights of expression from hidden layer to output;
ithe threshold values of individual hidden node,
lthe threshold values of individual output node.
Transport function is generally (0,1) S type function:
To the error function of P sample calculation, be:
In formula,
for desired output,
for the output of network actual computation.
When the desired output of output node is
time, as the formula (10), the calculating of output node is exported as the formula (11) in the output of BP model hidden layer.
The error that formula (10) and formula (11) substitution formula (9) is obtained to output node is:
BP algorithm is exactly continuous roll-off network weights and threshold values in network training process, and error is declined along negative gradient direction, and algorithm block diagram as shown in Figure 4.After test multiple network structure, selected best neural network structure as shown in Figure 7, wherein the number of hidden nodes is 40, and training function is transig function, and output layer adopts trainlm function as training function.
4. sample normalized
Because the input vector of sample is mistiming and energy, the dimension of time and energy is different, and between component, the order of magnitude of numerical value has very large difference, for a certain input node
kif the numerical value of this node is excessive, like this in the output of hidden layer,
kthe impact of node weights will be more much larger than other components, cause other components almost to lose regulating and controlling effect, so be necessary sample vector to be normalized, can be with reference to the difference being worth according to each component, its input amplitude is reasonably adjusted, make its variation range roughly be evenly distributed on interval (0,1), thus make network training at the very start give each input component with status of equal importance.
5. the fault localization based on wavelet transformation transient state energy
In the present invention, time window length is chosen 10ms after fault, sample frequency is 10kHz, line mode voltage is carried out to wavelet transformation, sampled data length 100, line mode voltage signal is carried out to small echo 7 Scale Decompositions, and then reconstruct obtains 3 ~ 7 layers of small echo high frequency coefficient sequence, obtain the 3rd yardstick to the high-frequency signal wavelet energy under seven foot degree and
(I=3,4,5,6,7):
In formula:
for energy signal;
for sample sequence length, N=100;
for
the high frequency coefficient that layer wavelet decomposition obtains.
From Fig. 5, can find out with Fig. 6: in DC line fault type, different or fault distance different situations, the energy distribution of AC line mode voltage is different, feature can be in order to transmission line malfunction classification and location accordingly.
Get respectively
e 3 ,
e 4 ,
e 5 ,
e 6 ,
e 7 use mapminmax function to do normalized to it, the data that normalization is obtained, as input vector, are selected transport function and learning rules, and three layers of BP network of structure are trained.Carry out localization of fault.
The invention has the beneficial effects as follows:
1, sample frequency is 10kHz, and sampling rate requires low, high compared with the reliability of travelling wave ranging.
2, utilize that a kind of outward appearance is obvious, position easily definite wavelet energy frequency band feature come looking up the fault position, improved the accuracy of localization of fault.Utilize Neural Network Based Nonlinear capability of fitting, carry out extra high voltage direct current transmission line earth fault distance measurement, its sample attribute is clear, sample set small scale, and convergence efficiency is high.
3, the present invention is not vulnerable to the impact of systematic parameter conversion and the impact of transition resistance.
Accompanying drawing explanation
Fig. 1 is the theory diagram of intelligent fault sorting technique of the present invention, in figure
e 3 ,
e 4 ,
e 5 ,
e 6 ,
e 7 for calculate the transient state energy of 3 ~ 7 layers of gained wavelet transformations according to formula (2).
Fig. 2 be cloud wide ± 800kV DC transmission system structural drawing, in figure, 1 is alternating current filter and compensation capacitors, 2 is converter power transformer, 3 is converter valve, 4 is smoothing reactor, 5 is DC filter group, 6 is earthing pole, 7 is DC line.
Fig. 3 is feed-forward type BP neural network, and topological structure is 5 × 40 × 1, and ground floor is input layer; The second layer is hidden layer, and node number is 40, and transport function is transig; The 3rd layer is output layer, and transport function is trainlm.
Fig. 4 is BP algorithm block diagram.
Fig. 5 is that when transition resistance is 10 Ω, emulation obtains the energy spectrogram of failed row swash mode voltage and 7 yardsticks of wavelet transformation apart from circuit head end M point 100km place fault.
Fig. 6 be 3 ~ 7 layer wavelet energy of line mode voltage waveform of the present invention after wavelet decomposition different faults apart from time wavelet energy figure.
Fig. 7 is neural metwork training performance plot of the present invention.Horizontal ordinate is iterations, and ordinate is iteration precision.
Fig. 8 is the anodal short circuit L-G fault that travels through 3 kinds of transition resistances in full line range, by emulation, tries to achieve the absolute error curve map of corresponding fault localization.
Embodiment
Below in conjunction with the drawings and specific embodiments, the invention will be further described.
Embodiment mono-: as shown in Figure 2, establishing a trouble spot every 10km in DC line, to carry out emulation be Δ F=10km to realistic model, and it is Δ R that fault ground resistance increases progressively with 10 Ω
f=10 Ω, consider earth fault:
(1) after DC line breaks down, starting element starts immediately, according to formula
(1)
Obtain AC line mode voltage
.
(2) line mode voltage signal is carried out to small echo 7 Scale Decompositions, then reconstruct obtains 3 ~ 7 layers of small echo high frequency coefficient sequence, obtain the 3rd yardstick to the high-frequency signal wavelet energy under seven foot degree and
(I=3,4,5,6,7):
In formula:
for energy signal;
for sample sequence length, N=100;
for
the high frequency coefficient that layer wavelet decomposition obtains.
(3) localization of fault.
Structure BP network, chooses
e 3 ,
e 4 ,
e 5 ,
e 6 ,
e 7 , do the data that obtain after normalized as input vector.Get input vector under different faults condition and form totally 1000 of the sample arrays of failure modes neural network.
Data are sent into neural network and realize range finding.
The present invention has utilized PSCAD/EMTDC electromagnetic transient simulation software building extra high voltage direct current transmission line electromagnetic transient simulation model, carries out emulation to circuit, and simulation time length is 5ms, and sample frequency is 10kHz.As shown in the table to the range finding result under different transition resistances and different faults distance condition:
By reference to the accompanying drawings the specific embodiment of the present invention is explained in detail above, but the present invention is not limited to above-mentioned embodiment, in the ken possessing those of ordinary skills, can also under the prerequisite that does not depart from aim of the present invention, make various variations.
Claims (4)
1. the extra high voltage direct current transmission line fault distance-finding method based on wavelet transformation transient state energy spectrum, is characterized in that: after DC line breaks down, and the two poles of the earth DC voltage gathering according to protection installation place
with
obtain line mode voltage
; To line mode voltage
carry out wavelet decomposition, obtain the high frequency coefficient of wavelet decomposition; By high frequency coefficient obtain high-frequency signal wavelet energy and
; Right
after being normalized, obtain the input sample of neural network; In neural network, to input sample, training obtains the result of finding range.
2. the extra high voltage direct current transmission line based on wavelet transformation transient state energy spectrum according to claim 1 is therefore distance-finding method is characterized in that the concrete steps of method are as follows:
(1) after DC line generation monopolar grounding fault, starting element starts immediately, now protects installation place to measure the two poles of the earth DC voltage and is respectively
with
, direct current two polar curve voltages are carried out to triumphant human relations boolean phase-model transformation and obtain independently line mode voltage
:
In formula:
,
be respectively positive and negative polar curve magnitude of voltage, k represents the 1st, 2,3 ... N sampled point, N=100;
(2) utilize db4 small echo to line mode voltage
carry out 7 layers of wavelet decomposition, obtain the high frequency coefficient of wavelet decomposition under different scale;
(3) obtain the 3rd yardstick to the high-frequency signal wavelet energy under seven foot degree and
(I=3,4,5,6,7):
In formula:
for energy signal;
for sample sequence length, N=100;
for
the high frequency coefficient that layer wavelet decomposition obtains;
(4) choose that (3) step obtains
e 3 ,
e 4 ,
e 5 ,
e 6 ,
e 7 with, it is done to normalized, the data that normalization is obtained, as the input vector of neural network, obtain the input sample of neural network;
(5) sample of the neural network obtaining in (4) step is sent in the neural network of fault localization and trains, the neural network that obtains training; Get physical fault Wave data and convert the wavelet energy obtaining, bring the neural network training into and carry out fault localization, output is the fault distance that neural network is calculated.
3. the extra high voltage direct current transmission line event distance-finding method based on wavelet transformation transient state energy spectrum according to claim 2, is characterized in that: the input sample selection rule of described neural network is as follows:
(1) along circuit whole process, choose trouble spot, choose fault distance change step and be
=10km;
(2) fault resstance: 0≤
≤ 100 Ω, choose fault resstance step-length and are:
=10 Ω;
Wherein
for the change step of fault distance,
for fault resstance,
for the change step of fault resstance.
4. therefore the extra high voltage direct current transmission line based on wavelet transformation transient state energy spectrum according to claim 2 is distance-finding method, it is characterized in that: the neural network of described fault localization adopts BP neural network model, network topology structure is 5 × 40 × 1, and ground floor is input layer; The second layer is hidden layer, and node number is 40, and transport function is transig; The 3rd layer is output layer, and transport function is trainlm, and training algorithm is selected adaptive learning rate algorithm, and maximum frequency of training is elected as 1000000 times, and objective function error is set as 1e
-7.
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